Education and the Public Interest: School Reform, Public Finance, and Access to Higher Education

EDUCATION AND THE PUBLIC INTEREST

EDUCATION AND THE PUBLIC INTEREST School Reform, Public Finance, and Access to Higher Education

EDWARD P. ST. JOHN University of Michigan, USA

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Contents

Figures and Tables

vii

About the Author and the Contributing Authors

xi

Acknowledgements

xiii

Introductio n

xvii

Part I. Public Policy and College Access Chapter 1. Globalization

3

Chapter 2. The Public Interest

25

Part II. State Indicators Chapter 3. Academic Access

57

Chapter 4. Financial Access

83

Chapter 5. Pathways and Markets

115

Part III. Student Outcomes: Reanalyses of the NELS Chapter 6. Access to Advanced Math

135

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Chapter 7. Enrollment

163

Chapter 8. Attainment

193

Part IV. The Public Interest Chapter 9. Improving Access and College Success

217

Chapter 10. Redefining the Public Interest

235

References

255

Index

269

Figures and Tables

Figure 2-1. Framework for assessing policy influence on educational opportunity: Linking education policy to educational outcomes

28

Figure 2-2. Pathways to College Network principles

44

Figure 2-3. A balanced access and attainment model

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Table 3-1. Trends in the implementation of high school reforms in the 50 states and the District of Columbia

59

Table 3-2. Trends in SAT scores, high school graduation rates, and college enrollment rates for high school graduates

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Table 3-3. Descriptive statistics for variables in the fixed effects analysis of SAT score, graduation rate, and enrollment rate averages across year for all U.S. states

62

Table 3-4. Three-step fixed effects regression for SAT combined scores in the states

67

Table 3-5. Three-step fixed effects regression for high school graduation rates in the states

69

Table 3-6. Three-step fixed effects regression for college enrollment rates in the states 71 Table 3-7. State reports on SAT scores: Change in average SAT scores 1990 to 2000 with policies significant in the fixed effects regression of combined SAT scores

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Table 3-8. State reports on change in graduation rates: Change in graduation rates 1990 to 2000 with policies significant in fixed effects regression

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Figure 4-1. Framework for assessing the impact of public finance strategies on postsecondary attainment

85

Table 4-1. Independent variables used in analysis of high school graduation rates

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Table 4-2. Independent variables used in analysis of college enrollment rates by high school graduates

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Table 4-3. Fixed-effects regression: The influence of population characteristics and state finance strategies on public high school graduation rates in the 1990s

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Table 4-4. Fixed-effects regression: The influence of population characteristics and state finance strategies on college enrollment rates in the 1990s

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Table 4-5. State reports: College enrollment rates, state grant funding, and public institution tuition and funding

95

Table 4-6. Estimated costs and benefits of meeting the equity standards in funding for need-based grants: Baseline, low-range, and high-range estimates

101

Figure 4-2. Estimation of costs for meeting the minimum equity standard in need-based state grants 102 Table 4A-1. Simulation of costs and benefits of meeting the minimum equity standard: State-by-state analysis—Baseline estimate 105 Table 4A-2. Simulation of costs and benefits of meeting the minimum equity standard: State-by-state analysis—Low-range estimate 108 Table 4A-3. Simulation of costs and benefits of meeting the minimum equity standard: State-by-state analysis—High-range estimate

111

Table 5-1. National percentage of FTE enrollment in public four-year, public two-year, private nonprofit, and private for-profit colleges

121

Table 5-2. Fixed effects regression analyses of impact of state school reform on college destinations: Public four-year, public two-year, private nonprofit, and private for-profit

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Table 5-3. Fixed effects regression analyses of the impact of public finances on college enrollment rates by type of institution: Public four-year, public two-year, private nonprofit, and private for-profit

126

Figures and Tables

ix

Table 6-1. Specification for individual-level variables in multilevel analyses of preparation

141

Table 6-2. State-level variable coding for two-level model used to analyze preparation

142

Table 6-3. Descriptive analysis of college preparation: All students

146

Table 6-4. Two-level multinomial logistic regression analysis of college preparation: All students 149 Table 6-5. Descriptive analysis of individual-level variables for analyses of college preparation: Low-income students 152 Table 6-6. Two-level multinomial logistic regression analysis of college preparation: Low-income students 154 Table 6-7. Descriptive analysis of individual-level variables related to college preparation: Middle-income students

156

Table 6-8. Two-level multinomial logistic regression analysis of college preparation: Middle-income students 157 Table 6-9. Descriptive analyses for individual-level variables related to college preparation: High-income students

159

Table 6-10. Two-level multinomial regression analysis of college preparation: High-income students

160

Table 7-1. Variable coding for analyses of enrollment and college choice using NELS 168 Table 7-2. Descriptive statistics on enrollment for all students

170

Table 7-3. Two-level logistic regression analysis of enrollment for all students

172

Table 7-4. Two-level multinomial logistic regression analysis of college choice for all students 174 Table 7-5. Descriptive statistics for enrollment for low-income only

176

Table 7-6. Two-level logistic regression analysis of enrollment for lowincome students 178 Table 7-7. Two-level multinomial logistic regression analysis of college choice for low-income students 180 Table 7-8. Descriptive statistics for enrollment by middle-income students

182

Table 7-9. Two-level logistic regression for enrollment by middle-income students 183

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Table 7-10. Two-level multinomial logistic regression analysis of college choice for middle-income students 184 Table 7-11. Descriptive statistics for enrollment by high-income students

186

Table 7-12. Two-level logistic regression for enrollment of high-income students 188 Table 7-13. Two-level multinomial logistic regression analysis of college choice: High-income students 189 Table 8-1. Variable coding for analyses of attainment using NELS

196

Table 8-2. Descriptive statistics for the analysis of degree attainment: All students

200

Table 8-3. Two-level multinomial logistic regression analysis of degree attainment: All students

204

About the Author and the Contributing Authors

Edward J P. St. J John Edward P. St. John is Algo D. Henderson Collegiate Professor at the University of Michigan Center for the Study of Higher and Postsecondary Education. He recently published Refinancing the College Dream: Access, Equal Opportunity, and Justice for Taxpayers (Johns Hopkins University Press, 2003) and co-edited Incentive-Based Budgeting in Public Universities (Edward Elgar, 2002) and Reinterpreting Urban School Reform: Have Urban Schools Failed, or Has the Reform Movement Failed Urban Schools? (SUNY Press, 2003). Professor St. John is series editor for Readings on Equal Education (AMS Press, Inc.) He holds an Ed.D. from Harvard University Graduate School of Education and M.Ed. and B.S. degrees from the University of California, Davis.

Contributing AuthoJrs Anna S. Chung is a Ph.D. candidate in economics at Indiana University Bloomington and research associate for the Indiana Project on Academic Success. Her dissertation research on students in for-profit postsecondary institutions has been supported by grants from the American Educational Research Association and the National Association of Student Financial Aid Administrators. She has also been awarded the Cameron Fincher Fellowship by the Association for Institutional Research for the best dissertation proposal of the year. Her other research on the economics of education xi

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focuses on the effects of public policy on access to higher education, labor market outcomes for students, and utilizing economic indicators in assessment of education policies. Choong-Geun Chung is statistician at the Center for Evaluation and Education Policy (CEEP) at Indiana University Bloomington. His research interests are statistical models for school reform, access and persistence in higher education, and issues in minority representation in special education. Glenda Droogsma Musoba is assistant professor of education at Florida International University. Previously she was policy analyst and associate director of the Indiana Project on Academic Success at Indiana University. She has a Ph.D. in higher education from Indiana University. Her research interests include higher education access and equity, persistence in higher education, education policy, K–16 education reform, and social justice. Ada B. Simmons is executive associate director of the Center for Evaluation and Education Policy (CEEP) at Indiana University Bloomington. Prior to her current appointment, she was Research Analyst in the campus’s institutional research office. Her research focuses on issues of educational equity, such as minority disproportionality in special education and access and attainment of underrepresented groups in higher education. She holds an Ed.D. from Indiana University.

Acknowledgements

This book would not have been possible without financial support from Lumina Foundation for Education along with the support of professional staff and graduate students at Indiana University. In 2002 I initiated two projects for Lumina Foundation that provided the basis for the analyses. Project funding only provided the opportunity for research, however, so the additional support from graduate students, support staff, and professional colleagues was crucial. The financial indicators project, initiated at the request of Lumina Foundation for Education, supported development of state indicators, used in part II, as well as the reanalysis of the National Education Longitudinal Study (NELS), presented in part III. Derek V. Price, Jerry S. Davis, and Robert C. Dickeson of Lumina Foundation for Education provided reviews and guidance for the indicators study. The advisory panel members for the indicators analyses included in part II were Derek V. Price, Jerry S. Davis, Jill Wohlford, and Deborah Bonnet (all of Lumina Foundation), Cheryl Blanco (Western Interstate Commission for Higher Education), Brian Fitzgerald (Advisory Committee on Student Financial Assistance), Susan Kleeman (Illinois Student Assistance Commission), Donald Heller (Center for the Study of Higher Education at Pennsylvania State University), Paul Lingenfelter (State Higher Education Executive Officers), Tom Mortenson (Pell Center for the Study of Opportunity in Higher Education), Laura Perna (University of Pennsylvania), Kenneth Redd (National Association of Student Financial Aid Administrators), Scott Thomas (Institute for Higher Education-University of Georgia), and Nick Vesper (State Student Assistance Commission of Indiana). Their guidance for and reviews of analyses included in this book are appreciated. The opinions expressed are xiii

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those of the author and do not necessarily represent policies or positions of Lumina Foundation for Education or members of the project advisory panel. In collaboration with the Indiana Commission for Higher Education (ICHE), Indiana University received funding for a study of academic preparation and college success that used national- and state-level databases to examine the pathways to college. This project enabled the research team to develop a set of indicators for state education policies that proved important in analyses presented in parts II and III of this book. Stanley Jones (ICHE Commissioner) provided the vision for the study of academic preparation and college success, and Jeffrey Stanley (Assistant to the Commissioner) provided expert guidance on the execution of the study. In addition, Ontario Wooden and Jesse Mendez provided analysis support on the financial indicators studies. Their hard work and thoughtful comments are greatly appreciated. JingJing Lou, a graduate student in policy studies at Indiana University, assisted with the review of the literature. She conducted reviews as part of an independent reading course and shared them with me. I am grateful for her goodwill and support. Amy Fisher, a graduate student at the University of Michigan, assisted with the development of the index. Colleagues at Indiana University and the National Advisory Committee on Student Financial Assistance were also very important in the formulation of the strategy used for these analyses. Don Hossler, Professor of Higher Education at Indiana University, not only shares my interest in student outcomes but he provided an outstanding sounding board for me as I pondered issues for this book. William Becker, Professor of Economics at Indiana University, has been a good critic as well as a friend. Bill’s probing critiques always challenge me to think through topics more thoroughly. Brian Fitzgerald, former Staff Director for the Advisory Committee of Student Financial Assistance, and William Goggin, Economist and current Staff Director for the Advisory Committee, encouraged me to undertake a reanalysis of the NELS. They argued that there had been serious statistical errors in the official analyses published by the National Center for Education Statistics and that a reanalysis was merited. Glenda D. Musoba, Choong-Geun Chung, Ada B. Simmons, and Anna S. Chung were collaborators and are acknowledged as chapter co-authors in parts II and III. Glenda was lead analyst for studies of academic preparation and access (chapter 3), while Choong-Geun was the lead analyst for the analyses of financial access (chapter 4). Anna collaborated on reanalyses of financial and education policy indicators (chapter 5) and also collaborated on the reanalyses of NELS in part III. Ada B. Simmons contributed to the simulation analyses in chapter 4. In addition, Choong-Geun completed preliminary statistical analyses of NELS for part III before he accepted a

Acknowledgements

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position with the Center for Evaluation and Education Policy at Indiana University. He was co-author of an earlier version of these analyses published in the Readings on Equal Education series. Anna collaborated on additional analyses for these chapters and reworked the entire set of analyses in part III as part of our response to reviewers. Leigh Kupersmith and Sarah Martin provided technical support in editing and manuscript production. Leigh was Publications Coordinator at the Indiana Education Policy Center when these projects were undertaken and assisted with many of the original reports. Sarah joined the Indiana Project on Academic Success as Publications Coordinator in May 2004 and provided support in every phase of manuscript production, from editing through development of references. This support is sincerely appreciated. Finally, the Center for American Progress funded a study, published as Affordability of Postsecondary Education: Equity and Adequacy Across the 50 States, that also supplied information for this book. Carmel Martin and Cindy Brown provided thoughtful reviews of that paper. They requested state-level analyses of the financial indicators database that proved a very useful step in my effort to build an understanding of finance policy. I also extended their approach to the analyses of school reform policies in this book. This support is gratefully acknowledged. Edward P. St. John Ann Arbor, Michigan

Introduction

In the U.S. as in many other countries, the goal of improving elementary and secondary (K–12) education as preparation for college has taken center stage in the debate about expanding college access. Yet voter resistance to taxation has made it difficult to finance school reform and to expand higher education using older progressive finance schemes. New strategies have emerged in education reform and public finance that focus on accountability and privatization. Policy indicators—including changes in test scores, school graduation rates, and college enrollment rates—are often used to rationalize these new strategies. Unfortunately, there has been a paucity of systematic assessments that examine whether the implemented policies and changes in public funding have resulted in improved student outcomes. This near void in research on the effects of education reforms and public finance strategies means that the U.S. and most other countries could be pursuing flawed reform strategies. Since a complex and diversified set of reform strategies is being used across countries—and across states in the U.S.—we can reasonably expect that different policies have different effects on outcomes. Thus, a sophisticated evaluation approach is needed to generate information about policy indicators and student outcomes that can inform policy deliberations. Detailed analyses that consider the effects of policies on different student outcomes are needed to complement state and national comparisons. Although the evaluation methods are necessarily complex and multileveled, it is necessary to find relatively simple ways to communicate vital information from research findings to policy makers and voters. Education and the Public Interest: School Reform, Public Finance, and Access to Higher Education introduces and tests a new framework for xvii

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assessing the impact of public policies on student outcomes. Chapter 1 reviews global developments in education and higher education finance. College students in the U.S., like students in most other countries, have faced higher tuition charges with increased availability of private capital in the form of student loans to pay a portion of college costs. Like most other developed countries, the U.S. has been in the midst of a period of education reform that emphasizes accountability. The great variation in accountability policies across the states in the U.S. during the 1980s has made it possible to assess the effects these policies have had on policy indicators and student outcomes. While privatization is being used internationally, student debt alone is probably not a sufficient means for equalizing college opportunity. The substantial variations in tuition charges and in state funding for grant programs (need- and merit-based) make it possible to assess the effects of these policies within a national context that emphasizes loans. Chapter 2 examines the strategies used in states in relation to the public interest in higher education and provides a logical basis for assessing the effects of the new accountability and finance policies. John Rawls’s theory of justice, adjusted for the pragmatic nature of pluralist society, provides a basis for examining (1) the basic right to a quality education, an aim of the accountability movement; (2) equal rights for access to quality education for diverse racial/ethnic and income groups; and (3) the public cost of diverse funding strategies relative to both basic and equal rights. Rather than merely using trend data to rationalize policies that may or may not influence their intended outcomes, part II demonstrates an analytic approach that uses state indicators to assess linkages between policies and educational outcomes, an approach that could be adapted to the comparative study of national systems. The analyses of state-level indicators control for demographic characteristics, the structural features of state systems of education, and tax rates in assessing the effects of K–12 policies and state finance policies on educational outcomes. Chapter 3 examines the associations between state education policies on indicators related to academic preparation. While many of the new K–12 policies—e.g., higher math standards, more course requirements, and exit exams—are said to be related to higher achievement, as measured by test scores (i.e., state averages on SAT exams), these same policies were also related to increases in dropout rates. In spite of rhetoric to the contrary, the new reforms have been largely unrelated to high school graduation rates. These contradictory effects are troubling, given that improvements are needed in both high school graduation and college enrollment rates. Chapter 4 examines the relationship between pricing strategies—tuition charges, along with state funding for need-based and non-need (merit) grants—and both high school graduation rates and college enrollment rates. Again, the findings only partly support

Introduction

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the rationales that have been used to argue for accountability and privatization. While funding for both non-need grants and need-based grants was associated with improved college enrollment rates in the 1990s, funding for non-need grants was also associated with reductions in high school graduation rates, contradicting claims about the indirect effects of merit aid on educational improvement (e.g., Bishop, 2002, 2004). Proponents of the academic preparation rationale—the set of beliefs that underlie accountability reforms—have not only overlooked the role of needbased financial aid in improvement of student outcomes, but they have incorrectly specified the nature of the access challenge. The structural constraints on state systems—especially the limited capacity of two-year and four-year colleges—are not only related to the extent of access but also create multiple pathways to college. Chapter 5 uses the notions of academic pathways and markets to reanalyze the indicators database. The results further illuminate the complexity of the college access challenge in the U.S. Specifically, the implementation of honors diplomas was negatively associated with enrollment in in-state public colleges. However, offering Advanced Placement courses was associated with enrollment in public fouryear colleges. At the same time, high tuitions in public colleges, coupled with high grants, were associated with the development of private markets for four-year colleges, which extended opportunity by expanding capacity. At the very least, there is a need for better coordination of academic and finance policies in the states. However, to understand more fully the nature of the problem, it is crucial to examine how state policies relate to student outcomes, using individual data. Part III undertakes a reanalysis of the National Education Longitudinal Study (NELS), which tracks students in the high school class of 1992 from middle school, with the 1988 base survey, through the college years, with the last follow-up in 2000. This is the most recent longitudinal survey tracking students from high school into college, and it has been used previously to build a rationale that advanced math courses taken in high school are the best predictors of college success (Adelman, 1999, 2004; NCES, 1997a, 2001c). Research using NELS has been used to argue for reform agendas that emphasize transforming comprehensive high schools into college preparatory high schools in the U.S. (e.g., Kazis, Vargas, and Hoffman, 2004; Pathways to College Network, 2004). Our analyses examine the relation between K–12 reform and preparation in advanced math as well as the effect of public funding strategies on access and persistence. These chapters were written for a general audience with a modest understanding of statistics as well as for technical readers. In addition to providing descriptive background, these chapters use two-level models to examine the effects of

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individual background and experience (level 1) and state policies (level 2) on a series of student outcomes. First, we examined the impact of K–12 policies on whether students completed advanced math courses (chapter 6). Given the emphasis on high school math in reports by the National Center for Education Statistics (NCES) and the American Council on Education (ACE), it was important to consider whether state education policies—often rationalized based on the assumption that they would improve math achievement—actually influenced course-taking behavior and/or high school dropout. Students who completed calculus or trigonometry/precalculus as their most advanced course were compared to students who did not have this level of math attainment. We found only modest associations between some of the new policies and completion of calculus when the entire population was considered, but no significant relationships with completion of trigonometry/precalculus. At a prima facie level this provides very weak support for the accountability regime. Presumably, requiring more math courses for graduation and raising standards for math education are associated with the availability of advanced math, at least calculus. The reanalysis of NELS also examined the statistical relationships of state funding for financial aid and tuition charges by public institutions in states with college enrollment (chapter 7), variables omitted from the NCES analyses (Becker, 2004; Heller, 2004). To overcome some of the selection bias included in these earlier studies, we included all high school students in the analyses. Students who had not taken advanced math, had not taken SAT exams, or had not even graduated from high school were included in the analyses along with students who had taken advanced math—because all types of students do have the opportunity to enroll in college, at least in some circumstances. Controlling for student background, we examined taking math courses, taking entrance exams, scores on entrance exams, and graduating from high school for associations with enrollment and college choices. As expected, we found an association between advanced math and college enrollment, especially enrollment in public and private four-year colleges. However, whether or not students graduated from high school also had a substantial statistical association with college enrollment, especially with enrollment in four-year colleges. Thus, if the K–12 reforms have an influence on high school dropout rates as well as on access to advanced math for some groups of students, then the consequences of the new policies are contradictory. The analyses of access and enrollment (chapter 7) also examined the relationships between state finance policies and college enrollment. Consistent with the indicators analyses of generations of research on the economics of higher education, these analyses reveal a positive relationship

Introduction

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between need-based grants and college enrollment. Also consistent with the market analyses using policy indicators, we found that high tuition in public colleges and high state grants were associated with enrollment in private colleges. Thus, the expansion of the private colleges was influenced by finance policies in the states. As a final step in the reanalysis of NELS, we examined persistence and degree attainment (chapter 8). Consistent with the NCES studies (Choy, 2002; NCES, 2001c), we found an association between completing advanced math in high school and eventual attainment of four-year and advanced degrees. However, we also found that after eight years low-income students had significantly lower odds of completing degrees and significantly higher odds of being still enrolled, even when controlling for high school math. The national system of finance created unequal odds of college success across incomes, controlling for high school preparation and other social and demographic factors. Thus, contrary to the findings of prior, flawed research, (a) advanced math in high school was not the primary determinant of college success and (b) there were substantial inequalities in educational attainment across income groups, after controlling for preparation. As a conclusion (part IV), I reconsider the access challenge along with the public interest in education. The challenge of improving high school preparation, access to higher education, and degree attainment is far more serious than previously portrayed in official statistical reports on the subject (see chapter 9). Family income and public finance play much more substantial roles in preparation than previously assumed in NCES studies. Further, the education reforms implemented in the 1990s missed the most critical aspect of the access challenge—equaling opportunity for low-income students. The gaps in opportunity were widened by the academic reforms, not narrowed. However, this is not to argue for casting these reforms aside. Rather, it is time to undertake serious reflection on the status of education and finance policies and to achieve better coordination between them— coordination that addresses inequalities created or made worse by 20 years of problematic reforms. Education and the Public Interest uses a new framework for evaluating education and finance reforms (chapter 10), one that could be adapted for comparative studies of countries. In particular, the literature on social justice merits consideration in efforts to evaluate and rethink education and public finance policy. If the education reforms now under way are setting a new standard for basic education, with preparation in math and other subjects sufficient for college enrollment, then equal access to quality schooling becomes more, not less, important. If such a basic standard is needed for economic citizenship—to be capable of supporting a family through a combination of education and employment—then the duty to improve

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education in ways that are fair and just, providing this basic right to all, is imperative. The American system of education has fallen farther from this standard as a consequence of the so-called “excellence” movement, rather than coming closer to the espoused goals of the movement. Is it not now time to rethink and craft education and finance policies to meet this just standard for educational improvement? The concluding chapter of this book summarizes lessons learned and makes a few recommendations for consideration by voters and policy makers.

I

PUBLIC POLICY AND COLLEGE ACCESS

Chapter 1 GLOBALIZATION

Higher education is in the midst of a massive transformation in its structure and finance in the United States, as it is globally. The old system of education was mostly publicly funded and controlled, with privately funded schools and institutions of higher education enrolling a small percentage of students. This system of public education, created in the U.S. in the 19th century, has been privatizing during the past two decades and appears to be moving rapidly, if variably, toward a new structural model. The new system of finance uses high tuition along with student loans and grants to stimulate market response. Increasing use of vouchers and charters in K–12 education mirrors the movement toward privatization of public higher education in the U.S. There has also been a push toward increased government accountability in both K–12 and higher education, changing the institutional obligations for receiving public support. The philosophy of the emerging system differs from the progressive values of earlier reforms that made education a mass enterprise. In addition to promoting the values of individual discretion and choice, the new reform advocates also argue for lower taxes. The notion that educating students in the new system will cost taxpayers less than the older system underlies many of the arguments for reform, especially those for the privatization of higher education. The U.S. is not alone in this transformation. Indeed, most social democracies are undergoing a similar transition in both education and social welfare. While it is vital to distinguish education from welfare, given the fact that investment in education would be needed even if there were no need for welfare,1 it is interesting that privatization is taking place in both welfare and 1

In a true socialist state—of which there is no historical example—all citizens would contribute to the social good through their work, while society would provide equal educational opportunities for all of its citizens. While none of the democratic societies

3

4

Chapter 1

education, both of which are humanitarian concerns critical in civil societies. My argument is that the intent to have a more just society should be maintained, even if the structure of education is transformed along with the means used to finance it. In fact policy makers in most countries should ponder how to retain the public interest in education in the face of the globalization that is driving privatization. In one form or another privatization of higher education is sweeping the globe. In the social democracies of North America, Europe, and Australasia, education systems are moving toward high tuition and high loans as a means of public finance that shifts more of the cost burden to families, while K–12 education systems are experimenting with charters, vouchers, and other methods of introducing market forces, with full or partial support (Henry, Lingard, Rizvi, and Taylor, 2001). It should be noted that most of the voucher experiments in the U.S. provide only partial support for high-need students (Metcalf and Paul, 2006), expecting charity from private schools to cover the remaining costs (St. John and Ridenour, 2001). In Russia, China, and other former Soviet countries, there is also rapid movement toward privatization of higher education and decentralization of control of both K–12 and higher education (Rizvi, in press). And developing countries face difficult choices about creating educational systems during a period when the market model dominates the international literature on education. Education and the Public Interest undertakes a critical examination of the globalization process in education, using a comparative study of U.S. states.2 While there has been a general pattern of movement toward privatization and accountability in the U.S., during the 1990s there was a great deal of variability in the rate and pattern of change across the states. This variability, coupled with the relatively large number of states (50), means that other nations can learn from the U.S. case, just as the states can learn from a comparative analysis. As background, I describe my approach to this comparative study of the states and how it can inform policy decisions in education and public finance in other nations as well as in the states.

2

reached this point of development in the 20th century, there was substantial evidence of movement in this direction before the new conservative turn in the last two decades of the century (Huber and Stephens, 2001). In the remainder of this book, “states” refers to states in the United States and “nations” refers to nation states.

1. Globalization

1.

5

GLOBALIZATION AND THE PUBLIC INTEREST

Policy debates about education in the U.S. tend to be domestically focused. Seldom do public officials discuss international educational trends in political campaigns, unless it is to bash U.S. schools for American students’ low test scores. Low achievement has been central to the education reform rationale in the U.S. since the publication of A Nation At Risk (National Commission on Excellence in Education, 1983). Many American readers would be surprised to learn how similar developments in the states are to global patterns in education and public finance. To understand these patterns, we should also consider globalization in trade and social welfare before focusing on the specific issues facing K–12 and higher education.

1.1

Globalization in economic and social policy

Globalization can be understood in the U.S. as an economic development involving corporations in an international labor market, changes in taxpayer support for social welfare and education, and the end of the Cold War. As corporations internationalize, they move more production jobs out of the U.S., along with the revenues that are no longer taxed in this country (Tabb, 2002). Princeton economist Robert Gilpin (2001) defines the global political economy as “the interaction of the market and such powerful actors as states, multinational firms, and international organizations” (pp. 17-18), a definition that elevates the corporation to the level of the nation state. He concludes his extensive treatment of the political economy with an argument about its governance: With the end of the Cold War and the triumph of neoliberalism, the purpose of governance seemed clear again; for most American officials, business leaders, and professional economists, the purpose of governance was to facilitate free trade, freedom of capital movements, and unrestricted access of multinational firms to markets around the globe. The global economy, according to this position, should be governed in accordance with policy prescriptions of neoclassical economics, and its rule should be based on market principles. (pp. 400-401) In a very real sense, this process of elevating the role of multinational firms implicitly to equal status with nation states is not unrelated to the end of the Cold War and the end of the neocolonial period. Historically most analysts in higher education compared national systems of education and social welfare based on the national origins of those systems, drawing parallels between systems (Clark, 1978; Kerr, 1978). During the earlier period, developing countries followed the developmental paths installed by

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the colonizing countries. When these systems were nationalized upon independence, the historic patterns usually remained. In the older system, the nation state still dominated the discourse because the role of the private economy was subjugated to a broader international conflict between socialist and capitalist systems. As the world entered the post-Cold War period, private corporations wielded more power because they were thought to be the economic engine that won that war. However, this rendition of the global period overlooks the roles social welfare and education played in the Cold War. Indeed, Western societies were better developed socially—and had better foundations for social justice—than the Soviet Socialist Republics. Ironically, the communist nations were founded on socially progressive principles but did not develop according to these intents, at least not as well as did the social democracies. However, the shift in the global period of economic development put a new economic system in place that devalued social welfare, emphasizing privatization of the formerly public systems (Huber and Stephens, 2001). The shift also reconceptualized the roles of taxes and debt in economic development and social agency (i.e., the public funding of social welfare and education). In the new global model, debt became a major means to generate revenue to fuel economic development in poor nations. The unevenness of tax revenues in indebted nations created problems with repayments at the same time developed nations reduced tax rates in favor of the wealthy and the private corporations (Stiglitz, 2002; Tabb, 2002). Some argued that the use of debt rather than international aid to promote development accentuated global inequalities (Tabb, 2002), while others argued that greater fiscal responsibility was needed in developed and developing nations (Stiglitz, 2002). Regardless of the reason, the role of debt changed after the Cold War. The former Soviet nations became indebted to Western nations through excessive borrowing from international agencies, while the U.S. failed to deal with its deficits and also amassed excessive debt. There was a bizarre parallel between the view toward taxes—with the unwillingness of voters in neoliberal democracies to be taxed at historic rates—and the view of the entitlement of nations, including the U.S., to build international debt. Regarding Russia’s failure to collect taxes and pay its debts, Joseph Stiglitz, winner of the Nobel Prize in economics and member of President Clinton’s administration, argues: And just as those who owe taxes must pay what they owe, those who owe money to banks—especially the banks that are now in the hands of government as a result of defaults—must be made to pay those debts. Again, this may entail an effective renationalization of the enterprise, a renationalization to be followed by a more legitimate privatization than had occurred previously. (2002, p. 191)

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This argument about taxes, debt, and responsible privatization has implications far beyond the former Soviet republics. Indeed, it has implications for privatization of public services and education in the U.S. While comparative scholars have examined transitions in the welfare nations,3 the linkage to globalization and higher education is generally less well understood. There is a trend in the U.S. and other Western social democracies toward privatization of welfare and social services, with private companies providing more services and government providing fewer services (Huber and Stephens, 2001). This not only places public service in the hands of corporate entities that are more concerned with profit than with meeting human needs, but reduces the public sector constituents who would support social welfare as it was known in the mid-20th century. There has been a strong push toward moving people from welfare to employment in the U.S. as well, a rationale that fueled changes in the welfare system in the late 1990s. These changes in the social safety net both accelerate income redistribution from lower- and middle-income families to wealthy families and increase demand for higher education (Phillips, 2003).

1.2

Privatization and taxation

When we examine U.S. higher education in isolation of global patterns of change it is easy to attribute privatization of public higher education to changes in political ideology (St. John, 1994, 2003). In addition, a reading of the broader literature on political economies reveals the link between the new global pattern of public finance—including the failure to stabilize tax revenues—and the transition of formerly public institutions. Unstable tax revenues and fees for service in education are tightly linked: “Most countries, facing severe budgetary constraints, have followed the Washington Consensus advice that fees would be charged. Their reasoning: statistical studies showed that small fees had little effect on enrollment” (Stiglitz, 2002, p. 76). Increasing fees for service in education became an acceptable alternative to raising taxes. While there was little negative enrollment effect, as measured by the rate of enrollment by traditional-age students, there was increased inequality in enrollment opportunity, at least in the U.S. (St. John, 2003). Both the changes in social welfare and in the financing of public higher education are appropriately viewed as part of the globalization process. The push for lower tax rates in the last part of the twentieth century also fueled the wealth redistribution. Lower tax rates not only concentrate 3

The literature more commonly refers to “welfare states.” However, since this book is using the term “states” to refer to state governments, it is more appropriate to refer to nations as such.

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wealth, but they reduce the resources available for social services and education. Funding for higher education has been easier to cut because it has been possible to develop the private market. There are substantial returns to individuals from their investments in higher education (Becker, 1964; Paulsen, 2001a). However, it is not as easy to reduce public social welfare and K–12 education because there is not the same type of private market for services, nor are the returns sufficient to charge high fees for services, especially to low-income recipients. So privatization of services was rationalized on the efficiency of the private sector, even though this means of delivery usually did not reduce costs (i.e., public colleges spend more per student in spite of the privatization process [St. John, 2003]). President George W. Bush has pushed for spending more public social welfare dollars in “faith-based” organizations and has introduced legislation toward this end. Before the welfare system emerged in the U.S., churches played a more substantial role in providing social services. However, prior to the Great Depression of the 1930s, there was no program that addressed poverty, and large numbers of American citizens lived in poverty and hunger. The private system of welfare, including services provided by nonprofit religious organizations, was inadequate, as became abundantly evident during the Depression. President Roosevelt began the development of the social welfare safety network as a means of ending the Great Depression. The return to private welfare—the “thousand points of light” of G. H. W. Bush (1988) and the new strategies of G. W. Bush—could move American society back to this earlier period.

1.3

Globalization and schools

There is a growing emphasis internationally on testing in K–12 education (OECD, 2001) and on school choice (Henry et al., 2001), suggesting some parallels between the issues facing K–12 reform in the U.S. and in other countries. Since publication of A Nation At Risk, the United States has been leading a global movement toward privatization and accountability in both K–12 and higher education (Henry et al., 2001). But how is this global leadership reflected in the patterns that typify the privatization of education related to the outcomes of education? The Organization for Economic Cooperation and Development (OECD) (2000, 2001) provides appropriate indicators for comparison for member countries that help define the status of participation, quality, and finance in developed countries. The OECD (2001) compared member nations on attainment across levels of education. Participation rates for early childhood education (for children under three years of age) varied from 100 percent in France to less than 10 percent in Ireland, Korea, Switzerland, and Turkey. The U.S. fell below the

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OECD average on this indicator. In contrast, the U.S. ranked highest on the percentage of 50- to 54-year-olds with upper secondary education but fell behind four other countries on the percentage of 25- to 29-year-olds with this level of attainment. This illustrates an erosion of opportunity to attain a high school education in the U.S. compared to other OECD nations as well as gains in educational attainment by other countries. More troubling, compared to other OECD countries, the U.S. ranked below all but one country (Poland) in literacy rates and was worst on underachievement in literacy. Thus, on one of the most fundamental measures of achievement, the U.S. was a remarkable underachiever. Achievement problems in the U.S. are not limited to literacy, however. The U.S. ranked above the OECD mean in fourth-grade math but significantly below the mean in eighth-grade math, indicating erosion in the U.S. standing in math between elementary and middle school. In science and math attitudes and achievement, the U.S. ranked relatively high on math attitudes in fourth grade but low on math achievement. Conversely, the U.S. ranked relatively low on attitudes toward science in eighth grade, as it did on achievement. In sum, while the U.S. may lead a charge toward privatization and accountability in education, levels of educational achievement and attainment in the U.S. do not reflect well on this leadership role. The OECD (2000) also provides comparative statistics on public spending on education. As a percentage of the GDP spent on education at all levels, the U.S. ranked third behind Korea and Sweden in 1997. However, the U.S. ranked below about half of the OECD countries on public expenditures on education. Only Korea was farther down the path to privatization, with both a higher total percentage of the GDP devoted to education expenditures and a higher percentage of private payments to education. In other words, the U.S. is a model for privatization of education among developed countries. But how does erosion of educational achievement relate to privatization in the U.S.? And how has accountability in K–12 education—standards, testing, and aligned curriculum—influenced achievement, high school graduation, and college enrollment? The states in the U.S. maintain substantial policy control over education although the federal government exerts substantial influence over the direction of education policy. A study of the impact of public finance and accountability policy in the U.S. can inform policy makers in other nations that are on the path toward increased privatization and accountability, the hallmarks of the globalization movement in K–12 education. However, the purpose of this assessment of the impact of education policy on attainment and achievement in U.S. education is not merely to criticize. Rather, since the shift toward privatization and accountability are well under way, it may not be possible to go back. We need to take a closer

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look at what is working and what is not and why. Given the growing national debt in the U.S., along with the unwillingness of voters to increase taxes, it is unlikely that we will be able to change the path we are on, at least the path toward privatization of higher education. Rather, the goal of educationists who care about equity and quality in U.S. education may be reduced to restructuring the path we are now on. To the extent that other countries are following the path to privatization, they too can learn from an analysis of the effects of education policies in the states.

1.4

Globalization in higher education

There is a remarkable parallel between the privatization process in higher education in the U.S. and the global pattern. The trends toward privatization in the U.S.—including movement toward high tuition and high loan aid—are evident internationally, largely as a result of an overt effort by international organizations to promote this strategy. The OECD has played a major role in promoting the privatization of colleges and the development of loan schemes internationally (Henry et al., 2001). Comparisons of the U.S. to other OECD countries in the financing of higher education indicates the U.S. ranks low on expenditures on education largely because the comparative statistics do not take the state role into account. In the U.S., states are the primary funding source of public colleges and universities. The OECD statistics (2000) reveal substantial variation across the member countries in the use of loans and nonrepayable subsidies. In the U.S. as in other OECD member countries, subsidies to students and their families take the form of loans that must be repaid and student subsidies that do not have to be repaid (i.e., grants and scholarships). In the OECD, some countries have made loans their primary means of providing subsidies to students. By 1997 New Zealand, Norway, and Sweden and provided more than half of their direct student support in the form of loans (OECD, 2000). Australia provided about half loans and half other forms of aid. Several other countries (Canada, Denmark, Germany, Mexico, the Netherlands, Switzerland, and the United Kingdom) made less substantial use of loans. However, about half of the OECD countries used nonrepayable aid as their primary forms of student subsidies in 1997 (OECD, 2000). The World Bank has played a substantial role in promoting loans as a means of financing higher education in developed countries (Johnstone, 1998; Woodhall, 1989) and in developing countries (Albrecht and Ziderman, 1992; Woodhall, 1992). Not only has its literature promoted loans as a means of financing the expansion of higher education but it has also revealed great diversity in repayment schemes. There is also substantially more

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history of income-contingent repayment in other contexts than in the U.S. (Johnstone, 1989; Lleras, 2004). Loans per se are not the focus of this assessment of the impact of public investment, however. The U.S. case study reveals that loans enabled the federal government to expand access at a lower cost to the federal government than the prior reliance on grant aid (St. John, 2003). Yet while federal loans are available in all states, there is great variation in state policies on and funding of grants, along with substantial variation in the extent of access across the states. Analyses of the effects of state financing policies—the use of subsidies to colleges and grants for students as complements to a federal system of moderate need-based grants and substantial federal loans—can inform policy makers in the U.S. and internationally about the role of tuition and grants. Thus, the analyses of the impact of state finance strategies are relevant outside of the U.S. because similar strategies are being used in developing countries and former Soviet countries.

2.

A COMPARATIVE STUDY OF STATES

There is not a consensus about the direction of education and public finance policy in the United States, although a majority has supported certain reform principles over the past two decades. There has been widespread public support for public accountability of education systems (Rose, Gallup, and Elam, 1997; Rose and Rapp, 1997) as well as for reduction in public taxation. No Child Left Behind (NCLB), the 2001 education reform act of the George W. Bush administration, and other reform legislation has supported rapid movement toward market-based models for organizing and financing education. However, while there may be majority agreement in these directions in public policy, many educators have been resistant to these reforms. And since education is a state responsibility there is a great deal of variation in the methods states are using to “improve” their educational systems. The two major components of the new model—reform of K–12 education and privatization of higher education—are examined below along with the fundamental financial challenge facing states.

2.1

K–12 reform and the academic preparation rationale

The education reform movement has dominated the education landscape over enough time for most college undergraduates to have felt the effects of this movement through their entire education in school. And while the public interest in education should be reconsidered if we are to build an

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understanding of the choices we face, the excellence movement is now the dominant view of education. To build an understanding of the U.S. context, it is important to review the key features of this movement and how it relates to the academic preparation rationale that is widely used in the policy discourse about college access. 2.1.1

The education excellence movement

The No Child Left Behind Act (NCLB) includes the following features that hasten privatization: • A requirement that states have testing systems that measure the “quality” of schools, • A method of withdrawing federal funding from schools that do not make progress educationally, • An emphasis on private options for individuals who have been served by schools that do not measure up, and • An emphasis on “research-based” reform in both the public and private systems of education. The educational testing movement has been central to education reforms in the U.S. during the last two decades. In 1990 Chester Finn published an article entitled “The Biggest Reform of All” in Phi Delta Kappan, a publication widely read by American educators. Finn had been Assistant Secretary of the Office of Educational Research and Improvement (OERI) during the second term of President Reagan. He had oversight during the transformation of the National Center for Education Statistics (NCES) and the National Institute of Education (NIE) from agencies that appeared neutral to agencies that essentially promoted the new reform agenda. Although the Reagan administration was out of office in 1990, there was still a Republican administration (George H. W. Bush) and strong commitment to the new reform initiatives. In the 1990 article, Finn argued that the major accomplishment of the Reagan reforms had been to shift the focus of policy from equalizing educational system inputs—including equity in resources— to raising educational outcomes. The testing movement was central to this shift. In 1980 few states used testing as a central part of education. By 1990 national education legislation had been rewritten to require states to have some form of accountability system. Thus, the foundation for NCLB had been laid more than a decade earlier. The idea that federal funding for compensatory and special education might be denied schools that did not show “improvement” on test scores would have seemed absurd two decades ago, but this plan received bipartisan support in Congress when NCLB was passed. Given the long history of testing, it was not a large step in 2001 to require states to have an

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“approved” testing system and to link scores to funding. Not only had the focus of education policy shifted from inputs to outputs, but schools’ ability to receive federal funds—originally targeting the neediest students—was linked to their ability to improve test scores. Education standards provided the structure linking testing and education practice. During the 1980s and 1990s, education standards were developed across the content areas. In some areas, like reading and language education, there was relatively high variability in the content of standards across states (St. John, Loescher, and Bardzell, 2003). In the case of reading there was disagreement about basic approaches during this period. Since groups of educators generally met to construct standards, there was some variability in the ways these standards were stated. However, in some other areas there was relatively high agreement about the content of standards. In math, for example, the standards promoted by the National Council of Teachers of Mathematics (NCTM) were adopted by most states during the 1990s. The common standards gave states a way of linking curriculum to standardized “criterion referenced” tests. With a relatively common curriculum linked to tests through these common standards, the foundations for NCLB were largely in place in many states, even before the act became law. However, the linkages between funding for privatized educational activities and results from this testing scheme were novel at the very least. NCLB’s focus on research-based reform, with the heavy emphasis on randomized experiments, was a new development that had a well-established foundation. In the 1980s and 1990s, school reformers used comparative studies of treatment schools and “comparison” schools with similar characteristics to “test” reform models like Success for All (Slavin and Fashola, 1998) and Accelerated Schools (Hopfenberg, Levin, and Associates, 1993). And in the 1990s random assignment of vouchers and scholarships had been used as a means of testing the impact of these new schemes (Peterson, 1998; Witte, 1998). This new emphasis on education research legitimized these experiments, focusing the attention of reformers, who had amassed large sums of money, to “test” their ideas. NCLB gave these reform advocates priority for public research funding as well. This feature of the legislation complicated the picture facing reformers in schools. Educators were being required to rationalize their plans based on research and the experiments that were privileged in the process. However, problems were created by emphasizing randomized experiments. This concept of experimental design had originated in agricultural research in the late 19th century. In the first half of the 20th century, quasi-experimental research designs had been created as a means of using social science databases. With advances in computer and information technology, it became possible to study the effects of reforms as natural experiments.

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The research-based component of NCLB can be viewed as a political instrument promoting particular types of reform, especially if there is little regard for whether the reforms really work when broadly implemented after their “experimental” period. If schools have reform models imposed on them because they worked in an experiment somewhere else, then it seems critical that a system of evaluation be developed to see whether the implemented reform has had the desired effects. This requires using large databases on individuals, or specially constructed databases on states or schools, to evaluate the effects of reforms. However, adjustments for selection effects are needed in some instances, an issue considered below. For example, this book undertakes an evaluation of high school reforms that were implemented in the 1990s. Given the variability in the implementation of educational standards during that decade, as well as the variations in curriculum and other reforms, it was possible to assess the effects of the type of reforms that are included in NCLB. To the extent that reforms became commonly used across all states—for example, NCTM standards were implemented in all states by 2000—there was no variation to study. The variable rate of implementation made it possible to study the effects of this reform. During the 1990s there was a great deal of variation in the types of education reform policies being implemented across the U.S. (see part II). The number of states requiring NCTM standards increased across the decade, and there was also an increase in the number of math courses required for high school graduation. However, other policies such as exit exams for high school graduation did not increase substantially across the decade. It is interesting to note that public funding per student in K–12 education did increase. Thus, while there was movement toward the policies embedded in NCLB during the decade, there was also a great deal of variation in these policies. This book uses a new approach for evaluating the effects of state education and public finance policies in the states, an approach that could be adapted to the comparative study of countries. Part II uses time series analyses of indicators data on states in fixed effects regression analyses, a statistical method that controls for differences across states. These analyses examined the direct relationship between education policies and both preparation and enrollment outcomes as well as the relationship between public finance policies (funding per student for grant programs and tuition charge in state systems) and rates of college enrollment within states. Part III uses two-level models—integrating state level indicators with student record information from the National Education Longitudinal Study (NELS)—to provide an assessment of the effects of state education policies on preparation in math and the effects of state public finance policies on

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enrollment and persistence. Since students reside in states, these analyses do not have the implicit problems with selection that are evident in many other national studies. In combination, these analyses illustrate it is possible to evaluate the effects of public policies. It is important to build an understanding of whether the implemented reforms had their intended effects and if they had unintended effects. There have been very few evaluations of the effects of implementing educational standards and standardized tests, especially high-stakes exit exams. Instead, the focus has been on funding experiments and using the results of the experiments to fuel the reforms. I am not arguing against the excellence goals but pointing out that after more than 20 years of implementation little is really known about the effects of the “excellence” reforms on student outcomes. The system of aligned curriculum, standards, and testing is now well established. However, it is important to know the effects of these policies—both intended and unintended—to help us refine and reinvent education policy in the U.S. once again. My argument is that state education and finance policies should be routinely evaluated. The implementation of accountability systems within states requires that schools and colleges be held accountable. However, this approach does not hold states accountable for the effects of policies they legislate and implement through regulation and funding. Kirst and Venezia (2004) point out that high school curricula and college admissions requirements are not well aligned in most states. Their research illustrates that we need to build a better understanding of the interfaces between K–12 systems and higher education systems. However, while changing requirements for high school graduation can improve this alignment, especially between high schools and four-year colleges, we also need to consider how the new requirements influence test scores, high school completion or dropout rates, and college enrollment rates for high school graduates. In other words, it is not sufficient to argue for new policies based on research that considers intermediate variables—like high school courses taken. We also need to assess the intended effects of these policies (e.g., the effect on dropout). States should routinely consider the effects of education reforms enacted based on academic rationales as well as the effects of finance policies implemented based on financial rationales (e.g., arguments about privatization or equal opportunity). 2.1.2

The academic preparation rationale

The academic rationale being used in the debate about college access—developed in parallel to the education excellence movement in K–12 education — is an argument to align higher education

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reform with the excellence movement. In a response to a USA Today editorial (“College aid,” 2001) advocating increased investment in need-based student aid for higher education, Chester Finn (2001) argued that better preparation of high school students was a higher priority than needbased grants and that if high schools did their job there would be less need for higher education. His response summarized in a few short paragraphs the rationale about higher education access that had evolved over the prior two decades. At the outset of his presidency in 1980, Reagan faced a challenging situation. There had been relative equality in college enrollment rates for Whites and African Americans in the middle 1970s, largely as a consequence of federal student aid programs targeting low-income students (St. John, 2003). In the U.S., the issues of poverty and race were inextricably linked at the time, even after President Carter had signed the Middle Income Student Assistance Act of 1978 (MISAA), which liberalized the provisions for student aid programs to include middle-income students. MISSA had been a way of assuaging middle-class voters tired of equity-based programs thought to privilege minorities. The Supreme Court’s 1977 Regents of the University of California v. Bakke decision had not only constrained the use of racial preferences in college admissions, but the decision was popular. Equity and race were intertwined in ways that led many low-income White voters to support Reagan, who had argued for “trickle down” economics and had cut taxes to stimulate the economy. After his early success in cutting taxes, Reagan soon turned his attention to education. Reagan’s tactics for higher education finance began to take shape in his first term as he began to deemphasize grants, retargeting them on lowincome students—undermining the intent of MISSA with respect to student grants—and extending loans as the central feature of federal student aid (St. John, 2003). This policy served two purposes: It targeted the supposedly scarcer resources on students who had the most need and it used loans as a means of financing middle-income students (St. John and Byce, 1982). The strategy worked well but it had an unintended effect: The enrollment rate for minority students eroded slightly in the late 1970s and early 1980s (NCES, 1990). However, the decline in this rate had begun in the late 1970s as a consequence of the shift of discretionary institutional aid from low-income to middle-income students as a result of extending Pell grants to middleincome students (St. John, 2003). The targeting of Pell grants on low-income students may have been the best choice if the decline in federal investment in student aid was unavoidable. Given the prospect of future cuts, a fortune that may have been unavoidable according to economist Lee Hansen’s persuasive argument that the federal investment in grants had been excessive (e.g., Hansen, 1983), the Reagan strategy may have been reasonable in the

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sense that it targeted those with greatest financial need for need-based aid (Hearn, 1993; St. John, 2003). It rapidly became apparent, however, that a new rationale was needed because of the wide support for federal student grant programs, especially for Pell grants.4 Pell was a large, need-based grant program with voucherlike features that had been implemented under the earlier Republican administration of President Nixon. With declining African American participation rates5 the Reagan administration was asked by African American leaders in higher education to study the problem. The principal investigators of the study (Pelavin and Kane, 1988) faced a difficult challenge. While inadequate student financial aid provided the most reasonable explanation for the problem, the administration was not interested in hearing this response. Ultimately the report focused on an alternative explanation for the disparity. The fact that students in the “college preparatory” track were more likely to take advanced math courses had long been known among both high school educators and college admission officers. The fact that high school math courses were correlated with college enrollment did not surprise anyone. The official report argued that taking algebra by eighth grade explained the differential in college-going rates. The report was soon revised and republished as a monograph by the College Board6 (Pelavin and Kane, 1990). The report not only helped the administration dodge the toughest issue—the consequences of reductions in federal grants in the early 1980s—but it also set up the academic preparation rationale that is currently being used to argue that school reform is the answer to the access problem. The U.S. Department of Education soon began using the national databases to build the academic preparation rationale. They developed better ways to compare the courses students took in high school (Adelman, 1995, 1999), creating an extremely valuable resource for the study of the transition from high school to college. However, studies that used the course file developed an “academic index” combining high school courses with indicators on the college application process as an explanation for college enrollment behavior (NCES, 1997a, 2001a). The analysis included indicators that combined completing advanced math courses with taking college entrance exams and completing applications for college. Students who took all of these steps were, of course, more likely to go to college. 4

At the time, Pell was called Basic Educational Opportunity Grants. The current name of the program is used in this book to avoid confusing readers. 5 This was a consequence of the reduction in grants that was largely overlooked in the policy literature (St. John, 2003). 6 The College Board is a nonprofit organization that administers the SAT, the major college entrance test in the U.S.

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Unfortunately, students from low-income families may have been discouraged from taking the advanced courses or from taking entrance exams because they did not think they could afford to attend college (St. John, 2002, 2003). Subsequent reviews indicated that the NCES reports had serious mathematical errors (Becker, 2004; Heller, 2004), but by that time the academic preparation rationale had become central to research being reported by the American Council for Education (ACE) (Choy, 2002; King, 1999a), the nation’s premier higher education lobbying organization. While these ACE reports are not official government reports, they are the products of the nation’s primary umbrella organization for higher education (Cook, 1998), which means they have political influence. If these reports carry forward arguments made in official governmental reports, they illustrate an alignment between the lobbying community and political positions of the administration. Therefore, it is important to consider and compare the arguments made—and the rationales that underlie analyses—in both types of public documents. By the early 21st century the arguments for reforming high schools had become intertwined with the arguments for expanding college access. Not only was there a need to evaluate the efficacy of the high school reform strategies that were being so widely used, but there was also a need to reanalyze the National Education Longitudinal Study (NELS), the major NCES database, since analyses of NELS had been used to construct the new academic preparation rationale. A reanalysis was also needed to untangle the relative effects of student aid—and the decline in federal need-based grants—from the effects of high school reforms. Federal grant aid continued to decline though the 1990s, but enrollment rates improved. The use of loans may have had a positive outcome: It has provided a relatively inexpensive method of expanding college access. Throughout the 1970s, slightly over 30 percent of high school graduates enrolled in college. By 1980 about 45 percent enrolled, a substantial gain in enrollment that would not have been possible if there had not been ample loan aid (St. John, 2003). There are compelling arguments for further expanding college enrollment rates in the U.S. (Kazis, Vargas, and Hoffman, 2004; Pennington, 2003). Therefore, it continues to be important to untangle the ways education reforms and changes in public finance influence college access.

2.2

Privatization of higher education

There were major changes in the financing of higher education in the U.S. after 1980, and while federal student aid policy may have been a catalyst, substantial changes also took place within states. The degree of

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decentralization in the governance of higher education in the U.S. is unique internationally. Not only is the federal role less dominant than in nations with national systems of higher education, but public universities in the U.S. have a history of autonomy in most states. Neither states nor the federal government has a direct role in the governance of colleges and universities in the U.S., nor have they historically. While the U.S. differs from other nations in this respect, the pattern of public finance in the states has some important similarities to other nations. States in the U.S. historically paid most of the educational costs, and some states have maintained a commitment to low tuition charges. In the 1980s, a large number of private nonprofit colleges enrolled less than a third of the students. For international readers to understand the U.S. system, and for readers in the states to situate the implications of the privatization process, it is important to reconsider the context of American higher education before 1980. While more than one-third of high school graduates attended college in most of the 1970s, colleges had experienced substantial enrollment growth from the end of World War II until 1980. Initially the growth was due to the return of veterans, with support from the GI Bill,7 but enrollment continued to grow as the percentage of high school graduates increased through about 1960. The enrollment rate of college-age students did not increase much during the late 1960s and 1970s, but enrollment numbers did rise when the baby boom—another artifact of World War II—reached college age. By the early 1970s, however, there was concern that enrollment would decline when the baby boom generation passed through higher education. It was widely expected that by 1980 there would be an enrollment decline (Carnegie Commission on Higher Education, 1973; Cartter, 1976; Freeman, 1976; NCES, 1980) and that a large number of small, not-for-profit private colleges would close (Breneman, Finn, and Nelson, 1978; Carnegie Commission, 1973). These predictions assumed that the college enrollment rate for high school graduates enrolling in colleges would remain relatively stable, causing enrollment to decline. It was also widely assumed that private colleges would be hardest hit by the downturn in enrollment, because they were more dependent on tuition and had higher tuition charges (Breneman, Finn, and Nelson, 1978; Carnegie Commission, 1973). Had these forecasters known that prices would go up and grant aid would decline they probably would have given even more dire predictions, because it was widely assumed that enrollment rates were responsive to net price (i.e., tuition minus grants), which increased during the two decades. The privatization process had enrollment effects that no one predicted. To 7

The GI Bill of Rights provided grant aid to veterans who returned to college after World War II (Jencks and Reisman, 1968) and stimulated growth.

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understand the American version of privatization, it is important to consider both the federal and state roles in the process as well as the role of institutional strategy. 2.2.1

The federal role in privatization

While the federal government led the movement to privatization of higher education, it appeared at the time to be an accidental process. The federal government stimulated changes through both federal policies and the rhetoric of policy makers. 2.2.1.1 The policy shift. The shift from grants to loans in the 1980s and 1990s in the U.S. has been widely documented (Fossey and Bateman, 1998; Hearn, 2001b; St. John, 2003). In 1980, 48 percent of federal aid was in loans, but in 2000 grants had dropped to 77 percent of federal aid (St. John, 2003). Had the Reagan administration fully funded the provisions of MISSA and the 1980 reauthorization of the Higher Education Act (HEA),8 the cost of federal grant programs would have been substantially higher (St. John and Byce, 1982). The reduction in grants was achieved through the budget process (Hearn, 1993; St. John, 1994). Expansion in loans also happened gradually as a result of political decisions (Hearn, 2001b; Hearn and Holdsworth, 2004). However, there was substantial analytic drama beneath the veneer of political decisions. In the middle 1980s there was a widely held belief that loans had a negative influence on educational opportunity (Astin, 1975; Kramer and VanDusen, 1986; Newman, 1985). Soon there were studies that found a positive association between loans and both enrollment and persistence (St. John and Noell, 1987),9 and the Reagan administration had begun to lump all forms of aid together in critiques of higher education (Bennett, 1987; Carnes, 1987; Finn, 1988a, 1988b). The Reagan team in the Department of Education— 8

9

The HEA is the major federal legislation enabling student financial aid. The liberal features of MISAA were incorporated in the 1980 reauthorization of the act. These studies, summarized in a paper released by the U.S. Department of Education that extolled the virtue of loans in promoting educational opportunity (St. John and Noell, 1987), were eventually published in their more complete form. While loans were positively associated with enrollment and persistence for all students, they had different effects on minorities and low-income students (St. John, 1989, 1990a, 1990b, 1991, 1999; St. John and Noell, 1989). Since I conducted these studies under contract with the U.S. Department of Education in the Reagan years, it was not possible to publish the full findings until I had left the consulting business to take an academic position. Publication of more complete results could have resulted in the loss of contracts or the layoff of analysts.

1. Globalization

21

Secretary William Bennett and Assistant Secretaries Bruce Carnes and Chester Finn, Jr.—had adopted positions critical of higher education. During a period when funding for grants was falling rapidly, the administration evolved the argument that colleges were wasteful and that they raised prices to capture more federal aid dollars. 2.2.1.2 The ideological battle. An ideological battle about higher education was waged in the media in the late 1980s, and higher education lost (St. John, 1994). The Bennett team in the U.S. Department of Education advanced the following arguments (Bennett, 1986, 1987; Carnes, 1987; Finn, 1998a, 1998b): • Colleges had been “greedy,” raising prices to capture more student financial aid dollars. • Colleges had been wasteful and unproductive, further causing college tuitions to climb. • “Reckoning” time had come for higher education; lower grant aid would induce greater efficiency—the “no frills college.” These arguments not only proved mostly false, but the strategies used by the Department of Education—the shift from grants to loans—induced privatization. The research indicates that • As a result of the reduction in federal grants, private colleges raised tuition in part to provide more institutional aid (McPherson and Schapiro, 1991). • Spending per student for educational purposes increased in both public and private colleges (Davis, 1997; Hauptman, 1990; St. John, 1994). • Public colleges increased tuition to make up for decreases in state funding (Hauptman, 1990; St. John, 1994) and to raise funds for educational purposes. • Faculty salaries and technology costs were the primary reasons for increased expenditures for educational purposes (Davis, 1997; St. John, 1994). • Loans made it possible for middle-income students to pay the higher cost of tuition charges at both public and private colleges (St. John, 2003). Rather than stimulating reduction in price and increasing efficiency, the new policies stimulated development of a new market system of higher education finance in the U.S. While the rationale used by the Bennett team was largely critical of higher education costs, their strategy—the shift from grants to loans, coupled with the critiques—set new market forces in motion, including higher prices.

22

Chapter 1

2.2.1.3 The state role in privatization. Three types of state financial strategies can influence enrollment patterns among qualified students: the development of public campuses, funding institutions on a per-student basis to reduce tuition on public campuses, and providing grant aid—need-based and non-need (mostly merit-based) aid—to students who enroll in public and private colleges. While there have been arguments that states should coordinate these strategies (Hearn and Anderson, 1989, 1995; Hearn and Longanecker, 1985), coordination of state financial strategies has been an elusive goal. During the last two decades of the 20th century, enrollment was supposed to have declined nationally, at least according to all prior predictions, so it is not surprising that few states founded new state university campuses during this period. However the western states did face expanding demand and should have either developed new campuses or invested in grant aid to induce expansion of private colleges (Zumeta, 2004). Western states were slow to develop new four-year campuses and instead tried to expand through distance education, including electronic courses and programs (Farmer, 2004). But desired efficiencies (i.e., lower instruction costs per student) were difficult to realize through these new ventures. As a consequence, some states have actually declined in their rankings in college access. For example, California dropped from having the best enrollment rate in higher education among the states in 1990 to nearly the worst (St. John, 2005). Prior to 1980 most states used funding institutions as a means of lowering tuition; but this practice changed after 1980. The percentage of educational revenues per full-time-equivalent (FTE) student subsidized through state appropriations declined from 76 percent in 1980-81 to 68 percent in 1996-97 (St. John, 2003). The student share of costs, generated through tuition charges, rose substantially. While there is still a substantial differential in the charges of public and private colleges, in the past decade public college tuition charges rose at a faster rate than private college tuitions (College Board, 2004). While tuition charges have crept upward in public colleges across the U.S., there is still substantial variation in public sector tuition charges across the U.S. The greatest area of variability across states, however, is funding for student grant programs. During the 1990s there was substantial growth in merit (non-need) grants, especially in southern states (Heller and Rasmussen, 2001). State funding for need-based grants did not increase at as high a rate, but was higher by 2000 than in 1990. Tuition increased in most states, and there were major differences across the states in college affordability, especially for low-income students. In Georgia, for example, students who maintained a high grade point average (GPA) in high school

1. Globalization

23

would not have to pay tuition, but low-income students who did not meet this GPA threshold might not be able to pay for college, even with federal grants and loans (Dynarski, 2002). In a few states there was a high level of need-based grants which helped equalize opportunity to enroll. However, in most states, need-based grants were not adequate for low-income students to pay the costs of attending public four-year colleges (St. John, 2004). During the decade of the 1990s there was a great deal of variation in state finance policies (see chapter 4). Some states maintained high tuition and high grant aid, while others did not. This variation makes it possible to analyze the relative effects of tuition, non-need merit grants, and need-based grants. 2.2.1.4 Institutional strategy. While we focus on the impact of state education and finance policies on college access and persistence, it is important to note that there have been substantial changes in institutional behavior during the past two decades. Private colleges were the first to develop aggressive, market-oriented behavior in the early 1980s, in response to the prospect of declining enrollment and the declining federal investment in student aid (St. John, 1994). However, by 2000 many public universities had also adopted marketoriented behaviors (Hossler, 2004). Four areas of behavior changed substantially as a new, market-oriented system of higher education emerged: • In the early 1980s, many colleges began to develop new strategic plans that focused on changing missions and increasing enrollment (Keller, 1983; Norris and Poulton, 1987). • In the late 1980s, enrollment management emerged as a means of focusing on improvement in enrollment and retention (Hossler, 1984, 1987; Hossler, Bean, and Associates, 1990), an approach that was widely adopted, especially in private colleges. • Aggressive price discounting was used in private colleges to increase ability to attract high-achieving students (McPherson and Schapiro, 1998), an approach also adopted in public universities marketing to outof-state students (Hossler, 2004). • In the 1990s many public universities adopted incentive budgeting systems (Priest, Becker, Hossler, and St. John, 2002; Slaughter and Leslie, 1997) as a means of increasing incentives to raise funds from external sources and provide incentives to improve retention. By 2000 a substantially different marketplace had developed in U.S. higher education. Colleges and universities used strategic planning and market-oriented financial management to generate revenues. In addition, the recruitment and retention of students were high priorities because institutions were increasingly dependent on revenues from tuition.

24

Chapter 1

The serious problems with the NCES research on college access and persistence have been extensively documented (Becker, 2004; Fitzgerald, 2004; Heller, 2004; Lee, 2004; St. John, 2002, 2004). This book builds on these prior reviews, rather than repeating them. The central issues in the debate are addressed in part III. There is a crucial need for better evaluative information on the effects of state education and finance policies in the U.S. Many reforms are being rationalized based on seriously flawed analyses. Whether or not the research used to rationalize new policies is flawed, the effects of the new policies should be evaluated. In a democratic society the public requires information of the effects of policies their elected officials have chosen. Further, public officials should revise their policies to improve effects, especially if they can do so with relatively similar costs.

2.3

Conclusion

Education and the Public Interest examines how claims about rights are balanced within the political process. My argument has been that the public finance and education policies used over the past two decades have not only emphasized quality education over equal opportunity but have also failed to meet their intended outcomes (St. John, 2003, 2005). The next chapter introduces the logical frameworks used in this book to evaluate the effects of policies. Outcomes related to quality, equity, and cost are considered. Achieving balanced approaches to policy that address both equity and quality within the constraints of tax revenues represents a minimum standard for public accountability. If public officials gain political capital from advocating educational accountability, then it is also crucial to consider the public accountability of these political officials. Evaluative information about the efficacy of public policies is a crucial form of public information.

Chapter 2 THE PUBLIC INTEREST

A decline of integrity in public service has been evident in recent decades, as a parallel of the globalization process. In Public Integrity, J. Patrick Dobel (1999) argues that when politicians base their actions exclusively on ideological and personal beliefs, the public integrity of democratic institutions can easily be lost. The educated public—voters and taxpayers who ponder the future of education and society—should also ponder whether public policies are being based on unsubstantiated rationales. Policy development should be informed by research that examines whether the policy pathways chosen by public officials have effects consistent with the public goals and the claims made by political interest groups. To assess the impact of changes in public policies on education and public finance on preparation for college, access to college, and success in college, it is necessary to start with an objective framework, one that represents the public interest in education. This book focuses on the integrity of policy research with respect to the positions taken in relation to the research findings. In the U.S., policy makers are critical of the quality of schools. I am concerned with research on the correlates of test scores—e.g., math courses in high school or reading instruction approaches in elementary schools—that has been used to rationalize policy. The previous chapter discussed research used by state and federal policy makers to criticize colleges for their inefficiencies when prices rise but seldom to acknowledge the ways policies influence prices to rise. A counterview is that prices rise largely as a consequence of policy (Heller, 2006). When competing explanations for rising prices were examined, it was evident that government policy did have a substantial influence in public colleges (i.e., colleges were compensating for the loss of government needbased grants and state subsidies), but it was also evident that education expenditures have risen in public and private college in the U.S. (St. John, 25

26

Chapter 2

2003). My argument is that policy researchers have an obligation to consider the multiple vantage points on critical issues. The easy path is to use research to argue a particular position or point of view, but integrity requires understanding competing arguments and constructing research to test them. Integrity in educational policy research requires balanced approaches that test competing arguments about educational outcomes and that evaluate the effects of policies based on research-informed rationales. Finding theoretically sound designs to examine competing arguments is crucial to policy researchers and an integral aspect of integrity in research. This requires stepping beyond one’s own position on policy to consider it along with others in analyses and interpretations of research. Research of this type could lead to arguments that favor one policy position over another or to policy arguments that balance aspects of multiple rationales. For example, in research on college access there is little reason to doubt that preparation, information, and finances are all important, so it is reasonable to expect policy rationales to balance these elements. The concept of integrity in public policy making implies using research in balanced ways to craft and refine policies to achieve outcomes related to generally accepted goals. While this sounds simple, most policy arguments about education lack this type of integrity, a point emphasized in the prior chapter. As a university-based policy researcher, I am not directly engaged in the formation of policy. Instead, I hope my efforts to conduct balanced policy research can be informative for people who are engaged in policy making. However, to maintain my integrity as a researcher I try to use the best available data and methods in research that assesses competing rationales. This chapter starts with a brief summary of the framework used in this study—a way of viewing policy on access developed in Refinancing the College Dream (St. John, 2003). This framework provides a way of examining competing arguments about preparation, college transitions, and public finance. Then, I reexamine John Rawls’s theory of justice, which provides the core assumptions for the framework. Next, I reexamine the ideological arguments and competing economic and educational rationales for reform. Then, I introduce the balanced access model as a means of assessing rationales for education reform, an issue that has become a focus of my more recent research on education policy (St. John, 2004). Finally, I discuss my approach to measuring the effects of policy in relation to generally accepted statistical methods.

2. The Public Interest

1.

27

FRAMING THIS STUDY

Education and public finance policies have become imbalanced as a consequence of the overemphasis on academic preparation. This study uses a framework for assessing the ways public policies influence educational opportunity (see Figure 2-1) that was developed as a conceptual guide for seeking a more balanced approach to the study of access. This framework examines a comprehensive set of linkages: For K–12 education, families have a substantial direct influence on educational attainment, but the percentage of the population attaining a high school education and students’ level of achievement in public school also can be influenced by school reform (education policy) and by the social context (social and economic forces) to which children are exposed (e.g., parents’ work settings). For postsecondary transition, access for qualified students is influenced by students’ aspirations (career and education), as well as by policy interventions (i.e., tuition and student aid, postsecondary information, and admissions policies). The percentage of the population completing a postsecondary education can be affected by changes in educational environments, labor markets, and public funding of colleges and students. Educational attainment results in personal growth, which influences earnings and congruence. Taxation of increases in earnings provides a rationale for public investment, along with economic development from research. Congruence between education and personal development provides the basis for personal satisfaction with education and a willingness to support higher education (as parents or taxpayers). (St. John, 2003, p. 54) Centered in John Rawls’s theory of justice, this framework also considers recent theory and research in economics, sociology, and education. It provides a way of seeing how imbalance can be created if academic preparation is overemphasized at the preparation stage. Ignoring equal opportunity to prepare, as a policy issue, creates inequalities down the line, in college transitions and success, just as emphasizing achievement outcomes can encourage better preparation. This framework was used in Refinancing the College Dream to examine the relative roles of financial aid, postsecondary encouragement, and school reform in changes in access (i.e., the national college enrollment rate) and the rates of college enrollment for African Americans, Hispanics, and Whites. Since Whites have substantially higher incomes on average than African Americans and Hispanics, these relative measures provided an indicator of equality of opportunity. The enrollment rate was used as a general indicator of individual access to equal education while the relative

Parents’ Occupation

Postsecondary Education (PSE) Transitions Aspirations Applications Access Financial Academic

Potential Earnings

Middle Class Professions

Undergraduate College choice Major choice Persistence Graduate Choice of field University choice Persistence

Elite Professions

Individual Employment Opportunity Skilled

Education Policy

K–12 Schooling/Reform Policy Interventions Colleges and Universities Early reading interventions Prices and subsidies Financial strategies Comprehensive school reform PSE information Academic strategies School choice Affirmative action/merit Learning environment Standards/tests

Attainment/Equity Reduced retention rate Reduced dropout rate Increased graduation rate Achievement Increased test-passing rate Improved test scores

Exposure to Work Environments

Congruence

Individual Development and Attainment

Earnings

Source: E. P. St. John, Refinancing the College Dream: Access, Equal Opportunity, and Justice for Taxpayers, Johns Hopkins University Press, 2003.

Parents’ Education

Family Influence

Social and Economic Forces

Figure 2-1. Framework for assessing policy influences on educational opportunity: Linking education policy to educational outcomes

28 Chapter 2

2. The Public Interest

29

measures of college enrollment across racial groups was used as a measure of equal opportunity. The key findings related to student outcomes and public finance are summarized below to help frame this study.

1.1

Student outcomes

In the late 1960s and the 1970s major educational reforms were implemented, and in the 1970s the available federal grant aid was targeted on financial need. Three major postsecondary encouragement programs were also implemented (i.e., the federal TRIO programs1), providing encouragement and support for disadvantaged high school students. College enrollment rates for high school graduates did not increase substantially during this period, but across racial groups enrollment rates were relatively equal. The research on student aid during that period indicated a positive association between grants and enrollment, so there was reason to argue that student aid programs played a role in equalizing opportunity, but all three types of programs could have had an influence. In the 1980s government grant dollars declined, student loans increased, and education reforms refocused on student excellence. There were not substantial changes in encouragement programs. However, college enrollment rates increased, while there was a growing disparity in enrollment rates across racial/ethnic groups. The education reforms of the 1980s may have contributed to the increase in college enrollment rates but could not explain the disparity in enrollment rates. Analyses of the effects of student aid during the period revealed that loans were associated with expanded college choice by middle income students—helping to explain the expansion of enrollment rates. However, several studies found that student grants were not adequate for low-income students.2 In the 1990s the patterns evident in the 1980s persisted: Federal grants remained lower than in the 1970s; education reforms continued to be focused on excellence; there were modest increases in state-level encouragement programs; and loans continued to be emphasized over grants, 1

Legislated in the Higher Education Act of 1965, the TRIO set of programs includes Upward Bound, which takes disadvantaged high school students to college campuses to support preparation and encourage application; Talent Search, which seeks out and engages talented middle and high school students and provides some support services during high school; and Student Support Services, which provides counseling and other support services for college undergraduates. 2 The final section of this chapter discusses the approach used to make judgments about the adequacy of student aid, based on the earlier research. While the advances in measurement methods since that research was conducted change the methods that should be used in research on student aid research, this same recent research essentially validates the interpretive position used to judge adequacy in the earlier research.

30

Chapter 2

although some states began to reinvest in need-based grants. Enrollment rates continued to climb and the disparities in enrollment rates narrowed slightly but remained more substantial than in the 1970s. The analyses of the effects of student grant aid in studies in the states of Washington and Indiana revealed that increased funding for state grants was associated with equalizing opportunity, although causal inferences could not be made. This previous study (St. John, 2003) sets the stage for the analyses provided in parts II and III of this volume. While the prior review indicated that student grant aid played a role in equalizing opportunity and that loans played a role in expanding opportunity, many questions remained about the role and impact of school reform. The studies in this volume were designed to test these lingering questions, focusing exclusively on the 1990s because there was high quality data available on states and college students during that period. The remainder of this chapter explains the philosophical position on justice that underlies the research framework and discusses the analytic approach used to evaluate the effects of policy on access and measurement issues related to this task.

1.2

Public finance

The earlier study examined trends in education and related expenditures per student (the sum of administration, instruction, and plant operation) in both public and private colleges, along with trends in state and federal funding per full-time equivalent (FTE) student in public systems of higher education (St. John, 2003). In the 1970s both education expenditures per student and tuition charges changed very little and government subsidies increased slightly. Education expenditures in public colleges rose in the 1980s and 1990s, however, but not as fast as tuition. In addition, government funding per FTE in higher education (students enrolled) declined. Our estimate of government spending—that subsidized loans cost the federal government 50 cents per FTE—was extremely high, given interest rates and default rates. One question that the earlier study addressed was whether rising education expenditures explained tuition costs. While this notion cannot be completely dispelled given the correlation between education expenditures and tuition charges, the relationship between the decline in government spending on public colleges and need-based grants provided a more compelling explanation for tuition increases. Indeed, a substantial portion of tuition increases went to grants in private colleges throughout the period, and there was a near direct substitution of tuition dollars for loss of state support in public colleges and universities.

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31

Another possibility is that both the decline in government spending per student3 and the rise in education expenditures in four-year colleges were outcomes of privatization. With the rise in prices, college must offer a better product—a reason to increase expenditures. More recent studies of incentive budgeting (Priest, Becker, Hossler, and St. John, 2002) and privatization (Priest and St. John, 2006) in public universities reveal that as public universities adapt to the new financial environment they develop budget systems to provide incentives to raise revenue from instruction and research. The problem of finding revenue to support educational activities has been pushed down to academic units. At the same time, there is a push toward cheaper education—lower tuition and lower expenditure per student—in public two-year colleges (St. John, 2003; Voorhees, 2001). This market differentiation is another outcome of the movement toward privatization of public colleges that needs to be better understood. The argument that the market forces related to privatization help explain changing patterns of finance within institutions merits more attention and is a focus of this study. Given the emphasis on market forces and privatization, complaints about spending in research universities are misguided. Market forces encourage some institutions to improve quality as a means of attracting students and encourage others to economize and offer a lower cost product.

1.3

Critical questions

The review of trends and research findings in Refinancing the College Dream addressed some questions about education reform and public finance but suggested others for more detailed investigation. Serious questions remain about whether education policies influence improvement in collegegoing rates. To untangle further how state education policies influence preparation for college, this study focuses on the linkage between policies on high school curriculum and outcomes related to student preparation. A second set of questions relates to the role and influence of privatization and other public finance strategies in promoting or inhibiting educational opportunity. Privatization is more than an ideology; it is also a means of shifting some of the burden of paying for higher education from taxpayers to students and their families. Rather than just consider the relationship between privatization and enrollment rates, we also need to examine enrollment distribution and whether there is equal opportunity to enroll in public four-year colleges for equally prepared students. 3

With the shift of students to private colleges and the increases in tuition in both public and private colleges, government spending per student in the U.S. system declined in the 1980s and 1990s (St. John, 2003).

32

2.

Chapter 2

FINDING JUSTICE IN EDUCATION AND PUBLIC FINANCE POLICY

Critical questions about education and public finance policies require that we situate the analysis in an understanding of the public interest. The earlier book used Rawls’s theory of justice as a starting point for reframing analysis of the linkages between public policies and college access. Below, I explore how Rawls’s theory relates to the topics of this study—the consequences of accountability schemes (i.e., aligning curriculum and testing with enrollment criteria for four-year colleges) and privatization strategies.

2.1

Defining the public interest

John Rawls’s theory of justice (1971, 2001) is a useful starting point for defining the public interest in the financing of schools, colleges, and students. His theory provides a moral lens for thinking about current education policy choices. In particular, his three principles provide a means of finding balance among competing interests in the education debates: • Principle 1 relates to basic rights, which all individuals have in a democratic society. The right to an education is widely accepted (Nussbaum, 1999; Sen, 1999), and in the U.S. equal access to college should be a right for those who qualify academically. • Principle 2 relates to equal opportunity, which argues that if there is an inequality it should favor the most disadvantaged. The historic emphases on equal opportunity in school desegregation and student financial aid are a few of many examples of this approach in education policy. • The “just savings principle” relates to cross-generation equity, which includes the use of taxation to support education. In the current context of majority concern about tax rates, it is important to balance taxpayer costs with concerns about equity and basic rights in education. Among the basic rights in principle 1, the right to an education is generally accepted. In fact, school enrollment is mandatory in the U.S., but there is still disagreement about what should be included in a general list of rights and liberties. In their discussions of women and developing countries, Nussbaum (1999) and Sen (1999) have explicitly argued for basic rights to education and literacy. In most countries, it is appropriate to consider education as a basic right, given that a K–12 education is a general requirement for youth and universal attainment of high school education is considered a national goal as well as an individual right. While it may seem extreme to argue that a college education should be a right for those who qualify academically, the National Center for Education Statistics has come

2. The Public Interest

33

close to making this argument in its official reports. Specifically, consider the following statement: Although there are differences by income and race/ethnicity in the fouryear college enrollment rates of college-qualified high school graduates, the difference between college-qualified low-income and middle-income students, as well as differences among college-qualified Black, Hispanic, Asian, and White students, are eliminated among those students who have taken the college entrance examinations and completed an application for admission, the two steps necessary to attend a four-year college. (NCES, 1997a, p. iii) While this statement and others like it in the NCES reports on access to higher education does not claim that access for prepared students is a right, it does argue that this standard has been attained. I have two concerns about this statement: (1) it reflects an appropriate basis for thinking about basic rights in the current period, but (2) it is based on fundamentally flawed research. The major error in the NCES analysis was that it included college applications (measured in response to questions asked during the senior year) as an indicator of preparation. In fact, many students in the National Education Longitudinal Study class of 1992 who went to college did not apply in advance and some who applied during their senior year did not go. When the academic indicators—high school courses, test scores, and so forth—are used as indicators of preparation, then it is evident that large numbers of students were left behind, as has been illustrated by numerous reanalyses (Advisory Committee on Student Financial Assistance (2002); Fitzgerald, 2004; Lee, 2004; St. John, 2003). The report introduced a standard that merits consideration even if the analyses on which it was based were fundamentally flawed. Principle 2 relates to equal opportunity and essentially argues that if there is an inequality it should favor the most disadvantaged. The historic emphases on equal opportunity in school desegregation and student financial aid are a few of many examples of this approach in education policy. The equal rights provisions of the federal constitution provided the basis for federal litigation of desegregation of higher education in the U.S. (Brown, Butler, and Donahoo, 2004; Conrad and Weerts, 2004), while federal legislation—the Higher Education Act of 1965 (HEA), as reauthorized over time for student financial aid programs (Title IV)—has emphasized the goal of equalizing educational opportunity (Advisory Committee on Student Financial Assistance, 2001; National Commission on the Financing of Postsecondary Education, 1973). Thus, to the extent that access to higher education is a goal of public policy in the United States, there is a legal basis

34

Chapter 2

for considering whether access is equal among diverse income and racial/ethnic groups when there is equal preparation. There is also a question in the U.S., and in other countries, about whether all groups have equal opportunity to prepare (Lleras, 2004). Initially, education in the U.S. was the sole responsibility of parents, local communities, and states—as the federal constitution left this responsibility to states. The federal government first got involved in education through the National Defense Education Act of 1958 (NDEA), an effort to provide general support for programs in science and math. Then the Elementary and Secondary Education Act (ESEA) of 1965 also established a federal role in compensatory education (providing additional financial support for educational programs for students who were at risk educationally) and special education programs (providing funds for students with special educational needs). The ESEA, as reauthorized, established a federal role in equalizing opportunity, while the NDEA demonstrated the federal interest in improving the basic quality of education—the basic right. The just savings principle relates to cross-generation equity, which includes the use of tax dollars to support education. In the current context of majority concern about tax rates, it is important to balance taxpayer costs with concerns about equity and basic rights in education. However, Rawls also argued that funding for programs that support the difference principle should be based on rights within a generation. In other words, once the public decides to provide education, the difference principle merits consideration, including equal rights to education. The just savings principle can be and has been used to make arguments to increase public funding. Historically, arguments about economic development—including the impact of funding for education on economic productivity—provided a rationale for public funding at the state level (Honeyman, Wattenbarger, and Westbrook, 1996; McKeown, 1996; Trammell, 2004) and federal programs (St. John and Parsons, 2004; Slaughter, 1991). While these arguments may be useful in constructing rationales for funding, they have not been persuasive for public finding in a period of declining taxpayer support. In particular, as the public funding for students enrolled in state systems of higher education4 goes down, as it did in the last two decades of the 20th century (St. John, 2003), there is reason to consider both equity and the extent of opportunity (supply) in relation to expenditures and demand. In the U.S., the taxpayer share of costs per student went down in the 1990s as spending increased (St. John, 2003). However, the states did not maintain equal opportunity for enrollment, especially for enrollment in public four-year colleges. Thus, a decline in public dollars per 4

State systems include public two- and four-year college and private nonprofit and for-profit colleges and universities.

2. The Public Interest

35

student in state systems is a false efficiency if realized at the cost of equal opportunity. The other caveat to the use of efficiency measures (or costs per student) in analyses of the impact of public policies is that the measure of efficiency implicitly assumes quality is maintained. Previously, I argued that an efficiency gain—that is a reduction in expenditures per student—was false if it resulted in a reduction in educational quality (St. John, 1994). Since expenditures per student enrolled in each sector of higher education in the U.S. did not decline in the 1980s or 1990s, the period studied in Refinancing the College Dream (St. John, 2003), I did not explicitly consider the issue of false efficiencies attributable to the shift in public finance strategies. However, given the complexity of the privatization process, we should reconsider the role of quality in the analysis of efficiencies.

2.2

Walzer’s critique of Rawls

Rawls’s theory has been criticized for the lack of constraint on redistribution of income. In particular, the difference principle is considered as having a high implication for public cost and income redistribution through the tax system. Financing equity can, in theory at least, be very expensive. Perhaps the best known of the critics is Michael Walzer, author of Spheres of Justice: A Defense of Pluralism and Equality (1983). Some of the specific criticisms in this text merit consideration, especially as they apply to the aim here of redefining the public interest in education and public finance of education. Walzer’s critique focuses primarily on the difference principle. He argues as follows: It is possible to set limits to the new conversion patterns, to recognize but constrain the monopoly power of the talented. I think this to be the purpose of John Rawls’s difference principle, according to which inequalities are justified only if they are designed to bring, and actually do bring, the greatest possible benefit to the least advantaged social class. More specifically, the difference principle is a constraint imposed on talented men and women, once the monopoly of wealth has been broken. It works this way: imagine a surgeon who claims more than his equal share of wealth on the basis of the skills he has learned and the certificates he has won in the harsh competitive struggles of college and medical school. We will grant the claim, if, and only if, granting it is beneficial in the stipulated ways. At the same time, we will act to limit and regulate the sale of surgery—that is, the direct conversion of surgical skill into wealth. (p. 14-15)

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While some other critiques argue for expanding Rawls’s notion of basic rights (Nussbaum, 1999, 2001), Walzer argued here that aspects of the difference principle may apply to pluralistic society even if we do not accept Rawls’s income redistribution. My arguments about the application of Rawls’s theory to education (St. John, 2003), summarized above, were similarly constrained. I argued that the principles have direct applicability to the process of public funding of education, in relation to access to educational opportunity—both as a basic right and an equal right. In addition, my argument was also constrained with respect to income: I argued for appropriate use of tax dollars rather than income redistribution, leaving arguments about tax rates to others. My argument was that when using tax dollars in the public interest, public officials have a fiduciary responsibility to taxpayers as well as to recipients of services. More generally, this dispute about the meaning of the difference principle—between Walzer’s view and Rawls’s statement—is inconsequential to my application of Rawls’s theory for defining the public interest in education. In a very real sense, the politics of the past 20 years have reversed the trend toward social equity implicit in the arguments of both Rawls and Walzer. The tax rates have gone down and there has been a redistribution of wealth back to the high income group (Fogel, 2000). Setting arguments about public and private benefits of education aside for a moment, along with the notions about wealth redistribution, the equity principle has merit. The central element of Walzer’s critique of Rawls is that the principles of justice should be constrained and contextualized to fit pluralistic societies. Consider his restatement as a comment on the American welfare state: What sort of communal provision is appropriate in a society like our own? It is not my purpose here to anticipate the outcomes of democratic debate or to stipulate in detail the extent or forms of provision. But it can be argued, I think, that the citizens of a modern industrial democracy owe a great deal to one another, and the argument will provide a useful opportunity to test the critical force of the principles I have defended up until now: that every political community must attend to the needs of its members as they collectively understand those needs; that the goods that are distributed must be distributed in proportion to need; and that the distribution must recognize and uphold the underlying equality of membership. (p. 84) This restatement of principles is more constrained than Rawls’s, but consonant with my discussion of the application of the principles to education above. Consider the adjustment of the argument about equal educational opportunity made in this text. The argument about equal

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opportunity for a higher education is adapted to focus on a community of prepared students. My argument was that financial need should be an integral part of the policy conversations about access and, specifically, that students who are equally prepared for a four-year education should have equal opportunity to attend (St. John, 2003). This argument is perfectly compatible with Walzer’s restatement of Rawls’s principles. Walzer makes the following argument about the public role in education: Educating citizens is a matter of communal provision, a kind of welfare. I would suggest that we commonly conceive of a more specialized education as a kind of office. Students must qualify for it. They qualify, presumably, by some display of interest and capacity; these two yield nothing like a right to a specialized education, for necessary specializations are a matter of communal decision, and so is the number of places available for specialized schools. Students have the same right that citizens generally have with regard to office holding: that they be given equal consideration in awarding the available places. And students have this additional right: that insofar as they are prepared for office holding in the public schools, they should, so far as possible be equally prepared. (pp. 209-210) Thus, there is little disagreement here with respect to my argument about the application of Rawls’s principles to education and Walzer’s more constrained restatement. However, I don’t agree with Walzer’s notion that education should be equated with social welfare, largely because the social and individual returns from education differ substantially from social welfare. Education is an investment with very substantial returns to society in the forms of social mobility and economic growth, two benefits that cannot be attributed to welfare. While Walzer does not explicitly consider financial aid for college students, he does consider vouchers for private schools. He is generally supportive of their use in a pluralistic society: “A voucher program for specialized education and on-the-job training makes a lot of sense” (p. 219). He also recognizes the limitations of the model as well: “The voucher plan assumes the activism of parents, not the community at large, but narrowly, on behalf of their own children. But its greatest danger, I think, is that it would expose children to a combination of entrepreneurial ruthlessness and parental indifference” (p. 219). In higher education the process of privatization is well under way, so we need better ways of understanding the public interest in education and how public policies promote related outcomes.

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Thus, my application of the theory of justice to education, above, provides an appropriate basis for assessing the impact of education policies, even given the narrower application of justice principles proposed by critics. In fact, Walzer’s critique is situated in a notion that education is linked to the welfare state and supposes that privatization (i.e., vouchers) loom on the horizon. By bringing forward the just savings principle and considering taxpayer costs in relation to achievement outcomes (as proxies for principle 1) and equity outcomes (as proxies for principle 2), it is possible to develop an analytic framework that is applicable to market systems of education finance.

3.

UNTANGLING RATIONALES FOR REFORM AND FINANCE

When research on education outcomes has been considered in the debates about college access and attainment in the 1990s, there has been a striking lack of balance in interpretation (St. John and Parsons, 2004). Two sorts of problems impede balance in policy research and policy debates. One is that different academic disciplines consider different explanations for outcomes. For example, educationists concentrate on education causes of inequality while economists focus on economic explanations. However, a range of ideological views—from liberal to neoconservative—can be found among educators, economists, and policy analysts. Increasingly, untested beliefs are used as the basis for interpreting research, advancing agendas rather than providing realistic assessments of the effects of policies. Given these patterns, it is crucial to consider competing rationales for reform before considering how best to assess these divergent claims. Debates on access and college success have reflected a progression of ideological arguments about education and public finance among economists and educationists. In the 1960s and 1970s there was a general consensus that further gains should be made in both access and equalizing opportunity, a belief rooted in the social progressive tradition (St. John and Parsons, 2004). There was more disagreement before 1980 about the extent and form of public investment in education than about whether these investments were needed (St. John, 1994). However, with the election in 1980 of President Ronald Reagan, a new conservative rationale took shape, arguing that the public had invested too much in education and welfare and that a reduction in tax rates was called for. After taking office, the Reagan administration began to focus on deficiencies in education and to use arguments about accountability rather than public subsidy as the primary public strategy. The administration’s arguments also had an influence on the economic rationales

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about loans. There have been responses by liberal educationists and economists to these new conservative arguments, reconstructing arguments for funding. A new liberal rationale has emerged that essentially argues for expanding opportunity, reconstructing arguments made by the new conservatives. However, until very recently there was very little balance in the rationales used to argue for education and finance reform.

3.1

Evolution of economic rationales

Economic rationales about education are important because of the substantial influence economists once had on education public policy. Human capital theory had substantial influence on public financing of education in the 1960s and 1970s, with the argument that both the public and private returns to investment in education merited consideration in public decisions about funding schools (Becker, 1964; Paulsen, 2001a). Human capital arguments were used to support the Higher Education Act (Slaughter, 1991) and continue to be used as a rationale for public funding of education (e.g., Honeyman et al., 1996). Most of the early arguments for funding supported subsidies to institutions and the view that funding led to better economic conditions. There is also empirical evidence that state investments in education are associated with growth of state economies (Paulsen, 1996a, 1996b, 1998), giving support to the economic argument. Two developments in economics merit attention relative to the shift in ideologies in public policy about educational opportunity. In the late 1960s economists began to question whether low tuition was both more costly and less equitable than high tuition and high need-based grants (Hansen and Weisbrod, 1967, 1969), an argument that had substantial influence on the reauthorization of the HEA in 1972 (Gladieux and Wolanin, 1976). This legislation expanded need-based grants—through Basic Educational Opportunity Grants (now the Pell grant program) and state student incentive grants—but did not provide incentives for states to increase tuition. Instead, tuition remained stable in the 1970s. In the 1980s, when the new conservative rationales came into play, colleges raised prices in response to reductions in direct state subsidies and federal funding for student aid (St. John, 1994, 2003). However, these new developments were being driven by another economic reality. The individual returns to education took an upturn in the late 1970s and have continued to be substantially higher for college graduates than high school graduates (Geske, 1996; Paulsen, 2001a, 2001b). The high private returns were used to argue for higher loans limits in the U.S. (Davis, 1997) and have been extended in new rationales for using private capital (Woodhall, 1988), including the argument for human capital contracts

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(Lleras, 2004). However, two problems with using private capital have surfaced, given the decline in need-based grants: Many low-income students cannot borrow or earn enough to pay the cost of attendance after grants, and some workers may not be paid sufficiently to merit loans. Some of the newer proposals, such as human capital contracts (Lleras, 2004), would partially remedy these problems because of income-contingent repayment, but they do change the implicit shift in the tax burden for promoting equity from the majority to low-income families. In other words, even if some loan scheme could be created that overcame the current limitations of using private capital, there continue to be equity issues that should not be dismissed without explicit consideration. Thus, several economic rationales have been used in support of different positions on college access and success. Econometric models are crucial to analyses of the effects of finance policies, as it is in arguments for these policies. However, we need to use the theory of justice to assess the impact of public policies because economic theory alone lacks adequate basis for considering public responsibility in education and public finance. Three specific rationales and indicators are used in this study. First, the new arguments about privatization are a central focus of this study. Public sector tuition, the average full-time tuition charge for public institutions in a state weighted by enrollment distribution in the state, provided a workable proxy measure for privatization. Rises in tuition charges in public colleges are related both to declines in state support and to increases in spending for education purposes within colleges. Both can be considered artifacts of privatization. Thus public tuition charges provide a good proxy measure of the extent of privatization. Second, a new argument has emerged about using merit aid to encourage preparation, enrollment, attainment, and economic productivity. This study uses state spending per FTE on non-need grants as a measure of state investment in this rationale. While there are a variety of non-need grant programs in states, most funding is for merit programs. And while there are other non-need grants, such as tuition subsidies for police officers, these programs reward a form of merit considered important in states. Therefore, this variable provides a reasonable proxy measure for state investment in the merit rationale. Third, the old argument about need-based student aid is also a focus of this study. There is a strong basis for considering the links between needbased grants and the opportunity to enroll in more expensive public and private four-year colleges. This study uses state funding of need-based grants per FTE as a measure of state investment in this strategy.

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41

Evolution of education rationales

In the 1960s and 1970s educationists were largely trusted to provide quality education. Local boards had control over schools and were involved in their regulation. States were involved in textbook selection for schools, but educationists were also integrally involved in this process. In higher education, the content of classes was, and still is, largely left to professors. Policy development was oriented toward expanding education and funding the expansion, as the post World War II baby boom worked its way through the education system. Three other educational rationales, in addition to the expansion of education, had political influence in the progressive period First, after the U.S. Supreme Court’s 1954 decision in Brown v. Board of Education, desegregation of education systems became paramount in education policy. At first, de jure segregation of schools in the southern U.S. was dismantled. Then the scope of desegregation was expanded to include de facto segregation in the rest of the country. Courts had a substantial influence over bussing and even education offerings but did not have success with desegregation per se, as the percentage of African American children in single-race schools remained high as a result of middle-class flight (Fossey, 2003; Orfield and Eaton, 1996). In higher education, the courts did not get involved until the late 1970s, and the student choice process substantially complicated these efforts (Brown et al., 2004; Conrad and Wertz, 2004). Nevertheless, racial isolation and affirmative action in admissions processes continue to be policy issues in both K–12 and higher education in the U.S. Second, public concern about education quality—especially in math and science—also has a relatively long history. After Sputnik was launched, the National Defense of Education Act of 1958 was passed with an aim to promote science education in the U.S. New math and science curricula were introduced, tested, and disseminated, but this policy priority did not hold much weight in policy debates of the 1960s and 1970s, as equity issues received more attention. Third, compensatory education (making up for deficits in preparation) and special education (addressing the needs of students with learning disorders or other special needs) were emphasized in the Elementary and Secondary Education Act (ESEA) of 1965 and subsequent legislation. In particular, Title I of the ESEA provided supplemental funding for schools with high percentages of low-income students so they could raise literacy and math skills. While schools had discretion about how to use these funds during the 1960s, federal policy has become more regulated over time (Wong, 2003). In special education, there were shifts from pull-out programs to mainstreaming special needs students (educating all students in regular

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classrooms) to exit exams for graduation that complicated the intent of these programs (Manset-Williamson and Washburn, 2003). More recently, the argument that improvements in academic preparation can reduce the gap for minorities and low-income students has been echoed by liberal groups (e.g., Kazis, Vargas, and Hoffman, 2004). A recent report from a national meeting on expanding opportunity stated the following “points of agreement” in the “next phase of education reform” (Kazis, 2004): • It’s not just about high schools; it’s also about postsecondary success. We can no longer accept high school as the terminal credential or learning experience for anyone. Policy must reflect the shift. • It’s not just about access to college; it’s also about postsecondary success. Too many policies focus on expanding access to college; too few focus on completion and success. Neither students nor society can afford this anymore. • It’s not just about efficiency of the pipeline; it’s also about equity. Getting more young people to and through postsecondary education is critical, but to do so in a way that promotes social cohesion and addresses the demographic trends in our country requires strategies that improve attainment and achievement for those who are most at risk of failing in high school and postsecondary programs. • It’s not just about attainment; it’s also about learning. The standards movement of the past decade has driven home the point that “seat time” and making it through school is no guarantee of learning. This is no less true of postsecondary institutions, but policies that address quality of college learning are in their infancy. • We’re all standards-based reformers now. There is no turning back from the dominant framework of high standards, common assessments of learning, and accountability systems linked to those assessments. Any reform effort must start from existing policies and work to make them more flexible and effective. Much remains to be done to improve on what has been built to date. • Older adolescents need more high-quality schools and learning options. We need more, different models for reaching, motivating, and engaging older adolescents in learning. Policy should stimulate alternatives, fund them adequately, and ensure that they do not degrade into a new tracking. • Policy matters: Finance, accountability, and governance all need to change to bring K–12 and higher education systems into a more coherent whole. Starting is the easy part. What is difficult is to specify the mix of policies that states can fit together to drive better postsecondary outcomes. (p. 9-10) This argument pulls together a new liberal position about access and represents a bold statement about reasserting the importance of equity, at

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least from the vantage point of educationists who do not think about economics. But we should also note what is missing. It does not focus on the market and the role of need-based student aid. This statement also carries forward standards and accountability without sufficient critical thought. The role and effects of standards, testing, and other accountability mechanisms must be evaluated rather than merely carried forward because they exist. It is crucial to balance economic and education arguments and to evaluate the consequences of the policy paths chosen, rather than to accept any single point of view or method as true without validating evidence. Such a balanced position is possible even if it remains elusive.

3.3

A glimmer of balance

Over the past several years, the Advisory Committee on Student Financial Assistance (ACSFA) has promoted a balanced framework that considers the roles of preparation and student aid (ACSFA, 2001, 2002; Fitzgerald, 2004). A shared agenda statement prepared by the Pathways to College Network (2004), an alliance of national organizations and funding agencies, comes closer to suggesting a balanced approach. Their agenda includes six principles (see Figure 2-2) similar to the points of agreement stated above. This agenda has more potential for contributing to the national discourse than the rationale of Double the Numbers because it points to the need for balance. It includes need-based financial aid and other forms of support along with an emphasis on preparation. And, with the focus on evaluation of the effects of reform rather than on holding schools and colleges accountable, this approach leaves room for and even encourages learning about reform. Fortunately, higher education in the U.S. is engaged in a process— reformulating agendas and coalitions—to address the equity gap that has developed. Any reasonable person should realize that reaching the underserved involves preparation and outreach along with financial support beyond the family ability to pay. As higher education moves toward privatized market systems from structured public systems, it is crucial periodically to assess the role and effects of finances as well as to adjust finance agendas to recognize the central importance of academic preparation to college success.

3.4

State education policy indicators

This volume examines the direct effects of education policies implemented within states. Using a range of policy indicators, these policies are related to the agendas that are now being promoted by various interest

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Figure 2-2. Pathways to College Network principles Principle One: Expect that all underserved students are capable of being prepared to enroll and succeed in college. All students must be challenged by high expectations. Principle Two: Provide a range of high-quality preparatory tools for underserved students and their families. Require a complete college-preparatory core curriculum. Make honors and college-credit courses available to all students. Provide early college awareness and broad support services to accelerate student learning. Principle Three: Embrace social, cultural, and learning-style differences in developing learning environments and activities for underserved students. Involve families in supporting learning. Affirm students’ social and cultural contexts. Create environments that support diversity and foster positive intergroup relations. Principle Four: Involve leaders at all levels in establishing policies, programs, and practices that facilitate student transitions toward postsecondary attainment from Elementary school to middle school, Middle to high school, High school to college, and College to work and further education. Principle Five: Maintain sufficient financial and human resources to enable underserved students to prepare for, enroll, and succeed in college. Staff schools and programs with well-qualified teachers, counselors, and leaders. Ensure equitable funding that addresses past deficiencies and meets student needs. Fund robust need-based financial aid. Principle Six: Assess policy, program, practice, and institutional effectiveness regularly. Use assessment models that demonstrate whether practices are working with underserved students. Focus on data that provide feedback for continuous improvement. Employ a variety of analytic tools, avoiding reliance on any single measure. Source: Pathways to College Network, 2004, A Shared Agenda: A Leadership Challenge to Improve College Success, www.pathwaystocollege.net.

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groups. In most instances we used actual policies, although in one instance we used a proxy for policy. Specifically, we used the percentage of students taking advanced placement (AP) courses within schools as an indicator of policies that encourage this practice. We could find no common indicators of state policies in this area. Otherwise, we selected policies that aligned with the new reform rationales and the underlying commitment to implement accountability as a means of educational improvement.

4.

THE BALANCED ACCESS MODEL

While the reframing of Rawls’s theory of justice provides a philosophical basis for this study, it does not provide the analytic framework. The balanced access model was originally developed as a means of examining the role of finances and the academic preparation in a reanalysis of statistics reported in NCES studies. Given the new focus on policy variables as indicators of policy rationales, it is import to refine the balanced access model to consider roles of education and finance policies in preparation, enrollment, and persistence. The refined model used in this book (Figure 2-3) provides an appropriate logical basis for examining the influence of K–12 policies and higher education policies on enrollment in higher education. This volume seeks a balanced approach to research that considers the role and influence of both education and finance policies. It is conceivable that analyses of the influence of both types of policies could support one rationale over the others. However, it is more likely that by examining the influence of both types of policies we can suggest revisions to rationales for both education reforms and public finance strategies. Research integrity requires using a balanced approach to the study of policy effects. In contrast, integrity in public policy requires balancing public interests in ways that are not only politically feasible but that also have a moral basis.

4.1

The logical model

At the center of this framework is the concept of the preparation pipeline used in most of the NCES studies of college access. Controlling for family education and income, the pipeline approach considers student expectations and plans, academic preparation (courses and grades), examinations (tests and scores), and college applications and admissions as steps toward enrolling in two- or four-year college. The selection bias in many of these reports came into play when the researchers excluded students who had not taken tests or applied for college from their access analyses (Becker, 2004).

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Figure 2-3. A balanced access and attainment model Academic Policies

Social Factors

Finance and Encouragement

High School Policy Funding Curriculum Standards Testing

Family Income and Education

Concerns About College Costs 1

Student Expectations and Plans

3

2 Encouragement

Academic Preparation

Direct link

3

Indirect link

Taking Examination 3 Application and Admission 3 Student Financial Aid

College Costs

Tuition and Grant $ 4

5

Enrollment in 2-Year and 4-Year College

7

6

Persistence to Degree

8

Source: Adapted balanced access model from E. P. St. John, Refinancing the College Dream: Access, Equal Opportunity, and Justice for Taxpayers, Johns Hopkins University Press, 2003.

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This restricted the analyses to people who thought they could afford to enroll. NCES failed to consider policy variables of any type. The fact that they failed to consider the direct role and influence of student aid on this population is well established (Fitzgerald, 2004; Lee, 2004) and is a serious problem, given their conclusion that preparation explained observed variation in the access outcomes (Choy, 2002; NCES, 1997a). The implication has been drawn that reforms in preparation can improve access. Yet if reform advocates are pushing for more accountability and higher standards, we should also recognize that these studies consistently look at math courses. NCES also failed to consider how different types of public policies—including standards, testing, and accountability—influenced math preparation. They found a correlation, but failed to consider what policies might influence the independent preparation variables included in their statistical equations. This logical model identifies the roles of both K–12 and higher education policies, providing a basis for a balanced assessment. Academic policies that can influence preparation and success include school funding, standards, graduation requirements, and other policies affecting high school students. These policies have a direct effect on preparation (i.e., requiring certain behaviors) and an indirect effect on college enrollment and success (i.e., through preparation). In addition, the refined model notes the effects of early financial concerns and commitments on preparation, along with other forms of encouragement. State and federal grant programs can have a direct influence on both college enrollment and college success.

4.2

Linking policy and outcomes

The refined logic provides a basis for examining the role and influence of both education and finance policies. Given these considerations, it is possible to assess six types of linkages using appropriate databases. Linkage 1: Family concerns about paying for college can have a direct influence on aspirations and preparation. Programs that provide guarantees of grant aid to students from low-income families have been shown to have an influence on preparation (St. John and Hu, 2006; St. John, Musoba, Simmons, Chung, Schmit, and Peng, 2004). In this volume the role of families is considered in both state- and individual-level analyses. In the state studies (part II), we used demographic indictors related to income, diversity, and education as logical controls for the roles of families at the state level. In contrast, in the individual-level studies (part III), we include family background variables similar to those considered in the NCES studies.

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Linkage 2: High school policies—funding, graduation requirements, and other policies—can influence whether students take preparatory courses. This is a direct link because the behavior is the object of the policy. In part II we examine the relationships between state education policies and state-level outcomes related to preparation, controlling for state demographics. Specifically, in chapter 3 we examine the effects of state education policies on SAT scores, high school graduation rates, and college enrollment rates— a sequence of outcomes that could be influenced by state K–12 policies. In the individual-level studies (part III) we focus on the effects of state education policies on completion of advanced math courses. We chose this outcome because it is the measure of preparation most often used in NCES analyses of the academic pipeline and in arguments for reform of high school curriculum. Linkage 3: Two types of linkages are evident in this category. First, encouragement programs can have a direct influence on preparation (Hossler, Schmit, and Vesper, 1999), but it was not possible to examine such programs as part of this study, and Indiana may have been the only state with such a program in the 1990s. Second, it is possible that state financial strategies have an influence on preparation. This argument has been made about merit grant programs in particular (Bishop, 2004) but could also apply to need-based grants and privatization. To test these propositions in the statelevel studies, we examined the relationships between state finance strategies used during the sophomore year (two years before graduation) and eventual graduation rates (see chapter 4). We also consider how finance policies during the first year after high school influenced enrollment rates, an outcome related to Linkages 3 and 4. Linkage 4: Tuition and state grant programs can have a direct influence on both whether students enroll and the type of colleges they enroll in—twoyear or four-year colleges. This topic is considered in both parts II and III. In chapter 5 we examine how state finance policies influenced enrollment rates in two-year colleges, public four-year colleges, and private colleges. These analyses provide new insights into the role of market forces across states. Then, in the analyses of NELS, we examine how state finance policies were associated with choices individuals made about enrollment in different types of institutions, including out-of-state institutions. Linkage 5: Currently the new education rationales argue for improved enrollments in four-year colleges. We conducted an initial test of this argument for chapter 5, examining how state education policies relate to enrollment rates in different types of public colleges. While we thought this analysis had sufficient logical basis at the state level, it held sway at the individual level. Instead, we consider the indirect effects, reflecting on the role of math preparation in college choice (chapter 6).

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Linkage 6: High school policies also have an indirect association with persistence in college—if high school courses are associated with persistence in college, controlling for other variables. Since this relationship is not direct but is conveyed through courses taken in high schools, we did not examine this direct linkage. However, we do consider the indirect effects in part III and IV. Linkage 7: State finance policies can have a direct association with enrollment and choice of four-year colleges, topics addressed in the reanalyses of NELS (chapter 7). Linkage 8: Finance policies can have a direct effect on persistence and degree attainment, a proposition that is well established in the literature. In this volume we examine this linkage as part of the analysis of NELS (chapter 8).

4.3

Examining the effects of policies rather than the effects of intervening variables

Perhaps the most distinctive feature of the analyses in parts II and III of this book is the focus on the effects of education policies and public finance rather than on intervening variables related to policy. Much attention is given to the role of math courses, but the role and influence of education policies that require math have received relatively little attention. Similarly, there is extensive literature on the effects of grants and loans, but relatively few studies have examined the influence of public finance policies on student outcomes. By examining the direct effect of policies rather than the effects of related intervening variables, we move the field of public policy forward. In fact, student aid packages are influenced by state grant programs, but a number of other forces influence these packages as well, including federal and institutional aid. So examining the direct links between state finance policies and student outcomes is new and necessary. In addition, the proxy variables for math courses have received an extraordinary amount of attention from researchers. Yet we know of no prior studies that examine how education policies influence the completion of different types of math preparation. The analyses of the direct of effects of K–12 policies have importance beyond debates about math courses, however, and relate directly to the discourse of accountability schemes.

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STATISTICAL CONSIDERATIONS

Since research on the direct links between policy and student outcomes is relatively rare in research on student outcomes related to access and attainment in higher education, it is important to consider how this approach relates to the debates about statistical methods in education policy research. In Refinancing the College Dream, I reviewed trends in indicators and evidence from prior research to build an understanding of the relationships between changes in policy and outcomes over three decades. This study focuses on the decade of the 1990s and uses two types of statistical models to examine the effects of policies. The analysis methods are inferential rather than causal but are an improvement over the description of trends and, nonetheless, have implications for policy. Five issues related to statistical methods and interpretation merit mention in framing the studies in this book.

5.1

Linkages between policies and outcomes

The accountability scheme currently in use in the U.S. and internationally uses indicators related to outcomes to assess quality but has little regard for factors that can influence outcomes or for the role policy plays in the outcomes. If such an accountability scheme achieved the opposite of its intended effects it could go unnoticed, given the many other factors that influence these outcomes. Analyses in chapters 3 and 5 use the types of indicators normally used in state accountability schemes, including test scores, graduation rates, and college-going rates. Chapters 4 and 5 examined the associations between state education policies and public finance strategies and these critical outcomes, controlling for appropriate demographic variables. Using regression, an inferential statistical method, to examine these relationships is a means of assessing the effects of policies in terms of the outcomes they are intended to influence. This provides a means of assessing the effects of state strategies—a step largely overlooked in the excellence movement.

5.2

Fixed effects regression of state indicators

The fixed effects regressions used in the analyses in part II provide control for the effects of the state context. When ordinary least squares (OLS) regression is used in a time series model with multiple years of data on the same states, it is not possible to control for the state effect. In contrast, the STATA program provides a means of controlling for each state, so the variance in outcomes attributable to the state context across the observations for the state are taken into account.

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Using the fixed effects method substantially improves the quality of the state-level models. The substantial R2 for the models presented in part II is largely attributable to the embedded control of variance attributable to state contexts. Had OLS regression been used, the R2 would have been much smaller. In addition, by controlling for these state effects, the fixed effects models measure the association between policy variables and the outcome measures.

5.3

Selection of variables and exclusion of cases

Perhaps the most serious problem with the NCES studies of access and persistence is that variable selection procedures were used that excluded cases from analysis (Heller, 2004). When individual records are used to report only on cases with certain selected characteristics, the results are misleading. For example, NCES selected students who had taken certain courses and who had applied for college, resulting in high rates of enrollment for the groups reported. The most serious methodological error was using college application to select cases for the analysis (Becker, 2004): Selecting students with certain high school courses or test scores excluded students who should be included in analyses. Rather than using variable selection to exclude cases, it is essential to code variables in ways that include all possible cases. The analyses of NELS in part III used an approach to variable coding that enabled us to include all possible cases in the analyses. For, example we coded variables for standardized tests in a design set that included students who did not take the tests along with categories of scores in the analyses. In addition to avoiding the mistake of falsely excluding cases, this approach allowed us to control for—and to consider the effects of—related variables that logically should be controlled for, according to sound theory.

5.4

Assumptions about aid adequacy (and the role of selection bias)

In the analyses of individual data in part III we do not use student record information of student aid packages. Because we used state-level policy variables, it was not necessary to examine the effects of student aid received. However, the analyses in this volume do rely on prior analyses of the effects of student financial aid. My approach to interpreting analyses of the effects of student aid using logistic regression analysis has been to consider aid variables and income variables in relation to each other—as a means of making judgments about aid adequacy. A positive association for aid variables in these models was

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assumed to mean aid was adequate, and negative association was assumed to be inadequate (St. John, 2003). When aid was not significant and income was not significant I interpreted the findings to mean aid equalized opportunity. This approach assumed that selection for award aid to students (i.e., needs analysis) was included in the effects of aid. Recently a few researchers have used adjustments for selection bias. In one recent study (e.g., Alon, 2005), the author presents both the logistic model without the selection variable and the regression with the selection. Alon’s analyses found income and parents’ education were the primary predictors of selection, so he excluded these variables when selection variable was included in the analyses of the effects of aid on persistence. In the model with income, parents’ education, and grant amount, income was positively associated with persistence and grant amount was neutral. In the second equation without income and parents’ education, the selection variable was negatively associated with the outcome while grant amount had a positive association. These findings are an improvement on the analysis of the effects of grants because they untangle the selection effect and the effects of aid amounts. However, these findings also illustrate that the earlier interpretation method was correct with respect to the notion of adequacy, although the statistical method left room for improvement. The analyses in part III examine the effects of state funding, as secondlevel variables, so it was not necessary to consider the direct effects of student aid amounts in the individual analyses. However, portions of this text carry forward prior conclusions about aid adequacy, an interpretation that still seems appropriate, given the state of knowledge.

5.5

Two-level models

Two-level models represent an improvement over single-level models in studies that consider the effects of education and finance policies on individual outcomes. In the analyses presented in part III, variables related to student outcomes similar to those used in the NCES studies—including background and preparation variables—are included in the first level. At the second level of these models we examine the significance of policy variables controlling individual characteristics. This two-level analysis made it possible to examine the effects of state policies. (See appendix for methods.)

2. The Public Interest

6.

53

STUDIES OF STATE INDICATORS AND STUDENT OUTCOMES

Education and public finance policies play a major role in promoting justice within democratic societies. For most of the 20th century, public decisions about investment in K–12 and higher education were guided by economic and education theories and research. Both sets of theories were used to argue for more public spending, as investments in the economy and in human capital. The new conservative shift of the 1980s changed the nature and substance of the education policy debates. Rather than arguing for expanding and equalizing opportunity, the dominant educational rationale became one of control and regulation of K–12 systems under the guise of accountability and standards. And rather than supporting growth in human capital, the new conservative rationale treated spending on education as an individual decision and benefit. It has taken proponents of education two decades to adjust to this shift, and the adjustment is not complete. Rawls’s theory of justice, especially his principles as reconstructed (St. John, 2003; this chapter), provides a basis for a framework for examining the public interest in education—one which treats the quality of education, the objective of accountability and standards, as a basic right and the equal opportunity to attain an education, the objective of public funding schemes, as a right related to the difference principle. This framework provides a balanced approach to the assessment of the impact of public policies on educational outcomes and is consistent with the history of education policy in the U.S. and the basis of constitutional support for education. This volume addresses important issues about public integrity as well as philosophical issues related to finding a more just approach to policy research. I am concerned about integrity in both research and the policy process. By using a balanced approach to research that considers alternative explanations—or rationales—for the role of policy in promoting and equalizing education opportunity, I hope these studies demonstrate one way of bringing integrity back into the research process.5 Excluding variables related to alternative or competing explanations for policy outcomes may reinforce beliefs but does little to inform policy makers about the consequences of policies. In addition, there is reason for concern about integrity in the policy process when most research is biased by beliefs and ideologies rather than balanced in the consideration of alternative policy rationales. 5

There is still plenty of room to improve methods used to evaluate the effects of public policies on educational outcomes. However, policy researchers should focus on the effects of policy rather than merely focus on intervening variables such as math courses completed.

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APPENDIX: USE OF HIERARCHICAL LINEAR MODELS By Anna S. Chung

In part III we analyze a number of factors affecting student outcomes. Because state-specific policies are of particular interest to us—and we know that every state has implemented the policies differently—we also want to know whether state policies had the same effect on student outcomes across states. From a data perspective, there are several nested mechanisms through which different factors are affecting students’ college access, enrollment, and attainment. Hierarchical linear models (HLM) are often used to answer research questions involving nested data (Raudenbush, 1988). When estimating the relationship between the factors and the outcome for a group of individuals in a traditional OLS model, we obtain one slope for each factor in the regression. Every slope in OLS is an estimate pooled within a population. In HLM, we produce a slope for each group within a given state. Utilizing multilevel models, we can account for the nested nature of our data, and we can observe whether the relationships we are testing differ across states. Hierarchical linear models are not the only method that accounts for variation between different groups. To separate fixed or random effects across states, one could use a fixed- or random-effects model. However, HLM also avoids another problem a traditional regression model would produce: aggregation, which would inflate the slopes on the state policy variables. There are two levels nested in our HLM analyses: Level-1 estimates the relationships among the individual-level variables and the outcome, and Level-2 produces the coefficient estimates for the state-level policy variables. Level-1 is frequently called the within-group model and Level-2 the between-group model, with state being a group in our analyses. It is important to keep in mind that while we distinguish a presence of two models, we think of them and estimate them as a whole. To estimate our models, we used HLM 6 (Raudenbush, Bryk, Cheong, and Congdon, 2004,) software. Besides reporting the odds ratios and the associated significance levels, we also listed the Level-2 variance component and the corresponding significance test in order to be able to interpret and evaluate the model. If the variance component was significant, we rejected the hypothesis of zero variance and concluded that there was a significant variation among states in the outcome of interest.

II

STATE INDICATORS

Chapter 3 ACADEMIC ACCESS

By Edward P. St. John and Glenda D. Musoba1

Increasingly, education reforms in the United States are rationalized as means of improving access to higher education through improvement in college preparation (Kazis, Vargas, and Hoffman, 2004). Many of the new state reforms—increasing math requirements for high school graduation, raising educational standards, aligning curriculum and standards, using standardized tests as high school graduation requirements, and using meritbased grant aid—are rationalized based on the assumption that preparation barriers impede access to higher education. The logic of this academic preparation rationale is supported by the No Child Left Behind Act (NCLB) and is reflected in policy changes over the past two decades. While the academic preparation rationale was based on analyses of the statistical relationship between high school courses and college enrollment (e.g., Adelman, 1995, 1999; NCES, 1997a; Pelavin and Kane, 1988), policies rationalized on the basis of these statistics have seldom been systematically evaluated. Specifically, the linkages between state education reforms and college enrollment have seldom been examined. This chapter discusses the evolution of the academic preparation rationale, our research approach, findings, and implications for reforms.

1

We thank Choong-Geun Chung and Ada B. Simmons for their support in an earlier version of analyses presented in this chapter: E.P. St. John, G.D. Musoba, and C.G. Chung, 2004, Academic Access: The Impact of State Education Policies, in Readings on Equal Education: Vol. 19. Public Policy and College Access: Investigating Federal and State Roles in Equalizing Postsecondary Opportunity, E.P. St. John, ed., AMS Press, Inc., New York, pp. 131-151.

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1.

Chapter 3

STATE EDUCATION REFORMS

States have used research from the National Center for Education Statistics (NCES) to rationalize their education policies, just as the federal government has used these reports to rationalize federal education legislation, such as NCLB. The types of education policies that are aligned with the new academic preparation rationale include • Instituting “honors” diplomas for high school programs with advanced math courses and other advanced courses • Implementing math standards that are compatible with the National Council of Teachers of Mathematics (NCTM) recommendations, a generally accepted approach • Encouraging high schools to offer advanced placement (AP) courses, especially in math • Requiring high school exit examinations for high school graduation • Requiring three or more math courses for high school graduation, as contrasted to the usual requirement of one or two courses • Encouraging students to take the SAT (or ACT)2 State policies on educational requirements changed substantially during the decade of the 1990s (see Table 3-1), as did other policies related to preparatory curricula. While only 12 states had math standards that were consistent with NCTM recommendations in 1991, that number grew to 100 percent adoption in 1997. Math standards progressively rose from only 11 states requiring three or more years of study in 1991 to 25 states having that policy in 2001, yet a relatively stable minority of states maintained local control. The average percentage of schools offering advanced placement (AP) courses in a state also grew steadily—from 44 percent to 55 percent. Other aspects of the education policy environment have remained relatively stable. In 1991 honors diploma policies had been implemented in 17 states. This remained relatively stable over the decade. The implementation of several other policies grew substantially. The number of states with policies requiring high school exit exams grew from 17 in 1991 to a high of 23 in 1999 but dropped to 20 in 2000. The average state participation rate in the SAT also was relatively constant over the decade. Advocates of the academic preparation rationale have argued that educational outcomes are not related to educational expenditures (Finn, 1990, 2001; Paige, 2003). Unlike prior periods when proposals for academic improvement were used to argue for increased education funding, this was not the case generally in the 1990s. However, K–12 instructional 2

Our models include only the SAT participation rate. In supplemental analyses, we found nearly identical results relative to the ACT participation rate.

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expenditures progressively rose from an average of $2,960 per student in 1991 to $3,650 per student in 2001, even when adjusted for inflation. Table 3-1. Trends in the implementation of high school reforms in the 50 states and the District of Columbia Education Reform Honors or advanced diploma (Number of states) NCTM (Number of states) AP courses (Average % schools participating) Exit exams (Number of states) 3+ math (Number of states) 1-2 math (Number of states) Local control (Number of states) Adjusted K–12 instructional expenditures 2 years prior/FTE (Average [$/1,000]) SAT participation rate* (National average) Number of states with full data

1991 17

1993 17

1995 17

1997 17

1999 18

2001 17

12

28

46

50

50

50

44.31

47.78

49.92

51.25

53.95

55.11

17

19

19

17

23

20

11

10

16

18

21

25

34

34

28

27

25

20

6

7

7

6

5

5

2.96

3.29

3.36

3.44

3.54

3.65

36.32

37.14

35.59

34.82

37.00

37.00

51

51

51

51

51

50

*Participation rates here are averages of the state average participation rates, therefore the averages are lower than the national averages reported by the College Board, which takes into account the size of the state cohorts. Sources of data: Provided in the “Research Approach, State Database” section of this chapter.

Arguments for the K–12 reforms have been largely based on correlation statistics between high school courses and subsequent educational attainment. However, these reformers have failed to evaluate systematically whether these education reforms have led to improvements in educational outcomes. Three outcomes are logically related to these new policies: test scores as a measure of achievement, high school graduation (and dropout) rates, and college enrollment rates for graduates. Trends in these indicators are displayed in Table 3.2, showing some evidence of improvement in two outcomes from 1991 to 2000. SAT scores improved modestly and college enrollment rates rose, but the high school graduation rates declined. Were these changes in outcomes related to the new education reform policies?

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Table 3-2. Trends in SAT scores, high school graduation rates, and college enrollment rates for high school graduates Combined SAT High School Grad Rate, College Enrollment Year National Average National Average Rate, National Average 1991 999 71.2% * 1992 1001 71.2% 53.31% 1994 1003 70.0% 55.67% 1996 1013 67.9% 56.39% 1998 1017 67.8% 56.85% 2000 1019 67.1% 56.88% *Data not available. Sources: SAT scores: Annual college-bound seniors reports, College Board; Graduation rates: Chance for College by Age 19 by State, Postsecondary Education Opportunity web site; College enrollment rates: Postsecondary Education Opportunity web site (www.postsecondary.org).

2.

RESEARCH APPROACH

We developed a state-level database from information on the 50 states to examine the relationship between the adoption of new education policies and outcomes thought to be related to them. The analyses in this chapter consider the effect of demographic characteristics and state education policies on each of these outcomes. The database, statistical methods, and data limitations are described below.

2.1

State database

A multiyear database (1991 through 2000) was compiled from a number of government and private sources. Variables were state-level policies and achievement data. For the outcome variables, recentered SAT scores were gathered from the annual national reports of the College Board, and high school graduation rates and college-going rates (enrollment the fall semester following high school graduation) were calculated by Tom Mortensen and are available as Excel tables in the Postsecondary Opportunity Newsletter under high school graduation (www.postsecondary.org). State policies for honors or advanced diplomas were collected in surveys by the Council of Chief State School Officers (CCSSO) (www.ccsso.org) and published in the Digest of Education Statistics (NCES, 1992, 1993, 2001b). This was coded as a dichotomous variable for each year with a value of one if the state had a policy and a value of zero if the state did not. Assessments of whether the state’s mathematics content standards were in compliance with NCTM recommendations were also collected in the same survey by the CCSSO (NCES, 1999). Each year of implementation coded to

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a dichotomous variable. The percentage of schools in each state participating in the AP program and offering AP courses was drawn from the College Board web site (www.apcentral.collegeboard.com) and by mail from the College Board staff. An alternative variable, the percentage of students in the state taking an AP exam, was also available for some years, but preliminary testing showed that these two variables functioned in a similar manner in the regression (both significant and in the same direction in a test run) and correlated highly with one another. Because the percentage of schools rather than students is under greater policy control, this variable was selected. State exit exam policy implementation dates are reported in the State Student Assessment Program 1998-99 school year database from NCES. This was cross-referenced with CCSSO survey data published in the Digest of Education Statistics (NCES, 1993, 2001b) and coded as a dichotomous variable, whether or not the state has an exam. Math graduation curriculum requirements were also compiled in the CCSSO surveys for 1992, 1995, 1998, and 2000 and were reported in the Condition of Education reports for those years. The number of credits in math required for graduation was collapsed into three groups: high (three or more), low (one to two), and states that allow local school boards to control graduation requirements. Data on the average K–12 school funding for each state was available from the NCES web site. Only instructional expenditures were counted, and expenditures were divided by the enrollment or FTE to get a per-student expenditure and rescaled by dividing by 1000. The state average SAT participation rate is published each year in the annual national report from the College Board and is available on their web site. If a data point for a state policy variable was missing for a particular year, the value from the prior year was carried forward. This was appropriate because most policies remain in place once implemented. In a year a particular policy was reported to be in development, we assumed it could not reasonably be expected to be fully implemented; therefore, in dichotomous variables it was coded “no” in those years. State-level demographic data provided controls for those demographic variables known to be associated with achievement. For instance, the state educational attainment level, as measured by the percentage of adults with a bachelor’s degree or higher, also serves as a proxy for the average parent education level in the state. State-level education data was collected from the Current Population Survey (CPS) conducted by the U.S. Census Bureau. Average state poverty levels, also collected by the U.S. Census Bureau, were used as an approximation for the financial status of the citizenry in the state. The ethnic composition of the state, as collected through the NCES Common Core of Data, represents the state demographic context.

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The descriptive statistics for the variables included in the indicators analyses of academic access are presented in Table 3-3. The poverty rate averaged 12.9 percent across states, while the percentage of minorities ranged from an average of 11 percent African American to 6.1 percent Hispanic and 4.8 percent other minorities. The average combined SAT score was 1056, while the average graduation rate was 72.6 percent, and the average enrollment rate of high school graduates was 52 percent. NCTM standards were implemented in 79 percent of the cases analyzed. The SAT participation rate averaged 36.6 percent. About half of the high schools had AP courses. There was substantial variation in math requirements across the cases. Table 3-3. Descriptive statistics for variables in the fixed effects analysis of SAT score, graduation rate, and enrollment rate averages across year for all U.S. states Independent Variables % Poverty % Black % Hispanic % Other minorities (= Native American + Asian) % Population with BA or higher Enrollment when the cohort was in 9th grade SAT participation rate Honors or advanced diploma policy State guidelines consistent with NCTM standards % of schools participating in the AP program High school exit exam required High (3 or 4) math credits required for grad Local board control of math requirements for grad K–12 instructional expenditures per FTE

Mean/Percentage 12.92% 11.01% 6.10% 4.81% 22.78% 66.00% 36.65% 33.66% 79.08% 50.82% 37.75% 34.77% 11.75% $3,380

Outcomes Measures Combined SAT score High school graduation College enrollment by high school graduates

2.2

1056 72.63% 52.12%

Statistical methods

The analyses below used a fixed-effects ordinary least squares regression procedure, which is most appropriate for time series or repeat measures data because it takes into account the association between the values within each state over time. Our regression analyses used data from 1991 through 2000 and were conducted with STATA 8.0 software. Fixed-effects regression provides a means to control for states’ effects. The statistical method used in this is a fixed effects version of ordinary least squares (OLS) regression. It is OLS because it uses a continuous outcome. However, for the sake of clarity,

3. Academic Access

63

we refer to this as OLS, even though a fixed-effects regression was used. For each year there are multiple variable observations for the states. The regression method allows for defining each state so that the variance related to the state is controlled for within the explained variance (R2). Consequently the fixed effects analyses of state indicators explain a more substantial portion of the outcomes than does OLS regression.3 These analyses present both the standardized and unstandardized coefficients for the variables in each regression. The unstandardized coefficients would be used to estimate effects, if this were an appropriate step. In these analyses we were focused on whether these education policies had an effect on key indicators rather than on the extent of effect. However, we present these coefficients for readers who might want to compare across studies. The standardized coefficient provides a means of comparing the relative effects of the different variables. Variables that have a larger standardized coefficient have more substantial effects than do variables that have the smaller coefficient. We present each of the analyses in three steps. The first step indicates how much variance is explained by demographic variables and by implementation of state education policies. The second step adds participation rate in the SAT, and the final step adds the education policy variables. This allows us to consider the added variance in the outcomes explained by SAT participation and by additional variables. We also consider whether the significance of variables changes across models, as a means of considering the meaning of relationships among variables.

2.3

Study limitations

Using a state-level database provides a logical approach for examining the relationship between policies and these state-level outcomes. Further, by controlling for demographic characteristics that can influence these outcomes, we use a logically sound approach for assessing the relationship between high school policies and outcomes related to academic preparation. This approach has some limitations. First, given the relatively small number of states and large number of policies, there were limits to the number of demographic variables we could have used in this model (see Table 3-3). Given these constraints, we went through a number of steps to refine the model. We combined CPS statistical categories on ethnicity to come up with three distinct measures: percentages of African Americans, Hispanics, and other minorities in the population. We tested different measures of educational attainment, including the 3

For the same models using OLS regression see St. John, Chung, Musoba, Simmons, Wooden, and Mendez (2004).

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percentages of both high school graduates and bachelor’s degree recipients in the state populations, but concluded that the single measure, i.e., percentage of the population with bachelor’s degree or higher, was appropriate. We also tested other measures of wealth and income but concluded that state poverty rate was the most appropriate predictor. Second, the SAT is not the only college entrance examination, but it is widely used as an outcomes indicator of education quality for K–12 education (College Board, 2001; LeFevre and Hederman, 2001), and scores for the ACT, the other major college entrance examination, were not available for all of the years studied. However, we took steps to consider the consequences of using SAT scores. We tested an SAT model with ACT participation rates but found it did not make a difference. Therefore, we concluded these analyses provide an appropriate measure of the effects of state education policies on educational achievement.4 Third, we also recognized that individual-level data is more typically used to study access outcomes. However, analyses of individual databases, such as the National Educational Longitudinal Study (NELS), could use multilevel models when the effects of state education policies were examined (see part II). This approach also presents problems, given that it is not possible to use the weights with hierarchical linear modeling. Fourth, no attempt was made here to estimate the magnitude of effects of state education policy variables. The focus is on whether the new education policies had the intended effects on college enrollment and/or had unintended effects on high school graduation rates. Given the findings, we did not think it would be productive to provide further estimates of effects.5 Finally, since our aim was to untangle the effects of education and public finance policies rather than to build the best statistical model, we did not provide a combined model with education and public finance policies in this chapter. Instead, our focus is on building understanding of the role and influences of both education and finance policies. We conducted analyses with combined models on college enrollment rates and found that the effects were similar in the combined model to the ones reported in these chapters. Separate treatment of the two models is consistent with the logic of the balanced access and attainment model.

4 5

We also used this model to predict ACT scores and had similar findings. Specifically we found no effects of state education policies on college enrollment rates and found negative effects on high school graduation rates. In the future it might be worth estimating the number of dropouts attributable to policies such as increasing math requirements for graduation. However, we did not think these analyses would provide a constructive contribution at the present time.

3. Academic Access

3.

65

FINDINGS

The analyses of the impact of state policies are presented in three parts: student achievement (SAT scores), high school graduation rates, and college enrollment rates. Fixed-effects OLS regressions for each of the three outcomes are examined below.

3.1

Student achievement outcomes (SAT scores)

The analysis of the effects of high school reforms on student achievement reveals that both demographic variables and state education policies were associated with SAT scores (Table 3-4). State demographic characteristics explained a substantial portion of the variance (see adjusted R2), but SAT participation rates and high school reform did improve prediction. Poverty rates had a negative association with SAT scores, while the percentage of other minorities in the population and the percentage of the population with bachelor’s degrees were positively associated with the outcome across all three models. However, the coefficient for two of these variables—poverty rate and percentage of other minorities—dropped in significance (from .01 to .05) when state education policies were considered. There is a confounding relationship between state education policies and these demographic characteristics. States with higher poverty and lower percentages of other minority populations6 apparently were quicker to implement some of these reform policies, an issue that should be examined further in the future. The percentage of African Americans in populations had a modest positive association with SAT scores before we controlled for SAT participation rates. Several of the states with high percentages of African Americans emphasize the ACT and/or have lower percentages of high school students taking the SAT. The percentage of the high school population taking the SAT was negatively associated with SAT scores, an expected finding (Powell and Steelman, 1996), before the final step. Interestingly, SAT participation rates ceased to be significant when other state policies were considered. Apparently, states that take action on the other policies are also states that encourage high school students to take the SAT. This is a reasonable intermediate hypothesis, given that such an approach would be consistent 6

Since the association of poverty with SAT scores was strongly negative before state education policies were considered but weaker after they were considered, it is apparent that high poverty (greater negative) would be mitigated by the policies. The reverse would be true for minorities, in which case the positive effects of the variables would be mitigated.

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with the academic access rationale described above. The percentage of the high school population taking the SAT was negatively associated with SAT scores, an expected finding (Powell and Steelman, 1996), before the final step. Interestingly, SAT participation rates ceased to be significant when other state policies were considered. Apparently, states that take action on the other policies are also states that encourage high school students to take the SAT. This is a reasonable intermediate hypothesis, given that such an approach would be consistent with the academic access rationale described above. Three of the state policies were statistically significant and positively associated with SAT scores. Having implemented NCTM math standards had the largest standardized beta and the strongest statistical association (.01). Both the requirement of three or more math courses for graduation and the percentage of high schools offering AP exams were significantly and positively associated with average SAT scores. These findings offer relatively strong support for the use of NCTM standards in math and modest support for AP courses and raising math requirements for graduation—as strategies for improving test scores. However, to test the notion that requiring math courses improves access we must look at the other outcomes as well.

Variables % Poverty % Black % Hispanic % Other minorities (= Native American + Asian) % Population with BA or higher Enrollment when the cohort was in 9th grade SAT participation rate Honors or advanced diploma policy State guidelines consistent with NCTM standards % of schools participating in the AP program High school exit exam required High (3 or 4) credits in math required for grad Local board control of math requirements for grad K–12 instructional expenditures per FTE/$1000 Model Statistics Adj R2 N P-value for F test that all ui=0 Note: *** p<0.01, ** p<0.05, * p<0.1 0.9794 510 0.000

Step 1 Demographic Unstand. Stand. Coeff. Coeff. Sig. -91.589 -0.055 *** 268.472 0.486 ** 56.570 0.069 1513.939 2.072 *** 1.523 0.112 *** 0.000 -0.042

0.9799 509 0.000

Step 2 Demographic & Participation Rate Unstand. Stand. Coeff. Coeff. Sig. -91.123 -0.054 *** 130.456 0.236 100.246 0.122 1540.952 2.109 *** 1.595 0.117 *** 0.000 -0.039 -0.532 -0.216 ***

Table 3-4. Three-step fixed effects regression for SAT combined scores in the states

0.9831 505 0.000

Step 3 Demographic, Participation & Policy Unstand. Stand. Coeff. Coeff. Sig. -51.862 -0.031 ** 152.927 0.275 -60.112 -0.074 598.809 0.829 ** 1.095 0.080 *** 0.000 0.079 -0.264 -0.108 -4.126 -0.030 * 10.083 0.067 *** 0.211 0.065 ** 2.676 0.020 3.699 0.026 ** -3.973 -0.020 3.360 0.042

3. Academic Access 67

68

3.2

Chapter 3

High school graduation

The analysis of high school graduation rates (Table 3-5) reveals a different set of relationships for demographic variables and state education policies. There are clearly substantial differences between the ways state policies influence achievement outcomes and high school graduation rates. A substantial part of the variance in high school graduation rates was explained by demographic differences across the states. Poverty rates were positively associated with high school graduation rates, while the percentage of other minorities and the percentage of the population with bachelor’s degrees had a negative association. Further, the significance of poverty and education level decreased when state education policies were considered, indicating a relationship between demographic characteristics and state education policies. Participation rates in the SAT had a modest positive association with high school graduation rates before the effects of state education policies were considered. This suggests that expanding participation in college entrance exams could improve graduation rates. One of the new state policies—high school exit exams—had a modest positive association with high school graduation rates, a finding that is contrary to some other studies (Jacob, 2001; Manset-Williamson and Washburn, 2003). Perhaps defining a minimum threshold of achievement enables more students to graduate, controlling for other requirements they face and the availability of resources. Other than exit exams, education policies did not improve high school graduation rates. Implementation of NCTM standards, the percentage of high schools providing AP courses, and the requirement of three or more math courses for high school graduation were negatively associated with graduation rates. While these policies appeared to have contributed to improvement of SAT scores (Table 3-3), they were detrimental to high school graduation rates (Table 3-5). Funding for K–12 schools was positively associated with high school graduation rates. This finding contradicts the claims of new conservative reformers who argue that funding does not make a difference in educational outcomes (Finn, 1990, 2001; Paige, 2003). While funding was not related to test scores (Table 3-4), it was the only policy variable with a strong positive association with high school graduation (Table 3-5).

Variables % Poverty % Black % Hispanic % Other minorities (= Native American + Asian) % population with BA or higher Enrollment when the cohort was in 9th grade SAT participation rate Honors or advanced diploma policy State guidelines consistent with NCTM % of schools participating in the AP program High school exit exam required High (3 or 4) credits in math required for grad Local board control of math requirements for grad K–12 instructional expenditures per FTE Model Statistics Adj R2 N P-value for F test that all ui=0 Note: *** p<0.01, ** p<0.05, * p<0.1 0.9121 510 0.000

Step 1 Demographic Unstand. Stand. Coeff. Coeff. Sig. 0.212 0.092 *** 0.206 0.270 -0.381 -0.336 -4.285 -4.251 *** -0.003 -0.146 *** 0.000 0.271

0.9133 509 0.000

Step 2 Demographic & Participation Rate Unstand. Stand. Coeff. Coeff. Sig. 0.211 0.091 *** 0.517 0.678 -0.478 -0.420 -4.351 -4.317 *** -0.003 -0.154 *** 0.000 0.267 0.001 0.349 **

Table 3-5. Three-step fixed effects regression for high school graduation rates in the states

0.9266 505 0.000

Step 3 Demographic, Participation & Policy Unstand. Stand. Coeff. Coeff. Sig. 0.144 0.063 ** 0.227 0.295 -0.292 -0.259 -2.592 -2.594 *** -0.002 -0.097 ** 0.000 0.324 * 0.000 0.028 -0.001 -0.005 -0.015 -0.073 *** -0.002 -0.343 *** 0.012 0.063 ** -0.034 -0.176 *** 0.018 0.066 0.022 0.196 ***

3. Academic Access 69

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Chapter 3

When we contrast the two sets of findings, it is apparent that the policies that are associated with improvement in achievement outcomes are negatively associated with high school graduation rates. The new education policies may have contributed to the modest increase in SAT scores during the 1990s, but they were also associated with the more substantial and troubling drop in graduation rates. Clearly, more balance is needed in education policy.

3.3

College enrollment rates

The ultimate test of efficacy of the new high school policies, according to the pipeline theory, is whether the policies improve college enrollment rates for students who graduate from high school. The evidence suggests they do not (Table 3-6). Two of the demographic variables were consistently associated in our analyses with the outcomes across the three steps. Poverty rates had a strong negative association with college enrollment rates, while the percentages of other minorities in the populations had a modest positive association with the outcomes. The size of the ninth-grade cohort was negatively associated with college enrollment rates and was strongest when the state education policy variables were considered. This suppressed negative association is related to the implementation of the state policies. Only one of the policy variables had a significant association with college enrollment rates. Having honors or advanced diploma programs decreased the percentage of high school graduates who went on to college. It is conceivable that ranking different types of diplomas discouraged some students from pursuing college. However it is also possible that other forces in states explain this finding. The bottom line is that the new state education policies had little effect on college enrollment rates for students who graduated from high school. Further, several of these policies were actually associated with lower high school graduation rates within states. These findings suggest compelling reasons to question the assumptions of the new conservative reformers.

Model Statistics Adj R2 N P-value for F test that all ui=0 Note: *** p<0.01, ** p<0.05, * p<0.1 0.7572 250 0.000

0.756 250 0.000

0.7726 248 0.000

Table 3-6. Three-step fixed effects regression for college enrollment rates in the states Step 2 Step 3 Step 1 Demographic & Demographic, Demographic Participation Rate Participation & Policy Unstand. Stand. Unstand. Stand. Unstand. Stand. Variables Coeff. Coeff. Sig. Coeff. Coeff. Sig. Coeff. Coeff. Sig. % Poverty -0.573 -0.296 *** -0.573 -0.296 *** -0.525 -0.270 *** % Black 0.321 0.415 0.321 0.415 0.947 1.227 % Hispanic -0.407 -0.454 -0.407 -0.454 -0.093 -0.104 % Other Minorities (= Native American + Asian) 3.104 3.883 ** 3.104 3.883 ** 3.525 4.428 ** % Population with BA or higher 0.002 0.132 0.002 0.132 0.002 0.104 Enrollment when the cohort was in 9th grade 0.000 -0.846 * 0.000 -0.846 * 0.000 -1.249 *** SAT participation rate 0.000 -0.174 -0.001 -0.344 Honors or advanced diploma policy -0.044 -0.285 *** State guidelines consistent with NCTM standards -0.003 -0.017 % of schools participating in AP program 0.001 0.238 High school exit exam required 0.006 0.042 High (3 or 4) credits in math required for grad 0.017 0.107 Local board control of math requirements for grad 0.002 0.010 K–12 instructional expenditures per FTE -0.021 -0.235

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STATE VARIATION IN PREPARATION

To build a better understanding of the role of education policies in changes in academic preparation, it can help to examine specific state reports on changes in SAT scores (Table 3-7) and high school graduation rates (Table 3-8). These changes are discussed below and presented at the end of the chapter.

4.1

Changes in SAT scores

There was a great deal of variation across the states in the changes in average SAT scores over the ten years studied, ranging from a 70-point gain in Wisconsin to an 8-point drop in New Mexico and Delaware (see Table 37). However, the reported relationship between education policies and changes in SAT scores does not provide a particularly convincing explanation for variations in the SAT scores. A comparison of the top five gainers and the lower five states illuminates the interpretation problem. Honors or advanced diplomas for graduates were negatively associated with SAT scores (Table 3-4), but most of the states in both groups did not have these policies. NCTM standards were also positively associated with SAT scores. All of the top states and bottom states had these policies, but the lowest five states had implemented these policies earlier, between 1992 and 1994, compared to 1995 or 1996 in four of the five top states. The top states did have more expansion of AP courses during the ten years. The number of math courses required for the general high school diploma was associated with higher average SAT scores. Individual states should carefully review state reports (Table 3-8) in relation to research on high school and college students. In Indiana, for example, we have studied the effects of high school courses on SAT scores for individual high school students and have traced the effects of these courses into and through two years of college (St. John, Musoba, and Chung, 2004b). Interpreted in relation to these analyses, the state SAT results for Indiana (see Table 3-7) do appear relevant. Specifically, the state of Indiana has taken steps to introduce college preparatory curricula in all high schools. Further, there was a statistical relationship between completion of both the preparatory and honors curriculum in Indiana and SAT scores (St. John, Musoba, and Chung, 2004b). Therefore, the explanation for the improvement in SAT scores in Indiana, in spite of the increase in SAT participation rates, is probably related to encouragement (e.g., Hossler, Schmit, and Vesper, 1999), improvement in state grants, and other factors.

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The state reports are potentially useful to states, but are appropriately interpreted with an understanding of other state education policies.

4.2

Changes in high school graduation rates

The state reports on high school graduation rates (see Table 3-8) also raise questions. Comparing the top five states and the bottom five states, for example, only five states had any improvement in high school graduation rates, ranging from a 6 percentage point improvement in New Jersey to a 1 percentage point gain in Utah. In contrast, lowest ranked states had drops in graduation rates of 11 percentage points in Georgia to 23 percentage points in Hawaii. Two areas of policy appear related to these differences. The states with most substantial gains in graduation rates also had greater gains in AP courses than the five states with the lowest gains. However, AP courses were negatively associated with graduation rates in the model. Although these findings are counterintuitive, the role of AP courses should also be considered in relation to school funding. The two states with the largest gains in SAT scores—New Jersey and Connecticut—were among the states with the most substantial increases in school funding. However, the three states with only a 1 percentage point gain in SAT scores had lower gains in funding—more similar to the majority of states. New Jersey increased funding by $2,452 per student and Connecticut increased funds by $2,301. In contrast, the lowest five states in changes in SAT scores had increases in funding between $1,553 in Georgia and $619 in Arizona. While school funding may provide the most compelling explanation for improvement in high school graduation in a few states, it does not explain the decline in the national average. The relationship between financial aid and high school dropout, therefore, merits serious attention (see chapter 6).

*State policies for offering honors diplomas were reported in 1992, 1995, 1998, and 2000. **If the original data said the policy was in development, one year was added beyond the year reported.

continued

Table 3-7. State reports on SAT scores: Change in average SAT scores 1990 to 2000 with policies significant in the fixed effects regression of combined SAT scores Year Math Percentage Change in Change in Credits in Guidelines of Schools % of SAT Score Math Credits in Consistent Participating Schools Math Between Required with in AP Participating Required for for Grad in Honors Diploma Offered 1990 SAT 2000 SAT 1990 and NCTM Program in in the AP 2000 in State* State Score Score 2000 Grad in 1991 Standards* 2000 Program Significance in the regression equation (read across) Neg. Pos. Pos. Pos. Pos. Pos. Wisconsin 1111 1181 70 none 1996 65.3 29.3 2 2 Illinois 1089 1154 65 none 1995 54.1 12.1 2 2 Minnesota 1110 1175 65 none 1995 44.6 14.6 1 Michigan 1063 1126 63 discontinued before 2000 1995 56.7 9.7 0 local Missouri 1089 1149 60 prior to 1992 1990 32.6 12.6 2 2 Arkansas 1077 1117 40 none 1993 33 14 3 3 948 988 40 discontinued before 1998 1992 67.7 10.7 2 3 North Carolina North Dakota 1157 1197 40 none 1993 8.8 4.8 2 3 Alabama 1079 1114 35 prior to 1992 1989 36.3 -8.7 2 4 Louisiana 1088 1120 32 prior to 1992 1995 24.6 3.6 3 3 D.C.** 950 980 30 none 1995 64.4 6.4 2 3 Oregon 1024 1054 30 none 1995 50.2 10.2 2 2 Washington 1024 1054 30 none 1995 58.1 10.1 2 2 Oklahoma 1095 1123 28 prior to 1992 1993 42 26 2 2 prior to 1992/statewide Indiana 972 999 27 1991 59.1 14.1 2 4 before 2000 Kansas 1129 1154 25 none 1991 24.4 6.4 2 2 South Dakota 1150 1175 25 none 1996 19.2 7.2 2 2

74 Chapter 3

*State policies for offering honors diplomas were reported in 1992, 1995, 1998, and 2000. **If the original data said the policy was in development, one year was added beyond the year reported.

continued

Table 3-7. (continued) State reports on SAT scores: Change in average SAT scores 1990 to 2000 with policies significant in the fixed effects regression of combined SAT scores Percentage Change in % of Schools of Schools Credits in Credits Year Math Change in Math in Math Participating Participating Guidelines SAT Score Required Required in AP in the AP Consistent Between 1990 2000 Program in Program Since for Grad for Grad SAT SAT with NCTM 1990 and Honors Diploma in 1991 in 2000 2000 1991 Standards** 2000 Offered in State* Score Score State Significance in the regression equation (read across) Neg. Pos. Pos. Pos. Pos. Pos. Ohio 1048 1072 24 between 1995 and 1998 1990 63.1 12.1 2 2 South Carolina 942 966 24 discontinued before 2000 1993 74 9 3 4 Georgia 951 974 23 prior to 1992 1992 65 16 2 3 1994 86.4 13.4 0 Local Massachusetts 1001 1024 23 none Hawaii 985 1007 22 between 1995 and 1998 1995 72.7 7.7 2 3 Mississippi 1090 1111 21 none 1993 38.7 7.7 2 3 Vermont 1000 1021 21 none 1995 72.2 16.2 3 2.5 1995 12.6 2.6 2 2 Alaska 1015 1034 19 none Rhode Island 986 1005 19 prior 1995 70.1 11.1 2 2 New Jersey 993 1011 18 none 1993 87.8 12.8 2 3 1993 78.6 15.6 2 2 Utah 1121 1139 18 none Wyoming 1072 1090 18 none 1990 33.3 2.3 0 3 Iowa 1172 1189 17 none 1987 33.3 15.3 0 Local Connecticut 1002 1017 15 none 1996 85.2 8.2 3 3 1994 42 5 2 4 Idaho 1066 1081 15 none New York 985 1000 15 prior to 1992 1995 76.7 11.7 2 2 Tennessee 1102 1116 14 prior to 1992 1991 53.1 9.1 2 3

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Honors Diploma Offered in State* Neg. prior to 1992 prior to 1992 none prior to 1992 none discontinued before 2000 none prior to 1992 between 1995 and 1998 none none between 1998 and 2000 none none none none none

Year Math Guidelines Consistent with NCTM Standards** Pos. 1991 1991 1995 1988 1993 1995 1995 1993 1985 9999 1996 1993 1994 1992 1992 1993 1992

*State policies for offering honors diplomas were reported in 1992, 1995, 1998, and 2000. **If the original data said the policy was in development, one year was added beyond the year reported.

Change in SAT Score Between 1990 SAT 2000 SAT 1990 and 2000 Score Score State Significance in the regression equation (read across) Texas 979 993 14 California 1002 1015 13 Maine 991 1004 13 Virginia 997 1009 12 1028 1039 11 New Hampshire Florida 988 998 10 Nebraska 1121 1131 10 Kentucky 1089 1098 9 Maryland 1008 1016 8 Pennsylvania 987 995 8 Montana 1082 1089 7 Nevada 1022 1027 5 Colorado 1067 1071 4 Arizona 1041 1044 3 West Virginia 1034 1037 3 Delaware 1006 998 -8 New Mexico 1100 1092 -8

Change in % Percentage of Schools of Schools Credits in Credits in Math Math Participating Participating Required Required in the AP in AP Program in Program Since for Grad for Grad in 2000 in 1991 1991 2000 Pos. Pos. Pos. Pos. 63.1 34.1 3 3 74.7 14.7 2 2 63.3 18.3 2 2 74.7 8.7 2 3 79.5 26.5 2 2 64.8 12.8 3 3 21.7 3.7 0 local 66.4 13.4 3 3 79.3 11.3 3 3 63.4 15.4 3 3 34.3 12.3 2 2 38.7 -5.3 2 3 49.9 6.9 0 local 51 -1 2 2 55.2 0.2 2 3 94.7 -2.3 2 50 22 3 3

Table 3-7. (continued) State reports on SAT scores: Change in average SAT scores 1990 to 2000 with policies significant in the fixed effects regression of combined SAT scores

76 Chapter 3

Table 3-8. State reports on change in graduation rates: Change in graduation rates 1990 to 2000 with policies significant in fixed effects regression Credits in Ten-Year Year Math Percentage of Change in % of Credits in Math Math Ten-Year Change in Guidelines Schools Schools Required Required Change in Grad Rate Consistent Participating in Participating in Year Exit Exam 1990 to with NCTM AP Program in the AP Program for Grad in for Grad in School Funding 1991 2000 Implemented** $/FTE*** 2000 Standards* Since 1991 2000 Significance in the regression Neg. Neg. Neg. Neg. Neg. Pos. Pos. equation (read across) New Jersey 6 1993 87.8 12.8 2 3 prior to 1991 2452 Connecticut 2 1996 85.2 8.2 3 3 none 2301 California 1 1991 74.7 14.7 2 2 ended 1993, just 1998 1179 Missouri 1 1990 32.6 12.6 2 2 none 1274 Utah 1 1993 78.6 15.6 2 2 none 1221 Maryland 0 1985 79.3 11.3 3 3 prior to 1991 1845 New Hampshire 0 1993 79.5 26.5 2 2 none 1525 Rhode Island 0 1995 70.1 11.1 2 2 none 2032 Virginia 0 1988 74.7 8.7 2 3 prior to 1991 1070 Louisiana -1 1995 24.6 3.6 3 3 1993 1569 Maine -1 1995 63.3 18.3 2 2 none 2305 Michigan -1 1995 56.7 9.7 0 local 1998 1863 Idaho -2 1994 42 5 2 4 none 1589 Massachusetts -2 1994 86.4 13.4 0 local just 1998 2435 Nebraska -2 1995 21.7 3.7 0 local none 1453 Texas -2 1991 63.1 34.1 3 3 1993 1444 West Virginia -2 1992 55.2 0.2 2 3 briefly 1993 2265 *If the original data said the policy was in development, one year was added beyond the year reported. **Data were available for 1991, 1993, 1995, 1998, and 2000. ***Based on K–12 instruction expenditures per student 1989-1999. continued

3. Academic Access 77

Table 3-8. (continued) State reports on change in graduation rates: Change in graduation rates 1990 to 2000 with policies significant in fixed effects regression Ten-Year Year Math Percentage of Change in % of Credits in Credits in Ten-Year Change in Guidelines Schools Schools Math Math Grad Rate Consistent Participating in Participating in Required Required Change in School Funding with NCTM AP Program in the AP Program for Grad in for Grad in 1990 to Year Exit Exam $/FTE*** 2000 Standards* 2000 Since 1991 1991 2000 Implemented** Significance in the regression Neg. Neg. Neg. Neg. Pos. Pos. Neg. equation (read across) Arkansas -3 1993 33 14 3 3 none 1556 Kentucky -3 1993 66.4 13.4 3 3 ended before 1993 1843 Vermont -3 1995 72.2 16.2 3 2.5 none 1793 Colorado -4 1994 49.9 6.9 0 Local none 967 North Dakota -4 1993 8.8 4.8 2 3 none 1142 Ohio -4 1990 63.1 12.1 2 2 1993 1526 Pennsylvania -4 9999 63.4 15.4 3 3 none 1686 Wyoming -4 1990 33.3 2.3 0 3 none 1087 Iowa -5 1987 33.3 15.3 0 local none 1244 Montana -5 1996 34.3 12.3 2 2 none 1297 Oklahoma -5 1993 42 26 2 2 none 1382 Oregon -5 1995 50.2 10.2 2 2 ended before 1993 1345 Alabama -6 1989 36.3 -8.7 2 4 prior to 1991 1339 Alaska -6 1995 12.6 2.6 2 2 none 929 Florida -6 1995 64.8 12.8 3 3 prior to 1991 951 Illinois -6 1995 54.1 12.1 2 2 none 1751 Minnesota -6 1995 44.6 14.6 1 1998 1401 *If the original data said the policy was in development, one year was added beyond the year reported. **Data were available for 1991, 1993, 1995, 1998, and 2000. ***Based on K–12 instruction expenditures per student 1989-1999. continued

78 Chapter 3

Table 3-8. (continued) State reports on change in graduation rates: Change in graduation rates 1990 to 2000 with policies significant in fixed effects regression Credits in Percentage of Change in % of Credits in Ten-Year Year Math Ten-Year Math Schools Math Schools Change in Guidelines Change in Required Required Grad Rate Consistent Participating in Participating in Year Exit Exam 1990 to with NCTM AP Program in the AP Program for Grad in for Grad in School Funding $/FTE*** 2000 Implemented** Since 1991 1991 2000 2000 Standards* Significance in the regression Neg. Neg. Neg. Neg. Neg. Pos. Pos. equation (read across) New York -6 1995 76.7 11.7 2 2 prior to 1991 1932 Washington -6 1995 58.1 10.1 2 2 none 1295 Wisconsin -6 1996 65.3 29.3 2 2 none 1723 Delaware -7 1993 94.7 -2.3 2 none 1344 Indiana -7 1991 59.1 14.1 2 4 1998 1961 D.C.** -8 1995 64.4 6.4 2 3 2000 1295 Kansas -8 1991 24.4 6.4 2 2 none 1126 Mississippi -8 1993 38.7 7.7 2 3 prior to 1991 1047 Nevada -8 1993 38.7 -5.3 2 3 prior to 1991 1209 New Mexico -8 1992 50 22 3 3 1993 1157 South Carolina -8 1993 74 9 3 4 prior to 1991 1394 North Carolina -9 1992 67.7 10.7 2 3 prior to 1991 1327 Georgia -11 1992 65 16 2 3 prior to 1991 1553 South Dakota -12 1996 19.2 7.2 2 2 none 1158 Tennessee -13 1991 53.1 9.1 2 3 prior to 1991 1447 Arizona -14 1992 51 -1 2 2 ended before 1993 619 Hawaii -23 1995 72.7 7.7 2 3 prior to 1991 1552 *If the original data said the policy was in development, one year was added beyond the year reported. **Data were available for 1991, 1993, 1995, 1998, and 2000. ***Based on K–12 instruction expenditures per student 1989-1999.

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Chapter 3

CONCLUSION

There is little reason to doubt the research finding that high school students who take advanced math courses are more likely to enroll in college. However, policies aimed at increasing the level of math achievement—successful completion of advanced classes and higher test scores—have both intended and unintended consequences. Our analyses help illuminate them: • A few of the new education reforms implemented in the 1990s (i.e., NCTM standards, AP courses, and requiring more math courses) had positive associations with higher SAT scores. • Many of these new policies were also negatively associated with high school graduation rates. • The level of funding for K–12 education was positively associated with high school graduation rates when the influences of demographic variables and state education policies were considered. • The new education policies had little association with college enrollment rates of high school graduates. These findings reveal problems inherent in leaping from correlation analyses of intervening variables (i.e., math courses) to the advocacy of college preparation reforms. Just because a behavior—such as taking advanced math courses in high school—is associated with college enrollment, it should not be assumed that a policy requiring students to take more math courses would increase college enrollment rates. In fact, implementing higher standards and more intensive math requirements apparently influenced the decline in high school graduation rates in the 1990s and had no impact on college enrollment rates. While advocates of these policies might review these analyses and claim that these policies had the intended effect—because they were associated with higher SAT scores— they should also consider the unintended consequences of these policies. The finding that some of the education policies that were positively associated with average SAT scores in states were also negatively associated with graduation rates may seem like a contradiction at first glance. Yet these findings are similar to other research that considers effects of reading and comprehensive reforms on both grade level retention and test scores in early primary grades. School reforms that used structured approaches were associated with both higher failure rates (holding students back) and higher test scores (St. John, Hossler, Musoba, Chung, and Simmons, 2006; St. John, Manset-Williamson, Chung, and Michael, 2005). In addition to focusing on

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requirements and standards, this research also shows it is crucial to use instructional methods that engage and support diverse learning patterns. More generally, these results illustrate that it is necessary to evaluate the effects of actual policies on intended outcomes (i.e., test scores and college enrollment rates) and unintended outcomes (e.g., dropout rates), rather than depend on research used to rationalize policies. Individual state reports are informative but are appropriately interpreted with an expert understanding of policies in each state. In addition, the K–12 reforms simply do not provide a sufficient explanation for the decline in high school graduation rates. While it is probable that researchers and policy makers who advocate such leaps in policy have noble intentions, it is crucial that they also be open-minded about contradictory evidence. New and contradictory evidence has emerged over time in access research. The shared goals should be to improve schools so more students can qualify for college and to enable more qualified students to attend college. The research on academic preparation has informed educators about the meaning of academic qualifications, but the policies that have flowed from these analyses have not adequately achieved these noble goals. The new policies have not prepared more children for college or enabled more children to go to college. Rather these new policies appear to have enabled only some children to attain higher achievement as measured by scores on standardized tests.

Chapter 4 FINANCIAL ACCESS

1

By Edward P. St. John, Choong-Geun Chung, Glenda D. Musoba, and Ada B. Simmons

There should be little doubt that financial aid plays an important role in promoting access for low-income students (Heller, 1997; Jackson and Weathersby, 1975; Leslie and Brinkman, 1988; St. John, 2003). Viewing state higher education systems as markets can help build a better understanding of the roles of financial aid and privatization (as measured by public tuition charges). However, past studies of academic preparation for college as a possible explanation for the enrollment disparity between minorities and Whites (e.g., NCES, 1997a; Pelavin and Kane, 1990) often did not consider need-based grants important as tuition prices rose in the 1990s. In addition, proponents of the academic preparation rationale have been successful in promoting investment in merit-based grants (Heller and Marin, 2002), a form of aid that has been thought to be associated with improvement in preparation and college enrollment (Bishop, 2004). Data on state indicators are useful for examining the relationships between state investment in grants—need-based and non-need (mostly merit2)—and both high school graduation rates and college enrollment rates. This chapter examines the impact of state financing strategies on college access in the 1990s and proposes a cost efficient approach for expanding 1

2

An earlier version of some of the analyses in this chapter was published in E.P. St. John, C.G. Chung, G.D. Musoba, and A.D. Simmons, 2004, Financial Access: The Impact of State Financing Strategies, in Readings on Equal Education: Vol. 19. Public Policy and College Access: Investigating Federal and State Roles in Equalizing Postsecondary Opportunity, E.P. St. John, ed., AMS Press, Inc., New York, pp. 109-129. While most of the non-need programs used by states have merit criteria, a few programs direct aid to special populations (e.g., tuition remissions for school teachers or police officers).

83

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access over the next decade. Because the debates about college access consider both academic preparation and financial access, these analyses consider the roles of both academic and financial factors. First, we describe the logical model and statistical methods used to develop a set of state financial indicators for the 1990s and to assess the impact of state financial strategies on college access. Then, using state financial indicators, we present analyses of the impact of state financial strategies on rates of high school graduation and college enrollment during the 1990s. We also examine how states compared in percentage changes in enrollment rates between 1992 and 2000.

1.

A BALANCED APPROACH

The conceptual model (see Figure 4-1) recognizes that there is an educational attainment pipeline in states that can be influenced by the public finance strategies used in the state. The educational attainment pipeline is defined in relation to • Demographic Context: This is the ethnic composition of the state’s population and the extent of wealth, poverty, and education.3 The demographic context represents the state-level equivalent of variables for family income and parents’ education, which are frequently used in studies of college access that use individual-level data. • Academic Preparation: While in studies of enrollment in four-year colleges it may be desirable to consider the specific courses that students take in high school, in most states students are qualified for enrollment in a two-year college if they receive a high school diploma. Therefore, in the current study, which aims to examine access to two-year and fouryear colleges, high school graduation rates represent the appropriate measure of academic preparation. • Postsecondary Attainment: There are two types of indicators of the impact of public finance on college attainment:

3

This study tested the use of both poverty rates and income per capita. However, the two variables were highly correlated; therefore, poverty rate was used as a predictor because it related more directly to the financial access issues that were of concern in this report. In addition, since tax rate was used in the analyses, we had an additional statistical control for the influence of wealth. In response to inquiries from reviewers of an earlier version of this analysis, the study team also tested the use of unemployment rates as a predictor variable. However, as expected, unemployment was very highly correlated with poverty rate; therefore, it was not included in the final model.

Tax Rates

Demographic Context Diversity Employment Wealth/Poverty Education

Financial Context K–12 Funding for public schools Expected college costs (indirect effect)

Academic Preparation High school courses High school graduation

College Costs Needs-based grants Non-need grants Tuition Funding public colleges

State System 2-year public 4-year public Private

Postsecondary Attainment College enrollment College graduation

Source: E. P. St. John, C. G. Chung, G. D. Musoba, A. B. Simmons, O. S. Wooden, and J. Mendez, Expanding College Access: The Impact of State Finance Strategies, Lumina Foundation for Education, Indianapolis, IN, 2004.

Public Finance Strategies (Focus on State Role)

State Postsecondary Education System

Educational Attainment Process

Figure 4-1. Framework for assessing the impact of public finance strategies on postsecondary attainment

4. Financial Access 85

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1. College enrollment rates of high school graduates (and possible enrollment in different sectors of state higher education systems); and 2. Roles of college graduation or degree attainment, which are not considered here because the data are not available. The systems of public finance are the primary means that states can use to promote educational attainment, especially college attainment, among state populations. The system of public finance in states links to the educational attainment pipeline in several ways: • Tax Rates: Historically, it was assumed that there were higher tax rates in states with wealthier populations, but many states either reduced some taxes or resisted tax increases in the 1980s and 1990s. Therefore, a state’s tax rate, controlling for the wealth of the population, can influence both academic preparation and college attainment. The tax rate measure used here was total taxes collected by a state divided by personal income (from the U.S. Census Bureau). • School Funding: The level of state funding for public K–12 education can be influenced by the wealth and tax rates in a state.4 The level of school funding could influence the high school graduation rate in a state and has a direct effect on the availability of certain high school courses. • Expected Tuition and Grants: Student expectations about tuition and grants can influence their desire to prepare for college (St. John, 2002). Therefore, there is reason to expect that average public tuition charges and average state grants two years prior to graduation can influence the high school graduation rate in a state. • Actual College Costs: States finance college access and persistence through student grants (need-based and non-need) and tuition subsidies to public colleges. At a given level of educational expenditures by public colleges, state subsidies to public colleges reduce tuition charges to college students. In addition to public finance strategies, the capacity of the state postsecondary education system has an influence on college access. Extensive two-year college systems or a large number of independent colleges can expand access or provide different forms of access for different groups. The percentages in these two sectors serve as proxies for capacity.5 In analyses of the impact of public finance strategies on access, it is 4

This study controls for the influence of school funding, but not school reform policies. High school graduation rates dropped during the 1990s, a period during which more stringent requirements were implemented. Therefore, to assess fully the impact of school funding on graduation rates, it would also be necessary to examine the impact of school reform policies. 5 The variables are proxies for capacity because these percentages are measured in relation to four-year college enrollment (not specified) and cohort size (specified). In combination, these variables provide a statistical control for the capacity of the state system.

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appropriate to control for the structure of state systems of higher education.6 In addition, some students who enroll in college attend colleges out of state, but out-of-state enrollment is not directly influenced by state finance strategies unless states provide grant subsidies to students who enroll out of state.7 Using this conceptual model, we examined the impact of demographic indicators and state financial strategies on academic preparation as measured by high school graduation rates8 and college enrollment rates. The study team developed a statistical model testing different measures. The information used in the study was developed as part of a state fiscal and financial aid indicators project being developed currently for Lumina Foundation for Education.

2.

RESEARCH APPROACH

As a part of this study, the project team developed a set of financial indicators that were used in the analysis of the impact of state financial strategies. Below, we describe the indicators database, model specifications, statistical methods, and study limitations. To test this model, the study team developed a set of financial and demographic indicators for each of the 50 states for the 1992, 1994, 1996, 1998, and 2000 fiscal years. All dollar amounts were adjusted to 2000 dollars. The analyses of high school 6

7

8

In earlier analyses the research team had developed separate analyses of enrollment rates in public two-year colleges, public four-year colleges, private colleges, and colleges in other states. The project advisory committee suggested modifying the base access model to consider the role of system complexity, as an alternative to presenting a larger number of statistical models. In particular, Laura Perna was helpful in conceptualizing the role of system capacity in the access models presented in these analyses. The early analyses included an analysis of the impact of state financial strategies on the percentage of high school graduates who enroll out of state. Chapter 8 provides the analyses of the impact of state financial strategies on the distribution of high school graduates within state systems. In analyses of the impact of state financial strategies on academic preparation, high school graduation rate is a more appropriate indicator than high school courses. First, high school graduation is a better indicator of preparation for two-year and four-year colleges than are specific courses related to a college preparatory curriculum, which may be a better indicator of preparation for four-year enrollment. Further, state policies that require more students to take advanced courses in high school could have the unintended effect of reducing high school graduation rates. Therefore, it is more appropriate to assess the effects of school reform policies on high school graduation rates, as an indicator of the efficacy of state reforms, than to use high school curricula to constrain analyses of college enrollment. Even if better indicators of high school curricula had been available, it would not have been inappropriate to use high school graduation rate in this study.

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graduation rates used financial indicators for two years prior to graduation to reflect the financial conditions that prevailed when students in a cohort were enrolled in high school and making future plans.

2.1

Model Specifications

When assessing the impact of state finance policies on high school graduation rates and college enrollment rates, it is appropriate to use linear models because the outcomes are continuous variables. This chapter presents the results of two fixed-effects regression analyses of the impact of public finance policies on access using the state indicators data. Variables used in predicting high school graduation rates are shown in Table 4-1. Table 4-1. Independent variables used in analysis of high school graduation rates Demographic Context Percentage of the state population below the poverty level Percentage of African Americans in the population Percentage of Hispanics in the population Percentage of other minorities in the population Size of the 9th grade cohort four years prior Percentage of the population with a bachelor’s degree or higher Financial Controls Tax rate K–12 expenditures (two years before graduation) Higher Education Finance Strategies Need-based grants per FTE (two years before high school graduation) Non-need grants per FTE (two years before high school graduation) Tuition charges weighted per FTE (two years before high school graduation)

Variables used in the analysis of the impact of public finance strategies on college enrollment of high school graduates are presented in Table 4-2. This approach provides a method of assessing the impact of financial aid— need-based and non-need—on enrollment rates in states, controlling for the effects of academic preparation.9 Thus, this approach overcomes the most serious limitations of prior efforts to relate variations in public finance strategies to enrollment rates.

9

This analysis considers the population that has graduated a more appropriate measure of preparation for enrollment in state systems (inclusive of community colleges) than is a higher standard like the percentage of population that completed advanced math.

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Table 4-2. Independent variables used in analysis of college enrollment rates by high school graduates Demographic Context Same independent variables used in the high school graduation model in Table 4-1 Financial Controls Tax rate System Capacity Percentage of FTE enrolled in private colleges Percentage of FTE enrolled in public two-year colleges Higher Education Finance Strategies Need-based grants per FTE (for first year of college eligibility) Non-need grants per FTE (for first year of college eligibility) Tuition charges weighted per FTE (for first year of college eligibility)

2.2

Statistical methods

Ordinary least squares (OLS) regression analysis is appropriate for the analysis of the impact of financial policies on graduation and enrollment rates because both outcomes are continuous variables. Further, most of the indictors used in the regression analyses were continuous variables, which also argue for use of OLS regression. The fixed-effects version of OLS used in this study provided a way to control for the state effects. The study team considered using OLS regression, but after careful review decided to use fixed-effects regression. The fixed-effects regression method was appropriate because it made it possible to control for the effects of state education policies, using coding (or effects) variables. In addition to the financial and demographic forces, academic policies in states can influence access (see chapter 3). Using the fixed-effects method, a variable is calculated for unspecified characteristics, including state education policies. Thus, in this model we are able to control for the effects of other policies in states without actually specifying those variables. The two regression tables provide both standardized and unstandardized coefficients, levels of significance (.1, .05, and .01). The lowest of these levels (.1) indicates only a modest association, and we are cautious about reaching any conclusions about these associations. In addition, R2 and Pvalues are presented as indicators of the quality of the models. In addition, we present a set of simulations of the effects of an alternative approach to financing higher education in the states: funding need-based grants on a per-FTE basis10 at a level equaling one-quarter of the weighted average public sector tuition charge—a level of funding considered a 10

Consistent with the method used to measure state investment in student aid, this indicator used undergraduate FTE students within each state’s system of public and private higher education.

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minimum equity standard, defined in this chapter as funding need-based grants at an amount per student equaling one quarter the weighted average public sector tuition charge (St. John, 2005).

2.3

Limitations

First, as in all regression analyses, the relationships of variables in these analyses should not be interpreted as causal. Rather, the sound logic in the selection of variables provides a basis for assuming that the variables included in the model have an influence on the outcomes. The logical framework, reviewed above, was used to guide the variable selection process. The analyses reported here represent the initial test of this new logical approach to the study of college access. Second, the analyses should not be generalized beyond the period studied. While similar financial conditions have persisted into the early 2000s, there have been major changes in the economies across the states that could limit the implications of these analyses. Thus, this study provides an evaluation of the impact of finance strategies used in the 1990s. Therefore, our analyses of effects (i.e., the simulations) consider the historical period studied. It would be necessary to estimate future changes in state populations to estimate the enrollment effects of finance policies in the future. Third, simulating the effects of policy alternatives is risky because it is not possible to model all factors that can influence the outcomes. In the simulations presented in this chapter we consider the alternatives within the period studied and estimate within the range of financing alternatives used by a few states during the period studied. We also present high- and lowrange estimates as a means of illustrating the range of possible effects.

3.

THE IMPACT OF STATE PUBLIC FINANCE STRATEGIES

While the first aim of this study—to measure the effects of state public finance strategies on access—may seem straightforward, it is a complex process further complicated by methodological considerations. To illuminate the role of public finance in promoting access, this chapter examines both academic preparation (high school graduation rates), as an indicator of the indirect effects of financial access, and college enrollment (by high school graduates), as an indicator of the direct effects of financial strategies.

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High school graduation rates

Logically, the availability of financial aid could have an indirect effect on college enrollment rates if it influences the will of low-income students to finish high school (Advisory Committee on Student Financial Assistance, 2002; St. John, 2003). We found that high school graduation rates were influenced by the demographic context of the state and the strategies used to finance higher education, controlling for public finance of schools (Table 4-3). Table 4-3. Fixed-effects regression: The influence of population characteristics and state finance strategies on public high school graduation rates in the 1990s Regression Coeff. Unstandard Standard Sig. % Poverty 0.063 0.025 % African American 0.774 0.803 % Hispanic 1.222 1.103 * % Other minorities -5.356 -5.366 *** Enrollment when the cohort was in 9th grade 0.000 0.262 % of population with bachelor’s degree or higher -0.310 -0.151 ** Tax rate (=state tax collection/personal income) 0.100 0.015 Per student K–12 expenditures ($/1,000) 2 years prior -0.003 -0.027 Per FTE need-based grant amount ($/1,000) 2 years prior 0.031 0.094 Per FTE non-need grant amount ($/1,000) 2 years prior -0.061 -0.097 ** Undergraduate in-state tuition and fees for public system -0.321 -0.371 *** ($/1,000) 2 years prior Adjusted R square 0.933 N 200 P-value for F test that all ui=0 0.000 (The null hypothesis of the F test is that the state-specific, fixed-effects terms are all zeros. The fixed-effects model can be judged significantly different from the OLS model when we reject the null hypothesis.) Note: *** p<0.01, ** p<0.05, * p<0.1

Three of the demographic variables were significant in the fixed-effects regression analysis. The percentage of Hispanics in a state’s population was positively associated with high school graduation. The percentage of the population that are other minorities and the percentage that had a bachelor’s degree or higher were negatively associated with high school graduation rates. The reasons why education level was negatively associated with high school graduation rates are complex and merit future study. The fixedeffects analysis statistically controls for the state context. First, we can only speculate about the explanations of the finding on education levels of the population. If states import highly educated citizens, they may have

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artificially depressed high school graduation rates in these statistical models because the native (nonimmigrant) population with children is less well educated. Alternatively, an educated citizenry could keep educational standards high, which may discourage low-achieving students. Second, high school graduation rates actually declined in the 1990s, which adds to the complexity of interpreting this finding. While the decline in graduation rates is partially attributable to the impact of state education reforms, the state reports did not provide a compelling portrayal of the relationship. The impact of school reforms is “controlled for” by the state variables implicit in fixed-effects models. Therefore the effects of some reforms could confound these analyses. This analysis of the influence of demographic variables raises questions about the influence of a state’s demographic composition on high school graduates. The fixed-effects approach controls for specific state contexts in a set of uncoded variables for the state. When this approach is used, the education level of the population has a substantially different effect on high school graduation rates than we would expect from research on academic preparation, suggesting that further research is needed on the effects of school reform policies on high school graduation rates. Tuition charges and state grants, both measured two years prior to graduation, also had an influence on high school graduation rates. Both tuition charges and non-need grants were negatively associated with high school graduation rates. Need-based grants were not significant, but had a positive association with high school graduation rates. Higher need-based grants could have a slight positive association with high school graduation rates, while higher merit-based grants would negatively influence graduation rates. This statistical association may be because students with low grades believe they cannot afford to attend college in their states if they do not maintain the grade point average necessary to attain merit-based grants.

3.2

College enrollment rates

State finance strategies had a substantial and direct effect on college enrollment by high school graduates, controlling for demographic contexts and the structure of higher education in the states. The fixed-effects regression analysis revealed that demographic context, state system capacity, and student grants have an influence on enrollment (Table 4-4). While the poverty level in a state was not statistically associated with high school graduation rates (Table 4-3), it was significantly and negatively associated with college enrollment rates (Table 4-4). Low-income families are apparently more disadvantaged with respect to college enrollment than they are with respect to high school graduation. The percentage of the

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population with college degrees was also positively associated with college enrollment rates, further indicating that demographic variables related to socioeconomic status (SES) have a substantial and direct influence on college enrollment among students who have prepared academically for college by graduating from high school. Table 4-4. Fixed-effects regression: The influence of population characteristics and state finance strategies on college enrollment rates in the 1990s Regression Coeff. Unstandard Standard Sig. % Poverty -0.462 -0.238 *** % African American -1.810 -2.326 % Hispanic -1.174 -1.316 * % Other minorities 2.388 3.001 Enrollment when the cohort was in 9th grade -0.000 -0.694 * % of population with bachelor’s degree or higher 0.299 0.182 * % Public 2-year institution FTE 0.211 0.328 * % Private institution FTE 0.643 1.089 *** Tax rate (=state tax collection/personal income) -0.071 -0.015 Per FTE need-based grant amount ($/1,000) 0.115 0.426 *** Per FTE non-need grant amount ($/1,000) 0.089 0.204 *** Undergrad in-state tuition and fees for public system ($/1,000) 0.100 0.146 Adjusted R square 0.789 N 244 0.000 P-value for F test that all ui=0 (The null hypothesis of the F test is that the state-specific, fixed-effects terms are all zeros. The fixed-effects model can be judged significantly different from the OLS model when we reject the null hypothesis.)

Note: *** p<0.01, ** p<0.05, * p<0.1

The capacity of state higher education systems also affects postsecondary opportunity. The percentages of students enrolled both in community colleges and private colleges were positively associated with enrollment rates in the states. Tax capacity was not associated with college enrollment. Although the direct effects of need-based aid were much more substantial (twice the standardized coefficient), both need-based and non-need grants were significant and positively associated with enrollment rates, with the more modest effects of non-need grants related to state contexts. In contrast, tuition charges were not significantly associated with enrollment. Thus, the most efficient way for states to expand access is to expand need-based grants.

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COMPARISON OF STATES

These analyses provide substantial new insights into the impact of public finance policies on access. Student financial assistance has both direct and indirect effects on access. If students feel they cannot afford to attend college, either because of high tuition charges or the restrictions on state grants, they are less likely to graduate from high school. State funding for need-based grants was positively and significantly associated with college enrollment by high school graduates, indicating an indirect effect on college enrollment through preparation.

4.1

Improving financial access

It is also apparent that state financial strategies play an important role in improving college enrollment rates, but they are not the only factors that influence this outcome. An examination of trends in the states between 1992 and 2000 reveals substantial variation in the extent of improvement in state participation rates during this period (Table 4-5). Ten states improved enrollment rates by at least 9.5 percentage points during the eight-year period: Florida, Indiana, Kansas, Kentucky, Massachusetts, Minnesota, North Carolina, South Carolina, South Dakota, and Tennessee. Funding for need-based state grants appears to have played an important role in improvement in access in three of the states. North Carolina was fourth among states in the total amount of increase in state need-based grant per FTE ($196.79). Other changes in North Carolina’s financial strategies included a higher-than-average increase in merit grants (11th among states) and a lower-than-average tuition increase (29th). Massachusetts was fifth in the total per-FTE increase in grants ($185.30) and actually substantially reduced tuition charges (by $801.89), an exception among states (50th in tuition increase). Indiana was eleventh in increased spending on need-based grant aid per FTE, but tightened the link between tuition and grants for lowincome students through the Twenty-first Century Scholars program (St. John, Musoba, Simmons, and Chung, 2002). Three states with improved enrollment rates were among those with substantially increased merit grant programs: Florida, Kentucky, and South Carolina. However, the link between state spending on merit grants and increased high school dropout (chapter 3) makes this strategy problematic. In fact these states had substantially lower than average high school graduation rates (chapter 3). One of the states could have benefited from the slight rise in Pell Grants in 2000. Minnesota is one of the few states that met the equity standard, but changes in the state’s financial strategy do not reflect this commitment,

South Carolina Tennessee North Carolina South Dakota Florida Minnesota Kansas Massachusetts Kentucky Indiana New Mexico Pennsylvania Nevada Arkansas Wyoming Ohio Maine

State

Change in Per-FTE Change in Per-FTE Change in Undergrad Change in 1992-2000 College Need-Based Undergrad Non-Need Undergrad In-State Tuition and Fees Per-FTE State and Local Enrollment State Grant Funding, in State Grant Funding, in for Public System, in Appropriation for Public 2000 Dollars 2000 Dollars 2000 Dollars System, in 2000 Dollars Rate, 1992-2000 % point $ amount $ amount $ amount $ amount change Rank change Rank change Rank change Rank change Rank 22.9% 1 $96.05 14 $456.66 3 $904.89 6 $989.59 22 15.5% 2 $41.60 21 -$2.96 43 $694.33 17 $620.09 37 15.4% 3 $196.79 4 $83.33 11 $500.19 29 $1,290.75 15 12.7% 4 -$31.11 40 -$4.77 46 $977.84 5 $547.91 39 12.1% 5 -$7.45 33 $282.85 5 $445.89 31 $1,399.04 13 10.3% 6 -$73.94 47 $0.04 26 $566.42 25 $741.81 31 10.2% 7 $12.04 25 $0.25 25 $435.72 32 $1,222.43 16 10.0% 8 $185.30 5 $9.58 18 -$801.89 50 $3,528.74 1 9.8% 9 $94.73 15 $159.51 7 $623.71 21 $1,179.20 17 9.5% 10 $107.09 11 $4.12 20 $651.32 20 $657.60 34 8.8% 11 $16.97 23 $306.47 4 $368.04 39 $1,490.55 10 7.7% 12 $137.62 6 -$0.57 40 $865.48 7 $497.76 40 7.5% 13 $117.68 9 $149.16 8 $425.54 35 $589.68 38 7.2% 14 $263.29 1 $88.14 10 $753.10 12 $1,049.32 20 6.0% 15 -$16.48 36 $0.00 34 $612.63 23 $943.34 23 5.8% 16 -$3.18 30 $91.05 9 $556.80 27 $3,412.25 2 5.7% 17 $119.36 8 $0.00 28 $693.03 18 $627.76 36 continued

Table 4-5. State reports: College enrollment rates, state grant funding, and public institution tuition and funding

4. Financial Access 95

Change in 1992-2000 Change in Undergrad Change in Per-FTE Change in Per-FTE Need-Based Undergrad Non-Need Undergrad In-State Tuition and Fees Per-FTE State and Local College Appropriation for Public for Public System, in State Grant Funding, in State Grant Funding, in Enrollment System, in 2000 Dollars 2000 Dollars 2000 Dollars 2000 Dollars State Rate, 1992-2000 % point $ amount $ amount $ amount $ amount change Rank change Rank change Rank change Rank change Rank Georgia 5.3% 18 -$42.05 41 $1,220.59 1 $300.73 42 $2,327.53 8 Connecticut 5.1% 19 $115.42 10 $4.14 19 $525.58 28 $2,506.41 6 Louisiana 5.0% 20 -$42.24 42 $528.18 2 $429.44 34 $707.75 33 Alaska 4.8% 21 -$56.53 45 -$3.01 44 $843.07 8 $231.16 46 Missouri 4.7% 22 $50.20 17 -$4.82 47 $782.72 11 $2,296.95 9 Arizona 4.2% 23 -$14.37 35 $0.00 33 -$599.31 49 $1,160.40 18 Rhode Island 4.1% 24 -$146.05 48 $0.00 37 $407.95 36 $1,454.20 12 Hawaii 3.7% 25 -$18.58 37 $0.00 35 $842.59 9 -$3,886.82 50 Montana 3.6% 26 $45.34 20 $29.85 15 $842.36 10 -$672.31 49 North Dakota 3.6% 27 -$66.85 46 -$1.15 41 $340.37 40 $465.04 41 West Virginia 3.3% 28 $137.56 7 $0.00 27 $394.75 38 $1,482.15 11 New Hampshire 2.8% 29 -$10.53 34 -$0.15 39 $1,981.38 1 $348.72 44 Iowa 2.7% 30 $9.30 26 -$1.32 42 $335.27 41 $897.94 27 New Jersey 2.7% 31 -$182.20 49 $36.71 13 $1,032.99 3 $652.99 35 Delaware 2.2% 32 -$24.65 39 $1.45 24 $469.98 30 $2,890.84 4 Colorado 1.6% 33 $103.10 13 -$17.50 49 $252.71 44 $729.73 32 Alabama 1.5% 34 -$4.82 31 -$4.02 45 $565.22 26 $796.65 29 continued

Table 4-5. (continued) State reports: College enrollment rates, state grant funding, and public institution tuition and funding

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Mississippi Virginia Michigan Texas Oklahoma Maryland Illinois New York Oregon Wisconsin California Nebraska Idaho Vermont Utah Washington

State

Change in 1992-2000 Change in Undergrad Change in Per-FTE Change in Per-FTE College Need-Based Undergrad Non-Need Undergrad In-State Tuition and Fees Per-FTE State and Local Appropriation for Public for Public System, in Enrollment State Grant Funding, in State Grant Funding, in System, in 2000 Dollars 2000 Dollars 2000 Dollars 2000 Dollars Rate, 1992-2000 % point $ amount $ amount $ amount $ amount change Rank change change change change Rank Rank Rank Rank 1.5% 35 -$2.04 28 $182.90 6 -$18.01 46 $2,454.77 7 1.4% 36 $245.87 2 $57.29 12 -$355.91 48 $3,043.46 3 1.2% 37 -$52.20 43 $0.00 36 $618.25 22 $1,003.65 21 0.0% 38 $89.19 16 $3.51 21 $750.85 13 $450.19 42 -0.9% 39 -$23.86 38 $21.56 16 $286.31 43 $2,751.90 5 -1.2% 40 $47.80 18 -$20.10 50 $1,138.76 2 $1,293.73 14 -2.3% 41 $47.36 19 -$12.74 48 $1,003.87 4 $1,135.51 19 -3.0% 42 -$274.48 50 $2.85 23 $431.72 33 $310.83 45 -3.2% 43 -$3.04 29 $0.00 32 $404.60 37 $31.84 47 -3.3% 44 $12.73 24 $16.32 17 $716.91 15 $915.72 25 -3.7% 45 $106.91 12 $0.00 29 -$83.29 47 $929.24 24 -4.0% 46 $23.10 22 $0.00 30 $585.64 24 $908.77 26 -4.2% 47 -$6.07 32 -$0.09 38 $738.85 14 $808.76 28 -10.5% 48 -$52.83 44 $3.31 22 $668.56 19 $405.40 43 -13.6% 49 $4.17 27 $0.00 31 $104.67 45 $751.09 30 -13.8% 50 $210.31 3 $36.53 14 $716.51 16 -$378.06 48

Table 4-5. (continued) State reports: College enrollment rates, state grant funding, and public institution tuition and funding

4. Financial Access 97

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given the increase in average tuition charges ($566.42) and the slight drop in need-based grants (-$73.94). Minnesota is one of the few states that estimate a total grant award and subtract Pell from the total, so the increase in Pell may be a mitigating factor in this state. However, in three of the states—Kansas, South Dakota, and Tennessee— the rise in college enrollment rates does not appear to be related to changes in financial strategies during the period. The role and influence of education reform merit attention in these states, as they do in other states.

4.2

Losing ground

Ten states also lost ground in access, with a drop of college enrollment rates of more than two percentage points: California, Idaho, Illinois, Nebraska, New York, Oregon, Utah, Vermont, Washington, and Wisconsin. Two distinct patterns are evident among these states. Five of the states that lost ground—Illinois, Nebraska, New York, Vermont, and Wisconsin—had substantial increases in net costs. Two of the states (New York and Vermont) had reductions in funding of grants, and all five had relatively large tuition increases. While New York was a state that met the equity standard on average during the eight-year period, the state had a $274.48 per-FTE drop in grants, coupled with a substantial increase in tuition. The other five states—California, Idaho, Oregon, Utah, and Washington —were among the western states facing increases in student demand. In particular, the growth of demand was problematic in California and Washington, states that made efforts to improve grants. They maintained a focus on equity but were not able to maintain the capacity to keep pace with population growth. The plight of the western U.S. illustrates the capacity challenge facing this region.

4.3

Rethinking high school graduation rates

The state reports on change in high school graduation rates (Table 3-8) also merit rethinking relative to the state reports on finance (Table 4-5). Consider the five states with the largest drop in high school graduation rate: • Tennessee had a 13 percentage point drop in high school graduation rates along with a $694 increase in tuition, a drop in non-need grants (-$2.96), and only a modest increase in need-based grants ($41.60). • South Dakota had a 12 percentage point drop in high school graduation rates along with a drop in need-based grants (-$31.11) and a large tuition increase ($977.84).

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• Georgia had an 11 percentage point drop in high school graduation rates along with a drop in need-based grants (-$42.05 per FTE), an increase in non-need grants ($1,220.59 per FTE), and a moderate tuition increase ($300.73)—all trends associated with low graduation rates. • Arizona had a 14 percentage point drop in high school graduation rates along with a decline in need-based grants (-$14.37) and a drop in tuition (-$599.31). The tuition drop did not improve graduations, but the financial aid situation could be a reason for the lack of response. • Hawaii had a 23 percentage point drop in high school graduation along with a drop in need-based grants (-$18.58) and a large increase in tuition ($842.59). In conclusion, the financial strategies appear related to the decline in high school graduation rates. Most states that had a drop in need-based grants and an increase in tuition also had a drop in high school graduation rates. It is time to reconsider the role of financial aid in high school graduation and academic preparation.

5.

DEFINING AN EQUITY STANDARD FOR STATE FINANCE

There is currently no generally accepted standard for the level of state investment in student need-based grants. Previously we have proposed a standard to coordinate equitably state funding of need-based grants with tuition (St. John, Chung, Musoba, Simmons, Wooden, and Mendez, 2004). The key feature of this standard was funding need-based grants at an amount equaling one-quarter of the weighted average public tuition charge for public sector institutions. This would provide sufficient funding for a fair system of public finance that realized the following aims: 1. Pell grants covered the approximate cost of room and board charges in public college for full need students in the 1990s, so states must provide sufficient need-based grant aid to equalize enrollment opportunity given tuition charges. 2. In a state finance system in which prices were fairly set, the amount of need would vary from the total tuition charge to no need at the midpoint of income. 3. About half of the students enrolling in public colleges would have some level of financial need after loans for tuition and living costs if tuition were fairly set. 4. The average grant for students in the lowest income quartile should equal the average the average for the public tuition charge and about half that amount for students in the lower-middle quartile. This should be

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sufficient to equalize enrollment opportunity and to enable market forces to work. 5. Students who enrolled in private colleges within the state should be eligible for need-based grants with their maximum award having a higher threshold than public colleges.11 (For example, the maximum award for private colleges could be set at one and one-half times the average public tuition charge and the maximum award for public colleges could be set at tuition at the public college attended.) 6. Using this approach, state funding for public college can be set at an amount enabling competitive funding for public institutions in the state, after tuition. 7. The aim of such a public funding system would be to optimize the role of the individual payment when it is affordable. The tuition should be set so that half of the population could pay tuition required on top of general per-student support to the institution (i.e., the general tuition subsidy), reducing tuition to the level of fairness. When we applied the central element of this standard to the states, we found that three states met the standard, on average, across the years from 1992 to 2000—California, New Jersey, and New York—states that are far from exemplary. While New York met the standard on average, it declined in college enrollment rate during the decade (see analysis above), probably because of the very substantial cuts in state investment. New Jersey also raised tuition and cut need-based aid during the decades. The New Jersey and New York examples illustrate that it is difficult for states to maintain an equitable finance system over time for higher education. California, too, dropped in enrollment rate largely due to constrained opportunity (the supply side). In the late 1980s the state scuttled plans for several new university campuses and did not sufficiently respond to population growth, illustrating fair finance alone cannot solve the access problem. Minnesota was in the top ten states with respect to the increase in enrollment rates, and it was very close to meeting the equity standard during the decade. However, even Minnesota—the state that pioneered the coordination of public tuition and state grants—had a decline in funding for state grants during the decade (see Table 4-5).

11

The key element of the standard sets grant funding at a rate based on FTE for all students in public and private college, creating additional revenue for grants to students in private colleges as public sector prices rise.

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101

The estimated effects of meeting the equity standard

To illustrate the effects of coordinating state finance policies, we present 12 simulations for each state of the effects of meeting the equity standard (Tables 4A-1, 4A-2, and 4A-3, at the end of this chapter). These simulations consider the entire time period, on average, rather than the annual level of effort. Given the decline in funding for need-based grants in the few states that met the equity standard on average, we recognize that using averages in this way can disguise short-term inequities (e.g., consider the enrollment decline in New York discussed above). Table 4-6 presents the estimated enrollment effects for the 1990s if all states had, on average, met the equity standard. (The method used to estimate the enrollment effects option is summarized in Figure 4-2.) The baseline of the enrollment effects for this option suggests that 120,500 additional high school graduates would have enrolled in college each year during the 1990s. Thus, an additional 1.2 million new high school graduates would have enrolled in college during the decade. Table 4-6. Estimated costs and benefits of meeting the equity standards in funding for need-based grants: Baseline, low-range, and high-range estimates Baseline Low-Range Effect High-Range Effect Estimate of Enrollment Effects High school graduation Rate increase 1.0% points 1.0% points 1.0% points New graduates 38,000 38,000 38,000 College enrollment Rate increase 3.8% points 1.1% points 6.5% points New enrollment 120,500 50,000 191,000 Estimate of Costs Cost per new student $4,400 $10,000 $3,000 Additional funding for need$533 $498 $568 based grants (in million $)

High- and low-range estimates of enrollment effects are also presented in Table 4-6. If, for some reason, the supply of opportunity (college openings) or the number of qualified high school graduates was severely limited, then the program could result in a smaller increase in college enrollment. For 12

We added these simulations to this volume as a means of responding to a reviewer’s request for inclusion of analyses of effects.

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Figure 4-2. Estimation of costs for meeting the minimum equity standard in need-based state grants State figures for the United States were estimated as follows: •

Step 1: For each state, the average tuition (weighted by enrollment) in the 1990s was multiplied by ¼, setting a new state grant standard. For the nation, the new grant standard was $650, based on an average national weighted tuition of $2,601.

•

Step 2: For each state, their actual average need-based grant in the 1990s was subtracted from the standard for the state, producing the additional grant funding necessary for the minimum equity standard. The national average shortfall, weighted for enrollment, was $264.

•

Step 3: Estimated increases in the rate and number of high school graduates were calculated for each state using the regression coefficient from a model predicting high school graduation rates. The national rate of increase (1.0%) was calculated by averaging state rates. Total new graduates (38,000) were calculated by summing state numbers.

•

Step 4: Estimated increases in the rate of college enrollment were calculated for each state using the regression coefficients from the model predicting college enrollment rates, taking into consideration the adjusted number of high school graduates from Step 3. The national rate of increase (3.8%) was calculated by averaging the rates of increase for all states. State increases were summed to produce a national total of new enrollment (120,500).

•

Step 5: Program costs for each state were calculated by multiplying the new college freshman enrollment (original freshman enrollment + increase) by the additional grant funding required for the minimum equity standard (Step 2). Costs for each state were summed to produce a national total of $533.1 million.

•

Step 6: For each state, the cost per new student enrolled was calculated by dividing total costs by the number of new students. Nationally, the cost per new student enrolled was $4,400.

•

Step 7: Low- and high-range effects were produced following the same steps, but applying 95% confidence limits around the regression coefficient for college enrollment.

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example, in California, the constraints on opportunity (capacity of public four-year colleges) limited enrollment rather than student aid (see discussion above). Prior analyses of high school preparation, however, indicate there are ample qualified high school graduates to fill the seats if sufficient aid is available (Advisory Committee on Student Financial Aid, 2002; Lee, 2004). At the other extreme, if the percentage of students graduating collegequalified from high school had been higher and there had been ample postsecondary opportunity, then more students would probably have enrolled. The high-range estimates illustrate this type of scenario. The analysis of the costs and effects of coordinating public finance strategies is also presented in Table 4-6. States would have needed to invest an additional $533 million per year per student cohort (about $2.1 billion) to meet the minimum threshold based on data from the 1990s. Assuming about four cohorts enrolled, the cost would be four times $533 million. This amount represents a substantial increase in state grant programs based on the latest data from the National Association of State Student Grant and Aid Programs (2004). We estimate that this additional investment would have resulted in 1.2 million more freshman enrollments over the decade. According to baseline estimates, the average cost would be $4,400 in new grant dollars per additional student enrolled across the U.S. Of course, the investment necessary to reach this minimum equity standard varies substantially across the states. The tables in the appendix of this chapter provide state-by-state estimates of the effects of meeting the equity standard.

5.2

The state role reconsidered

During the past two decades, many states have substantially changed public higher education funding strategies, shifting a substantial portion of the burden from taxpayers to students and families. Since federal grants have not kept pace with the rising prices of public four-year colleges, it is crucial that states make a sufficient investment in need-based grants to ensure equal opportunity for all students who take the steps to prepare for college. In the 1990s only four states maintained an average investment in needbased grants at a level equaling a reasonable equity standard. Three of these states had substantial increases in tuition without sufficient new funding for grants to maintain equal opportunity. Over the decade, most states that gained ground in enrollment rates made substantial new investments in needbased grants. In contrast, states that lost ground had increases in net costs or failed to expand their state capacity to keep up with population growth. Had the states coordinated increases in state grants with tuition charges in a way that met the equity standard, according to our simulations between .5 and 1.9 million more students would have had the opportunity to enroll in

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college. The baseline estimate of 1.2 million provides the most reasonable estimate of the effects of the failure of states to invest in need-based grants at an appropriate level given public sector tuition charges. Clearly states have failed to coordinate finances in ways that would promote equal opportunity for enrollment in postsecondary education in the U.S.

6.

CONCLUSIONS AND IMPLICATIONS

While there has been substantial disagreement in the policy literature about the underlying causes of the current access challenge, there is general agreement that college access should be expanded in the U.S., especially for college-prepared, low-income students. The analyses reveal that finances have a modest indirect effect on academic preparation, as indicated by analyses of high school graduation rates, as well as a substantial direct influence on college enrollment. There is also a substantial body of research indicating state grants influence persistence (St. John, 2003; St. John, Cabrera, Nora, and Asker, 2000), though it was not yet possible to examine persistence using state indicators. State-level studies have found that funding for state need-based grants helped equalize the opportunity for persistence in Washington state (St. John, 1999) and Indiana (Hu and St. John, 2001; St. John, Hu, and Weber, 2000, 2001). Further, Indiana’s Twenty-first Century Scholars Program has been shown to influence academic preparation, college enrollment, and college persistence for low-income high school students (St. John, Musoba, Simmons, and Chung, 2002; St. John, Musoba, Simmons, Chung, Schmit, and Peng, 2004). Thus, state grants improve retention as well as access. During the 1990s, states allowed public tuition charges to rise when lacking tax revenues for continuity in funding for state colleges and universities. However, funding of state grants did not increase at rates needed to ensure financial access for low-income students. Had states coordinated increases in state grants with tuition increases, many more college-qualified students would have had the opportunity to enroll. Investing in student grants when tuition increases is a more efficient use of tax dollars than providing general subsidies to public colleges. When states shifted away from investing in institutions and allowed tuition to climb, a pattern evident over the past two decades, they had a moral—if not a legal—obligation to provide additional grant aid for qualified low- and middle-income students. In the 1990s, most states fell short of meeting the minimum equity standard of funding for need-based grants at a level equaling one-quarter of public sector tuition changes. This pattern has continued in the first years of the 2000s as well.

b

a

continued

Numbers are reported rounded for the columns Increase in Enrollment, Cost of Additional Grant Funding, and Cost per New Student. The United States figure is the sum of all states’ values. The formula presented is not working for the United States since the United States value is the weighted sum of individual states’ funding amounts, weighted by the number of students.

State Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware Florida Georgia Hawaii Idaho Illinois Indiana Iowa Kansas Kentucky

90s Avg Tuition (A) $2,195 $2,601 $2,243 $2,102 $1,369 $2,459 $3,416 $3,703 $1,592 $2,004 $1,558 $2,185 $4,213 $3,283 $2,512 $1,959 $2,069

Cost of Grant Gap in New Increase in Additional Funding per Grant Cost per Targeted Enrollment as 90s Avg Number of Grant Result of FTE Funding per New Funding per Fundingb 90s Avg FTE 90s Avg Filling Gap Freshmen Student FTE 90s Avg (F) (E) (C) (D=B-C) (B) (G=D*(E+F)) (H=G/E) $549 $16 $533 3,200 24,174 $14,600,000 $4,500 $650 $24 $626 500 2,487 $1,900,000 $3,500 $561 $27 $534 2,700 16,714 $10,300,000 $3,900 $525 $230 $296 1,100 13,695 $4,400,000 $4,000 $342 $371 $0 0 163,058 $0 $0 $615 $244 $371 1,900 19,015 $7,700,000 $4,200 $854 $399 $455 2,200 20,620 $10,400,000 $4,700 $926 $52 $874 900 4,406 $4,700,000 $5,100 $398 $146 $252 3,800 53,474 $14,400,000 $3,800 $501 $19 $482 4,600 37,183 $20,200,000 $4,400 $389 $21 $369 600 7,130 $2,900,000 $4,600 $546 $21 $525 1,100 6,969 $4,200,000 $4,000 $1,053 $894 $160 2,800 76,639 $12,700,000 $4,600 $821 $496 $325 2,800 34,671 $12,200,000 $4,400 $628 $409 $220 1,100 21,942 $5,000,000 $4,800 $490 $122 $367 1,500 17,203 $6,900,000 $4,700 $517 $305 $212 1,200 21,186 $4,800,000 $4,000

Table 4A-1. Simulation of costs and benefits of meeting the minimum equity standard: State-by-state analysis—Baseline estimatea

4. Financial Access 105

b

a

continued

Numbers are reported rounded for the columns Increase in Enrollment, Cost of Additional Grant Funding, and Cost per New Student. The United States figure is the sum of all states’ values. The formula presented is not working for the United States since the United States value is the weighted sum of individual states’ funding amounts, weighted by the number of students.

State Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota

90s Avg Tuition (A) $2,183 $3,734 $4,059 $3,669 $3,651 $3,125 $2,010 $2,952 $2,588 $2,162 $1,601 $4,920 $4,105 $1,568 $3,349 $1,385 $2,525

Cost of Gap in New Increase in Grant Additional Grant Funding per Enrollment as 90s Avg Targeted Cost per Grant Number of Funding per Result of Funding per FTE New Fundingb Freshmen FTE 90s Avg Filling Gap FTE 90s Avg 90s Avg Student (D=B-C) (E) (C) (G=D*(E+F)) (H=G/E) (F) (B) $546 $39 $507 3,300 25,049 $14,400,000 $4,300 $934 $254 $679 1,300 7,334 $5,900,000 $4,400 $1,015 $297 $718 5,000 27,440 $23,300,000 $4,700 $917 $358 $559 4,800 40,196 $25,200,000 $5,200 $913 $362 $551 7,700 56,959 $35,600,000 $4,600 $781 $734 $47 400 30,070 $1,400,000 $4,100 $503 $13 $489 2,000 17,330 $9,500,000 $4,700 $738 $115 $623 4,800 28,693 $20,900,000 $4,300 $647 $26 $621 900 5,703 $4,100,000 $4,600 $540 $66 $475 1,400 12,509 $6,600,000 $4,900 $400 $123 $277 400 4,425 $1,300,000 $3,100 $1,230 $41 $1,189 2,100 7,192 $11,100,000 $5,200 $1,026 $1,034 $0 0 51,626 $0 $0 $392 $313 $79 200 9,579 $800,000 $4,000 $837 $1,160 $0 0 111,533 $0 $0 $346 $121 $226 2,000 35,745 $8,500,000 $4,200 $631 $69 $563 700 5,772 $3,600,000 $5,400

Table 4A-1. (continued) Simulation of costs and benefits of meeting the minimum equity standard: State-by-state analysis— Baseline estimatea

106 Chapter 4

b

a

Numbers are reported rounded for the columns Increase in Enrollment, Cost of Additional Grant Funding, and Cost per New Student. The United States figure is the sum of all states’ values. The formula presented is not working for the United States since the United States value is the weighted sum of individual states’ funding amounts, weighted by the number of students.

State Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming United States

90s Avg Tuition (A) $3,725 $1,810 $2,595 $5,122 $3,387 $2,838 $3,008 $1,924 $1,751 $2,036 $6,349 $3,159 $2,160 $2,273 $2,722 $1,635 $2,601

Grant Gap in New Increase in Cost of Enrollment as 90s Avg Targeted Funding per Grant Cost per Additional FTE Funding per Result of Grant New Funding per Number of FTE 90s Avg 90s Avg FTE 90s Avg Filling Gap Student Freshmen Fundingb (B) (C) (D=B-C) (G=D*(E+F)) (H=G/E) (E) (F) $931 $307 $624 10,600 64,539 $46,900,000 $4,400 $452 $170 $283 1,400 17,565 $5,400,000 $3,900 $649 $197 $452 1,900 15,327 $7,800,000 $4,200 $1,281 $736 $544 9,700 73,746 $45,400,000 $4,700 $847 $167 $680 900 6,140 $4,800,000 $5,100 $709 $239 $471 2,400 19,313 $10,200,000 $4,300 $752 $15 $737 900 5,049 $4,400,000 $4,800 $481 $146 $335 2,300 25,962 $9,500,000 $4,100 $438 $112 $326 8,800 97,524 $34,600,000 $4,000 $509 $21 $488 1,900 13,444 $7,500,000 $4,000 $1,587 $589 $999 1,000 3,475 $4,400,000 $4,600 $790 $287 $503 4,500 34,694 $19,700,000 $4,400 $540 $385 $155 1,100 28,714 $4,600,000 $4,000 $568 $203 $365 1,000 10,528 $4,200,000 $4,100 $681 $327 $353 2,900 33,982 $13,000,000 $4,500 $409 $11 $398 300 3,187 $1,400,000 $4,200 $650 $386 $264 120,500 1,470,914 $533,100,000 $4,400

Table 4A-1. (continued) Simulation of costs and benefits of meeting the minimum equity standard: State-by-state analysis— Baseline estimatea

4. Financial Access 107

b

a

Cost per New Student (H=G/E) $9,700 $8,200 $8,700 $9,500 $0 $9,700 $10,800 $10,600 $8,600 $9,200 $10,300 $9,500 $10,700 $10,000 $11,400 $10,800 $9,400

continued

Numbers are reported rounded for the columns Increase in Enrollment, Cost of Additional Grant Funding, and Cost per New Student. The United States figure is the sum of all states’ values. The formula presented is not working for the United States since the United States value is the weighted sum of individual states’ funding amounts, weighted by the number of students.

State Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware Florida Georgia Hawaii Idaho Illinois Indiana Iowa Kansas Kentucky

90s Avg Tuition (A) $2,195 $2,601 $2,243 $2,102 $1,369 $2,459 $3,416 $3,703 $1,592 $2,004 $1,558 $2,185 $4,213 $3,283 $2,512 $1,959 $2,069

Cost of Grant Gap in New Increase in Enrollment as 90s Avg Additional Funding per Grant Targeted Grant Number of FTE Funding per Result of Funding per Fundingb 90s Avg FTE 90s Avg Filling Gap Freshmen FTE 90s Avg (F) (C) (D=B-C) (E) (B) (G=D*(E+F)) $549 $16 $533 1,400 24,174 $13,600,000 $650 $24 $626 200 2,487 $1,700,000 $561 $27 $534 1,100 16,714 $9,500,000 $525 $230 $296 400 13,695 $4,200,000 $342 $371 $0 0 163,058 $0 $615 $244 $371 800 19,015 $7,300,000 $854 $399 $455 900 20,620 $9,800,000 $926 $52 $874 400 4,406 $4,200,000 $398 $146 $252 1,600 53,474 $13,900,000 $501 $19 $482 2,000 37,183 $18,900,000 $389 $21 $369 300 7,130 $2,700,000 $546 $21 $525 400 6,969 $3,900,000 $1,053 $894 $160 1,200 76,639 $12,400,000 $821 $496 $325 1,200 34,671 $11,600,000 $628 $409 $220 400 21,942 $4,900,000 $490 $122 $367 600 17,203 $6,500,000 $517 $305 $212 500 21,186 $4,600,000

Table 4A-2. Simulation of costs and benefits of meeting the minimum equity standard: State-by-state analysis—Low-range estimatea

108 Chapter 4

b

a

Cost per New Student (H=G/E) $9,200 $10,100 $10,400 $11,500 $10,300 $10,300 $9,900 $9,800 $10,600 $11,300 $7,700 $11,000 $0 $9,300 $0 $9,500 $12,300

continued

Numbers are reported rounded for the columns Increase in Enrollment, Cost of Additional Grant Funding, and Cost per New Student. The United States figure is the sum of all states’ values. The formula presented is not working for the United States since the United States value is the weighted sum of individual states’ funding amounts, weighted by the number of students.

State Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota

90s Avg Tuition (A) $2,183 $3,734 $4,059 $3,669 $3,651 $3,125 $2,010 $2,952 $2,588 $2,162 $1,601 $4,920 $4,105 $1,568 $3,349 $1,385 $2,525

Cost of Grant Gap in New Increase in Additional Enrollment as 90s Avg Targeted Funding per Grant Grant Result of Number of Funding per FTE Funding per Fundingb 90s Avg FTE 90s Avg Filling Gap Freshmen FTE 90s Avg (E) (F) (B) (C) (D=B-C) (G=D*(E+F)) $546 $39 $507 1,500 25,049 $13,400,000 $934 $254 $679 500 7,334 $5,300,000 $1,015 $297 $718 2,000 27,440 $21,200,000 $917 $358 $559 2,000 40,196 $23,600,000 $913 $362 $551 3,200 56,959 $33,100,000 $781 $734 $47 100 30,070 $1,400,000 $503 $13 $489 900 17,330 $8,900,000 $738 $115 $623 1,900 28,693 $19,100,000 $647 $26 $621 400 5,703 $3,800,000 $540 $66 $475 500 12,509 $6,200,000 $400 $123 $277 200 4,425 $1,300,000 $1,230 $41 $1,189 900 7,192 $9,600,000 $1,026 $1,034 $0 0 51,626 $0 $392 $313 $79 100 9,579 $800,000 $837 $1,160 $0 0 111,533 $0 $346 $121 $226 900 35,745 $8,300,000 $631 $69 $563 300 5,772 $3,400,000

Table 4A-2. (continued) Simulation of costs and benefits of meeting the minimum equity standard: State-by-state analysis—Lowrange estimatea

4. Financial Access 109

b

a

Cost per New Student (H=G/E) $10,000 $9,400 $9,600 $10,700 $11,100 $9,100 $10,900 $9,200 $8,900 $9,500 $10,300 $10,000 $9,700 $9,700 $10,700 $10,000 $10,000

Numbers are reported rounded for the columns Increase in Enrollment, Cost of Additional Grant Funding, and Cost per New Student. The United States figure is the sum of all states’ values. The formula presented is not working for the United States since the United States value is the weighted sum of individual states’ funding amounts, weighted by the number of students.

State Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming United States

90s Avg Tuition (A) $3,725 $1,810 $2,595 $5,122 $3,387 $2,838 $3,008 $1,924 $1,751 $2,036 $6,349 $3,159 $2,160 $2,273 $2,722 $1,635 $2,601

Cost of Gap in New Increase in Grant Enrollment as 90s Avg Additional Grant Funding per Targeted Grant Result of Funding per FTE Number of Funding per Freshmen Fundingb FTE 90s Avg Filling Gap FTE 90s Avg 90s Avg (E) (D=B-C) (C) (G=D*(E+F)) (F) (B) $931 $307 $624 4,300 64,539 $43,000,000 $452 $170 $283 500 17,565 $5,100,000 $649 $197 $452 800 15,327 $7,300,000 $1,281 $736 $544 4,000 73,746 $42,300,000 $847 $167 $680 400 6,140 $4,400,000 $709 $239 $471 1,100 19,313 $9,600,000 $752 $15 $737 400 5,049 $4,000,000 $481 $146 $335 1,000 25,962 $9,000,000 $438 $112 $326 3,700 97,524 $33,000,000 $509 $21 $488 700 13,444 $6,900,000 $1,587 $589 $999 400 3,475 $3,800,000 $790 $287 $503 1,800 34,694 $18,400,000 $540 $385 $155 500 28,714 $4,500,000 $568 $203 $365 400 10,528 $4,000,000 $681 $327 $353 1,200 33,982 $12,400,000 $409 $11 $398 100 3,187 $1,300,000 $650 $386 $264 50,000 1,470,914 $498,300,000

Table 4A-2. (continued) Simulation of costs and benefits of meeting the minimum equity standard: State-by-state analysis—Lowrange estimatea

110 Chapter 4

b

a

Cost per New Student (H=G/E) $3,100 $2,400 $2,600 $2,600 $0 $2,700 $3,200 $3,500 $2,500 $3,000 $3,000 $2,700 $3,000 $2,900 $3,100 $3,100 $2,600

continued

Numbers are reported rounded for the columns Increase in Enrollment, Cost of Additional Grant Funding, and Cost per New Student. The United States figure is the sum of all states’ values. The formula presented is not working for the United States since the United States value is the weighted sum of individual states’ funding amounts, weighted by the number of students.

State Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware Florida Georgia Hawaii Idaho Illinois Indiana Iowa Kansas Kentucky

90s Avg Tuition (A) $2,195 $2,601 $2,243 $2,102 $1,369 $2,459 $3,416 $3,703 $1,592 $2,004 $1,558 $2,185 $4,213 $3,283 $2,512 $1,959 $2,069

Cost of Grant Gap in New Increase in Additional Enrollment as 90s Avg Targeted Funding per Grant Result of Grant FTE Funding per Number of Funding per Freshmen Fundingb 90s Avg FTE 90s Avg Filling Gap FTE 90s Avg (E) (F) (C) (D=B-C) (G=D*(E+F)) (B) $549 $16 $533 5,000 24,174 $15,600,000 $650 $24 $626 900 2,487 $2,100,000 $561 $27 $534 4,200 16,714 $11,200,000 $525 $230 $296 1,800 13,695 $4,600,000 $342 $371 $0 0 163,058 $0 $615 $244 $371 3,000 19,015 $8,100,000 $854 $399 $455 3,500 20,620 $11,000,000 $926 $52 $874 1,400 4,406 $5,100,000 $398 $146 $252 6,000 53,474 $15,000,000 $501 $19 $482 7,200 37,183 $21,400,000 $389 $21 $369 1,000 7,130 $3,000,000 $546 $21 $525 1,700 6,969 $4,600,000 $1,053 $894 $160 4,400 76,639 $12,900,000 $821 $496 $325 4,400 34,671 $12,700,000 $628 $409 $220 1,700 21,942 $5,200,000 $490 $122 $367 2,300 17,203 $7,200,000 $517 $305 $212 1,900 21,186 $4,900,000

Table 4A-3. Simulation of costs and benefits of meeting the minimum equity standard: State-by-state analysis—High-range estimatea

4. Financial Access 111

b

a

Cost per New Student (H=G/E) $2,900 $3,000 $3,200 $3,500 $3,100 $2,600 $3,200 $2,900 $3,100 $3,200 $2,000 $3,700 $0 $2,600 $0 $2,700 $3,600

continued

Numbers are reported rounded for the columns Increase in Enrollment, Cost of Additional Grant Funding, and Cost per New Student. The United States figure is the sum of all states’ values. The formula presented is not working for the United States since the United States value is the weighted sum of individual states’ funding amounts, weighted by the number of students.

State Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota

90s Avg Tuition (A) $2,183 $3,734 $4,059 $3,669 $3,651 $3,125 $2,010 $2,952 $2,588 $2,162 $1,601 $4,920 $4,105 $1,568 $3,349 $1,385 $2,525

Cost of Gap in New Increase in Grant Enrollment as 90s Avg Additional Grant Funding per Targeted Result of Number of Grant Funding per Funding per FTE Freshmen Fundingb FTE 90s Avg Filling Gap 90s Avg FTE 90s Avg (E) (D=B-C) (C) (G=D*(E+F)) (F) (B) $546 $39 $507 5,200 25,049 $15,300,000 $934 $254 $679 2,100 7,334 $6,400,000 $1,015 $297 $718 7,900 27,440 $25,400,000 $917 $358 $559 7,600 40,196 $26,700,000 $913 $362 $551 12,100 56,959 $38,000,000 $781 $734 $47 600 30,070 $1,500,000 $503 $13 $489 3,100 17,330 $10,000,000 $738 $115 $623 7,700 28,693 $22,700,000 $647 $26 $621 1,400 5,703 $4,400,000 $540 $66 $475 2,200 12,509 $7,000,000 $400 $123 $277 700 4,425 $1,400,000 $1,230 $41 $1,189 3,400 7,192 $12,600,000 $1,026 $1,034 $0 0 51,626 $0 $392 $313 $79 300 9,579 $800,000 $837 $1,160 $0 0 111,533 $0 $346 $121 $226 3,200 35,745 $8,800,000 $631 $69 $563 1,100 5,772 $3,800,000

Table 4A-3. (continued) Simulation of costs and benefits of meeting the minimum equity standard: State-by-state analysis—Highrange estimatea

112 Chapter 4

b

a

Cost per New Student (H=G/E) $3,000 $2,500 $2,800 $3,100 $3,500 $2,900 $3,300 $2,700 $2,600 $2,600 $3,200 $2,900 $2,600 $2,700 $3,000 $2,800 $3,000

Numbers are reported rounded for the columns Increase in Enrollment, Cost of Additional Grant Funding, and Cost per New Student. The United States figure is the sum of all states’ values. The formula presented is not working for the United States since the United States value is the weighted sum of individual states’ funding amounts, weighted by the number of students.

State Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming United States

90s Avg Tuition (A) $3,725 $1,810 $2,595 $5,122 $3,387 $2,838 $3,008 $1,924 $1,751 $2,036 $6,349 $3,159 $2,160 $2,273 $2,722 $1,635 $2,601

Cost of Gap in New Increase in Grant Enrollment as 90s Avg Additional Grant Targeted Funding per Grant Result of Funding per FTE Number of Funding per Fundingb Freshmen FTE 90s Avg Filling Gap FTE 90s Avg 90s Avg (E) (F) (D=B-C) (B) (C) (G=D*(E+F)) $931 $307 $624 16,900 64,539 $50,900,000 $452 $170 $283 2,200 17,565 $5,600,000 $649 $197 $452 3,000 15,327 $8,300,000 $1,281 $736 $544 15,400 73,746 $48,500,000 $847 $167 $680 1,500 6,140 $5,200,000 $709 $239 $471 3,700 19,313 $10,800,000 $752 $15 $737 1,500 5,049 $4,800,000 $481 $146 $335 3,600 25,962 $9,900,000 $438 $112 $326 13,800 97,524 $36,200,000 $509 $21 $488 3,000 13,444 $8,000,000 $1,587 $589 $999 1,600 3,475 $5,000,000 $790 $287 $503 7,200 34,694 $21,100,000 $540 $385 $155 1,800 28,714 $4,700,000 $568 $203 $365 1,700 10,528 $4,400,000 $681 $327 $353 4,600 33,982 $13,600,000 $409 $11 $398 500 3,187 $1,500,000 $650 $386 $264 191,000 1,470,914 $568,000,000

Table 4A-3. (continued) Simulation of costs and benefits of meeting the minimum equity standard: State-by-state analysis—Highrange estimatea

4. Financial Access 113

Chapter 5 PATHWAYS AND MARKETS By Edward P. St. John, Anna S. Chung, Glenda D. Musoba, and Choong-Geun Chung

The access debates in the U.S. have been framed as though there were just one avenue to college, frequently characterized as the academic pipeline. Arguments for preparation for four-year colleges, derived from correlations between advanced math courses in high school and enrollment in senior institutions, now dominate the access debate. Yet the implementation of the policies advocated by proponents of the academic preparation rationale has had little effect on college enrollment rates in states and has had negative effects on high school graduation rates (chapter 3). Because the decline in the pool of high school graduates is associated with K–12 policies implemented in the 1990s (chapter 3), these policies may have actually reduced college participation rates for the cohorts of high school students who would have graduated in the 1990s. The analysis of financial access (chapter 4) revealed some of the ways the concept of net price has oversimplified the role of public finance in college access because needbased and non-need grants have had very different effects on high school graduation than they have had on enrollment. These findings raise basic questions about the apparent effects of education and finance policies on preparation and access. This chapter initiates the process of rethinking the assumptions made on both sides of the access debate. If we take the trends in globalization— increased emphasis on privatization and accountability—as a starting point, then we need to think more critically about the meaning of the public good and the rationing of access to these services through school reform and public finance policies. This chapter introduces two ideas into the debate:

115

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x Educational pathways: There are multiple pathways into college markets from high schools, and the effects of school reforms can be evaluated relative to these pathways into the educational marketplace x Financial markets: There are multiple higher education markets in states—two-year and four-year, public (or publicly assisted) and private—and access to these markets can be influenced by the changes in public finance strategies implicit in privatization, given the great variation in costs across different types of colleges. These propositions create a more complex picture of access than does the notion of an educational pipeline, but they also introduce a different way of thinking about the problems facing students, taxpayers, and educators. This chapter reexamines the assumptions about access and the new propositions and presents analyses of access testing these newer concepts.

1.

RETHINKING ASSUMPTIONS ABOUT ACCESS

Both rationales—for academic preparation and for financial access— have been predicated on the notion of a single pathway into college, and both oversimplify the problem. The academic preparation argument rests on the notion that all students need the same college qualifications, but the policy seems unrelated to enrollment, using a single enrollment rate as an outcome (chapter 3). In contrast, the net price notion assumes the rate of flow into college is regulated by costs, a belief that is supported by the analysis of enrollment rates, but not by the analysis of high school graduation rates (chapter 4). The advocates of academic reform have argued for new educational standards that would define an academic threshold for access, a concept closely linked to research on advanced math education. Rather than a pipeline keeping children on track to college, the system’s leaks (i.e., dropouts) are accelerated by screening mechanisms along the path (chapter 3), including increased graduation requirements, standards, and exit exams. However, family concerns about costs are also an invisible screen. Many students fall off the preparatory path and drop out of high school (chapter 4) before they even reach some of the screening mechanisms that are thought to be important, like entrance exams and college applications. Students who make it down the path may not make it into the type of college they aimed for—or into any college—because they cannot afford to attend. The logic of the new academic preparation rationale seems to have incorrectly specified the ways different forms of preparation relate to college access in addition to having overlooked the role of finances in preparation.

5. Pathways and Markets

117

In contrast, the advocates of economic equity have argued that there is a single financial mechanism—net price—which regulates the flow of students into the system: As net prices went up, the rate of flow would, logically, go down. However, in the 1980s and 1990s in the U.S., net prices increased due to decreases in need-based grants and increases in tuition charges, and college enrollment rates increased substantially (St. John, 2003). These trends illustrate the need to rethink the role of prices and subsidies in access. While the notions of differentiated price effects and multiple markets have been used to explain this anomaly (St. John 1994, 2003; St. John and Starkey, 1995a, 1995b), these propositions need further examination. This chapter extends the public interest framework, informed by the analyses in prior chapters, to offer two new propositions: (1) that there are multiple academic pathways into and through college and (2) that there are multiple financial markets for students, depending on their qualifications and financial aid support. The public interest framework provides a means of thinking differently about equal access in this diverse marketplace.

1.1

Multiple academic pathways

Historically, progressive arguments about K–12 education have emphasized local control and adequate funding. The U.S. system has not had the strong emphasis on student tracking evident in European systems. Rather than using exams to determine whether students can attend college preparatory high schools, the comprehensive high school in the U.S. has had multiple pathways into college—community colleges as well as diverse types of four-year colleges. However, the development of new standards and testing policies in the states has moved the entire American system in the direction of European academic high schools but has located the screening functions in different places in the pipeline than in the European system. Rather than sorting students into different high schools, as in Europe, the U.S. system has increased the risk that students will be screened out of high school altogether (i.e., higher dropout rates). Raising the graduation requirements, including additional math requirements, was based on the belief that a more rigorous college preparatory curriculum was necessary for all students in the U.S. system (see chapter 1). Raising the state-level standards and implementing testing systems integrated a new rationale in the transition between high school and college, and thus added academic features more like the European model, albeit in a different configuration. Creating better defined academic pathways to college is not necessarily a problem for equal access, especially if students have had the same chances of preparing regardless of race and income and if all qualified students have had the opportunity to enroll in college. If after having met the new

118

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standards—the requirements aligned with the standards and college admissions criteria—low-income students fail to gain access to four-year colleges while their wealthier peers have the opportunity to enroll, then there is a systemic inequality. Under these conditions the new system of access would be assumed “equitable” whether or not inequalities persisted in the opportunity prepare. However, neither of these equity standards—equal opportunity to prepare or equal opportunity for prepared students to enroll in four-year colleges—have been met in the U.S. In fact we may have been closer to meeting these standards, especially equal access to college for prepared students, in the mid-1970s than we are in this new century (St. John, 2003). The analyses of academic preparation and financial access (chapters 3 and 4) illustrate inequalities in the opportunity to prepare that were increased by policies implemented after 1992. In addition, it is now well documented that many low-income students who qualify for admission to four-year colleges lack the financial opportunity to enroll (Fitzgerald, 2004; Lee, 2004). While NCES reports (1997a, 2001a, 2001c) claimed that access is equitable in the U.S. for students who have taken the steps to prepare for college, reviewers of these reports have documented that the NCES studies are fraught with statistical errors (Becker, 2004; Heller, 2004). Fitzgerald’s reanalysis (2004) of the statistics reported by NCES indicates that millions of qualified, low-income students are being left behind each decade. Lee’s (2004) reanalysis of the NELS database (used by NCES, 1997a, 2001a, 2001c) found that large numbers of low-income students were left behind regardless of the combination of variables used to define the academic screen. Thus, in the American context, where prepared students have different chances of entry based on their incomes, there is reason to be concerned about equity in both preparation and in the opportunity to enroll. However, before we further reanalyze the NELS database to examine the effects of school reforms and public finance policies (see part III), it is important to consider whether the new education policies in states have really moved the U.S. system closer to the more rigidly defined European model. The new American model advocates college preparatory high schools for all students, not just students with high scores on high school entry exams, as in Europe. The analyses of pathways in this chapter examine this proposition. Given that the new education policies have had little significance in expanding enrollment rates for high school graduates in the states, it is worthwhile considering whether these policies have had any effect on enrollments at different types of institutions.

5. Pathways and Markets

1.2

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Multiple financial markets

The shift toward privatization, with the emphasis on individual fees for service, can also change the nature and function of the markets within state systems of higher education. The consequence of the shift is most easily understood when viewed as a transition from a structural response to demand—expanding subsidies to expand supply (i.e., building more and larger colleges)—to a market strategy that stimulates supply by raising prices and using private capital to subsidize demand (i.e., loans to students). Within the U.S., students have extensive access to loans, although subsidized loans alone are insufficient for low-income, college-prepared students to gain access, especially given the low debt limits for subsidized loans in relation to the cost of attending public colleges. In the U.S. system they also need grants from the federal government, states, and/or institutions for financial access. Using the older structural strategy of providing supply, states faced higher costs per student at four-year colleges than at two-year colleges. In the old system, need-based grants equalized opportunity for lowincome, prepared students to enroll in college. As prices have risen, the national strategy of cutting grants and raising prices has undermined the equalizing federal function of need-based grants. State grants have become the equalizing mechanism, and there has been a great deal of variability in the state investment in grants. In recent years there has been a push to expand the number of graduates based on the assumption that the labor market demands more collegeeducated citizens. The underlying question is whether the market approach increases supply of educational opportunity. The market strategy can expand supply by (a) expanding opportunities in the public system through sharing costs of operation between taxpayers and consumers and (b) expanding the supply in private colleges by lowering the tuition gap between public and private colleges and stimulating the development of private colleges (forprofit and not-for-profit). To understand the effects of privatization we need to examine the impact of state financial strategies in the student environment in higher tuition public colleges (i.e., four-year colleges), lower tuition public colleges (i.e., two-year colleges), and private colleges. In other words, it is crucial to examine how state financial strategies influence the distribution of students within state market systems.

2.

REANALYSIS

Because this is a relatively novel approach to the study of enrollment and college choice, the analysis in this chapter is exploratory. While some

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findings on state policy screening functions are noteworthy, they are not central to the overall position of the text, but are considered along with other findings in our conclusions about college access and social justice in part IV.

2.1

Research approach

These analyses applied the same statistical methods and were subject to the same sample limitations as the analyses discussed in chapters 3 and 4, so these discussions are not repeated here. We tested the use of combined models with both K–12 and public finance policies. The effects for the policy variables in the combined models were very similar to those with the two types of analyses, so we present and discuss the two sets separately. The analyses in this chapter provide fixed-effects regressions of the enrollment rates for four educational markets within states: public four-year colleges, public two-year colleges, nonprofit colleges, and for-profit (proprietary) colleges. The regression models for the analyses of the effects of K–12 policies (educational pathways) use the same independent variables in the enrollment rate model in chapter 3. The analyses of effects of public finance policies on enrollment rates used most of the independent variables included in the analysis of enrollment rates in chapter 4 with the exception of the variables for percentages of enrollment in different sectors.1

2.2

Trends in enrollment rates across institutional type

Not only do the rates for college enrollment vary across the states, but the rates of enrollment in different types of colleges also vary for residents of different states. The analyses in this chapter adapted the same access prediction model presented in chapters 3 and 4 to examine the flows into different types of colleges within states. The outcomes were state-level enrollment rates for public four-year, public two-year, private nonprofit, and private for-profit institutions. Part of the access problem in the U.S. is that enrollment rates for public four-year colleges did not increase relative to enrollment rates for public two-year and private colleges for 1992 through 1998 (see Table 5-1). Unfortunately, it was not possible to provide this breakdown for 2000 because of changes in IPEDS.2 These trends illustrate that expansion in 1

2

Elimination of these variables was necessary because rather than being a factor influencing enrollment—as they were in the models used in chapter 4—enrollment rates (i.e., measures of percentage of enrollment) for public two-year colleges and private colleges are outcomes in these analyses. The Integrated Postsecondary Education Data System (IPEDS) is the core postsecondary education data collection program for NCES.

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public four-year college enrollments for first-time freshmen was faster than in public two-year and private colleges (Table 5-1). Between 1992 and 1998 there was a two-percentage-point increase in the enrollment rate for public four-year colleges, but there were smaller percentage increases in enrollment rates for private colleges (0.5%) and for-profit colleges (0.4%) and in out-ofstate enrollment (0.6%). In addition, the enrollment rate for two-year colleges was lower in 1998 than it had been in any of the prior years. In contrast, the national trends in total enrollment (Table 5-1) indicate that enrollments in public two-year colleges grew far more rapidly than enrollments in public four-year colleges (St. John, 2003). This combination of trends suggests that many students transfer from four-year to two-year colleges, possibly after a period of stopout in a reverse direction of transfer that merits further study. Table 5-1. National percentage of FTE* enrollment in public four-year, public private nonprofit, and private for-profit colleges Public 4- Public 2- Private Private Out-of-State No Year Year Year Nonprofit For-Profit Enrollment Enrollment 1992 20.0% 16.7% 7.0% 0.7% 9.9% 45.7% 1994 20.9% 17.6% 7.3% 0.7% 10.5% 42.9% 1996 21.8% 18.0% 7.4% 0.9% 10.4% 41.5% 1998 22.0% 16.0% 7.5% 1.1% 10.5% 42.8% * Full-time equivalent

2.3

two-year, Total 100.0% 100.0% 100.0% 100.0%

Academic pathways

Academic preparation strategies provide means of regulating access to public four-year colleges. Our statistical models for these analyses were consistent with the specifications of the academic access model described in chapter 3, and the multiple outcomes were used in the different models presented in Table 5-2. A series of fixed-effects regressions were used to examine enrollment rates for the sectors of colleges within states: public four-year colleges, public two-year colleges, proprietary schools, and private, nonprofit colleges. The analysis of the impact of preparation regulations on access to different types of college is examined after discussing the influence of demographic variables. 2.3.1

Demographic contexts

Poverty rates were negatively associated with the enrollment rates for public four-year colleges but not with enrollment rates for public two-year colleges or private four-year colleges. The percentages of African Americans in state populations were positively associated with the enrollment rates in public four-year colleges and private nonprofit colleges but not with the

Note: ***p<0.01, ** p<0.05, * p<0.1

N P-value for F test that all ui=0

Variables % Poverty % Black % Hispanic % Other minorities (= Indian + Asian) % Population with BA or higher Enrollment when cohort in 9th grade SAT participation rate Honors or advanced diploma policy State guidelines consistent with NCTM recommended standards Percentage of schools participating in the AP program High school exit exam required High (3 or 4) credits in math required for grad Local board control of math requirements for grad K–12 instructional expenditures per FTE Adj R2 -0.016 0.021 0.097 -0.099

-0.002 0.003

0.018

-0.008 0.96 199

0.00

0.224 *

0.001

Public 4-Year Institutions Unstand Stand Beta Sig. -0.258 -0.159 *** 1.833 2.775 ** 0.275 0.356 -0.459 -0.674 0.000 0.032 0.000 -0.016 -0.001 -0.263 -0.021 -0.161 ** 0.000 0.000

0.00

-0.021 0.94 196

-0.007

-0.001 0.013

0.001

-0.221

-0.031

-0.003 0.075

0.249

Public 2-Year College Institutions Unstand Stand Beta Sig. -0.202 -0.100 -0.311 -0.377 -0.634 -0.660 3.721 4.336 ** 0.000 0.012 0.000 -1.054 ** 0.000 -0.142 -0.030 -0.183 ** -0.005 -0.029

0.00

0.001 0.96 194

-0.005

0.000 0.001

-0.001

0.029

-0.042

-0.005 0.009

-0.357 ***

0.00

0.001 0.82 169

-0.002

-0.003 -0.002

0.000

0.169

-0.122

-0.189 * -0.108

-0.327

Private For-Profit Private Nonprofit Institutions Institutions Unstand Stand Beta Sig. Unstand Stand Beta Sig. 0.052 0.055 0.002 0.013 1.338 3.417 *** 0.226 3.271 -0.455 -1.003 * 0.219 2.865 * 1.006 2.517 * -0.043 -0.481 0.000 -0.013 0.000 0.112 0.000 0.705 * 0.000 1.088 0.000 -0.327 0.000 1.010 -0.002 -0.030 -0.001 -0.085 -0.001 -0.007 -0.002 -0.100

Table 5-2. Fixed effects regression analyses of impact of state school reform on college destinations: Public four-year, public two-year, private nonprofit, and private for-profit

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enrollment rates for public two-year colleges. The percentages of Hispanics in state populations were modestly (.1 alpha) and negatively associated with enrollment rates in private nonprofit colleges and modestly and positively associated with enrollment rates in proprietary institutions. The percentages of other minorities in states were positively associated with enrollment rates in both public two-year and private nonprofit colleges. Further, this variable had the largest standardized beta coefficient, indicating a strong influence. The largest “other” minority group was Asian Americans, a group that attended college at higher rates than other racial/ethnic groups. However, the size of the student population was positively associated with enrollment in private colleges and negatively associated with enrollment in two-year colleges. In contrast, the percentages of state populations with bachelor’s degrees were not associated with enrollment in any type of college. 2.3.2

Findings on education policies

First, having honors diplomas was negatively related to enrollment in public colleges, both two-year and four-year, suggesting that advanced diplomas functioned as a screening device, rationing access when opportunity was limited. This would constrain college access for students in schools lacking the advanced courses required to complete these diplomas, thus easing access for students from elite schools. These findings fit well with the regulatory interpretation of the role of education reforms. Second, the percentage of high schools offering Advanced Placement (AP) courses was significantly and negatively associated with enrollment rates for private colleges and modestly (.1 alpha) and positively associated with enrollment rates for public four-year colleges. It is conceivable that as AP courses become more prevalent, they could become a force in determining who goes on to public four-year colleges, part of raising the stakes along with entrance exams and higher standards. This further suggests the hypothesis that states use standards and other accountability schemes to regulate access to public four-year colleges, rather than to expand access. Public four-year colleges may be more likely than private colleges to collaborate with public schools on the transfer of AP credits and other initiatives—like combining the last two years of high school and the first two years of college—because of their shared interest in the state system. In the longer term, as policies that promote college preparation during high school are expanded, private colleges may adapt their admissions policies to ensure they maintain their share of enrollment within states. Third, high school exit exams were modestly (.1 alpha) and negatively associated with the rate of enrollment in proprietary schools. Recall that exit

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exams were the one reform policy that was positively associated with high school graduation rates (chapter 3). These findings support the argument for alignment of high school preparation with admission standards for four-year colleges. In contrast to these findings, neither K–12 funding per student nor math course requirements were associated with enrollment rates in any type of college. However, readers are reminded that school funding was associated with higher high school graduation rates (chapter 3), so it has had an indirect effect on college enrollment because it increased the size of the collegeprepared population in each of the states. 2.3.3

Understanding the policy shift

The new education policies set in motion a pattern of academic pathways in the U.S. more like the European model than the traditional American model of education. Yet the single pipeline framework appears to have had an unintended inequitable effect in the U.S.—reducing high school graduation rates. The analysis of academic pathways found most of the state K–12 policies were significantly related to enrollment rates in different types of colleges (Table 5-2). These findings confirm that education policies regulate the flow into educational systems rather than expand access to them. Although the new U.S. policies may now resemble the European model more than the traditional American methods of regulation of education, the accountability mechanisms in the U.S. system are implemented at different points in the pipeline than is the case in the European system. Not only do the U.S. policies screen students out of high schools altogether, but they alter the flow of students to different types of colleges and do not influence the overall rate of enrollment. Thus, there is a clear need to rethink the academic pathways from K–12 to higher education in the U.S. These findings raise questions for policy makers, voters, and researchers. In the analysis of the pipeline it became apparent that the new reforms did not expand college access. Rather, they constrained high school graduation and were relatively benign with respect to college enrollment. The pathways analyses provided an alternative explanation for these findings. The new policies essentially function as mechanisms for regulating access to public colleges. Increasing the emphasis on AP courses was positively related to enrollment in public four-year colleges. In addition, honors diplomas constrained enrollment to public colleges (both two-year and four-year). While the new policies appear to be more like the European model of education than the traditional American model, the accountability mechanisms impede improvements in preparation, which appears not to be the case in the European system. Not only do these policies screen students

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out of high schools altogether, by inducing dropout (chapter 3), they are related more to the flow of students to different types of colleges than to the overall rate of enrollment. Thus, there is a clear need to rethink the academic pathways from K–12 to higher education in the U.S.

2.4

Higher education markets

One major adaptation was made to the financial access model (chapter 4) so that we could consider the role of multiple markets. We did not include variables related to the percentage of enrollments in each of the sectors because these were so strongly related to the distribution outcomes. Otherwise the model is identical to the one used in chapter 4, with demographic, intervening, and financial variables. Fixed effects OLS regressions examine enrollment rates for in-state public four-year colleges, public two-year colleges, and private nonprofit colleges. 2.4.1

Demographic variables

A comparison of the academic pathways model (Table 5-2) and the market model (Table 5-3) is revealing. In particular, when we control for tax rates as intervening variables and public finance variables (as in Table 5-3), poverty rates were significantly and negatively associated with enrollment in public four-year colleges. The percentage of African Americans in states’ populations was not significantly associated with enrollment in public fouryear colleges but was positively associated with enrollment in private nonprofit colleges. In addition, the percentage of Hispanics in state populations was positively associated with enrollment in both two-year colleges and private nonprofit colleges. 2.4.2

The impact of intervening variables

States’ tax rates were positively associated with enrollment rates in public four-year colleges but were negatively associated with enrollment rates for proprietary schools. School funding was also related to both higher graduation rates (chapter 3). This adds substance to the new understanding that is emerging. States with higher tax rates can afford to expand access to public four-year colleges. The alternative to adequate taxes appears to be implementation of regulatory controls on access to public four-year colleges, rationalizing new education policies that essentially constrain access by reducing high school graduation rates.

Variables % Poverty % Black % Hispanic % Other minorities Enrollment when cohort in 9th grade % of population with BA or 0.118 higher Tax rate: State tax/income 0.593 0.018 Adjusted per FTE needbased undergrad state grant amount (1,000) Adjusted per FTE non-need 0.049 undergrad state grant amount (1,000) Adjusted undergrad in-state -0.026 tuition and fees (1,000) 0.95 Adj R2 N 199 0.00 P-value for F test that all ui=0 Note: *** p<0.01, ** p<0.05, * p<0.1 -0.108 0.053

0.038

-0.249

0.151 *** 0.076

0.109 ***

-0.042 0.90 196 0.00

0.083

-0.275 *

0.069

-0.019 0.180

0.047

Public 2-Year College Institutions Unstand Stand Beta Sig. -0.148 -0.073 -1.307 -1.584 -0.673 -0.700 3.077 3.579 ** 0.000 -0.744 *

0.083

Public 4-Year Institutions Unstand Stand Beta Sig. -0.231 -0.142 *** 0.342 0.517 0.342 0.442 0.766 1.121 0.000 -0.092

0.95 194 0.00

-0.025

-0.001

0.094 0.016

0.009

-0.246 *

-0.003

0.040 0.113

0.011

Private Nonprofit Institutions Unstand Stand Beta Sig. 0.049 0.051 1.501 3.831 *** -0.384 -0.845 1.102 2.750 ** 0.000 0.409

0.70 169 0.00

0.006

-0.001

-0.113 0.002

0.019

0.169

-0.030

-0.246 * 0.092

0.129

Private For-Profit Institutions Unstand Stand Beta Sig. 0.025 0.150 0.163 2.350 0.176 2.288 -0.290 -3.260 0.000 1.238

Table 5-3. Fixed effects regression analyses of the impact of public finances on college enrollment rates by type of institution: Public fouryear, public two-year, private nonprofit, and private for-profit

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127

The impact of public finance on market analyses

Two of the three state finance variables were associated with enrollment rates in at least one sector (Table 5-3). But all three of the variables merit discussion in relation to the findings in chapter 4. First, given the substantial influence of funding for need-based grants on enrollment rates (chapter 4), it is interesting to note that this variable was not significantly related to enrollment in any particular type of institution (Table 5-3). Putting these findings together, it is apparent that need-based grant aid is the most important predictor of access, but it did not favor enrollment in one set of institutions over another. Second, state funding for non-need grants was positively and significantly associated with enrollment rates in public four-year colleges. This finding is consistent with prior research (Heller and Marin, 2002, 2004), which finds that merit programs influence more middle-income students to stay in state and to enroll in public four-year colleges. Further, given that the new merit grants are frequently indexed to tuition, it is not surprising that these grants were positively associated with enrollment rates for public four-year colleges. Finally, tuition charges were modestly (.1 alpha) and negatively associated with enrollment in both public two-year colleges and private colleges (Table 5-3). Students attending two-year colleges are generally the poorest and most likely to be adversely affected by tuition, so the finding on two-year colleges was expected.

3.

CONCLUSIONS

The analyses testing the pathways and markets concepts reveal that college access is far more complex than assumed in either the academic or financial rationales for college access. Both K–12 and public finance policies influenced the follow of students across different types of institutions in the 1990s. It is appropriate to reconsider the evidence presented above relative to the core arguments of the two rationales.

3.1

The academic preparation rationale reconsidered

The academic preparation rationale for college access in the U.S. has posited that improving preparation will expand enrollment in four-year colleges. This argument assumed that the courses students took in high school, especially advanced math courses, determined college access.

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The findings on AP courses reinforce the new academic preparation rationale, albeit in a backward way. Increasing advanced high school courses has been associated since 1992 with redirecting enrollment from private nonprofit colleges to public four-year colleges. This could result from smoother transitions in college admissions and from the reluctance of private colleges to allow students to transfer AP credits. Regardless of the reason, this finding seems to support the academic preparation rationale. At the very least these analyses suggest it is important to monitor and evaluate the role and influence of K–12 policies in regulating the flow of students into different types of colleges. To the extent that the new U.S. system is following a European model of tighter linkages between high schools and colleges, we might expect new patterns of student flow—or academic pathways—will be created as an artifact with changes in preparation relative to the supply of different types of college opportunities.

3.2

The financial access rationale

The financial access rationale was part of the ethos of the Great Society of the late 1960s and was informed by economic research on student demand and human capital. In the 1960s and early 1970s, economists argued that providing aid to low-income students would equalize the opportunity to attend (Carnegie Commission on Higher Education, 1973; Committee on Economic Development, 1973; Hansen and Wiesbrod, 1969; National Commission on the Financing of Postsecondary Education, 1973). These arguments were influential in the creation of the federal Pell Grant program in 1972 and the near full funding of the program in the middle 1970s. During that brief period, high school graduates from diverse ethnic backgrounds attended college at nearly equal rates. High school graduation rates were also higher than is currently the case. While a disparity in opportunity developed in the 1980s and persisted through the 1990s, few analysts used the economic rationale to examine these developments. Instead, economists tended to focus on Pell funding (Hansen, 1983; Kane, 1995) but overlooked the role of other federal student grant programs (St. John, 1994, 2003). More recently, the Advisory Committee on Student Financial Assistance (2001, 2002) refocused attention on the influence of grant aid on equalizing opportunity, drawing attention to the relationship between the decline in federal grant aid and the large number of college qualified, low-income students being left behind. The analysis of financial access (chapter 4) confirmed that need-based grants helped promote access in the states, as measured by enrollment rates. However, the analyses of the role of the financial market in this chapter revealed a more complicated picture. Need-based grants were not

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significantly associated with enrollment in any type of institution. But tax rates were positively associated with enrollment in public four-year colleges and not other sectors. The ability to enroll large percentages of high school graduates in public colleges is dependent in part on the willingness of the state’s citizens to pay their fair share of taxes. In contrast, merit grants were associated with enrollment rates in public four-year colleges. Rather than providing access to a type of college, needbased grants simply expanded opportunity. Merit grants, in contrast, appear to be a grant aid strategy that helped states to align their educational reforms with access to public four-year colleges, further complicating the picture. In other words the merit aid programs implemented after 1992 rationed access to public four-year colleges for students who met predefined academic criteria. In a sense this policy shifts the screening devices of admissions and prices to student achievement in high school. But these policies complicate school reform because parents may have to choose high school districts based on their perceptions of their children’s ability to attain grades or test scores (depending on the criteria used for these programs). Tax rates also came into the picture in the market analysis. States with higher tax rates had better access to public four-year colleges. This brings us back to the underlying issues: privatization and accountability. Globalization of higher education involves privatizing public colleges and increasing the use of loans. In the U.S., these policies were also accompanied by a decline in federal need-based grants (St. John, 1994, 2003)—a development that explains the new inequality and the relative decline in opportunity to attend public four-year colleges. Public four-year colleges remain a costly enterprise for taxpayers, with costs either being borne by families in payment for attending or by taxpayers subsidizing these costs—either through reductions in tuition (i.e., tuition subsidies) or through grants for low-income students who also borrow. Merit grants logically align admissions to four-year colleges with the new high school curriculum reforms, providing a financial incentive for high-achieving students who prepare in specified ways. It has been argued that this is an appropriate alignment, that the financial incentives provided by merit grants encourage more students to prepare for college (Bishop, 2002, 2004). However, most of the evidence does not support this claim. There appears to be little change in test taking after the implementation of merit programs (Heller and Rogers, 2003), although more time may be needed to evaluate this aspect of the new merit programs. The merit programs help students who would have gone to college to enroll in fouryear colleges. However, these analyses suggest that merit grants serve another function related to college access.

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Historically, states developed master plans that set parameters related to college admissions. For example, the California 1960 master plan for higher education set limited access to the University of California campuses to the top 10 percent, while state colleges were to provide access to the top third. Many states followed this type of differentiated system, developed in the 1960s (Halstead, 1974). In the new context, state merit grants appear to be functioning as screening devices, rationing access to public four-year colleges. However, the prospect of stimulating market forces through competition between public and private four-year colleges has not yet been an outcome of state grant programs. It has long been argued that grant aid can stimulate efficient use of tax dollars in a high-tuition, high-grant environment (McPherson, 1978; Zumeta, 2004). However, during the 1990s, tuition was functioning as a sorting mechanism, reducing access to private colleges and to public two-year colleges. But state spending on need-based grant aid in the 1990s was not sufficient to have a positive association with enrollment rates for four-year colleges, a finding that supports the argument that need-based aid was not sufficient during that decade in the U.S. (Advisory Committee on Student Financial Assistance, 2002; Fitzgerald, 2004).

3.3

Finding perspective

Interpreting these findings from both American and European perspectives helps us untangle their meaning. The American perspective is situated in a history that once emphasized a general education high school and mass access to low-tuition colleges. In contrast, in the European model, college access has been more limited but free to those who qualify. From the U.S. perspective, the comparative study of states reveals the complexities of the new approaches to education reform and public finance. The education reforms pushed a new single model of more schools—forcing more children out of the system because the alternative paths were no longer acceptable. Indeed, the older vocational and general education options in American high schools no longer seemed appropriate, given the call to improve preparation. The role of finances was largely overlooked in the debates about academic access. The new U.S. higher education system enabled more students to enroll at lower per-student cost for taxpayers because it induced students to enroll in two-year and private colleges. However, the lack of public will to use tax dollars for higher education undermined realization of both the academic and financial rationales. While the new developments in the American educational system may bring it closer to the rationales used in European education, changing from comprehensive high schools with multiple tracks to high schools with a

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single college preparatory curriculum seems odd from the European vantage point. The European model has had more success at expanding college enrollment rates than the U.S. model (Conklin and Curran, 2005). The reasons for making this shift in the U.S. have been derived from the neoconservative myth of an academic pipeline. This mythical pipeline does not represent many of America’s predominately minority communities, where the college preparatory curriculum has limited availability (Trent, Gong, and Owens-Nicholson, 2004) and where African Americans have had less opportunity to take advanced courses. The pipeline model, used as the core concept of the academic access rationale, also disguises the redistribution of opportunity resulting from the new education and finance policies. The reanalysis of NELS considers which groups in American society benefited from the redistribution of opportunity.

III

STUDENT OUTCOMES: REANALYSES OF THE NELS

Chapter 6 ACCESS TO ADVANCED MATH By Edward P. St. John and Anna S. Chung

Admissions practitioners and researchers have long known that students in a college preparatory curriculum take advanced math courses and that the “hard” sciences require more preparation in math. More than a half century ago college entrance exams were developed to measure math knowledge and skills—outcomes of high school preparation and student ability—so colleges could make informed admissions decisions. Although the correlation between completion of advanced math courses in high school and college enrollment (e.g., Choy, 2002) should not have been a surprise to the policy community, when policy researchers first began to report on this correlation (e.g., Adelman, 1995; Pelavin and Kane, 1990) it seemed to catch the academic world by surprise, as does stating the obvious sometimes. Perhaps there was a lack of general understanding of high school preparation and the admissions process. Variables related to preparatory curriculum had been considered in access studies for decades (e.g., Jackson, 1978; Manski and Wise, 1983; St. John and Noell, 1989). The new studies had findings consonant with these earlier studies, but they left out financial aid variables, resulting in imbalanced research in a field with a prior history of balance. Arguments were being advanced in the national discussion on education reform for a rationale requiring advanced curricula for all students without considering (a) whether policies aimed at improvement in math preparation were associated with higher dropout rates, an unintended consequence, or (b) whether requiring advanced courses for all students was appropriate for a society that depends on a technically trained workforce along with a substantial number of four-year college graduates. But the more troubling issue was that this new enthusiasm for reform overlooked whether prepared, low-income students—who had taken the courses—could afford to enroll and persist in college.

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Therefore, it is important to consider the first of these overlooked issues: What influence did the K–12 policies in place at the time the 1992 cohort entered college (i.e., the database for the new studies of preparation and access) have on completion of advanced math courses or dropout? This question could have been addressed by any of these new studies that focused on preparation as an explanation for unequal access. This chapter examines evidence related to the role and influence of state education policies on preparation. The indicators analyses in chapter 3 and 5 reveal that strengthening graduation requirements after 1992 was not only associated with higher SAT scores but also with higher dropout rates. These analyses did not consider whether students actually completed higher levels of math as a consequence of these policies. This chapter examines the effects of state policies in place the year the high school class of 1992 graduated from college. We reexamine the NELS database because research using this database (e.g., Adelman, 1995; Choy, 2002; NCES, 1997a, 2001c; Pelavin and Kane, 1990) has been the centerpiece of arguments for reforming high school graduation requirements to improve access.

1.

BACKGROUND

The American Council on Education (ACE) commissioned a paper by Susan Choy (2002), a senior research administrator for the private firm that had completed most of the government-sponsored analyses of access and attainment using the NELS database (e.g., NCES 1997a, 2001a). The paper’s executive summary included the following points about access: x A young person’s likelihood of attending a four-year college increases with the level of the parents’ education. This is true even for the most highly qualified high school seniors. x Taking challenging mathematics courses can mitigate the effects of parents’ education on college enrollment. The association between taking a rigorous high school math curriculum and going to college is strong for all students, but especially for those whose parents did not go beyond high school. x More at-risk students apply to college if their friends plan to go. College outreach programs as well as parental and school support with the application process have also proven worthwhile. x The price of attending college is still a significant obstacle for students from low- and middle-income families, but financial aid is an equalizer, to some degree. Low-income students enroll at the same rate as middleincome students if they take all the necessary steps toward enrollment. (p. 5)

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The first of these points is troubling, given the general knowledge that parents’ education and family income are highly correlated and part of socioeconomic status (Becker, 2004). These two variables are correlated because the level of parents’ degree attainment influences the type of jobs they can get and, thus, has a very substantial influence on income. Given this relationship, we could substitute income for education in the first point. If we did, the point would be that there are differentials in college enrollment related to income and these differentials are true even if we control for the influence of preparation, including preparation in advanced math. Instead, the NCES contractors went to great lengths to show in regression equations that parents’ education was significant and income was not, ignoring the statistical errors embedded in the method and the conclusion, restated above, that was based on these equations (Becker, 2004; Heller, 2004). What is sad about this statement is the apparent cover-up. Choy essentially argues that if disparity is due to parents’ education rather than to income then neither the government (i.e., state and federal funding) nor institutions (i.e., quality of schools and colleges) are at fault for the inequality. In fact the reviews of NCES reports that preceded this ACE report reveal that the cover-up was probably intentional (Heller, 2004). It appears that policy makers and the leading national lobby organizations did not want to call attention to these underlying inequalities in access attributable to the growing income disparities in the U.S. The second of these statements indicates that taking advanced math courses can help students “mitigate” deficits attributable to being born into a family with less well educated parents. But what is the point being made? The first statement clearly indicates that preparation does not overcome the inequality in access that is attributable (to income and) parents’ education. In the past 15 years there has been a great volume of government reports—and reports by nonprofit agencies like ACE—that have made policy arguments based on correlations between advanced courses and subsequent outcomes, especially with college enrollment and degree attainment. Sometimes the reports emphasizing these correlations are accompanied by arguments for adding to the requirement for high school graduation. The new agenda argues for the transformation of American high schools from comprehensive high schools with multiple tracks to college preparatory high schools. This may or may not be a reasonable intermediate social goal,1 but wouldn’t it have been more honest to debate the question rather than to advance the argument in so many quasi-official2 documents without making 1

2

We should think of this as an intermediate goal because the intended outcome is improvement in access and improved equity in access among prepared students. The term “quasi-official” is used because a reviewer pointed out that the reports by ACE and other groups were not official in the same sense as government documents. Reflecting

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it explicit? This chapter examines whether and how current policies influence preparation in advanced math and high school dropout, two other questions that have not been systematically examined. We did not test the assumption about the supposed linkage between advanced math and the labor market. Linkages between high school math and two-year colleges should one day be reexamined with an explicit focus on the thresholds of preparation necessary to enter the mid-skilled labor market (Grubb, 1996b). The third statement implies there is evidence that outreach and encouragement programs have made a difference, a claim for which there was at best weak support over the past two decades (St. John, 2003). The ACE report made this argument without presenting evaluation evidence on effects of outreach and encouragement. An honest and balanced assessment of encouragement programs would consider their role in providing information about both finances and preparation on preparation, access, and attainment. The problem is that telling the truth in most U.S. states about affordability for low-income students might discourage preparation. Our hypothesis is—and evidence from analyses of indicators (chapter 4) support this proposition—that students understand affordability through experience, which is why we see a statistical relationship between funding for student aid during the sophomore year and high school dropout. If encouragement programs focus only on preparation, then they risk perpetrating the failure of this nation—or at least of most states—to provide balanced and honest information about college preparation, access, and affordability. The final point has to do with the core problem and focuses on the role of prices and student aid. Of course the role of need-based financial aid is to equalize opportunity of similarly prepared students across income groups. The goals should be to provide equal opportunity for low- and high-income students with equal preparation to be able to pay for continuous enrollment in a four-year college. Unfortunately, it appears that federal grant aid programs are not funded at a sufficient level to achieve this goal (St. John, 2003) and that most states have not met a reasonable equity threshold (chapter 4). In fact this ACE report and the many NCES reports on which it is based have failed to examine the relationship between student aid and either enrollment or persistence. Further NCES argued that it could not be done, in spite of prior studies that had used their databases to examine the effects of student aid (e.g., Jackson, 1978; Manski and Wise, 1983). This chapter focuses on the second part of the myth that has been promoted by NCES and ACE: that inequalities in preparation can be on this comment, I realized the term needed modification. However, with the modification, there is reason to question why the position was taken by the higher education advocacy community. These authors probably had the best of intentions, but they failed to provide a balanced interpretation of statistics.

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overcome by changing high school requirements, a core assumption in arguments for addressing the access challenge through K–12 reforms. To address this issue, we designed models that control for the individual-level variables that NCES deemed important, but we also considered state education policies in the second level of the two-level statistical model. The logic for these analyses is based on the balanced access and attainment model, presented in chapter 2. The specifications for the model, with other methodological considerations, are discussed below before the findings. With the background provided by the analyses in this chapter, we can examine the other aspects of the NCES/ACE propositions about access and attainment. As a conclusion to this section, chapter 8 reexamines these core propositions with evidence from the reanalysis.

2.

RESEARCH APPROACH

The reanalyses of NELS included in part III provide two-level multinomial logistic regressions that reexamine the three key components of the NCES claims about access. The multilevel models were needed to control for the individual-level variables included in the NCES studies, while also considering the association of state-level policy variables. This chapter focuses on the association between state policy on K–12 education and college preparation. The model specifications are summarized before discussing the statistical methods and limitations.

2.1

Model specifications

Using multinomial models, we compared three groups—graduates with high school calculus, graduates with high school trigonometry/precalculus, and dropouts (regardless of math preparation)—to graduates with less than trigonometry/precalculus. This coding for the outcome variables provided the best possible comparisons and allowed us to retain as many of the cases as possible. Given the debates about math preparation, it was appropriate to focus on that. However, given what we had learned from the time series analyses (chapter 3 and 5), we also knew that high school dropout could be influenced by math requirements, so we were obligated for the sake of truthseeking to include dropout as a variable.3 3

While it may be possible for this set of outcome variables to be refined or improved upon in future studies, they represent the best possible combination we could develop at this time. A prior version of this analysis (St. John and Chung, 2004a, 2004b) did not consider dropout. We were reminded by our reviewers that dropout was central to this study, so the analyses were revised for this book.

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The individual-level variables included in the analyses of preparation (Table 6-1) were selected to include the types of individual variables included by NCES and that should controlled for, logically, given general understanding of theory, as defined in the balanced access model. The coding was developed both to retain all possible cases and to provide appropriate and logical comparisons. Logistic models appropriately use dichotomous coding (for discrete variable-to-variable comparisons) or design coding (a set of variables compared to the same variable). Gender was appropriately treated as a dichotomous comparison variable. In this case we compared males to females because there were more females. All else being equal, it is appropriate to use the larger of the two dichotomous variables as the comparison. Race/Ethnicity was coded as a design set, to enable comparison among groups. Asian American/Pacific Islanders, Hispanics, and African American non-Hispanics, were compared to others, a group that included Whites, Native Americans, missing, and other groups. The population size for the other groups was too small for the explicit coding of each group. No disrespect is intended from this coding. Indeed, an effort has been made to be as respectful as possible for group differences given the state of the data and our knowledge of methods. Family Income was coded as a design set of variables. Middle income— the middle two quartiles of the population with reported incomes—was treated as the comparison group for high- and low-income students, an approach consistent with the methods used by NCES. Students from families with incomes over $75,000 and less than $25,000 were compared to middleincome students. In addition, students with missing information on income were coded as a discrete category. We did not want to exclude this group, as has been the case in many prior analyses of NELS. However, we could include “missing” in the comparison group because the comparison across groups has been a focal point in the debate about prior analyses of NELS. By including those with missing income as a coded group in a design set, we were able to maximize the number of cases included while retaining the capacity to make comparisons across income groups.

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Table 6-1. Specification for individual-level variables in multilevel analyses of preparation Coding Gender Male Female Race/Ethnicity Asian/Pacific Islander Hispanic African American, not Hispanic White, Native American or other/missing Family income group Low (less than $25,000) High ($75,000 or more) Multiple response or missing Middle ($25,000-$74,999) Parents’ highest education level HS, some college College grad M.A. or equivalent Ph.D., M.D., other No college indicated (including missing) Postsecondary education plans Vocational, trade, or business HS Will attend college Will finish college Advanced degree Other SAT or ACT participation Yes No or missing or refusal or don’t know GPA last year reported

Dichotomous Comparison Design Design Design Comparison Design Design Design Comparison Design Design Design Design Comparison Design Design Design Design Comparison Dichotomous Comparison Continuous

Parents’ education was also recoded as a design set of variables. In this case we coded as variables students who had at least one parent with high school and some college, college graduation, a master’s degree or equivalent, or a doctoral degree. The comparison group included students whose parents reported having a high school education, less than high school, or did not report. In this case we included the nonreport, or “missing,” in the comparison group because in our analyses they appeared similar to students whose parents had high school education or less. Postsecondary education plans were coded into a design set, with different levels of plans being compared to plans for high school or less, with missing code as part of the comparison group. This approach approximates the hierarchy embedded in the different levels of plans but does not assume a continuous ordinal relationship among these responses.

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We used two variables related to prior preparation and achievement as controls for these individual preparatory forces in the estimation of math preparation. We included taking the SAT/ACT because this variable has been treated as central in the debates about access. We did not include scores because we expect that the courses taken would influence the score. In addition, we acknowledge that high school dropouts were less likely to take the exams. However, since dropouts can take the exam and go on to college, we decided to include this variable. In addition, we included the last reported grade point average (GPA) as a continuous variable. This provided a means of controlling prior measures of achievement. The state-level variables included the indicators of the policies in place at the time students graduated—the rules and regulations their high schools would have been subject to at the time of their graduation in 1992. We used the same coding of second-level variables (Table 6-2) as was included in the analyses of academic access (chapter 3). Therefore the discussion of these variables is not repeated. Table 6-2. State-level variable coding for two-level model used to analyze preparation Coding Honors or advanced diploma policy Yes, the state offers an honors diploma Dichotomous No Comparison State guidelines consistent with NCTM Yes, consistent with NCTM Dichotomous No Comparison High school exit exam required Yes, exit exam required Dichotomous No Comparison Math required for graduation High (3 or 4 required) Design Low (1 or 2) Comparison Local control Design Percentage of schools participating in the AP Continuous

2.2

Limitations and statistical methods

Researchers in access and attainment must keep abreast of the evolving methods used to analyze student outcomes as well as theory and prior research. A few of the major limitations are discussed below before the description of the statistical methods.

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Limitations

Because a complete specification of all variables that influence preparation, access, and attainment is not possible and a reduced model must be used, it is crucial to use theory and prior research to guide the selection of variables. In this case we have been careful to think through the debates about theory and variables in sociology, economics, and education. We used our best judgment about variable selection, with theory guiding the selection of the types of variables that should be included. Logical rather than statistical relationships guided variable selection, although steps were taken to avoid excessive correlations among variables. Some of the statistical issues being debated are extremely complex. For example, NCES (1997a) created a preparation index that included variables that were more restrictive than the measures used as outcomes (Becker, 2004). Specifically, including application to college (a variable created from a question asked during the senior year) in the preparation index excluded students who actually enrolled in college. The independent variable (the index) was more restrictive than the outcome (enrollment). Including a variable related to taking SAT/ACT exams could have a similar problem, but since it was possible for dropouts to take the exam and for students to drop out after taking the exam, inclusion of this variable did not pervert or inverse the logic of the model used. The point is that we made the best decisions we could about variable selection given what was known from prior analyses. In addition, there is not a single best method for analysis of student outcomes, especially when the role and influence of policy variables is being considered. Every statistical method has limitations. In this study we chose multilevel models because we wanted to examine the role and influence of policy variables as well as to examine further evidence related to individual variables. We used logistic regression because the outcomes were discrete and related. The fact that multiple outcomes were being compared made it necessary to use the multinomial version of logistic regression. While this approach is a step forward, at least in comparison to most prior research, our method could be improved upon. Finally, there are selection problems with this outcome, as there are with most others. U.S. high schools have tracking systems—combinations of college preparatory, regular, or technical courses—that have a very substantial influence on whether students are placed into preparatory programs in which they can take advanced math courses. In this model we have included individual-level variables that relate to ability, especially the GPA variables. In addition, we control for individual background and goals. So the individual aspect of the choice to take advanced math is reasonably well specified. However, the outcome includes selection as it relates to the

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placement on preparatory course tracks within high schools or the placement in preparatory high schools rather than vocational or comprehensive high schools. These other forces can mitigate the effects on individual access to advanced math. These complexities in the distribution of opportunity to take advanced math courses are intrinsic to the milieu in which policy operates. Implementation of state policies on math requirements for graduation is carried out in contexts that include school structures and the constraints of the backgrounds and abilities of the populations schools serve. In vocational high schools, for example, additional required math could take the form of business math courses (i.e., applied math problems) instead of advanced math courses. A similar pattern could evolve for many students within comprehensive high schools. Given the state of knowledge and data, this two-level model provides a reasonable way to estimate the effects of state policies using a cohort panel survey of this type, but it does not resolve problems associated with the examination of math courses as either an independent or outcome variable. The fact is that the earlier studies used the high school track rather than courses (e.g., Jackson, 1978; St. John and Noell, 1989) because the high school track determined the types of courses a student had access to in the first place. Shifting the variables from the high school track to math courses (e.g., Adelman, 2004; Pelavin and Kane, 1990) does not change school practices, nor does it minimize the need to at least acknowledge these limitations. 2.2.2

Statistical methods

The analyses in this chapter include two types of tables: tables that present the descriptive breakdowns for the variables included in the models and tables that present the multinomial logistic regressions. The descriptive tables present the differences for the independent variables related to the sample. We do not include measures of statistical differences for these variables because this approach can be misleading. The differences themselves have no meaning. Differences in means of independent variables may or may not be related to the outcome. If we had the entire population, we should not even use these statistics because any reported difference would be an actual difference. Therefore we wanted to avoid misleading readers about the meaning of differences for independent variables in relation to the outcomes. We took this step because of the overemphasis on differences in the means of independent variables in the NCES studies. The statistical differences for independent variables are presented for the logistic regression models. Three levels are reported: .01, .05, and.1. The

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first two of these are generally considered significantly different. The third is a measure of association but cannot be considered to meet the threshold of statistical difference. We present only odds ratios (a type of beta coefficient) for the independent variables. Readers should consider whether the odds ratio is above one, in which case it has a positive association with the outcome, or below one, in which case it has a negative association. Our discussion focuses on whether variables have a significant association with an outcome and whether the relationship is positive (odds ratio above one) or negative (odds ratio below one). At times we comment on very large or very low odds ratios, but these comments should be interpreted as comparisons across variables. Extremely large or small odds ratios indicate a substantial association for that variable, but statistical comparisons cannot be made across variables, especially those outside a design set. Within a design set of variables, it is possible to make comparisons regarding significance and magnitude, since these variables make similar comparisons. However, even in this case odds ratios for different variables in the design set are not exactly comparable. For example, when there are differences in the size and significance of odds ratios for some racial/ethnic groups and not for others, caution is needed when comparing across groups. In addition, we present the significance of the state-level variables. This provides an indicator of whether the second-level variables had an influence on the outcome as a full set of variables. It is possible for the set of secondlevel variables to be significant as a model indicator and for none of the individual second-level variables to be significant. It is also possible for one or more of the second-level variables to have a significant association without having a significant association for the set of variables. When these anomalies are evident they are discussed.

3.

ANALYSES

The analyses of academic preparation are presented in four parts. The descriptive statistics and logistic analyses of preparation for the general sample are followed by analyses of low-, middle-, and high-income students.

3.1

The sample of all students

More than half of the population in the high school class of 1992 graduated high school and did not complete advanced math courses. A slightly higher percentage of females than males graduated without math, while slightly higher percentages of males dropped out (see Table 6-3). The

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Table 6-3. Descriptive analysis of college preparation: All students

Level 1: Individual Gender Male Female Race Asian/Pacific Islander Hispanic African Am., not Hispanic White, Native Am., or other/missing Family income group Low (less than $25,000) High ($75,000 or more) Multiple response/missing Middle ($25,000-$74,999) Parents’ highest education level HS, some college College grad M.A. or equal Ph.D., M.D., other No college Postsecondary education plans Vocational, trade, or business after HS Will attend college Will finish college Advanced degree Other SAT or ACT participation Yes No or missing or refusal or don’t know

HS Grad with Trigonometry/ Precalculus HS Grad with Only Calculus Count Row % Count Row

Dropout Count Row %

HS Grad without Advanced Math Count Row

1,101 1,205

16 17

644 591

9 8

1,408 1,211

21 17

3,650 3,992

54 57

264 202 156 1,684

25 10 10 18

264 75 61 835

25 4 4 9

95 573 464 1,487

9 29 30 16

438 1,100 865 5,239

41 56 56 57

397 451 303 1,155

10 30 13 20

175 339 167 554

4 23 7 9

1,240 72 562 745

31 5 23 13

2,190 641 1,383 3,428

55 43 57 58

801 495 313 204 493

16 27 29 33 9

331 273 234 197 200

7 15 22 32 4

893 122 68 25 1,511

18 7 6 4 28

2,927 916 460 194 3,145

59 51 43 31 59

67

6

17

2

334

31

672

62

153 1,198 789 99

9 22 25 4

35 469 656 58

2 9 21 2

384 619 292 990

23 11 9 41

1,111 3,173 1,412 1,274

66 58 45 53

2,134 172

25 3

1,182 53

14 1

779 1,840

9 35

4,443 3,199

52 61

continued

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Table 6-3. (continued) Descriptive analysis of college preparation: All students Level 2: State Honors or advanced diploma policy Yes, the state offers an honors diploma No State guidelines consistent with NCTM Yes, consistent with NCTM No High school exit exam required Yes, exit exam required No High (3 or 4) credits in math required Yes, required No Local board control of math requirements Yes, present No Percentage of schools participating in the AP

Count

% (of total number)

17 34

33 67

12 39

24 76

17 34

33 67

11 40

22 78

6 45

12 88 Mean 44

percentages of both groups that completed advanced math courses and graduated high school were nearly equal (both at 25%). Half of the Asian American students in the class of 1992 graduated with advanced math courses, while more than half of the other racial/ethnic groups graduated without advanced math. This is prima facie evidence of the need to improve the percentage of students with advanced math, especially if we assume that such requirements are necessary for admission to four-year colleges. About 30 percent of both Hispanics and African Americans in the class of 1992 dropped out. In addition about 31 percent of the low-income students in the class dropped out, a higher percentage than for other income groups, including “missing.” In contrast only 28 percent of the students from homes with parents who did not graduate college had dropped out of high school. These percentages are nearly equal because of the high correlation between the parents’ education and family income. The majority of students whose parents had advanced degrees (master’s or doctorates) had taken advanced math courses, and more than half of the high-income quartile met this threshold in preparation. Again we see the relationship between parents’ education and family income. It is specious to argue that advanced math overcomes deficits caused by parents’ education, however, when family income is every bit as related to preparation as is parents’ education, at least when descriptive statistics are examined. There was also a relationship between aspirations and completion of advanced math, although not as much so as for family background. About 45

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percent of the students who aspired to attain advanced degrees had graduated without advanced math and more than half of the students who expected to graduate from college (58%) graduated high school with advanced math. If the purpose of encouragement programs is to promote planning for college, then this type of data provides weak support for the claims made by Choy (2002) about encouragement. Encouraging students could influence access to advanced math when students are tracked in other ways or enrolled in schools that do not have these offerings. At the time students in the high school class of 1992 graduated high school, only 11 states required either three or four math courses as the standard for graduation. Twelve states had implemented NCTM standards. Seventeen states had implemented an honors diploma and the same number had required passing exams to graduate high school. The regressions assess whether these policies were associated with completion of advanced math courses or high school dropout, compared to graduating without advanced math. The multinomial logistic regression analysis (Table 6-4) revealed that most of the individual-level variables were significantly associated with the outcomes. These analyses indicate significance controlling for other variables in the two-level model. Males were more likely than females to take advanced math courses—and to drop out—than to graduate without advanced math. African Americans and Hispanics were more likely than Whites/others to drop out and less likely to complete either type of advanced math. Asians/Pacific Islanders were more likely than other racial/ethnic groups to complete both types of advanced math courses. Both income and parents’ education were significantly associated with dropout and completion of advanced math. Low-income students were more likely to drop out than middle-income students, while high-income students were more likely than middle-income students to complete math courses. Students who did not have income reported did not differ significantly from middle-income students in preparation. All levels of parents’ education above high school were associated with completion of advanced courses. In addition, having parents with college degrees was negatively associated with dropout, but the other levels of parents’ education did not have a significant association. Taken together these findings confirm that both family income and parents’ education were associated with academic preparation, not a surprising finding. What is surprising is that a decade of research on this database was reduced to a conclusion that parents’ education and not income was associated with enrollment and that advanced math “mitigated” the effects of parents’ education on enrollment (Choy, 2002).

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Table 6-4. Two-level multinomial logistic regression analysis of college preparation: All students

Level 1: Individual Gender Male Female Race Asian/Pacific Islander Hispanic African American, not Hispanic Other Family income group Low (less than $25,000) High ($75,000 or more) Multiple response or missing Middle ($25,000-$74,999) Parents’ highest education level HS, some college College grad M.A. or equal Ph.D., M.D., other Other Postsecondary education plans Vocational, trade, or business after HS Will attend college Will finish college Advanced degree Other SAT or ACT participation Yes No Cumulative GPA for last year attended

HS Grad with Trig/Precalculus Odds Ratio Sig. 1.094

HS Grad with Calculus Odds Ratio Sig.

Dropout Odds Ratio Sig.

1.378

***

1.270

***

* ***

2.065 0.836 0.695

*** * ***

4.096 0.610 0.599

*** *** ***

0.856 1.270 1.672

0.936 1.229 0.910

**

0.901 1.342 0.959

**

1.660 0.927 1.143

1.307 1.847 2.093 3.198

*** *** *** ***

1.273 1.928 2.931 4.079

** *** *** ***

0.886 0.553 1.067 0.898

1.199

0.694

***

***

0.531

***

1.503 2.781 3.352

*** *** ***

0.671 1.923 4.458

* *** ***

0.463 0.358 0.454

*** *** ***

7.082

***

9.523

***

0.522

***

1.002

*

1.007

***

0.989

***

continued

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Table 6-4. (continued) Two-level multinomial logistic regression analysis of college preparation: All students

Level 2: State Yes the state offers an honors diploma Consistent with NCTM Percentage of schools participating in AP HS exit exam required High (3 or 4) credits in math required Local board control of math requirements Mid (1 or 2) credits in math required for grad

HS Grad with Trig/Precalculus Odds Ratio Sig. 0.736

HS Grad with Calculus Odds Ratio Sig. 1.001

Dropout Odds Ratio Sig. 1.084

1.237 1.005

1.197 1.012

0.850 1.008

0.940 1.092

0.760 1.357

0.837 0.917

1.337

0.921

0.870

Variance Component Sig. Random Effect Level 2 effect 0.225 *** Note: *** p<0.01, ** p<0.05, * p<0.1

*

Variance Component Sig. 0.261 ***

Variance Component Sig. 0.127 ***

Postsecondary plans were significantly associated with preparation, especially with completion of high school. All levels of college plans were negatively associated with dropout compared to completing high school without advanced math. Having plans to attend college had a modest (.1) negative association with completion of calculus compared to not planning to enroll in college. Having plans to finish college and to complete an advanced degree were positively associated with taking both types of advanced math compared to graduating without advanced math. As expected, taking the SAT or ACT was positively associated with completing both types of advanced math compared to graduating without advanced math, while this variable was negatively associated with dropout. The state-level variables had a significant association with each of the outcomes, as measured by component variance. However none of these variables was significant. The percentage of high schools with AP courses in the state had a slight positive association (.1 alpha) with completion of calculus compared to completion of high school without advanced math. The other variables had modest positive or negative associations but were not significant. While subsequent changes in education policy were associated with SAT scores and dropout (chapter 3), these same policies were not significant for the class of 1992.

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151

Low-income students

Given the finding that low-income students were more likely to drop out in 1992, the critical challenge for educational reformers should have been to reform high schools to enable more students to graduate. Instead the assumption was made that the goal should be to require advanced math for all students because of the well-known correlation between advanced math and eventual college success. However, it was possible with the data on the 1992 cohort to assess how existing policies—and some states required three or four math courses and exit exams for graduation—influenced graduation and completion of advanced math course by low-income students. This step was not taken by the researchers using the NELS data, so it is taken below. It is remarkable that so few low-income students in the class of 1992 (Table 6-5) graduated high school with advanced math courses: 9 percent of males and 11 percent of females graduated with trigonometry or precalculus and only 5 percent of males and 4 percent of females had calculus. More than half graduated without advanced math and nearly a third did not graduate. African American and Hispanic students dropped out at higher rates than other groups. Clearly both reduction in dropout and improvement in advanced curriculum were issues. Within the sample of low-income students, there was some variation in the percentage of students completing advanced math courses in relation to parents’ education. However the majority of low-income students whose parents had an advanced degree—master’s or doctorate—either dropped out or graduated without advanced math. A substantially higher percentage dropped out than completed advanced math among students in both groups of students whose parents had advanced degrees. This should have provided a clue to the NCES analysts and contractors that even low-income students with high levels of parents’ education were at risk. This hardly seems like evidence to argue that math courses mitigate the negative effects of having parents without college degrees (e.g., Choy, 2002). What is really remarkable is that the majority of students who dropped out planned to attain some type of college education. Surprisingly 49 percent of the low-income students who expected to attain advanced degrees graduated without advanced math and 21 percent dropped out. Having plans to go on to college—the intermediate outcome that can be influenced by encouragement—did not seem to be the problem. The more serious problem related to the funding and structure (i.e., level of offerings and tracking) of schools that served low-income students. Given these findings, the argument—stated as a conclusion from a decade of research (Choy, 2002)— that outreach and encouragement can enable more students to prepare and go to college seems even more troubling.

Table 6-5. Descriptive analysis of individual-level variables for analyses of college preparation: Low-income students HS Grad with Trig/ HS Grad with HS Grad without Dropout Precalculus Only Calculus Advanced Math Count % Count % Count % Count % Gender Male 171 9 95 5 651 34 1,004 52 Female 226 11 80 4 589 28 1,186 57 Race 59 22 51 19 37 14 121 45 Asian/Pacific Islander Hispanic 57 7 27 3 316 38 429 52 African American, not Hispanic 54 7 21 3 270 34 446 56 227 11 76 4 617 29 1,194 56 White & other Family income group Low (less than $25,000) 397 10 175 4 1,240 31 2,190 55 Parents’ highest education level HS, some college 182 12 77 5 422 27 879 56 53 25 21 10 26 12 114 53 College grad M.A. or equal 17 23 14 19 13 17 31 41 Ph.D., M.D., other 7 39 1 6 3 17 7 39 Other 138 6 62 3 776 36 1,159 54 Postsecondary education plans Vocational, trade, or business after HS 17 4 3 1 185 40 252 55 Will attend college 43 7 11 2 183 29 384 62 Will finish college 199 16 73 6 246 19 759 59 Advanced degree 115 19 70 12 123 21 290 48 Won’t finish HS or will finish HS or missing 23 2 18 2 503 48 505 48 SAT or ACT participation Yes 364 19 162 9 348 18 1,030 54 No 33 2 13 1 892 43 1,160 55

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Most low-income students already aspire to enroll in college and attain degrees. Encouraging more students to have this aspiration will not change course-taking unless there are radical and fundamental changes in high schools. Encouragement and outreach can influence attitudes but cannot influence the structure and capacity of schools. The multilevel, multinomial logistic regression analysis for low-income students (Table 6-6) reveals a pattern at the individual level similar to that of the general population. Controlling for other variables in the model: x Males were more likely than females to graduate with calculus than without advanced math; males were also more likely to drop out. x Asian Americans were more likely than Whites to graduate with advanced math (both categories) than to graduate without advanced math. x Having parents with advanced degrees was associated with graduation with advanced math, especially for graduation with trigonometry/precalculus compared to graduation without advanced math. x Having parents who had graduated college compared to parents with no college decreased the odds of dropout. These findings illustrate that social background factors influence the educational attainment of low-income students. However, these associations did not change the high percentage of students who dropped out, nor did they increase the percentage of students who took advanced math. These variables are related to family and social class, factors students are born with. In addition, the two variables related to preparation were significantly associated with the outcomes as well. Students who took the SAT/ACT were more likely than students who did not to take the test to have graduated with advanced math than to have graduated without advanced math. However encouraging a high school senior to take the SAT will not influence the academic track they are on in high school. So, with respect to policy, this variable is an artifact of the structure of schools rather than a policy lever. High school GPA was also associated with the outcomes. Having high grades substantially improved the odds of graduating with calculus compared to graduating without advanced math. This variable was also negatively related to dropout compared to graduation without advanced math. Perhaps encouraging low-income students to perform better within the structure of schools is a potentially important policy lever. But doesn’t this also mean that providing professional development for teachers should be a priority? Don’t better prepared teachers enable more students to achieve in their high school classrooms regardless? Similar to the analysis of the entire sample, the state policy variables helped explain variation in the outcomes (i.e., significance of level 2 random effect), but once again none of the variables was significant. Five of the six policy variables had a negative, nonsignificant association with dropout.

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Table 6-6. Two-level multinomial logistic regression analysis of college preparation: Lowincome students

Level 1: Individual Gender Male Female Race Asian/Pacific Islander Hispanic African Am., not Hispanic White, not Hispanic or Native Am. or other or missing Parents’ highest education level HS, some college College grad M.A. or equal Ph.D., M.D., other Other Postsecondary education plans Vocational, trade, or business after HS Will attend college Will finish college Advanced degree Other SAT or ACT participation Cumulative GPA for last year attended Level 2: State State offers an honors diploma Yes, consistent with NCTM Percentage schools partic. in AP Exit exam required Math credits required for grad High (3 or 4) credits Local board control of math requirements

HS Grad with Trig./Precalculus Odds Ratio Sig.

HS Grad with Calculus Odds Ratio Sig.

Dropout Odds Ratio Sig.

1.064

2.073 ***

1.338 **

2.541 *** 0.802 0.658 **

6.673 *** 0.916 0.726

0.715 1.278 1.486 **

1.223 2.135 *** 3.173 *** 11.968 **

1.090 1.506 4.270 *** 4.670

0.900 0.332 ** 2.126 6.560

1.058

0.422

0.539 ***

1.809 ** 2.839 *** 4.296 ***

0.772 1.884 * 4.510 ***

0.476 *** 0.366 *** 0.472 ***

10.877 ***

11.222 ***

0.723 **

1.002

1.007 **

0.990 ***

0.654 1.070 1.005 1.026

0.989 1.005 1.009 0.777

0.978 0.723 1.008 0.754

1.072 1.089

1.013 1.034

0.690 0.632

Variance Component Sig. 0.264 **

Variance Component Sig. 0.189 ***

Variance Random Effect Component Sig. Level 2 Effect 0.423 *** Note: *** p<0.01, ** p<0.05, * p<0.1

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This does not support the argument that strengthening graduation requirements and implementing math standards could reduce dropout, contrary to the position of proponents of these high school reforms. In addition, we know from the analysis of indicators that after 1992 these policies were associated with higher dropout rates (chapter 3). Perhaps too little attention was given to the encouragement of low-income students in the class of 1992 and subsequent cohorts.

3.3

Middle-income students

Middle-income students include about half—the middle two quartiles— of the NELS sample with reported incomes. Students who did not have incomes reported did not differ significantly from middle-income students on any of the outcomes in the analysis of the entire population (Table 6-4), so middle-income students may be representative of this group as well. More middle-income students graduated with advanced math than dropped out, but the majority graduated without advanced math (Table 6-7). A slightly higher percentage of females had advanced math courses while a slightly higher percentage of males dropped out. Asian Americans had a higher percentage of graduates with advanced math than other racial/ethnic groups. Higher percentages of Hispanics and African Americans than Whites/others and Asian Americans dropped out of high school. The majority of students whose parents had college degrees or advanced degrees had completed advanced math, a contrast to the low-income populations. The association between parents’ education and completion of advanced math was more evident among middle-income students. Could the high school improvement strategies being promoted by national advocacy groups be targeted to this population? There was also a stronger relationship between plans and attainment of advanced math for middle-income students than for low-income students, although the majority of middle-income students aspiring to complete college and attain degrees graduated without advanced math. Most middleincome students also took college entrance exams. Are middle-income students the target population for the new encouragement programs? Certainly they can afford to pay to take these tests, while low-income families often cannot. It is wise policy to focus reforms on the majority of students, but it is also important to consider minority populations, including low-income students, and to target programs on these groups. The logistic regression analyses for middle-income students (Table 68) revealed a similar pattern of significant relationships among variables as did the prior two analyses at the individual level. Asian Americans were more likely than Whites/others to have graduated with calculus.

Gender Male Female Race Asian/Pacific Islander Hispanic African American, not Hispanic White, not Hispanic or Native American or other or missing Family income group Low (less than $25,000) High ($75,000 or more) Multiple response or missing Middle ($25,000-$74,999) Parent’s highest education level HS, some college College grad M.A. or equal Ph.D., M.D., other Didn’t finish HS or HS grad or GED or don’t know or missing Postsecondary education plans Vocational, trade, or business after HS Will attend college Will finish college Advanced degree Won’t finish HS or will finish HS or missing SAT or ACT participation Yes No or missing or refusal or don’t know 19 20 25 16 16 20

20 19 25 31 29 12 10 11 24 25 6 27 5

557 598 100 86 76 893

1,155 521 258 166 47 163 43 76 639 350 47 1,065 90

HS Grad with Trigonometry Precalculus Only Count Row %

530 24

11 16 248 257 22

204 138 109 45 58

554

95 30 29 400

272 282

13 1

3 2 9 19 3

7 14 20 28 4

9

23 6 6 9

9 10

HS Grad with Calculus Count Row %

222 523

84 102 225 79 255

355 63 36 9 282

745

25 83 96 541

419 326

6 27

20 15 9 6 33

13 6 7 6 20

13

6 16 20 12

14 11

Dropout Count Row %

Table 6-7. Descriptive analysis of individual-level variables related to college preparation: Middle-income students

2,130 1,298

286 475 1,530 688 449

1,677 561 231 59 900

3,428

187 331 279 2,631

1,698 1,730

54 67

67 71 58 50 58

61 55 43 37 64

58

46 62 58 59

58 59

HS Grad w/o Adv. Math/Math-Taking Missing Count Row %

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Table 6-8. Two-level multinomial logistic regression analysis of college preparation: Middle-income students

Level 1: Individual Gender Male Female Race Asian/Pacific Islander Hispanic African American, not Hispanic White, not Hispanic/Native Am./other/missing Parent’s highest education level HS, some college College grad M.A. or equal Ph.D., M.D., other Didn’t finish HS/HS grad/GED/don’t know/missing Postsecondary education plans Vocational, trade, or business after HS Will attend college Will finish college Advanced degree Won’t finish HS/will finish HS/missing SAT or ACT participation Yes No or missing or refusal/don’t know Cumulative GPA for last year attended Level 2: State Honors or advanced diploma policy State guidelines consistent with NCTM Percentage of schools participating in AP High school exit exam required Math credits required for graduation High (3 or 4) credits in math required Local board control of math requirements Mid (1 or 2) credits in math required for grad Random Effect Level 2 Effect

Note: ***p<0.01, **p<0.05, *p<0.1

HS Grad with Trigonometry Precalculus Only Odds Ratio Sig.

1.087

HS Grad with Calculus Odds Ratio Sig.

1.126

Dropout Odds Ratio Sig.

1.317 *

1.514 0.793 0.788

***

3.051 0.640 0.757

*** **

0.833 1.057 1.945 ***

1.373 1.640 2.388 3.250

*** *** *** ***

1.406 2.056 3.364 4.415

** *** *** ***

0.841 0.514 ** 1.034 1.066

1.402 1.382 2.524 2.544

*** ***

0.926 0.704 2.093 3.714

*** ***

0.558 0.432 0.317 0.312

6.409

***

9.210

***

0.467 ***

1.000

1.006

***

0.987 ***

0.843 1.096 1.005 0.847

1.004 1.162 1.014 0.636

** **

1.401 0.775 1.005 0.667

1.080 1.330

1.441 0.948

**

Variance Component Sig.

Variance Component Sig.

0.148 ***

0.084 ***

** *** *** ***

1.188 0.931 Variance Component Sig.

0.119 *

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African Americans were more likely than Whites/others to drop out than to graduate without advanced math. There was also a strong pattern of association between postsecondary plans and math attainment among middle-income students. Higher levels of plans were positively associated with higher math attainment, while planning to enroll in college was negatively associated with dropout. Taking entrance exams was positively associated with completing advanced math and negatively associated with dropout. GPA had a significant positive association with completion of calculus and a negative association with dropout, with both outcomes being compared to graduation without advanced math. Once again the second-level variables related to state policies improved prediction (i.e., random effect). Interestingly, a higher percentage of schools participating in the AP program as well as a high number of credits in math required from graduation were positively associated with advanced math attainment. However, the requirement of a high school exit exam was negatively associated with advanced math attainment. This pattern of second-level results was unique to the sample of middle-income students.

3.4

High-income students

The majority of high-income students were from families with college degrees (Table 6-9). In addition, very few high-income students dropped out of high school. Males and females attained advanced math at about the same rate—and more than half had this level of course attainment—while a slightly higher percentage of males than females dropped out (6% compared to 3%). Most high-income students planned to attain at least some college, including more than half of the dropouts. The multilevel, multinomial logistic regression analyses (Table 6-10) revealed different patterns of association at the individual level than were evident for the other two income groups. Gender was not significantly associated with preparation among high-income students. Asian Americans had higher odds of attaining advanced math than Whites/others, compared to graduation without advanced math, but there were no other significant differences in preparation among racial/ethnic groups. Having parents who had attained higher degrees was associated with completion of advanced math for high-income students, but parents’ education was not associated with dropout. Of course, as for the other groups, test taking was associated with preparation. GPA was positively associated with both types of advanced math compared to not taking math and graduating. There was also a different pattern of association for state policy variables for high-income students than for other groups. The second-level variables were significant for advanced math but not for dropout, an artifact

Level 1: Individual Gender Male Female Race Asian/Pacific Islander Hispanic African American, not Hispanic White, not Hispanic or Native American or other or missing Family income group Low (less than $25,000) High ($75,000 or more) Multiple response or missing Middle ($25,000-$74,999) Parent’s highest education level HS, some college College grad M.A. or equal Ph.D., M.D., other Didn’t finish HS or HS grad or GED or don’t know or missing Postsecondary education plans Vocational, trade, or business after HS Will attend college Will finish college Advanced degree Won’t finish HS or will finish HS or missing SAT or ACT participation Yes No or missing or refusal or don’t know 29 31 29 28 30 30 30

24 33 29 35 14 11 14 31 33 17 33 12

224 227 51 19 17 364 451

61 137 107 135 11 3 11 206 219 12 429 22

HS Grad with Trig/Precalculus Only Count Row %

329 10

1 4 96 230 8

27 86 91 132 3

339

73 8 7 251

180 159

25 5

4 5 15 34 11

11 21 25 34 4

23

42 12 12 21

23 22

HS Grad with Calculus Count Row %

28 44

5 10 28 17 12

25 15 9 10 13

72

5 7 3 57

47 25

2 23

18 13 4 3 17

10 4 2 3 16

5

3 10 5 5

6 3

526 115

19 53 324 205 40

144 173 162 109 53

641

44 35 30 532

315 326

40 60

68 68 50 31 56

56 42 44 28 66

43

25 51 53 44

41 44

HS Grad w/o Adv. Dropout Math/Math-Taking Missing Count Row % Count Row %

Table 6-9. Descriptive analysis of individual-level variables related to college preparation: High-income students

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Table 6-10. Two-level multinomial regression analysis of college preparation: High-income students

Level 1: Individual Gender Male Female Race Asian/Pacific Islander Hispanic African American, not Hispanic White, not Hispanic/Native Am./other/ missing Parent’s highest education level HS, some college College grad M.A. or equal Ph.D., M.D., other Didn’t finish HS/HS grad/GED/don’t know/missing Postsecondary education plans Vocational, trade, or business after HS Will attend college Will finish college Advanced degree Won’t finish HS/will finish HS/missing SAT or ACT participation Yes No or missing or refusal or don’t know Cumulative GPA for last year attended Level 2: State Honors or advanced diploma policy Yes the state offers an honors diploma No State guidelines consistent with NCTM Yes, consistent with NCTM No Percentage of schools in AP program High school exit exam required Yes, exit exam required No Math credits required for graduation High (3 or 4) credits in math required for grad Local board control of math requirements Mid (1 or 2) credits in math required for grad Random Effect Level 2 Effect Note: *** p<0.01, ** p<0.05, * p<0.1

HS Grad with Trigonometry/ HS Grad with Precalculus Only Calculus Odds Ratio Sig. Odds Ratio Sig.

Dropout Odds Ratio Sig.

0.981

1.086

1.995

1.639 * 1.161 1.036

3.317 *** 0.535 0.526

1.324 0.257 1.480

1.918 3.077 ** 2.526 ** 5.078 ***

2.456 4.315 ** 5.742 *** 8.833 ***

0.321 0.470 0.242 0.384

0.800 0.728 2.102 * 2.687 **

0.677 0.422 1.397 3.982 ***

1.082 0.582 0.480 1.376

3.745 ***

5.294 ***

0.100 ***

1.005 *

1.007 **

1.003

0.537 **

1.011

2.271

1.715 *

1.234

0.960

1.003

1.008

1.044

1.432

1.166

0.676

0.995 1.712

1.121 0.761

0.616 3.416

Variance Variance Component Sig. Component Sig. 0.226 *** 0.344 ***

Variance Component Sig. 1.329

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of the low number of high-income dropouts. In addition, two of the policy variables had significant associations with completion of trigonometry/ precalculus compared to graduation without advanced math. When states had an honors diploma, high-income students were less likely to graduate with trigonometry/precalculus than to graduate without advanced math, controlling for other factors. In addition, there was a modest positive association (.1 alpha) between implementation of NCTM standards and completion of trigonometry/precalculus among high-income students. These significances are not an artifact of sample size, given that this sample is smaller than that for middle-income students and the general population. Rather, they relate to differences in access to advanced math for highincome students compared to other income groups, a probable artifact of structural variables. High-income students have access to better quality schools that are more sensitive to the intents of new policies, like the implementation of new math standards.

4.

CONCLUSION

The debate about college access and attainment has often been reduced to a focus on the relationship between advanced math and college enrollment, and the correlation is being used to rationalize new policies that encourage math preparation. The analysis of the entire population found the percentage of high school students with AP courses in a state was modestly associated with completion of calculus, but none of the other policy variables had a significant association. The analysis of policy indicators for the rest of that decade (chapter 3) found the policies implemented during the past decade did not improve preparation. Instead, the implementation of the new policies after 1992 was associated with increased dropout rates. The structure and quality of schools help explain why policies alone have had so little influence since 1992. Middle-income students (Table 6-8) were an exception to the pattern observed for the population (Table 6-4), low-income students (Table 6-6), and high-income students (Table 6-10). For middle-income students, high levels of math requirements were significantly and positively associated with completion of calculus. The lingering question is “How can the benefits of math reform be extended to low-income students?” The argument that parents’ education rather than income explains differences in preparation and access (Choy, 2002) appears especially problematic, at least with respect to educational opportunity for low-income student. When we divided the student population of 1992 into income groups it was evident that large numbers of poor students lacked access to advanced

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math courses but still planned to go to college—at least some day. What are the constraints on access to advanced math courses for low-income students? If these barriers were removed, would improvement in the percentage of prepared students yield more attainment, or more dreams deferred due to financial needs? These questions should not be overlooked in the enthusiastic rush to raise high school graduation requirements. The more difficult challenges for K–12 reformers are improving the structure, funding, and quality of high schools. And they should not overlook the serious challenge of ensuring financial access for low-income, prepared students.

Chapter 7 ENROLLMENT By Edward P. St. John and Anna S. Chung1

Debates about college access have too frequently pitted proponents of school reform against proponents of student aid. While political conservatives rally around the argument that schools have failed and need reform (Finn, 2001; Paige, 2003), liberals are often split between the financial and academic rationales. Proponents of school desegregation have refocused on improvement in high school curriculum (Trent, Gong, and Owens-Nicholson, 2004), as have advocates of small schools (Kazis, Vargas, and Hoffman, 2004). These arguments have increased the focus on academic preparation and could further remove the inadequacy of federal student aid from the center of the education policy debates. The preceding analyses (chapters 3, 5, and 6) illustrate uneven consequences from the new education policies, especially related to the access of low-income students to advanced curriculum. However, preparation is not the only barrier to access. Even if low-income students are fortunate enough to attend high schools with advanced courses, and to take these courses, they could still face financial barriers to college. No doubt school reform is needed in the U.S., but financial access is also needed. Improvements in preparation create more need for financial aid, not less. The Advisory Committee on Student Financial Assistance has kept student aid on the policy table during the debates about college access in the U.S. (ACSFA, 2002; Fitzgerald, 2004). ACSFA commissioned reviews of NCES access studies (Becker, 2004; Heller, 2004) that brought attention to the need for more balance in access research (St. John, 2004). While the logic of the balanced model has been used to reexamine statistics reported by

1

The authors thank Choong-Geun Chung for his technical assistance with a prior version of the analyses in this chapter.

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NCES (Fitzgerald, 2004; St. John, 2002, 2003), a further reanalysis of NELS (the National Education Longitudinal Study of 1988) is still needed to assess the effects of financial aid. There was a long history of research using longitudinal databases to analyze college enrollment before NCES introduced the student transcript files (Adelman, 1995).

1.

FINDING BALANCE IN ACCESS RESEARCH

Longitudinal studies tracking four high school cohorts have been widely used to address the role of high school preparation and financial aid in college access. The National Longitudinal Study of the High School Class of 1972 (NLS-72) (NCES, 1994) was used for early studies of the impact of financial aid on enrollment, controlling for the influence of academic preparation (Jackson, 1978; Manski and Wise, 1983; St. John and Noell, 1989). Research using NLS data was largely dependent on student selfreports on academic preparation and financial aid. Then the high school classes of 1980 and 1982 were followed by the High School and Beyond (HS&B) study (St. John, 1990a, 1991; St. John and Noell, 1989). HS&B supplemented self-reported data of student financial aid with actual student aid transcripts and experimented with academic transcript data. The most recent cohort, the NELS class of 1992, included high school course transcripts (Adelman, 1995) as a supplement to self-reports but not student aid transcript information (DesJardins, McCall, Ahlburg, and Moye, 2002). Over time, innovations in analytic models have followed data availability for high school courses and student aid. Consistent with prior studies of college enrollment that have used the longitudinal databases to assess the effects of policy variables (Jackson and Terkla, 1984; St. John, 2001; St. John, Asker, and Hu, 2001), the enrollment model used in this study controls for the influence of individual background, high school experience, and achievement/ability (Level 1). This reanalysis also takes a step forward in the analysis of NELS by including information on state funding for student financial aid, providing a means of linking public finance into the access analysis. The review below briefly summarizes our approach to controlling for the impact of high school courses and achievement, along with the reasoning for integrating state-level variables.

1.1

The influence of high school curriculum on college enrollment

A range of approaches has been used to examine the influence of high school courses on college enrollment using the longitudinal databases. Many

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early studies used the curriculum type that students had completed and achievement indicators like test scores and/or grades (Jackson and Terkla, 1984). Typically the earlier studies compared students enrolled in vocational or college preparatory curriculum to those enrolled in regular curriculum, and they generally found that completing a preparatory curriculum was associated with college enrollment while vocational curriculum was not. In analyses of NLS, some studies were limited exclusively to high school graduates (Manski and Wise, 1983) while others were not (St. John and Noell, 1989). Most studies using NLS-72 data (NCES, 1994) controlled for grade point average in high school and/or test scores as means of controlling for variations in student achievement (Jackson, 1978; Manski and Wise, 1983). Studies that compared NLS (the 1972 cohort) and HS&B (the 1980 and 1982 cohorts) tended to carry forward similar methods across cohorts (e.g., Jackson, 1988; St. John and Noell, 1989). The transitions in variables used to examine the influence of curriculum began with the HS&B database. In particular, Pelavin and Kane (1988, 1990) found a correlation between algebra courses and college enrollment, a finding that influenced subsequent analyses using NELS. The NCES analyses (1997a, 2001b) expanded on this methodology by examining the relationship between advanced high school math courses and college enrollment. More recently researchers have focused on the relationship between advanced math during high school and eventual college attainment (e.g., Adelman, 2006; Choy, 2002). However, many of these studies artificially constrained the population to students who met certain qualifications examining college enrollment (Becker 2004; Heller, 2004). In addition, the use of advanced math courses as independent variables in analyses of enrollment overlooks the role and influence of high school tracks.2 Informed by the evolution of methods and the analysis of access in advanced math courses (chapter 6), this chapter uses the types of math courses taken in high school as independent variables in analyses of enrollment and college choice but also uses test scores to control for achievement and considers whether students took college entrance exams. The study examines all high school students, but controls for whether students graduated from high school. This combination of preparation variables provides a similar set of measures to those used in the NCES studies but does not use artificial screens inherent in NCES’s use of the academic index. 2

This chapter carries this new tradition forward—using advanced math rather than high school tracks—because these variables are at the center of the debate over access. Chapter 6 addresses the limitations of this approach.

166

1.2

Chapter 7

From price response to public funding

Many of the studies of college enrollment using longitudinal databases have used the concept of student net price. The early economic studies of student demand examined the influence of the amount of tuition charges, and reviews (Jackson and Weathersby, 1975; McPherson, 1978) focused on the influence of net prices (tuition minus grants). Policy researchers developed alternative approaches for assessing the impact of student aid and other public finance strategies. More recent studies considered grant amounts as well as tuition charges response, but these studies have also been used to revise price response ratios (see Heller, 1997; Leslie and Brinkman, 1988). While the price-response approach was logically consistent with economic theory, it did not address the range of policy alternatives that have been considered by policy makers since the 1970s. Prior to the 1972 reauthorization of the Higher Education Act, there was a debate about the merits of student aid as contrasted to institutional subsidies (Gladieux and Wolanin, 1976). A few researchers adapted enrollment models to examine the influences of public funding for colleges and the receipt of student aid on enrollment (Weathersby, Jackson, Jacobs, St. John, and Tingly, 1977). That approach also involved controlling for the regions of the U.S. that students were from, as a proxy for regional policy differences (Jackson, 1978, 1988; St. John and Noell, 1989). Given the ambiguity about self-reported data on student aid in NELS (DesJardins, McCall, Ahlburg, and Moye, 2002), a new approach was needed to examine the impact of public finance strategies. By incorporating state finance indicators into a second level of enrollment and college choice models, this chapter further shifts the focus of analysis from price response to the impact of public finance strategies. Specifically we incorporated variables for state funding of need-based and non-need grant programs, weighted per FTE, along with the weighted public-sector tuition charge, into the second level of a multilevel analysis of enrollment. Since students’ states of origin are known, this method reduces the selection bias problem that has plagued research on student aid (Becker, 2004). These analyses not only control for the influence of advanced math courses and dropout, along with other individual characteristics in the first level of the model, they also consider the influence of state finance strategies in the second level. Using this approach it is possible to consider the influence of state finance variables controlling for the influence of individual-level variables. Our focus here is on building an understanding of the role and influence of public finance given the prior preparation of college-age freshmen.

7. Enrollment

2.

167

RESEARCH APPROACH

This section reexamines the effects of math courses and state funding for higher education on college enrollment and college choice using NELS. Separate analyses are presented for all students, low-income students, middle-income students, and high-income students. First, we describe the specifications for the statistical models and methods.

2.1

Model specifications

The analyses of college enrollment and college choice for each group used similar logical and statistical models to examine the influence of individual variables related to background, preparation, and postsecondary plans and state variables related to funding of student grants and tuition (see Table 7-1). With the addition of variables related to math course (i.e., calculus, trigonometry, and dropout, compared to other math plus graduation) the individual-level variables are the same as for the analysis of preparation. At the state level, average levels of freshman-year funding were used, coded as continuous variables, instead of education policy variables. Our assumption here is that education policies influence enrollment indirectly, through preparation, as indicated in the balanced access model. Consistent with the logic of the balanced access model, the outcome for the enrollment analyses compared students enrolled in any type of institution to nonenrollment (or missing), and the analyses of college choice compared enrollment in different types of institutions (less than four-year or four-year and above) to nonenrollment. These analyses did not consider out-of-state enrollment, although this could be done in future studies.3 3

We agree with an external reviewer of this volume that it is desirable to consider out-of-state enrollment. The fact that we would have had to draw another sample of variables for NELS to consider this question discouraged us from taking this step. Instead we decided to use the distinction between two-year and four-year enrollment, consistent with our original logical model (the balanced access model), to inform our coding for this analysis.

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Table 7-1. Variable coding for analyses of enrollment and college choice using NELS Individual Level Variables Coding Gender Male Dichotomous Female Comparison Variable Race Asian/Pacific Islander Design Set Hispanic Design Set African American, not Hispanic Design Set White, not Hispanic or Native American or Comparison Variable other or missing Family income Low (less than $25,000) Design Set group High ($75,000 or more) Design Set Multiple response or missing Design Set Middle ($25,000-$74,999) Comparison Variable Parent’s highest HS, some college Design Set education level College grad Design Set M.A. or equal Design Set Ph.D., M.D., other Design Set Didn’t finish HS or HS grad or GED or don’t Comparison Variable know or missing Postsecondary Vocational, trade, business after HS Design Set education plans Will attend college Design Set Will finish college Design Set Advanced degree Design Set Won’t finish HS or will finish HS or missing Comparison Variable Std test (1992 Quartile 1 Low Design Set NELS test) Quartile 2 Design Set quartile Quartile 3 Design Set Quartile 4 High Design Set Missing or test not comp Comparison Variable SAT or ACT Took SAT or ACT Dichotomous participation Did not take SAT nor ACT, or missing or Comparison Variable refusal or don’t know Preparation HS grad with trigonometry/precalculus only Design Set HS grad with calculus Design Set HS grad no trigonometry/precalculus or Design Set calculus, or missing Non-HS grad Comparison Variable State Level Need-based grant in 1,000 dollars Continuous Financial indicators Non-need-based grant in 1,000 dollars Continuous Public undergraduate in-state tuition in $1,000 Continuous

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169

Statistical methods

Three sets of analyses are presented for the entire sample and each of the income groups. First, descriptive statistics are presented on enrollment, broken down by the variables in the model. This provides an overview of the relationship of the independent variables and enrollment rates, a basic check for differences in enrollment rates for different variables. Second, logistic regression was used to examine whether or not students enrolled in college. We present odds ratios and three alpha levels (.01, .05, and .1) for each independent variable. The third level (.1) is discussed as a measure of association and does not indicate significance. In addition, a measure of significance for the second-level effect is presented, to indicate whether the state policy variables had a significant association. Third, multinomial logistic regression was used to examine enrollment in less-than-four-year or four-year colleges. (Hereafter in text we refer to lessthan-four-year colleges as two-year colleges.4) These analyses indicate whether the individual- and state-level variables had different effects on enrollment in two-year colleges than in four-year colleges. Measures of statistical significance are also discussed. Limitations of the study approach were discussed in chapter 2.

3.

ANALYSES

Analyses of enrollment and college choice for the sample of the population and for each income group are presented below. Descriptive statistics and two-level analyses logistic regression analyses are discussed.

3.1

The population

College enrollment rates varied substantially according to background characteristics (Table 7-2). Females enrolled at a higher rate than males (58% compared to 51%). The rates were lower for low-income students (38%) and students with multiple or missing responses (47%) than for middle- or high-income students (62% and 83% respectively). There were also substantial variations in enrollment rates by race/ethnicity, parents’ education, and postsecondary plans. These findings are similar to NCES (1997a, 2001c) analyses of the database. 4

Some colleges offer courses beyond the two-year degree but less than a four-year degree. These institutions have been included with the two-year colleges in these analyses.

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170 Table 7-2. Descriptive statistics on enrollment for all students

Individuals Gender

Male Female Race Asian/Pacific Islander Hispanic African American, not Hispanic White/other Family income Low (less than $25,000) group High ($75,000 or more) Multiple response or missing Middle ($25,000-$74,999) Parents’ HS, some college highest College grad education level M.A. or equal Ph.D., M.D., other No college Postsecondary Vocational, trade, business after HS education plans Will attend college Will finish college Advanced degree No college Std test (1992 Quartile 1 low NELS test) Quartile 2 quartile Quartile 3 Quartile 4 high Missing or test not comp SAT or ACT Took SAT or ACT participation No Preparation HS grad w/ trig./precalc. only HS grad with calculus HS grad no advanced math Non-HS grad State Need-based grant $/1,000 Financial indicators Non-need-based grant $/1,000 Public in-state tuition $/1,000

College Attendance Not Attending or Missing Attending Count Row % Count Row % 3,443 50.6 3,360 49.4 4,061 58.0 2,938 42.0 776 73.1 285 26.9 816 41.8 1,134 58.2 670 43.3 876 56.7 5,242 56.7 4,003 43.3 1,530 38.2 2,472 61.8 1,253 83.4 250 16.6 1,122 46.5 1,293 53.5 3,599 61.2 2,283 38.8 2,691 54.3 2,261 45.7 1,380 76.4 426 23.6 915 85.1 160 14.9 545 87.9 75 12.1 1,973 36.9 3,376 63.1 332 30.5 758 69.5 685 40.7 998 59.3 3,542 64.9 1,917 35.1 2,368 75.2 781 24.8 577 23.8 1,844 76.2 647 27.8 1,682 72.2 1,239 48.3 1,328 51.7 1,791 67.1 879 32.9 2,432 86.2 390 13.8 1,395 40.9 2,019 59.1 6,321 74.0 2,217 26.0 1,183 22.5 4,081 77.5 2,014 87.3 292 12.7 1,132 91.7 103 8.3 4,183 54.7 3,459 45.3 175 6.7 2,444 93.3 Mean 0.207 0.026 2.279

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Enrollment rates also varied for postsecondary goals and type of preparation. Students who did not expect to attend or complete college enrolled at lower rates than those who planned to complete college or obtain advanced degrees. Students who did not take the SAT/ACT enrolled at lower rates than those who did. Students who completed advanced math courses enrolled at higher rates than those who did not take advanced math and graduated. High school dropouts enrolled at substantially lower rates than students who graduated without advanced math. While the NCES studies (1997a) excluded students who did not prepare from some of their analyses of enrollment, we wanted to test the effects of different types of preparation, including high school dropout, on enrollment because this intermediate outcome was influenced by state education policies (see chapters 4 and 6). The simple averages for state funding of grants and tuition are also presented in Table 7-2. Consistent with the trend analyses in chapter 4, these averages indicate that tuition was more than ten times higher than average per-FTE funding for need-based grants and nearly 90 times higher than average funding per-FTE for merit grants. Within the regressions, the statelevel variables are weighted per FTE. The averages reported here were not weighted to a national average but are, rather, simple averages for the states. 3.1.1

College enrollment

The logistic regression analysis of college enrollment (Table 7-3) indicated that most of the individual-level variables except for race/ethnicity were significantly associated with enrollment. Males were less likely to enroll than females. Being from a low-income family reduced the odds of enrollment compared to being from a middle-income family, while having a high family income improved the odds. In addition, each level of parents’ education including high school or some college improved the odds of enrollment compared to less than a high school diploma. The odds ratios increased across the levels of parents’ education, as would be expected. There was a very substantial association between taking the SAT/ACT and enrollment (4.2 odds ratio). Controlling for taking these tests, students with low test scores (bottom two quartiles) had lower odds of enrolling than students who did not take the test, and students with high scores (top two quartiles) had higher odds, indicating a linear association. Taking advanced math courses improved the odds of enrolling in college compared to graduating without advanced math. However, dropping out substantially reduced the odds of enrolling compared to graduation without advanced math (.089 odds ratio). When considering the role of preparation in access, it is important to realize that many state education policies reduced college enrollment after 1992 through their association with dropout (chapter 3).

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Chapter 7 Table 7-3. Two-level logistic regression analysis of enrollment for all students Level 1: Individual Odds Ratio Sig. Gender Male 0.757 *** Female Race 1.189 Asian/Pacific Islander Hispanic 1.065 African American, not Hispanic 1.059 White/other Family income group Low (less than $25,000) 0.842 *** High ($75,000 or more) 1.243 ** Multiple response or missing 0.901 Parents’ highest education level HS, some college 1.359 *** 1.831 *** College grad M.A. or equal 2.700 *** 2.348 *** Ph.D., M.D., other Postsecondary education plans Vocational, trade, business after HS 1.244 ** Will attend college 1.429 *** Will finish college 2.008 *** Advanced degree 2.130 *** No college Std test (1992 NELS test) quartile Quartile 1 low 0.779 *** Quartile 2 0.988 Quartile 3 1.203 ** Quartile 4 high 1.607 *** SAT or ACT participation 4.208 *** Preparation HS grad with trigonometry/precalculus only 2.314 *** HS grad with calculus 2.757 *** Non-HS grad 0.089 *** HS grad no trig/precalculus or calculus, or missing Level 2: State Financial indicators Need-based grant in 1,000 dollars 1.755 *** Non-need grant in 1,000 dollars 1.141 In-state tuition in 1,000 dollars 0.902 ** Variance Random Effect Component Sig. Level 2 effect 0.058 *** Note: *** p<0.01, ** p<0.05, * p<0.1

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Finally, it is important to note that two of the state-level variables were significant: State funding for need-based grants was associated with higher odds of college enrollment while tuition charges were associated with lower odds of enrollment. Funding for non-need grants was positive but not significant. Readers are reminded that at the time, in 1992, non-need grants were substantially less well funded than in 2000. Given the findings from the analyses of state indicators (chapter 4), we expected that a more current sample would indicate a positive and significant association for non-need aid.

3.1.2

College choice

The multinomial analyses of enrollment in two-year and four-year colleges (Table 7-4) indicated similar patterns of association, especially for individual-level variables. Males were less likely than females to enroll in either type of college. However, controlling for other factors, students of color were more likely to enroll in four-year colleges than Whites/others. African Americans and Hispanics were significantly more likely than Whites/others to enroll in four-year colleges than not to enroll, while there was also a positive association (.1 alpha) for Asian Americans compared to Whites/others. African Americans were less likely than Whites to enroll in two-year colleges than not to enroll, controlling for other factors. Low-income students and students with no reported incomes were less likely than middle-income students to enroll in two-year colleges compared to not enrolling. In addition, students from high-income families were more likely than students from middle-income families to be enrolled in four-year colleges than not to be enrolled. This illustrates that variations in preparation play a large role in enrollment of low-income students in two-year colleges. In contrast, parents’ higher education was associated with enrollment in both types of institutions, although students whose parents’ had a doctoral degree were not more likely to enroll in two-year colleges than not to enroll. The size of the odds ratios for parents’ education were larger for enrollment in four-year colleges compared to nonenrollment than were the ratios for twoyear colleges, possibly indicating that parents’ education had a more substantial influence on enrollment in four-year colleges. Controlling for preparation and parents’ education, high-income students were more likely than middle-income students to enroll in four-year colleges than not to enroll (Table 7-4), while there were not significant differences for other income groups on this outcome. This reinforces the argument that there are college-prepared low- and middle-income students who do not enroll in four-year colleges as a consequence of financial barriers (ACSFA, 2002).

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Chapter 7 Table 7-4. Two-level multinomial logistic regression analysis of college choice for all students Four-Year Two-Year Level 1: Individual Odds Ratio Sig. Odds Ratio Sig. Gender Male 0.741 *** 0.806 *** Female Race Asian/Pacific Islander 1.062 1.202 * Hispanic 0.938 1.292 *** African American, not Hispanic 0.782 ** 1.764 *** White/other Family income group Low (less than $25,000) 0.775 *** 0.888 High ($75,000 or more) 0.864 1.471 *** Multiple response or missing 0.844 ** 0.957 Middle ($25,000-$74,999) Parent’s highest education level HS, some college 1.337 *** 1.305 *** 1.323 *** 2.017 *** College grad M.A. or equal 1.497 *** 2.915 *** 0.942 2.367 *** Ph.D., M.D., other No college Postsecondary education plans Vocational, trade, business after HS 1.410 *** 0.808 Will attend college 1.329 *** 1.453 *** Will finish college 1.759 *** 2.478 *** Advanced degree 1.579 *** 3.093 *** No college Std test (1992 NELS test) quartile Quartile 1 low 1.066 0.487 *** Quartile 2 1.298 *** 0.720 *** Quartile 3 1.360 *** 1.176 ** 1.051 1.766 *** Quartile 4 high Missing or test not comp SAT or ACT participation 2.099 *** 12.841 *** Preparation 1.436 *** 3.073 *** HS grad with trigonometry/precalculus HS grad with calculus 0.773 3.701 *** Non-HS grad 0.105 *** 0.054 *** No trigonometry/precalculus Level 2: State Need-based grant in 1,000 dollars 1.904 * 1.067 Non-need grant in 1,000 dollars 3.195 0.423 In-state tuition in 1,000 dollars 0.85 * 1.138 * Variance Variance Random Effect Component Sig. Component Sig. Level 2 effect 0.173 *** 0.095 *** Note: *** p<0.01, ** p<0.05, * p<0.1

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Students who aspired to attain technical education were more likely to enroll in two-year colleges than not to enroll, but odds of their enrollment in four-year colleges did not differ significantly from odds of their nonenrollment. Having higher aspirations was positively associated with enrollment in both two- and four-year colleges compared to nonenrollment, although the odds ratios were larger for enrollment in four-year colleges. High school preparation and tests were also associated with enrollment in two-year colleges and in four-year colleges compared to nonenrollment, although the odds were greater for enrollment in four-year colleges compared to nonenrollment than for enrollment in two-year colleges. Students who had completed advanced math courses were substantially more likely to enroll in four-year colleges than not to enroll. Students with trigonometry were also more likely to enroll in two-year colleges than not to enroll, but having calculus was not significantly associated with college choice. In addition, having scores on entrance exams in the lower two quartiles was negatively associated with enrollment in four-year colleges compared to nonenrollment, controlling for the positive effects of taking the exam and other factors. Having scores in the lowest quartile was not significantly associated with enrollment in two-year colleges compared to nonenrollment, but students with scores in the three high test quartiles were more likely to enroll in two-year colleges than not to enroll. The amount of funding for need-based grants was positively associated with enrollment in two-year colleges compared to nonenrollment, but only at a .1 alpha, indicating a modest association. Neither type of grant funding was significantly associated with enrollment in four-year colleges. Given the findings for high-income students, there was reason for concern about financial access to four-year colleges based on the experiences of students in the 1992 cohort. In addition, higher tuition in public colleges, an indicator of privatization, was modestly and negatively associated with enrollment in two-year colleges compared to nonenrollment and modestly and positively associated with enrollment in four-year colleges. The combination of college prices and student financial aid in place in 1992 seemed to have influenced students to enroll in two-year colleges.

3.2

Low-income students

The descriptive statistics for low-income students (Table 7-5) illustrate that a very high percentage of low-income students with parents who had advanced degrees actually enrolled in college. However, only a miniscule percentage of low-income students had parents with advanced degrees, so it simply does not make sense to emphasize parents’ education as a determinant among low-income students. These statistics also further

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Table 7-5. Descriptive statistics for enrollment for low-income only College Attendance Not Attending/Missing Attending Individuals Count Row % Count Row % Gender Male 652 33.9 1,269 66.1 Female 878 42.2 1,203 57.8 Race Asian/Pacific Islander 180 67.2 88 32.8 Hispanic 281 33.9 548 66.1 African American, not Hispanic 290 36.7 501 63.3 White/other 779 36.8 1,335 63.2 Family income Low (less than $25,000) 1,530 38.2 2,472 61.8 Parent’s highest HS, some college 674 43.2 886 56.8 education level College grad 145 67.8 69 32.2 M.A. or equal 50 66.7 25 33.3 Ph.D., M.D., other 12 66.7 6 33.3 No college 649 30.4 1,486 69.6 Postsecondary Vocational, trade, business after HS 110 24.1 347 75.9 education plans Will attend college 217 34.9 404 65.1 Will finish college 656 51.4 621 48.6 Advanced degree 344 57.5 254 42.5 No college 203 19.4 846 80.6 Std test (1992 Quartile 1 low 242 23.9 771 76.1 NELS test) quartile Quartile 2 354 40.4 523 59.6 Quartile 3 346 54.9 284 45.1 Quartile 4 high 318 77.9 90 22.1 Missing or test not comp 270 25.1 804 74.9 SAT or ACT Took SAT or ACT 1,153 60.6 751 39.4 participation Did not take SAT/ACT 377 18.0 1,721 82.0 Preparation HS with trig/precalculus only 325 81.9 72 18.1 HS grad with calculus 153 87.4 22 12.6 HS/No advanced math 989 45.2 1,201 54.8 Non-HS grad 63 5.1 1,177 94.9 State Mean State financial Need-based grant $/1,000 0.207 indicators Non-need-based grant $/1,000 0.026 Public in-state tuition $/1,000 2.279

illustrate the link between income and education. Most families with parents who had college degrees had at least mid-level incomes. Enrollment rates were low for students who aspired to attain vocational degrees, who expected to attend college but not to finish, or who did not expect to enroll. In contrast, more than half of the students enrolled who, as high school students, expected to complete college. In addition, students who had taken steps to prepare for college enrolled at higher rates than students who did not. Students who took advanced math

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courses and took entrance exams enrolled at higher rates. However, a few low-income students who had not completed high school actually enrolled in colleges, further illustrating that excluding these students from analyses of enrollment was a problematic approach. 3.2.1

College enrollment

The logistic regression analyses of enrollment by low-income students (Table 7-6) indicate that many individual-level variables were associated with enrollment. Males were less likely to enroll in college than females among low-income students. Asian Americans and African Americans were more likely than Whites/others to enroll in college among low-income students, controlling for other variables. Students whose parents had completed some college or who had received a college degree were more likely than students whose parents did not enroll in college to have enrolled themselves. However, having advanced degrees was not significantly associated with this outcome, an artifact of the low number of low-income students in these categories. Aspirations for attending college, for completing a degree, and for receiving an advanced degree were positively associated with college enrollment and were also positively associated with enrollment among low-income students. College preparation was also important. Students who took the SAT/ACT had 3.8 times the odds of enrolling as students who did not. Receiving a score in the highest quartile also improved the odds, compared to not having a score reported. Completing trigonometry/precalculus and calculus were positively associated with enrollment, compared to having graduated without advanced math. Compared to students who completed regular math, lowincome students who did not complete high school had only 0.08 the odds of enrolling in college. This illustrates that high school completion is a crucial threshold of preparation for low-income students. Controlling for individual-level variables, state funding for need-based grants was significantly and positively associated with enrollment by lowincome students. Each one-thousand dollars of differential in per-FTE funding for need-based grants improved the odds of college enrollment by 2.26 times compared to the odds of nonenrollment. These findings confirm the conclusion from the analyses of indicators that improved funding of need-based grant aid enables college enrollment among qualified lowincome students.

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Chapter 7 Table 7-6. Two-level logistic regression analysis of enrollment for low-income students Level 1: Individual Odds Ratio Sig. Gender Male 0.789 *** Female Race Asian/Pacific Islander 1.848 ** Hispanic 1.202 African American, not Hispanic 1.343 ** White, not Hispanic or Native American or other or missing Parent’s highest education level HS, some college 1.238 ** College grad 2.149 *** M.A. or equal 1.943 * Ph.D., M.D., other 1.892 No college Postsecondary education plans Vocational, trade, business after HS 1.259 Will attend college 1.511 *** Will finish college 1.862 *** Advanced degree 1.938 *** Won’t finish HS or will finish HS or missing Std test (1992 NELS test) quartile Quartile 1 low 0.838 Quartile 2 0.982 Quartile 3 1.147 Quartile 4 high 1.649 *** Missing or test not comp SAT or ACT participation 3.817 *** Preparation HS grad with trigonometry/precalculus only HS grad with calculus 2.263 *** Non-HS grad 0.488 HS grad no trigonometry/precalculus or calculus, or missing 0.897 Level 2: State Financial indicators Need-based grant in 1,000 dollars 2.263 *** Non-need grant in 1,000 dollars 0.488 Public system undergraduate in-state tuition in 1,000 dollars 0.897 Random Effect Variance Component Sig. Level 2 effect 0.001 * Note: *** p<0.01, ** p<0.05, * p<0.1

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College choice

The analyses of college choice (Table 7-7) further illustrate the roles of student background and preparation. Among low-income students, Asian Americans were more likely than Whites/others to enroll in four-year and two-year colleges than to not enroll, while African Americans were more likely to enroll in four-year colleges but not two-year colleges. Parents’ education had a more substantial association with enrollment in four-year colleges than two-year colleges among students in the lowest income quartile. Preparation was also important in college choice for low-income students. Completing advanced math courses was associated with enrollment in four-year colleges compared to nonenrollment, but was not significantly associated with enrollment in two-year colleges. Completing the ACT or SAT substantially improved the odds of enrolling in a four-year college compared to not enrolling (14.3 odds ratio) and was positively associated with enrollment in two-year colleges (2.0 odds ratio). Having high scores (top two quartiles) substantially improved the odds of enrollment in fouryear colleges, but was not significantly associated with enrollment in twoyear colleges. Having low scores reduced the odds of enrollment in four-year colleges, but was not associated with enrollment in two-year colleges. In addition, for low-income students, aspiring to complete a college degree or advanced degree improved the odds of enrollment in four-year colleges compared to nonenrollment, while all types of postsecondary goals had an association with enrollment for low-income students. Even vocational goals had a modest association with enrollment in two-year colleges compared to nonenrollment for low-income students. The state-level finance variables had a significant association with college choices by low-income students. State funding for need-based grants was associated with enrollment in two-year and four-year colleges compared to nonenrollment. In contrast, state funding for non-need grants and public sector tuition charges was not significantly associated with either outcome. Raising funding levels for need-based programs expands access to two-year and four-year colleges for students with the greatest financial need. These findings illustrate that state finances played a crucial role in ensuring financial access for low-income students in 1992.

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Table 7-7. Two-level multinomial logistic regression analysis of college choice for lowincome students Four-Year Two-Year Level 1: Individual Odds Ratio Sig. Odds Ratio Sig. Gender Male 0.787 *** 0.873 Female Race 1.702 ** 2.207 *** Asian/Pacific Islander Hispanic 1.000 1.281 African American, not Hispanic 0.971 2.470 *** White/other Parent’s highest education level HS, some college 1.116 1.187 * 1.584 ** 2.187 *** College grad M.A. or equal 1.393 2.479 ** Ph.D., M.D., other 1.194 3.904 No college Postsecondary education plans Vocational, trade, business after HS 1.394 * 1.061 Will attend college 1.455 ** 1.481 Will finish college 1.803 *** 2.265 *** Advanced degree 1.855 *** 2.616 *** Won’t finish HS or will finish HS or missing Std test (1992 NELS test) quartile Quartile 1 low 0.968 0.705 * Quartile 2 0.998 1.078 Quartile 3 1.013 1.610 *** Quartile 4 high 0.930 3.023 *** Missing or test not comp SAT or ACT participation Yes 1.957 *** 14.345 *** No or missing or refusal or don’t know Preparation HS grad with trigonometry/precalculus only 1.286 2.651 *** HS grad with calculus 0.884 4.005 *** Non-HS grad 0.078 *** 0.057 *** HS grad no trig/precalculus or calculus, or missing Level 2: State Financial indicators Need-based grant in 1,000 dollars 2.604 *** 1.756 ** Non-need grant in 1,000 dollars 1.380 0.124 Undergraduate in-state tuition in 1,000 dollars 0.891 1.001 Variance Variance Sig. Random Effect Component Sig. Component Level 2 Effect 0.132 *** 0.152 *** Note: *** p<0.01, ** p<0.05, * p<0.1

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181

Middle-income students

The descriptive statistics on college enrollment for middle-income students (Table 7-8) further illustrate the expected pattern of relationships between SES variables and enrollment rates. Students whose parents had some college enrolled at higher rates than students who had not graduated. While students who had taken entrance exams enrolled at higher rates than those who did not, only one-third of middle-income students in the lowest SAT quartile enrolled in any college. Furthermore, the majority of middleincome students in each category of high school graduates—calculus, trigonometry, and no advanced math—enrolled in college, while most nongraduates did not enroll (only 9%). 3.3.1

College enrollment

The logistic regression analysis (Table 7-9) found that variables related to background, goals, and preparation, were associated with enrollment among middle-income students. Males were less likely to enroll than females. Students whose parents had some college or college degrees were more likely to enroll, while having a parent with an advanced degree was not significantly associated with enrollment. Taking the SAT was positively associated with enrollment compared to not taking the test. Both types of advanced math preparation were positively associated with enrollment compared to graduation without math, while nongraduation substantially reduced the odds of college enrollment. State funding for need-based grants significantly improved the odds of college enrollment by middle-income students, while non-need grants and tuition charges were not significantly associated with enrollment. These findings further indicate the importance of state need-based grants in expanding college access for prepared students. 3.3.2

College choice

The multinomial analyses of college choice (Table 7-10) further illustrate the influence of background and preparation on enrollment in both two-year and four-year colleges. Among middle-income students, African Americans and Hispanics were more likely than Whites to enroll in four-year colleges than not to enroll, and African Americans were less likely than Whites to enroll in two-year colleges. All levels of parents’ education above high school improved the odds of enrollment in four-year colleges, compared to

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Table 7-8. Descriptive statistics for enrollment by middle-income students College Attendance Not Attending or Missing Attending Individuals Count Row % Count Row % Gender Male 1,671 56.7 1,275 43.3 Female 1,928 65.7 1,008 34.3 Race Asian/Pacific Islander 302 74.2 105 25.8 Hispanic 306 57.7 224 42.3 African American, not Hispanic 260 54.2 220 45.8 White/other 2,731 61.2 1,734 38.8 Income Middle ($25,000-$74,999) 3,599 61.2 2,283 38.8 Parent’s highest HS, some college 1,659 60.2 1,098 39.8 education level College grad 765 75.0 255 25.0 M.A. or equal 458 84.5 84 15.5 Ph.D., M.D., other 134 83.8 26 16.3 No college 583 41.6 820 58.4 Postsecondary Vocational, trade, business after HS 158 37.3 266 62.7 education plans Will attend college 319 47.7 350 52.3 Will finish college 1,821 68.9 821 31.1 Advanced degree 1,077 78.4 297 21.6 No PSE plans 224 29.0 549 71.0 Std test (1992 Quartile 1 low 265 33.6 523 66.4 NELS test) Quartile 2 587 52.4 533 47.6 quartile Quartile 3 958 71.0 391 29.0 Quartile 4 high 1,236 86.3 197 13.7 Missing or test not comp 553 46.4 639 53.6 SAT or ACT Took SAT or ACT 3,077 78.0 870 22.0 participation Not indicator of test 522 27.0 1,413 73.0 Preparation HS grad with trig/precalc only 1,021 88.4 134 11.6 HS grad with calculus 504 91.0 50 9.0 HS grad no advanced math 2,009 58.6 1,419 41.4 Non-HS grad 65 8.7 2,444 93.3

7. Enrollment Table 7-9. Two-level logistic regression for enrollment by middleincome students Level 1: Individual Odds Ratio Sig. Gender Male 0.726 *** Female Race Asian/Pacific Islander 1.072 ** Hispanic 1.145 African American, not Hispanic 0.918 ** White/other Parent’s highest education level HS, some college 1.540 ** College grad 1.840 *** M.A. or equal 3.001 * Ph.D., M.D., other 2.406 No college Postsecondary education plans Vocational, trade, business after HS 1.117 Will attend college 1.407 *** Will finish college 1.946 *** Advanced degree 2.078 *** Won’t finish HS or will finish HS or missing Std test (1992 NELS test) quartile Quartile 1 low 0.836 Quartile 2 1.054 Quartile 3 1.343 Quartile 4 high 1.710 *** Missing or test not comp SAT or ACT participation Yes 4.356 *** No or missing or refusal or don’t know Preparation HS grad with trigonometry/precalculus only 2.597 *** HS grad with calculus 2.541 *** Non-HS grad 0.103 *** HS grad no advanced math Level 2: State Financial indicators Need-based grant in 1,000 dollars 1.792 *** Non-need grant in 1,000 dollars 1.416 Public undergrad in-state tuition in $1,000 0.882 Random Effect Variance Component Sig. Level 2 effect 0.025 **

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Table 7-10. Two-level multinomial logistic regression analysis of college choice for middle-income students Two-Year Four-Year Level 1: Individual Odds Ratio Sig. Odds Ratio Sig. Gender Male 0.723 *** 0.739 *** Female Race Asian/Pacific Islander 0.994 1.020 Hispanic 0.937 1.640 *** African American, not Hispanic 0.660 ** 1.538 *** White/other Parent’s highest education level HS, some college 1.523 *** 1.435 *** College grad 1.367 *** 2.055 *** M.A. or equal 1.900 *** 3.537 *** Ph.D., M.D., other 1.172 2.291 ** No college Postsecondary education plans Vocational, trade, business after HS 1.219 0.667 Will attend college 1.175 1.622 ** Will finish college 1.538 *** 2.704 *** Advanced degree 1.363 ** 3.337 *** Won’t finish HS or will finish HS or missing Std test (1992 NELS test) quartile Quartile 1 low 1.206 0.475 *** Quartile 2 1.565 *** 0.680 ** Quartile 3 1.698 *** 1.233 Quartile 4 high 1.272 * 1.843 *** Missing or test not comp SAT or ACT participation Yes 2.196 *** 11.712 *** No or missing or refusal or don’t know Preparation HS with trigonometry/precalculus only 1.584 *** 3.328 *** HS grad with calculus 0.692 * 3.600 *** Non-HS grad 0.130 *** 0.066 *** HS grad no advanced math Level 2: State Financial indicators Need-based grant $/1,000 1.902 *** 1.108 Non-need grant in $/1,000 6.370 * 0.414 In-state tuition $/1,000 0.841 * 1.144 Variance Variance Component Sig. Component Sig. Random Effect Level 2 effect 0.134 *** 0.124 *** Note: *** p<0.01, ** p<0.05, * p<0.

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not enrolling, for middle-income students, while having a parent with an advanced degree was not significantly associated with enrollment in twoyear colleges. Compared to graduation without advanced math, both types of advanced math courses improved the odds of enrollment in four-year colleges for this group, while high school dropout reduced these odds. The chances of enrollment in two-year colleges compared to nonenrollment were higher for middle-income students who completed trigonometry than for those who graduated without advanced math. However, there was only a modest association (.1 alpha) with this outcome for students with calculus. Failure to complete high school substantially lowered the odds of enrollment in two-year colleges, compared to nonenrollment, by middle-income students. Taking the SAT or ACT very substantially improved the odds of enrollment in four-year colleges (11.7 odds ratio), compared to nonenrollment, and improved the odds of enrollment in two-year colleges (2.2 odds ratio) for middle-income students, while high test scores improved the odds of enrollment in both types of institutions and low scores reduced the odds of enrollment in four-year colleges. For middle-income students, state funding of need-based aid was positively associated with enrollment in two-year colleges, while funding for non-need grants substantially improved these odds but was only modestly significant (.1 alpha). State tuition charges, our primary indicator of privatization, reduced the odds of enrollment in two-year colleges compared to nonenrollment. However, the state finance variables were not significantly associated with enrollment in four-year colleges compared to nonenrollment by middle-income students.

3.4

High-income students

The descriptive statistics for students in the high-income quartile (Table 7-11) also indicate differentials in college enrollment rates by family background and preparation variables. High-income students enrolled at an 83.4 percentage rate. African Americans and Hispanics enrolled at somewhat lower rate than other racial/ethnic groups among high-income students. Students from high-income families whose parents had completed college or advanced degrees enrolled at higher rates than students whose parents had only some college or had not gone to college. High-income students who aspired to complete college and to attain advanced degrees enrolled at higher rates than high-income students with lower-aspirations. In addition, there were strong differentials in enrollment rates among students who had prepared for college compared to students who had not. High-income students who had completed entrance exams had higher

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Table 7-11. Descriptive statistics for enrollment by high-income students College Attendance Not Attending or Missing Attending Individuals Count Row % Count Row % Gender Male 626 81.7 140 18.3 Female 627 85.1 110 14.9 Race Asian/Pacific Islander 147 85.0 26 15.0 Hispanic 50 72.5 19 27.5 African American, not Hispanic 46 80.7 11 19.3 White/other 1,010 83.9 194 16.1 Income group High ($75,000 or more) 1,253 83.4 250 16.6 Parents’ highest HS, some college 180 70.0 77 30.0 education level College grad 348 84.7 63 15.3 M.A. or equal 334 90.5 35 9.5 Ph.D., M.D., other 349 90.4 37 9.6 Didn’t finish HS or HS grad or 42 52.5 38 47.5 GED or don’t know or missing Postsecondary Vocational, trade, business after HS 10 35.7 18 64.3 education plans Will attend college 48 61.5 30 38.5 Will finish college 555 84.9 99 15.1 Advanced degree 602 89.7 69 10.3 No college 38 52.8 34 47.2 Std test (1992 Quartile 1 low 32 50.8 31 49.2 NELS test) Quartile 2 99 69.2 44 30.8 quartile Quartile 3 254 84.4 47 15.6 Quartile 4 high 617 92.4 51 7.6 Missing or test not comp 251 76.5 77 23.5 SAT or ACT Took SAT or ACT 1,167 88.9 145 11.1 participation No 86 45.0 105 55.0 Preparation HS grad with trigonometry/ 414 91.8 37 8.2 precalculus only HS grad with calculus 321 94.7 18 5.3 HS grad no advanced math 507 79.1 134 20.9 Non-HS grad 11 15.3 61 84.7 State Mean Financial Need-based grant in $1,000 0.207 indicators Non-need-based grant in $1,000 0.026 Public undergraduate in-state tuition in $1,000 2.279

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enrollment rates than those who had not. Also high-income students with high test scores and who had completed advanced math enrolled in college at higher rates than those without the same levels of preparation. 3.4.1

College Enrollment

The logistic regression analyses for high-income students (Table 7-12) had many similarities to other groups at the individual level. High levels of parents’ education were positively associated with enrollment compared to no college. Aspiring to complete college was positively associated with enrollment compared to nonenrollment. Taking the SAT/ACT was positively associated with enrollment. Completion of advanced math improved the odds of enrollment compared to high school graduation without math, while dropout reduced the odds. However, unlike other groups, SAT/ACT scores were not significantly associated with enrollment. The state financial variables were also associated with enrollment, but differently than for other groups. State funding for need-based grants was negatively associated with enrollment, while state funding for non-need grants had a modest (.1 alpha) and negative relationship with the outcome. 3.4.2

College Choice

The multinomial analysis of college choice (Table 7-13) revealed a different pattern for high-income students than was evident for other income groups. Only one individual-level variable was significantly associated with their enrollment in two-year colleges compared to nonenrollment—not completing high school significantly and substantially reduced the odds of this outcome. Having parents with an advanced degree—a master’s or a doctorate—significantly improved the odds of their enrollment in four-year colleges. Taking the SAT/ACT improved the odds of enrollment in a fouryear college compared to nonenrollment, as did completing an advanced math course. Dropping out of high school substantially reduced the odds of enrollment in four-year colleges compared to nonenrollment for this group, as for other groups. Having an SAT/ACT in the lower-middle quartile reduced the odds of enrollment, but other scores were not statistically significant. Gender and ethnicity were not significantly associated with college choice by high-income students controlling for other variables. State financial variables were associated with high-income students’ college choices but differently than for other income groups. State funding for need-based grants reduced the odds they would enroll at four-year colleges compared to not enrolling after high school. Further, public sector tuition charges were negatively associated with enrollment in two-year colleges compared to nonenrollment for high-income students.

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Chapter 7 Table 7-12. Two-level logistic regression for enrollment of highincome students Level 1: Individual Odds Ratio Sig. Gender Male 0.872 Female Race 0.677 ** Asian/Pacific Islander Hispanic 0.823 African American, not Hispanic 0.898 White/other Parents’ highest education level HS, some college 1.337 1.812 ** College grad M.A. or equal 2.839 *** Ph.D., M.D., other 2.433 *** Didn’t finish HS Postsecondary education plans Vocational, trade, business after HS 0.709 Will attend college 1.233 Will finish college 2.535 *** Advanced degree 2.560 ** Won’t finish HS or will finish HS or missing Std test (1992 NELS test) quartile Quartile 1 low 0.854 Quartile 2 0.658 Quartile 3 1.050 Quartile 4 high 1.282 Missing or test not comp SAT or ACT participation Yes 3.616 *** No or missing or refusal or don’t know Preparation HS grad with trigonometry/precalculus only 1.739 *** HS grad with calculus 2.355 *** Non-HS grad 0.062 *** HS grad no trig/precalculus or calculus, or missing Level 2: State Financial indicators Need-based grant in $1000 0.669 ** Non-need grant in $1000 0.178 * Public system in-state tuition in $1000 0.882 Random Effect Variance Component Sig. Level 2 effect 0.001 Note: *** p<0.01, ** p<0.05, * p<0.1

7. Enrollment Table 7-13. Two-level multinomial logistic regression analysis of college choice: High-income students Two-Year Four-Year Level 1: Individual Odds Ratio Sig. Odds Ratio Sig. Gender Male 1.026 1.078 Female Race 0.895 0.765 Asian/Pacific Islander Hispanic 1.300 0.738 African American, not Hispanic 0.701 1.460 White/other Parent’s highest education level HS, some college 1.638 1.269 1.820 1.890 * College grad M.A. or equal 1.955 2.696 *** 1.029 2.507 ** Ph.D., M.D., other No college Postsecondary education plans Vocational, trade, business after HS 0.502 0.671 Will attend college 1.140 1.111 Will finish college 1.942 2.525 ** Advanced degree 1.289 2.878 *** No college Std test (1992 NELS test) quartile Quartile 1 low 1.253 0.750 Quartile 2 1.534 0.533 ** Quartile 3 1.443 1.032 Quartile 4 high 0.943 1.091 SAT or ACT participation 1.228 7.377 *** Preparation HS grad with trig/precalculus only 1.517 2.307 *** HS grad with calculus 0.791 3.111 *** Non-HS grad 0.159 *** 0.052 *** HS grad no trigonometry/precalculus or calculus, or missing Level 2: State Financial indicators Need-based grant in $1,000 1.224 0.476 ** Non-need grant in $1,000 0.031 0.293 0.588 ** 1.080 Undergrad in-state tuition in $1,000 Variance Variance Random Effect Component Sig. Component Sig. Level 2 effect 0.270 ** 0.048 Note: *** p<0.01, ** p<0.05, * p<0.1

189

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CONCLUSIONS

The analyses of enrollment and college choice by students in the 1992 cohort support two conclusions: (1) High school preparation plays a role in access, but the role is more complex than previously assumed, and (2) state financial strategies have a substantial and direct influence on enrollment and college choice. The analyses of the sample for the entire 1992 cohort confirm a relationship between state financing of higher education and college enrollment, controlling for individual background. Similar to the time series analysis of financial indicators for the 1990s, these analyses of the 1992 cohort indicate that need-based grants expand access. There is also little doubt that preparation plays a role. These analyses also verify that advanced math courses play a central role in access to four-year colleges. However, high school dropouts had substantially reduced odds of enrollment. While state education policies were not associated with dropout for the class of 1992 (chapter 6), the analyses of indicators revealed that policies implemented after 1992 were associated with increased dropout rates (chapter 3). At the very least, these reanalyses of the 1992 cohort reveal that state finance policies influence enrollment independent of the modest effects of K–12 policy on preparation. While there were differences in the effects of state finance policies across groups, there is evidence that balanced approaches to finances can expand opportunity across income groups. Both low- and middle-income students were responsive to grants in their enrollment decisions. For low-income students, state funding of need-based grants was positively associated with enrollment in both two-year and four-year colleges. Middle-income student enrollment in two-year colleges was related to funding of state grant programs for the 1992 cohort. Wealthy students enrolled at extremely high rates, while funding for student aid reduced their odds of enrolling in fouryear colleges compared to nonenrollment. In a sense, these findings reflect the structural constraints on enrollment opportunity—the limited capacity of state education systems. If we interpret these findings with the understanding of financial access from the analyses of state indicators, we can conclude that high-income students benefit from high-tuition and high-loan policies because of expanded opportunity, low- and middle-income students benefit from high-grant and high-tuition policies with respect to enrollment opportunities in two-year colleges, and low-income students have expanded opportunity to enroll in four-year colleges when high-grant policies accompany high tuitions. This high-grant/high-tuition combination has an

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equalizing effect across income groups while the high-loan/high-tuition combination does not. These findings are informative relative to the debates about college access. The arguments for improvement of preparation, especially the focus on math courses, relate primarily to preparation for enrollment in four-year colleges. Time series analyses indicated a relationship between K–12 policies and preparation (chapter 3), although the cohort analyses indicated these relationships were modest. The more problematic issue is that the privatization model currently being used in the U.S. is not providing equal enrollment opportunity for equally prepared students across income groups. These analyses support the position that access policy that focuses on preparation without considering financial access can result in increased inequalities in enrollment opportunity, especially in the opportunity to enroll in four-year colleges.

Chapter 8 ATTAINMENT By Edward P. St. John and Anna S. Chung

The agendas for improving educational opportunity in the U.S. (see chapter 2) focus on success in college as well as access. With the availability of National Education Longitudinal Study (NELS) data of the 1992 cohort through 2000 it was possible to examine the influence of preparation and state funding on college success, as measured by persistence and attainment after eight years. This chapter considers the influence of finance policies on college success using degree attainment. We compare different levels of degree attainment and current enrollment to dropout for students in the 1992 cohort who entered college within the first two years after high school. The public finance indicators provided a basis for assessing the direct effects of finances on persistence, a topic considered below before findings on persistence and attainment are presented. This chapter closes with a summary of the reanalysis of NELS, comparing our findings to the American Council on Education (ACE) summary report of National Center for Education Statistics (NCES) research.

1.

ASSESSING THE EFFECT OF PUBLIC FINANCES

There has been a long history of research on the direct effects of financial aid and college prices on persistence (Leslie and Brinkman, 1988) as well as a recent evolution of models used for this purpose (St. John, Cabrera, Nora, and Asker, 2000). Building on this prior research, this chapter examines the direct effects of state finance policies on persistence and degree completion. The state financial indicators provided a set of measures appropriate for this purpose.

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When financial variables are used in persistence models, it is crucial to link appropriate treatment variables with persistence outcomes. Early persistence to graduation models examined either (a) the different types of aid, e.g., grants, loans, and other forms (e.g., Astin, 1975); or (b) the effect of receiving any type of aid compared to not receiving aid (Terkla, 1985). It was difficult to consider aid amounts because both aid amounts and tuition charges could vary across years. When year-to-year persistence models were developed, the base year of aid (e.g., the freshman package in a first-tosecond-year analysis) was appropriately set as the base year or term of enrollment (St. John, Kirshstein, and Noell, 1991), an approach also used in within-year persistence models (St. John, Andrieu, Oescher, and Starkey, 1994). These models made it possible to examine the impact of aid and tuition amounts as well. This study examines attainment rates eight years after high school. Using the two-level models, it is possible to examine the effects of state financial policies, as second-level variables, on attainment status eight years after high school. However, the analyses are limited to the students who enrolled after high school, so that we can make cohort comparisons for students who enrolled in college, controlling for individual-level variables related to background and preparation. The state financial indicators are used at the second level, providing measures of public support. In these analyses we consider both the level of funding during the freshman year (1992) and change in amounts over the eight-year period studied (1992-2000). When examining degree attainment by 2000, it was appropriate to use both the initial funding and the change in funding because (1) the state funding level during the freshman year influenced college choices (chapter 7) and (2) changes in funding could influence students’ decisions about year-to-year persistence and reenrollment. This approach provides means of examining the influence of public finance strategies on attainment, controlling for individual background and preparation—an approach that has seldom been used in the past.1 The analyses provide new insights into the relationship between state finance policies and degree attainment.

1

We know of only one prior study that used a two-level model to examine the effects of state finance policies on degree attainment (St. John and Chung, 2006). The analyses included here improves on the prior model by including change in amounts as well as base amounts instead of the average funding level used in the prior study.

8. Attainment

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195

RESEARCH APPROACH

Increasingly, policymakers in the U.S. concerned about long-term persistence rates are considering the outcome of degree attainment. Reports of six-year degree completion rates, however, consistently overlook whether, controlling for preparation, students with different financial means have differing opportunities to attain degrees. Below, we describe the logical model for examining attainment, in this case after eight years, and we discuss the methods and limitations for the analyses that follow.

2.1

Model specifications

The outcome variables for the analysis of attainment compare attainment by 2000 of two-year degrees, four-year degrees, advanced degrees, or “still enrolled” to dropouts among those who enrolled after high school in the class of 1992. The individual-level variables used to examine attainment (Table 8-1) were very similar to those used to examine enrollment and college choice. The only difference is that high school dropouts are not specified.2 While there is some degree of attainment among dropouts, including “still enrolled” and two-year degrees, not all cells had sufficient numbers to complete this analysis. Therefore it was appropriate to revise the model to focus exclusively on math attainment. The model specifications differ from the enrollment/choice model at the state level because of the addition of variables related to change in the three financial indicators. The analysis of degree attainment used multilevel multinomial logistic regression, an approach consonant with the multiple outcomes and the use of variables related to individuals and their states of residence during high school. Three levels of significance are presented for independent variables (.01, .05, and .1). The third of these is a modest association. In addition, a measure of the second-level effects is provided. As with any analysis of student outcomes in higher education, there are limitations to the method used here. As noted in chapter 2, using state finance variables does not present the same sort of selection problem as analysis of the amounts of aid received. These analyses provide an indicator of the relationship between state funding and attainment rates, but we cannot claim causality from these analyses.

2

The number of dropouts who enrolled was small, so it was not possible to include this variable. Most dropouts did not complete advanced math.

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Chapter 8 Table 8-1. Variable coding for analyses of attainment using NELS Individual Level Gender Race

Family income group

Parents’ highest education level

Postsecondary education plans

Std test (1992 NELS test) quartile

SAT or ACT participation Preparation

State Level State financial policy indicators

Variables Male Female Asian/Pacific Islander Hispanic Black, not Hispanic White, not Hispanic or Native American or other or missing Low (less than $25,000) High ($75,000 or more) Multiple response or missing Middle ($25,000-$74,999) HS, some college College grad M.A. or equal Ph.D., M.D., other Didn’t finish HS or HS grad or GED or don’t know or missing VOC,TRD,BUS after H.S Will attend college Will finish college Advanced degree Won’t finish H.S or will finish H.S or missing Quartile 1 low Quartile 2 Quartile 3 Quartile 4 high Missing or test not comp Took SAT or ACT Did not take SAT/ACT, or missing or refusal or don't know Trigonometry/precalculus only Calculus No trigonometry/precalculus or calculus, or missing 1992 need-based grant in $/1000 1992 non-need grant in $/1000 1992 in-state tuition in $/1000 Change in need-based grants in $/1000 Non-need grants in $/1000 Change in in-state tuition in $/1000

Coding Dichotomous Comparison Variable Design Set Design Set Design Set Comparison Variable Design Set Design Set Design Set Comparison Variable Design Set Design Set Design Set Design Set Comparison Variable Design Set Design Set Design Set Design Set Comparison Variable Design Set Design Set Design Set Design Set Comparison Variable Dichotomous Comparison Variable Design Set Design Set Comparison Variable

Continuous Continuous Continuous Continuous Continuous Continuous

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Since not all students are eligible for state grants, the distribution of funding is not equal across all students. It can be assumed that need-based grants are distributed based on family income and other need-related indicators and that non-need grants are distributed based on achievement and other nonfinancial indicators. Since variables related to these selection processes—family income and test scores—are included at the individual level, there are some endogenous relationships among variables at the individual level. However, these limitations are not a problem with respect to the relationships between second-level variables and the attainment outcomes. While there are correlations between changes in base finance variables and changes in these amounts, these correlations do not preclude inclusion of both sets of variables. The effects of student aid on enrollment merit consideration along with findings of persistence. For example, if financial aid is not significantly associated with persistence for a given student but it is significant in enrollment, then some of the attainment is indirectly attributable to the enrollment effect. If students do not think they can afford to enroll they will not enroll. Thus, given the influence of state grants on enrollment, there is a residual effect on attainment: Students who gain access because of financial aid would not have had the opportunity to attain their education without this support.

2.2

Statistical methods and limitations

These analyses are presented in a form consistent with the analyses of preparation and access in prior chapters. The descriptive table indicates differences in rates of attainment and persistence for the independent variables. No significances are reported before the descriptive tables because readers are cautioned in this volume about reading too much into statistical differences for descriptive differences. The multilevel, multinomial logistic regression tables present three levels of significance for the independent variables (.01, .05, and .1). The third of these levels (.1 alpha) is a modest association and should not be considered statistically significant. In addition, the variance component of the secondlevel variables is presented as an indicator of whether these variables had a significant association with the outcome. Similar to the prior analyses, it is important to note that these multilevel analyses examine the effects of state policy controlling for the effects of individual-level variables related to attainment. However, this is not a causal model. The individual-level variables related to financial aid have been omitted because of concerns that they are self-reported, as noted earlier.

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ANALYSIS

States that have accountability systems sometimes reward campuses with high degree attainment rates with extra funding (Zumeta, 2001). If differences in attainment are attributable to family income, however, then states using this type of funding scheme may be rewarding campuses based on the social class of their students rather than on the value added from the education provided. The analyses summarized below add substance to the policy debates about persistence rates and degree attainment.

3.1

Attainment rates

The descriptive statistics (Table 8-2) provide indicators of the rate of attainment among students in the class of 1992 who enrolled during the first year after high school. Less than one quarter had dropped out. About fourtenths of both males and females had attained a bachelor’s degree (38.1% and 39.8% respectively) and a small group (4.3% of males and 5.9% of females) had attained advanced degrees. More than one-tenth (13.5% of males and 10.8% of females) were still enrolled in 2000. There were variations in attainment rates for variables related to individual background. Students whose parents had college degrees had attained advanced degrees at a much higher rate (15.1%) than other groups (e.g., 10.9% for students whose parents had master’s degrees and lower rates for students whose parents had lower levels of educational attainment). Students from high-income families also attained degrees at much higher rates than students from middle- and low-income backgrounds. There were also substantial variations in attainment rates for variables related to preparation. Students with higher aspirations, higher tests scores, and advanced math had higher levels of attainment than their peers. However, it should also be noted that less than one-third (32.2%) of the students who did not have advanced math were dropouts. More than half of the students who graduated without advanced math had attained degrees: 25 percent with associate degrees, 26 percent with bachelor’s degrees, and 2.5 percent with doctorates. The patterns of relationships are complex. As expected, these simple statistics illustrate variability in attainment rates for variables related to background and preparation. If we control for these individual-level variables, what effect do state financial policies have on attainment? As noted in Table 8-2, the change in funding for grant programs was modest over the eight years. However, after increasing by an average of $73 per FTE, state funding for non-need grants had increased about three times. State funding per FTE for need-based grants rose at a more modest rate (by

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$29, from a base of $219 per FTE). In contrast, tuition charges rose by about 25 percent (by $545 per FTE). The multilevel, multinomial analyses of attainment compared to dropout (Table 8-3) present different levels of attainment and persistence compared to dropout. We examine each below before examining the role of finances across models. While these results are discussed below as bivariate comparisons, the reader is reminded that these relationships are correctly interpreted within the entire model, which is why we provide cross-model comparisons along with a discussion of specific findings.

3.2

Attainment of associate degrees

Two background variables were significantly associated with the attainment of two-year degrees compared to dropout. Males were less likely than females to attain two-year degrees than to drop out (Table 8-3). Students whose parents had attained college degrees were less likely than students who had not gone to college to have attained two-year degrees. Three of the variables related to preparation were significantly related to attainment of two-year degrees. Having a test score in the lower-middle quartile was positively associated with attainment of two-year degrees compared to dropout. In contrast, students with scores in the highest quartile were less likely to have attained two-year degrees than to drop out. In addition, students who had taken calculus in high school were less likely to attain two-year degrees than to drop out. Readers are reminded that students with advanced math were substantially more likely to have enrolled in fouryear colleges after high school. Further, none of the financial variables was significantly associated with two-year degree attainment compared to dropout. The second-level variables were not significant according to the measure of variance.

3.3

Attainment of bachelors’ degrees

Most of the variables related to background had a significant association with attainment of four-year degrees only compared to dropout (Table 8-3): x Males were less likely than females to have attained four-year degrees. x Asians were more likely than Whites/others to have attained four-year degrees. x Hispanics were less likely than Whites/others to have attained four-year degrees. x Low-income students were less likely than middle-income students to have attained four-year degrees.

Postsecondary education plans

Parents’ highest education level

Family income group

Race

Individuals Gender

Male Female Asian/Pacific Islander Hispanic Black, not Hispanic White, not Hispanic or Native American or other or missing Low (less than $25,000) High ($75,000 or more) Multiple response or missing Middle ($25,000-$74,999) HS, some college College grad M.A. or equal Ph.D., M.D., other Didn’t finish HS or HS grad or GED or don’t know or missing VOC,TRD,BUS after H.S Will attend college Will finish college Advanced degree Won’t finish HS or will finish HS or missing 25.9 6.5 20.4 18.4 21.5 10.8 6.8 3.2 26.8 35.2 27.0 16.9 10.9 29.7

516 84 274 774 702 159 63 18 706 186 257 675 275 255

Certificate/ Associate Degree Count Row % 698 17.2 950 19.9 74 9.7 265 24.1 177 22.0 1,132 18.3

72 206 1,713 1,307 152

491 825 446 1,688 1,065 792 579 382 632 13.6 21.6 43.0 51.9 17.7

24.6 63.8 33.2 40.1 32.7 53.9 62.7 68.7 24.0 3 10 189 243 13

44 152 65 197 97 101 101 84 75 0.6 1.0 4.7 9.6 1.5

2.2 11.7 4.8 4.7 3.0 6.9 10.9 15.1 2.8

MA, PhD, BA Professional Count Row % Count Row % 1,551 38.1 176 4.3 1,899 39.8 282 5.9 403 53.0 64 8.4 236 21.5 24 2.2 232 28.9 22 2.7 2,579 41.7 348 5.6

Table 8-2. Descriptive statistics for the analysis of degree attainment: All students

75 140 449 266 138

308 95 196 469 436 137 79 37 379

14.2 14.7 11.3 10.6 16.0

15.4 7.3 14.6 11.1 13.4 9.3 8.6 6.7 14.4

Still Attending Count Row % 551 13.5 517 10.8 102 13.4 197 17.9 141 17.6 628 10.2

36.4 35.7 24.0 17.0 35.1

31.9 10.7 27.1 25.7 29.4 19.1 10.9 6.3 32.1

continued

192 340 958 428 302

637 138 364 1,081 958 280 101 35 846

Dropout Count Row % 1,093 26.9 1,127 23.6 117 15.4 376 34.2 231 28.8 1,496 24.2

200 Chapter 8

Quartile 1 low Quartile 2 Quartile 3 Quartile 4 high Missing or test not comp SAT or ACT Took SAT or ACT participation Not took SAT nor ACT, or missing or refusal or don't know Taking advanced Trigonometry/precalculus only 182 9.1 math courses Calculus 30 2.7 No trigonometry/precalculus or 1,436 25.0 calculus, or missing State Need-based grant in $1,000 Financial indicators Non-need-based grant in $1,000 Public system undergraduate in-state tuition in $1,000 Change in need-based grant from 1992 to 2000 Change in non-need-based grant from 1992 to 2000 Change in public system undergraduate in-state tuition from 1992 to 2000

Individuals Std test (1992 NELS test) quartile

Certificate/ Associate Degree Count Row % 306 31.8 448 28.5 381 18.9 150 6.0 363 20.1 917 13.9 731 32.3

Mean 0.219 0.027 2.420 0.029 0.073 0.545

1,194 762 1,494

59.8 69.0 26.0

BA Count Row % 112 11.6 374 23.8 834 41.4 1,539 61.7 591 32.8 3,239 49.2 211 9.3 156 159 143

7.8 14.4 2.5

MA, PhD, Professional Count Row % 3 0.3 31 2.0 78 3.9 262 10.5 84 4.7 447 6.8 11 0.5

Table 8-2. (continued) Descriptive statistics for the analysis of degree attainment: All students

182 65 821

9.1 5.9 14.3

Still Attending Count Row % 153 15.9 215 13.7 233 11.6 194 7.8 273 15.1 678 10.3 390 17.2 284 89 1,847

14.2 8.1 32.2

Dropout Count Row % 388 40.3 502 32.0 490 24.3 349 14.0 491 27.2 1,299 19.7 921 40.7

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Chapter 8

x High-income students were more likely than middle-income students to have attained four-year degrees. x Each level of parental postsecondary attainment, including some college, was positively associated with four-year attainment compared to dropout. Student aspirations were significantly associated with attainment of fouryear degrees compared to dropout. Students who aspired to finish college or to attain an advanced degree had higher odds of this level of degree attainment. In addition, most of the preparation variables were significantly associated with attainment of four-degrees compared to dropout: x Students with test scores in the bottom two quartiles were less likely than students with missing tests to have attained four-year degrees. (The lower-middle quartile was only modestly significant.) x Students with high test scores were more likely than students who did not take these tests to have attained four-year degrees. x Students who took the ACT or SAT were more likely than students who did not take these tests to have attained four-year degrees. x Students who took calculus or who took trigonometry/precalculus were more likely than students with lower levels of math preparation to have attained four-year degrees. While none of the finance variables was significant, the second-level variables had a significant relationship with the outcome. Therefore it is important at least to note the direction of effects. The base amounts of funding for need-based and non-need grants were positively related to the outcome, as was change in non-need grants. The amount of tuition charged was also positively related to attainment of four-year degrees. Change in non-need grants, which was modest, and change in tuition were negatively related to attainment of four-year degrees compared to dropout. The individual associations were weak, but the total effect was significant.

3.4

Attainment of advanced degrees

Most background variables were also significant in attainment of advanced degrees (master’s or Ph.D.) compared to dropout (Table 8-3): x Males were less likely than females to have attained an advanced degree. x Asian Americans were more likely than Whites/others to have attained an advanced degree, while Hispanics had a lower probability of this outcome. x Students from high-income families and who had no reported income were more likely than middle-income students to have attained advanced

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degrees. However, there was not a significant difference on this outcome for low income compared to middle income. x Students whose parents had attained bachelors’ or advanced degrees were more likely to have attained advanced degrees than students with parents who had not attended college. Aspirations also played a role in attainment of advanced degrees. Students who aspired to attain four-year degrees or higher also had higher odds of attaining advanced degrees than did students who did not aspire to complete degrees. Further, academic preparation was associated with attainment of advanced degrees compared to dropout: x Students with test scores in the high quartile were more likely than students who did not take the test to have completed advanced degrees. In contrast, students with scores in the three lower quartiles were less likely to complete advanced degrees than students who did not take the NELS test. x Taking the ACT/SAT substantially increased the odds of attaining advanced degrees. x Both levels of advanced math were significantly and positively associated with attainment of advanced degrees compared to lower levels of high school math attainment. The second-level variables were significantly associated with attainment of advanced degrees, as measured by the second-level effect. In addition, funding for non-need grants had a modestly significant (.1) association with the outcome, along with a substantial odds ratio (10.1 per thousand dollars per FTE). Funding for need-based aid had a positive but nonsignificant association with persistence. In addition, tuition had a positive and insignificant association.

3.5

Still enrolled

A student who was still enrolled after eight years could be at any of the levels of attainment, from no degree to being enrolled in a second advanced degree program. Students who did not have degrees had probably previously stopped out. The analyses did not find significant associations for student aspirations in this group, but did find significant relationships related to background, preparation, and finances. First, six of the variables related to background were significantly associated with current enrollment compared to dropout without a degree (Table 8-3):

Level 1: Individual Gender Male Female Race Asian/Pacific Islander Hispanic Black, not Hispanic White/other Family income group Low (less than $25,000) High ($75,000 or more) Multiple response or missing Middle ($25,000-$74,999) Parents’ highest education level HS, some college College grad M.A. or equal Ph.D., M.D., other HS or less (or not indicated) Postsecondary education plans Vocational, trade, or business after HS Will attend college Will finish college Advanced degree Won’t finish HS or will finish HS or missing 1.520 *** 0.704 *** 0.982

0.838 ** 1.746 *** 1.141

1.165 1.786 3.024 3.997

0.790 1.115 1.735 *** 1.833 ***

0.903 0.876 0.917

1.054 0.974 0.972

0.920 0.764 ** 0.899 0.760

1.102 0.883 0.880 0.854

* *** *** ***

0.734 ***

0.773 ***

Certificate/Associate B.A. Degree Degree Odds Ratio Sig. Odds Ratio Sig.

0.407 0.680 1.969 ** 2.831 ***

0.902 1.589 ** 3.415 *** 4.574 ***

0.741 2.160 *** 1.357 *

1.563 ** 0.645 * 0.839

0.541 ***

continued

0.968 0.923 1.014 1.172

1.083 1.073 1.592 *** 1.997 ***

1.121 1.260 1.268 **

1.823 *** 1.264 ** 1.552 ***

1.098

M.A., Ph.D., Still Professional Degree Attending Odds Ratio Sig. Odds Ratio Sig.

Table 8-3. Two-level multinomial logistic regression analysis of degree attainment: All students

204 Chapter 8

Random Effect Level 2 effect Note: *** p<0.01, ** p<0.05, * p<0.1

Level 1: Individual Std test (1992 NELS test) quartile Quartile 1 low Quartile 2 Quartile 3 Quartile 4 high Missing or test not comp SAT or ACT participation Taking advanced math courses Trigonometry/precalculus only Calculus No advanced math Level 2: State Finance Financial indicators Need-based grant: $/1,000 Non-need grant: $/1,000 Undergraduate in-state tuition: $/1,000 Change in need-based grant Change in non-need grant Change in undergraduate in-state tuition 1.613 10.994 * 1.062 0.823 0.921 0.910 Variance Component Sig. 0.078 **

0.534 *** 0.835 * 1.150 1.409 *** 4.788 *** 2.587 *** 3.983 ***

1.352 1.658 1.100 0.835 1.146 0.864 Variance Component Sig. 0.075 ***

1.054 1.203 * 1.084 0.657 *** 0.988 0.996 0.610 **

1.175 1.405 1.084 0.918 1.224 0.979 Variance Component Sig. 0.016

3.064 *** 6.274 ***

8.555 ***

0.159 *** 0.633 ** 0.843 1.422 **

B.A. Degree Odds Ratio Sig.

Certificate/ Associate Degree Odds Ratio Sig.

M.A., Ph.D., Professional Degree Odds Ratio Sig.

Table 8-3. (continued) Two-level multinomial logistic regression analysis of degree attainment: All students

Variance Component Sig. 0.007

1.022 0.214 ** 0.980 1.128 1.457 0.929

1.313 ** 1.320

1.115

0.727 ** 0.803 * 0.849 0.865

Still Attending Odds Ratio Sig.

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x All three ethnic groups—Asian/Pacific Islander, Hispanic, and African American—were significantly more likely to be still enrolled than were White/other. x Students who did not have income reported were more likely than middle-income students to be still enrolled than to have dropped out. x Students whose parents had advanced degrees—master’s degrees or doctorates—were more likely to be still enrolled than students whose parents had not gone to college. Three variables related to preparation were significantly associated with this outcome: x Students whose test scores were in the lowest two quartiles were less likely than students who did not complete the NELS test to be still enrolled than to have dropped out. x Students who took trigonometry or precalculus in high school were more likely than students who took no advanced math to be still enrolled than to have dropped out after eight years. While the second-level variables did not have a significant effect as a group of variables, state funding for non-need grants was significant and negatively associated with this outcome. Thus, state funding for non-need grants was associated with attainment of advanced degrees and reduced odds that students would take four more years to complete their degrees.

3.6

Comparison

In multinomial analysis it is appropriate to compare the models as well as to consider each comparison. Looking across these analyses, four general understandings emerge. First socioeconomic status (SES) has a significant association with degree attainment. Not only was parental education significant, but lowincome students were less likely than other income groups to attain bachelor’s degrees, controlling for other factors. The significance of both of these variables indicates inequalities in opportunities for prepared students and provides evidence of the indirect effects of aid. Given the association between the two variables as part of SES, it would be inappropriate to reach conclusions about one of the variables alone, especially if the other is not significant. However, in this case both variables are significant providing evidence of inequalities in opportunity, controlling for preparation and other factors. Second, student aspirations were associated with degree attainment. Students who as high school students planned to attain a college degree were more likely to have attained an advanced degree eight years later, controlling for the effects of SES and preparation. This is good news for proponents of

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early awareness programs. If these programs had an influence on aspirations, then they are likely to influence attainment, just as K–12 policies could improve attainment through preparation. Third, these findings further confirm the importance of preparation in long-term degree attainment. Students who acquire strong foundations have higher odds of degree attainment in college. For the class of 1992 there were modest associations between K–12 policies and completion of advanced math (chapter 6). Further, subsequent policy changes seem to have promoted high school dropout as well as improvement in test scores. Finally, there was only modest evidence that state finances have an additional influence on degree attainment beyond their effect on initial enrollment. State funding for non-need (merit grants) was associated with the attainment of advanced degrees. It is possible that the savings from the undergraduate period attributable to merit aid can be used to pay for graduate education. It is also possible that receipt of merit aid has a residual effect by boosting aspirations. In either event this finding seems consonant with the argument frequently made in favor of merit aid that these grants improve the educational level of the labor force. However, some caution is needed because this finding was only modestly significant and there was a negative association between merit aid and current enrollment compared to dropout. In addition, three general findings merit consideration: Low-income students were less likely to attain four-year degrees; there were positive nonsignificant associations between funding for need-based aid and all types of attainment outcomes; and the second-level variables contributed to the prediction of attainment of bachelor’s degrees, advanced degrees, and current enrollment—all compared to dropout. In combination these findings provide modest support for additional funding for degree attainment by lowincome students. However, the argument that there are additional prepared students who could have benefited from college was not negated by modest findings about state finance variables. It is evident that substantially more college-qualified, low-income students could enroll if they faced lower net prices. It is simply inappropriate to consider the statistical associations for aid variables in regression models without also considering the role of income, a position that is also true in reverse: It is inappropriate to consider the findings on the influence of income in attainment without also considering the role of financial aid (Becker, 2004). Too frequently the policy argument about college access and attainment is cast as a debate between two types of policies: those that promote preparation and reward achievement and those that argue for reducing financial inequalities. In a very real sense, the preparation rationale has won

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the debate. This book presents a plea for a balanced approach, not for abandoning efforts to improve high schools. More than in any other chapter of this book, these findings point to the complexity of the challenges facing states that aim to improve educational opportunity. First, consistent with other research, this chapter provides evidence that supports the academic rationale. Most indicators of high school preparation were strongly associated with degree attainment. Further, merit aid had a modest association with the attainment of advanced degrees. Second, there are still significant inequalities in the opportunity to attain a college degree. The association of low parents’ education and low income with lower degree attainment, controlling for preparation, shows lingering problems with inequality as a consequence of public finance policies. Arguing whether SES differences are attributable to parents’ education (e.g., first generation) or to income is more than silly, it is dangerous. Social class is too frequently overlooked in U.S. education policy. In this study, both parents’ education and family income were positively associated with degree attainment, controlling for preparation. This means that inequalities persist in U.S. education. Except that official and quasi-official publications have claimed that opportunity is equal once preparation is taken into account (i.e., Choy, 2002; NCES, 1997a, 1997b, 2001c) this finding would hardly be surprising, but it is all the more important because of the statistical errors in previous research (Becker, 2004; Heller, 2004). Third, there is evidence that, controlling for other factors, degree aspirations held in high school continue to be a positive force through high school graduation. This should be encouraging for proponents of postsecondary encouragement programs in the U.S. What is still missing is sound evaluation of these programs. Finally, it continues to be difficult to measure the effects of finance policies on persistence and degree attainment. If new students enroll in degree programs because of need-based aid and this same aid is not negatively associated with degree attainment, then we can conclude that the aid influences attainment through the gains in enrollment. This argument can be made from the evidence about the high school class of 1992. There is also weak and indirect evidence to support the argument that finances can equalize opportunity for low-income students. We can point to positive insignificant associations for non-need, significance of second-level effects, and continuing inequalities. However, this evidence is weak at best. The safest conclusion is that it continues to be difficult to measure the direct attainment effects of need-based financial aid.

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UNDERSTANDINGS FROM THE REANALYSIS OF NELS

The federal government invested millions in the collection and analysis of NELS, resulting in the following propositions (restated from chapter 6): x A young person’s likelihood of attending a four-year college increases with the level of the parents’ education. This is true even for the most highly qualified high school seniors. x Taking challenging mathematics courses can mitigate the effects of parents’ education on college enrollment. The association between taking a rigorous high school math curriculum and going to college is strong for all students, but especially for those whose parents did not go beyond high school. x More at-risk students apply to college if their friends plan to go. College outreach programs as well as parental and school support with the application process have proven worthwhile. x The price of attending college is still a significant obstacle for students from low- and middle-income families, but financial aid is an equalizer, to some degree. Low-income students enroll at the same rate as middleincome students if they take all the necessary steps toward enrollment. (Choy, 2002, p. 5) The analyses presented in the chapters in Part II were structured to test these propositions, to the extent possible, with data from the high school class of 1992 and the policy indicators for the base year of the indicators study. It is now possible to restate these propositions based on a balanced analysis. Reconstructed Understanding 1: A young person’s likelihood of attending a four-year college increases with the level of their parents’ education and income. This is true even for the most highly qualified high school seniors. The structural constraints on academic preparation for low-income students appear to be serious because of the resistance of these constraints to policy differences (evident from chapter 6) and to changes in policy (chapter 3). Low-income students have lower odds of preparing for college—as measured by advanced math and graduation—than do middle- and highincome students. Being prepared for college does not fully mitigate the effects of income or parents’ education on enrollment, especially enrollment at four-year colleges (chapter 7). In contrast, state funding for financial aid has a substantial influence on college choices, especially for low-income students (chapter 7), but has not kept pace with increases in tuition (chapters 4 and 5).

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Regardless of how one splits the variables related to income and preparation, preparation alone does not ensure access to four-year colleges. For low-income students, controlling for preparation and other individual variables, state finance policies were directly associated with enrollment in two-year colleges and four-year colleges compared to no enrollment for lowincome students (chapter 7). In addition, low-income students had lower odds of completing advanced degrees (this chapter). They must contend with complex issues related to work and debt in order to steer a path toward a four-year degree. Since information related to work is inconsistently available, a great deal remains unknown about the role of finances in educational attainment by low-income students. Reconstructed Understanding 2: Taking challenging mathematics courses does not overcome the inequality related to family income, especially inequality in college enrollment. Public policies that require rigorous high school math and impose standards do not prove to have a positive influence on improving opportunity to graduate from high school and to complete advanced math courses for those whose parents have low incomes. Having high standards (i.e., requirements, as well as standards and tests) and expectations did not influence opportunity—either for the base year of 1992 (chapter 6) or for the subsequent years through 2000 (chapter 3). The rationale used to argue for these reforms used flawed statistical analyses. When high-income students were examined separately, there was a weak positive association between math standards and attainment of advanced math, but a significant negative association with this outcome for higher diploma options (i.e., honors diplomas). So even for high-income students these policies have not improved math attainment (chapter 6). In the trend analyses there were relationships between these new policies and (a) improvement in SAT scores and (b) reductions in graduation rates. Certainly the relationships between graduation rates and school reforms merit attention, given findings on high school completion rates, along with the difficult issues related to improving the opportunity to prepare for college. The challenge overlooked when correlations between advanced math and college success are used to guide policy is that curriculum and instruction that is engaging, encouraging, and enabling may be necessary to increase the percentage of students who successfully complete advanced math courses in high school. More recent reform strategies, including university/high school partnerships (Rodrigues, 2004) and theme schools (Martinez and Doniskeller, 2004) merit further field testing. It is too early, however, to claim the success of any particular new methodology or reform experiment

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now under way in expanding opportunity to complete advanced courses because we lack evidence to evaluate them. The use of advanced math courses as proxy variables for preparation limits understanding of the role of policy because it does not take the role of high school tracks into account. If students are tracked into lower-level math courses in middle school as an artifact of the elementary school they attended, then it is exceedingly difficult to graduate from high school with advanced math. As long as the focus on advanced math preparation continues to dominate policy research on college access, it will remain difficult to evaluate the effects of K–12 reforms on college success. Reconstructed Understanding 3: Plans for enrollment exceed the opportunity to enroll, especially for low-income students. College outreach programs, as well as parental and school support with the application process, face difficult challenges given the rigid structure and poor funding of public schools, along with the financial constraints facing college-prepared, low-income students who plan to enroll. NCES (1997a, 2001a) and ACE (Choy, 2002) based their arguments about the efficacy of outreach on the correlations between aspirations and enrollment. There was little or no evidence about the effects of outreach programs in the NCES/ACE reports. Instead, these reports consistently made claims about outreach based on correlations of non-policy variables. These reports also failed to consider the large numbers of low-income students who planned to—but who did not—go on to college (chapter 6). Planning to go to college is certainly positively associated with enrollment in college—a correlation similar in significance to advanced math—but a large number of students who plan to enroll do not enroll (chapter 7). The confusion about interpretation—and the resulting half-truths—are a consequence of overlooking the role of unequal access to preparatory education. No matter how tight or loose the academic standards (GPAs and test scores, coupled with courses), analyses of the class of 1992 show that substantial numbers of prepared students are left behind (Lee, 2004). In fact, the reanalysis of the NCES reports reveals that these reports indicated about 400,000 qualified students were denied the opportunity to enroll in four-year colleges (Fitzgerald, 2004). These are serious problems given the ambiguity of the findings on preparation, access, and attainment in this and prior studies of the 1992 cohort. The purpose of outreach and encouragement programs is defeated if they result in more students who aspire to go to college but cannot do so for financial or other reasons. The barriers for low-income students who aspire to go to college relate both to limited access to advanced preparatory courses in high school and to the insufficient need-based grant aid for prepared, low-

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income students. Low-income students with college aspirations, and who intend to prepare academically, may encounter structural constraints, including tracking systems and schools that restrict entry into advanced courses. There is evidence, however, that comprehensive encouragement programs—combining academic reforms, encouragement, and financial aid guarantees—can improve odds of enrollment (St. John, Musoba, Simmons, Chung, Schmit, and Peng, 2004) and equalize odds of persistence (St. John, Gross, Musoba, and Chung, 2006) for low-income students. A missing link in outreach and encouragement programs as conventionally constructed is the commitment to financial support. Reconstructed Understanding 4: The price of attending college is still a significant obstacle for students from low- and middle-income families, and need-based financial aid is not sufficient to equalize enrollment opportunity. Not only do low-income students enroll at a lower rate than middle-income students, controlling for preparation, but substantial debt and work hours are necessary to maintain enrollment by the otherwise average low-income student. The NCES/ACE reports argued that opportunity was equal if students took the steps to enroll—including taking entrance exams and applying for college during their senior year of high school. Taking entrance exams and applying in advance require money—because neither step is free—and often indicate the expectation of being able to pay for college. It is not true that all students who apply for college have funding for college. A more recent ACE report illustrates that low-income students in public colleges can expect about $8,000 in annual debt (Hartle, Simmons, and Timmons, 2005), an extraordinary amount of debt for a family earning $25,000 annually. This debt includes unsubsidized loans which can go into repayment immediately, a feature of aid that puts pressure on the student to work longer hours. The findings in this chapter with respect to the effects of state funding for student aid were ambiguous. The fact is that state grants now comprise a modest portion of the aid packages for the typical low-income students. In order to develop a better understanding of the degree attainment process, more attention should be given to the role of debt. In conclusion, there is substantial evidence that low-income students in the United States not only face barriers to enrollment in college preparatory curricula in high schools, but if they do prepare adequately they also face financial barriers that inhibit enrollment and timely degree attainment in four-year colleges. Controlling for preparation and income, minority students who enrolled in 1992 were more likely to be still enrolled than to have dropped out eight years after initial enrollment. Students of color who desire to attain four-year degrees must be prepared for this long-term effort.

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To be effective in promoting attainment , states must reconsider the role of student financial aid. State investment in need-based grant aid is crucial in expanding opportunity while merit aid is associated with attainment of advanced degrees. With rising prices in the public sector across the states, investment in student aid becomes more central to the state role in promoting academic success for college students.

IV

THE PUBLIC INTEREST

Chapter 9 IMPROVING ACCESS AND COLLEGE SUCCESS

In the U.S. the debate about the public financing of higher education has been subsumed in a larger debate about college access. One argument assumes that improvement of schools is the primary means of improving access for low-income students while another assumes that middle-income students face more substantial access problems than low-income students. Arguments about middle-class affordability have also been persuasive, influencing expansion of merit grants, a public finance strategy that is aligned logically with the new academic rationale for reform. These arguments have proven questionable, however, given the overwhelming evidence of unequal educational opportunity in the U.S. This chapter reconsiders the primary strategies for improving college access—school reform, financial support, and encouragement—based on the research evidence from studies in parts II and III. Unfortunately, it was not possible with these databases to examine the impact of college encouragement and outreach programs, although there is evidence from state-level studies that comprehensive interventions can improve access, college choice, and persistence for diverse groups (Heller 2004; Musoba, 2004b; St. John, 2004; St. John and Hu, 2006; St. John, Musoba, Simmons, and Chung, 2002). In this chapter, I consider the linkages between policies on K–12 preparation and public finance and the outcomes with which they are associated before looking at the more general issues of improving postsecondary access and attainment. The conclusion considers the implications for educational and finance reform in the U.S. and in other nations.

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1.

SCHOOL REFORM

The primary claim of the majority of education reformers in the U.S. has been that improvement in K–12 education systems is necessary to improve preparation for and access to college for the majority of students and to overcome the academic barriers to access for low-income and minority students. While few observers raise doubts that reform of high schools is crucial to improving academic preparation, we should also assess evidence of linkages between public policies on K–12 education and student outcomes related to preparation, college access, and college success.

1.1

K–12 policies and preparation for college

The research used to defend the academic preparation rationale usually considers the relationship between advanced math and subsequent outcomes (e.g., Adelman, 1999, 2004; Choy, 2002; NCES, 1997a) but does not consider the impact of state education policies on whether students actually take these courses. Instead, the proponents of this rationale have assumed that because (a) there is a correlation between advanced math and college access and success, (b) policies that promote preparation must improve college access and success. This assumption—that if a is true then b must be true—takes an illogical leap and should not be accepted without being examined in relation to empirical evidence. The studies in this volume provide the most comprehensive assessment to date of whether b is associated with a. If public policies on K–12 education (b) do not link to preparation (a), then claims about access and persistence cannot be true. Thus, the test of the linkage between b (policy on K–12) and a (preparation) is a crucial missing link in the arguments currently being made about college access and success. 1.1.1

Findings

The studies in part I examined the relationship between state education policies in the 1990s and two state-level indicators of preparation: average SAT scores and high school graduation rates. The analyses of these indicators revealed an apparent contradiction: Many of the new reform policies (standards, more math requirements, and so forth) were positively associated with average SAT scores, but these same policies were also negatively associated with high school graduation rates. Given the importance of high school graduation in college enrollment (chapter 7), there is substantial reason to consider graduation rates as an indicator of

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preparation along with test scores. On closer examination, these findings are largely consistent with research on school reform. Our findings on the effects of reform strategies on achievement are similar to the other recent study of the effects of accountability on test scores. Hanushek and Raymond (2004) examined the impact of two types of policy—those with consequences (e.g., exit exams) and those without (e.g., report cards)—on change in state scores on the National Assessment of Educational Progress (NAEP) cohorts, using STATA statistical software in a study with a design similar to ours (in chapter 3). They found that policies with consequences were associated with test scores, similar to our analysis. However, they did not consider the impact on dropout but instead considered special education referral, an equity measure less directly related to policy outcomes (St. John, Manset-Williamson, Chung, Simmons, Musoba, Manoil, and Worthington, 2003). In our indictors study of preparation (chapter 3), exit exams were the only state policy variable that was positively associated with high school graduation rates. Defining a standard for graduation related to a test score may be a more appropriate means of expanding opportunity than requiring more courses in math or more advanced course work. Some prior research has found exit exams to be negatively associated with high school graduation rates (Manset-Williamson and Washburn, 2003), but this prior research did not control for funding. In this analysis, exit exams had a positive effect only when school funding was considered (chapter 3). There is a missing link in the theory of accountability schemes: The role of funding is important in assessing accountability reform. We need to reintegrate thinking about education and public finance. Research on school reform using the equal opportunity framework (chapter 2) finds similar patterns of relationships between reform approaches and educational outcomes for equity and achievement. Studies of early reading programs (St. John, Manset-Williamson, et al., 2003; St. John, Manset-Williamson, Chung, and Michael, 2005) have found that systematic educational practices (e.g., use of lectures, basal readers, worksheets) are both positively associated with test scores (i.e., pass rates on third-grade reading) and negatively associated with passing more children on grade level (i.e., failure in early primary grades). Similar patterns of outcome are evident in comprehensive high school reforms (St. John, Hossler, Musoba, Chung, and Simmons, 2006). Community building and networking among teachers was positively associated with higher pass rates in classrooms with comprehensive reform in the Michigan studies. Thus, the findings from the analyses of school reform state-level outcomes are consistent with findings on research that consider school practices. Both sets of studies indicate that the new accountability regime—standards, testing, and curricular

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alignment—is related to achievement measures and equal opportunity outcomes. The finding that school funding had a positive association with state SAT scores (chapter 3) should not be overlooked. In an analysis of SAT takers, Musoba (2006) also found that school funding was positively associated with SAT scores. The consistent claim from the new conservatives has been that school funding is not related to achievement (Finn, 1990, 2001; Paige, 2004). Yet our analyses found a direct positive association between funding levels and test scores (chapter 3), with a stronger effect (larger standardized coefficient)1 than the other policy variables. Thus the level of funding for schools may be more important to the goal of improving the quality of high school education, even as measured on test scores, than the myriad reforms now being pushed through federal legislation (No Child Left Behind Act of 2001) and state policies. Musoba (2006) reached a similar conclusion in her two-level study of the effects of state education policies on SAT scores. Findings on student outcomes (chapter 6) added further evidence of the role and influence of state education policies on preparation. The critical question regarding preparation, given the widely disseminated research on correlations between advanced math and educational attainment (Adelman, 2004; Choy, 2002; Pelavin and Kane, 1990) is this: Did the K–12 reforms of the 1990s influence math preparation? To address this question we conducted multilevel multinomial analyses of whether students in the 1992 cohort from NELS took advanced math, comparing completion of trigonometry/precalculus or calculus and high school dropout to less math attainment. The first level of the model included individual characteristics related to student background similar to those used by NCES. The second level included a set of policies—including exit exams for graduation, math requirements, and other academic reforms—that were implemented in some, but not all, states. The key finding, at least from the perspective of the preparation rates, was related to the direct link between policy and attainment in calculus for the class of 1992 (chapter 6). In the analysis of the entire cohort there was not a significant association between requiring more math courses and taking advanced math courses, but there was a significant positive association between the number of math courses required and completion of calculus for middle-income students. There was also a significant association for highincome students between being in a state that meets math standards and completion of calculus in high school. These policies provided opportunities for middle-income students to take calculus and to improve their test scores. 1

There is no way to determine significant difference in the size of coefficients in regression analysis. However, standardized coefficients are generally accepted measures of difference.

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However, these policies enabled only some students to gain access to calculus. Advanced math instruction was not available to low-income students in many American high schools in 1992. More critically, increasing requirements and raising standards does not replace the central role of public funding of instruction. Higher funding for instruction, at the discretion of schools and school districts, enables schools to maintain smaller class sizes and to hire better qualified teachers. The new requirements do not replace sound judgment or the local capacity to improve schools. 1.1.2

Implications for policy

Two policy issues emerged from these findings, both related to the apparent contradictions in the role and influence of the new K–12 policies: the unintended impact on high school graduation rates (chapter 3) and the differential in benefits of these policies across income groups (chapter 6). There was evidence that K–12 policies have an influence on preparation but that not all students benefit from these policies. The most critical issue was that the new K–12 policies influenced outcomes related to achievement but were also negatively associated with high school graduation rates. Specifically, requiring more math courses for high school graduation was associated with higher SAT scores in states, lower graduation rates in states, and individuals completing calculus. Given the very substantial role of high school graduation in college enrollment (chapter 7), the negative effect of requiring more math on graduation rates should not be overlooked. Stiffer requirements replace local discretion and, as a consequence, undermine some aspects of local control of schools. However, since the advanced math courses did not reach all schools equally, the local discretion in implementation of stiffer requirements could explain variation in outcomes across income groups, at least for the 1992 cohort. Specifically, the findings on math preparation indicate a differential pattern of school reform. Whether or not more math courses are required for high school graduation, advanced math should be available in schools serving lowincome students as well as schools that serve middle-income and wealthy students. The critical issue appears to be access to advanced math courses, but heaping on requirements and requiring more tests do not provide this access. Schools can respond to more requirements (i.e., more courses for graduation) and stiffer requirements (e.g., higher standards) by introducing watered-down courses (e.g., business math I and II), thus increasing inequality across schools and reducing postsecondary attainment. However, there are compelling reasons to provide advanced math courses in schools

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serving low-income students and to encourage all students to take advanced courses in high school.

1.2

K–12 policies and access to college

The new agendas for K–16 reform (see part I) assume that because advanced courses correlate with college enrollment attainment, policies that promote or require improvement in K–12 curricula will improve college access. However, the analyses of state indicators (part II) and student outcomes (part III), as summarized above, indicated that (a) the linkages between policies and preparation do not fully achieve their intended effects and (b) the unintended effects of the K–12 school reforms have included an increase in dropout (chapter 3). With this background we can assess the relationships between K–12 policies and college access by reviewing the research on state indicators and student outcomes. 1.2.1

Findings

First, the analyses of state indicators revealed that many of the K–12 policies were not directly associated with college enrollment rates for high school graduates. Since there is some evidence that the new K–12 policies were associated with higher test scores within states (chapter 3) and completion of advanced math courses for high-income students (chapter 6), we should also expect to find an association between K–12 policies and college enrollment rates. One policy variable, having honors diplomas, was positively associated with high school graduation rates. The analyses of student outcomes revealed, as expected, that math courses were positively associated with enrollment in general and especially with enrollment in four-year colleges. It is appropriate to conclude that raising state math requirements and standards had a modest indirect effect because of the association between completion of calculus and college enrollment, especially for middle-income students. However, given the weak linkages between state education policies and college enrollment rates (chapter 3), readers should be skeptical of arguments that adding graduation requirements will influence college access for low-income students. These linkages are indirect at best and not evident for low-income students. Very few low-income students had advanced math courses and the education policies were not associated with this outcome (chapter 6). In addition, the student-level analyses found that high school graduation had a substantial association with enrollment in college and especially with enrollment in four-year colleges. The effect, as measured by size of odds

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ratios,2 was more substantial for high school graduation than for advanced math. It was necessary to include students who had not graduated in the analyses because some nongraduates enrolled in college. When this step was taken, an adjustment that helps overcome the specification error, we found that graduation, an outcome overlooked in the NCES analyses, was very important. Linking the individual-level findings with the insights gained from state indicators, some observations regarding policy are apparent: x The K–12 reforms (e.g., requiring more math courses for high school graduation) had an unintended effect of reducing high school graduation rates (in state-level analyses). However, this policy was positively associated with access to calculus for middle-income students in the 1992 cohort. x Individual two-level analyses found that high school dropout had a substantial negative association with college enrollment, including enrollment in four-year colleges while advanced math courses had a positive association. At the very least, school reform had contradictory and countervailing effects on college enrollment. x The finding from analyses of state indicators—that most of the K–12 reforms were not significantly associated with college enrollment—is better understood in relation to these contradictory findings about math requirements. It is crucial to evaluate the effects of K–12 policies both on achievement (i.e., test scores and advanced courses) and also on equity (e.g., graduation and enrollment rates). 1.2.2

Implications for policy

Many of the new education reformers make faulty assumptions based on the NCES pipeline analyses. They often claim that K–12 policies—high standards and more math requirements—are directly linked to college access. However, this linkage is ambiguous at best. The negative, unintended effects—the decline in high school graduation rates attributable to these policies—are a much more serious policy issue than the positive association for wealthy students. In American education, there has been a myth— perpetuated by neoconservatives (Finn, 1990, 2001; Paige, 2002)—that standards and tests matter but funding does not. The analyses in this book indicate that raising math requirements improves access to advanced math for middle-income students and indicate a strong positive relationship between school funding and educational outcomes. 2

While there is not a measure of significant difference between the size of the odds ratios, large differences in the size of odds ratios indicate differences in effects. Since these variables are part of the same design set, it is possible to compare odds ratios.

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Public finance is far more important than standards within K–12 policy, at least with respect to academic preparation and college access as outcomes. The funding for schools was positively associated with college enrollment rates when we also considered the largely negative effects of K–12 policies (chapter 3). However, in the public finance model (chapter 4) funding for schools was not significantly associated with high school graduation rates. So, school funding is important in relation to school reform. This partially supports arguments that funding is needed to make the new reforms work, but we should also be cautious about the substance of this claim, given the contradictory findings on the effects of reform policies. The stronger, more compelling evidence of the role of policy in college enrollment is related to the direct effects of public finance strategies, considered below (section 2).

1.3

K–12 policies and enrollment in four-year colleges

The publications on the college pipeline have failed to consider sufficiently the role of structural capacity—whether states have the capacity in their public and private four-year college systems for students who prepare academically. Five of the ten states that declined in the percentage of high school graduates who enrolled in higher education after high school were from the western U.S. (chapter 4), the region of the country with the most substantial population growth. However, the size of the college-age population is now on the upswing in most states, so there is reason for wider concern about access. The findings in college destinations are appropriately interpreted in relation to the structural capacity of state systems. 1.3.1

Findings

The college pipeline rationale has assumed that the more students prepare, the more access they have to college. On a prima facie level, it seems reasonable to assume that better preparation should result in improved enrollment rates. However, the analyses of state indicators found a different set of relationships in the states, controlling for other variables related to state contexts. The analyses of pathways and markets (chapter 5) added to our understanding of this failure of school reform to influence college enrollment rates, showing that K–12 policies were apparently associated with the redistribution of students within state systems. For example, the percentage of schools with AP courses was positively associated with enrollment in public four-year college, but not in private colleges. Interestingly, private colleges are generally less likely to accept transfer credits of this type, a policy that may explain the redistribution effect. In

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addition, funding for merit grants was positively associated with enrollment in public four-year colleges, but not in private colleges, indicating an association with the flow of students. This may be consonant with the policy intent of keeping bright students in state. However, it may also be costly to states to accommodate these students in public four-year colleges, rather than to distribute them across state systems. The distributional effects of state education and finance policies merit more serious attention. 1.3.2

Implications for policy

These findings suggest a radically different notion of the relationship between preparation and access than has been promoted by neoconservatives and neoliberals in the U.S. In most European countries, the screening process is usually applied before students enroll in high school. However, in the U.S., academic screening is applied at the point of college admission. The contradiction in the U.S. context is that the new K–12 policies have been rationalized based on the notion that improving preparation improves access. Some of the new policies raise entry requirements and limit access to four-year colleges (e.g., honors diplomas). These policies result in the opposite of their intended effect.

1.4

K–12 policies and academic success in college

When pondering the role of K–12 policies in academic success in college, it is important to distinguish between (a) the measured effects of K– 12 policies on preparation and (b) the correlation of advanced math courses and college success. A summary of the findings follows: 1. In the state-level analyses, the new requirements had positive associations with SAT scores, but negative effects on graduation from high school. 2. In the student-outcome analyses, state standards were associated with advanced calculus, but not trigonometry, for middle-income students. 3. In the student-outcome analyses, high school graduation had a substantial association with enrollment, including enrollment in four-year colleges. Thus, while advanced math was associated with college enrollment, there is little reason to conclude that K–12 policies have been especially effective in promoting academic success, especially for low-income students. 1.4.1

Findings

There was a relatively consistent pattern of statistical association between advanced math and continued college enrollment. When attainment

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outcomes were broken down (chapter 8), it was also apparent that the significance of high school math courses was stronger for advanced college degrees and bachelor’s degrees than for current enrollment. In addition, students who had taken advanced math did not differ significantly from dropouts with respect to completion of two-year degrees. In other words, the linkages between advanced preparation in high school and advanced degree attainment are stronger for attainment of four-year degrees than for attainment of two-year degrees. In addition, it is important to interpret the findings on academic success in relation to the findings on preparation. Recall that for the 1992 cohort, low-income students did not benefit from the new policies. They did not get the same opportunities as wealthier students to take advanced math courses. Some high schools did not offer the advanced courses needed for academic preparation for four-year college, and the newer, more restrictive K–12 policies did not change this disparity (chapters 3 and 6). Therefore, to the extent that claims about K–12 reform and preparation are valid, they pertain primarily to wealthy and middle-income students, who received more substantial benefits from these K–12 reforms. 1.4.2

Implications for policy

There is ample reason to examine the linkage structure of K–12 policies with preparation, college enrollment, and academic success in college. Since the linkages between K–12 reforms and academic preparation are countervailing, there is also reason to be cautious about rushing to implement policies that raise standards without (a) taking steps to ensure equal access to advanced high school courses and (b) providing encouraging environments in high schools that enable more students to complete advanced courses. The new agenda oversimplifies the role of K–12 reform. Raising standards and holding schools accountable has not proven to be an effective approach to improving opportunity for all children. The new requirements replace discretion in local schools, and they have improved the quality of majority-serving schools. Rather than reduce inequality, they accentuate disparities in educational opportunity.

2.

PUBLIC FINANCE

Public finance is a second component of a comprehensive approach to equalizing opportunity for students to prepare academically, for prepared students to enroll in four-year colleges, and for enrolled students to attain

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their educational goals. But many reform advocacy groups have emphasized academic preparation while ignoring the role of finances. The analyses of state indicators and student outcomes in this book have used a balanced approach that considers the role of finances along with academic policies. The findings are summarized below, along with the policy implications.

2.1

Public finance and academic preparation

Advocates of the academic preparation rationale often argue that merit scholarships improve student preparation (e.g., Bishop, 2004; Kazis, Vargas, and Hoffman, 2004) but fail to consider the unintended effects of these finance policies. The analyses in parts II and III of this book have added to the growing research evidence that the new reforms have caused serious problems with their unintended effects. 2.1.1

Findings

The analyses of state indicators (part II) revealed that funding for nonneed scholarships, which are mostly merit programs, have had positive effects on college enrollment but negative effects on high school graduation rates (chapter 4). There may be a positive incentive for some students to perform better in high school as a consequence of merit aid, as Bishop (2002, 2004) has argued. Certainly our finding—that raising math standards and requiring more math courses for graduation were associated with SAT scores (chapter 3)—is an analogous finding. Like non-need aid, higher math standards and more requirements were negatively associated with high school graduation rates in the states. In other words, the association found between merit aid and test scores (Bishop, 2004) only means there is measurable marginal effect among students who would probably enroll anyway. The more substantial effect was the discouragement of middleability, low-income students who could not afford to go to college—students who did not achieve high enough grades to earn merit grant funding. While the analyses of student outcomes did not explicitly consider the association between merit aid and preparation (part II), there is some evidence from other studies that financial aid guarantees for low-income students improves preparation, as measured by college application (Musoba, 2006; St. John and Hu, 2006; St. John, Musoba, Simmons, and Chung, 2002) and advanced courses (St. John and Hu, 2006). A clearly important issue appears to be ensuring financial support for low-income students who take the steps to prepare for college and gain admission. The notion that merit scholarships achieve this social goal is not supported by the evidence.

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Implications for policy

The use of student financial aid to encourage academic preparation has gained support across the U.S., as is evident in the growth of the number of states with merit programs (Heller and Rasmussen, 2001). However, it crucial to consider how these programs have actually influenced academic preparation in high school, a topic that has not yet been sufficiently studied. In addition, the analyses of state indicators revealed a seriously problematic unintended consequence: There is a negative statistical association between merit aid and high school graduation rates. This topic needs further study at the individual level for further verification. Prior studies indicate that financial aid guarantees for low-income students encourage preparation, especially when they do not have excessively high achievement requirements (e.g., Musoba, 2004a). The challenge is to motivate students who might not otherwise prepare to do so. Conventional notions that high merit deserves high financial compensation will not solve the problem of creating opportunities for the great majority of students. A further rethinking of incentive structures and student preparation is long overdue.

2.2

Public finance and college enrollment

The relationship between need-based student grant aid and college enrollment is long established in the research literatures of economics and higher education finance (St. John and Paulsen, 2001). Consistently over time, reviews have pointed to the responsiveness of low-income students to need-based aid (Heller, 1997; Leslie and Brinkman, 1988). The analyses of state indicators and college students support these findings and have similar implications relative to this longstanding body of research. 2.2.1

Findings

The analyses of state-level indicators found a direct link between funding for both need-based and non-need (merit) aid and student enrollment (chapter 4). The standardized coefficient for need-based aid was more than twice the size of the coefficient for non-need aid, indicating a more substantial effect overall. These analyses considered state funding per FTE—rather than aid amounts received by students—as a means of exploring the linkage between financial indicators and student outcomes. However, the two unstandardized coefficients were closer in size, indicating

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the two forms of aid had price effects on enrollment.3 The only crucial caveat to the effects of non-need aid was the finding on high school graduation rates. The student-level analyses examined the impact on enrollment of state funding per FTE for need-based and non-need grants and of tuition charges in states, as state-level variables, controlling for other student characteristics. The analyses (chapter 7) led to the following findings: x When the whole population was examined, tuition charges were negatively associated with enrollment and funding for need-based grants was positively associated with this outcome. x For high-income students, state funding for grants actually limited enrollment opportunity. x Enrollment by middle-income students was positively associated with state funding for need-based grants and negatively associated with tuition charges for public institutions in the state. x Enrollment in both two-year and four-year colleges by low-income students was positively associated with need-based grants. As the 1992 cohort enrolled near the beginning of the period studied with the indicators data, it is important to note that, for a more recent high school class, funding for non-need grants may have also been statistically significant by 2000, as it was in the analysis of state indicators. Non-need aid increased more substantially during the decade than did need-based aid (Heller and Marin, 2004; Heller and Rasmussen, 2001). The findings from the individual-level analyses should be interpreted that the expanded merit aid of the late 1990s did not influence college enrollment.

2.3

Public finance and academic success in college

College success, as measured by persistence and degree attainment, should not be overlooked in research on educational opportunity. Indicators of degree completion rates in states were not available for this project, but it was possible to examine college success using NELS. While state funding for financial aid was not significantly associated with either persistence or attainment in these studies, these findings are best interpreted with an understanding of the influence of the types of colleges attended and of family income. The combination of the two analyses found that family income, as measured in high school, was substantially associated with persistence and degree completion. High-income students were more likely than middle3

The differential size of the standardized coefficients could have been related to the relative infrequency of merit programs compared to need-based programs across the U.S. in the 1990s.

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income students to have “persisted,” as measured by current enrollment after eight years or degree completion, compared to dropping out without a degree (chapter 8). In addition, in the attainment analyses, low-income students were less likely than middle-income students to have attained a four-year degree than to have dropped out and were also more likely to be still attending after eight years than to have dropped out. In contrast, high-income students were more likely than middle-income students to have attained bachelor’s degrees and advanced degrees. The current system of public finance substantially adds to class differences in American society. Given these findings, i.e., the nonsignificance of state funding for grants in persistence and degree attainment, it is possible that financial aid was not adequate to equalize the odds of college success, controlling for preparation. This adds to the substantial body of literature that indicates adequate needbased financial aid is crucial to persistence and attainment (Leslie and Brinkman, 1988; St. John, 2003; St. John, Cabrera, Nora, and Asker, 2000). However, the pathways through college now require complex decisions about borrowing, including loans without interest subsidies, and work, including work to pay off loans. These other forces were outside of the analyses presented because of data problems. While state funding for grants was sufficient to stimulate enrollment and even a modest degree of college choice, it was not sufficient to equalize the opportunity for low-income students to complete college degrees. Controlling for prior preparation, low-income students took more time to degree completion. Given current financial policies in the states, the six-year indicator of degree attainment does not provide appropriate success indicators. Controlling for preparation and background, low-income students were more likely to be currently enrolled after eight years than middleincome students, while high-income students were more likely to have attained bachelor’s or advanced degrees. Clearly the use of such measures in accountability systems rewards colleges that enroll substantial percentages of high-income students.

3.

RETHINKING PUBLIC POLICY

There have been three stages in the policy debates about college access in the U.S. First, expansion of supply was the strategy in early U.S. history, when supply (e.g., land grant colleges) preceded demand. Second, in the 1960s and early 1970s, colleges were expanded to stimulate demand, and arguments by economists about need-based grant aid had substantial influence on policies that promoted access, college choice, and persistence (Gladieux and Wolanin, 1976; National Commission on the Financing of

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Postsecondary Education, 1973). Third, by the middle 1980s, arguments about the role of student financial aid had given way to newer studies that examined the correlations between high school math and educational attainment (Adelman, 1995, 1999, 2004, 2006; Choy, 2002; King, 1999a, 1999b; NCES, 1997a, 1997b, 2001c; Pelavin and Kane, 1988, 1990). Some analysts went so far as to argue that academic preparation should predominate over the role of finances in policy development and research on access (Gladieux and Swail, 1999; King, 1999b). Unfortunately, the federally sponsored research that focused on the role of preparation systematically also overlooked the role and influence of student financial aid. Nor did these studies examine the impact of other public finance policies. Rather, the studies used correlations between math preparation and educational outcomes to build an academic preparation rationale. Using a systematic and balanced approach, this book has examined the influence of both public finance policies and K–12 reform policies on educational outcomes. The findings of these studies are strikingly similar to prior generations of research on student financial aid using longitudinal databases (Heller, 1997; Jackson, 1978, 1988; Leslie and Brinkman, 1988; Manski and Wise, 1983; St. John, 1989, 1990a, 1990b; St. John, Kirshstein and Noell, 1991; St. John and Noell, 1989). Our models included the financial variables overlooked by NCES, to bring their logic into a balanced assessment. Rather than use self-reported data on financial aid, as was used in some of the prior studies, this study used actual data on college tuitions and state funding for grant programs. However, the findings of this study are consonant with other research on student price response (St. John, 2003). Funding for need-based grants substantially improved college enrollment rates in states and was positively associated with college enrollment by lowand middle-income students. In addition, tuition charges were negatively associated with enrollment, especially for middle-income students. Equal opportunity for prepared students to enroll in four-year colleges remains one of the more perplexing issues facing policy makers. The studies of state indicators (part II) and college students (part III) confirm that a market model of raising both tuition charges and funding for grants stimulated growth in enrollment. In the 1980s most states ceased to build new four-year colleges, but demand continued to grow and private colleges responded. State grant programs were an especially important force in enabling this market expansion of private colleges. In addition, it was easier for states to respond to growth in demand in the 1990s by expanding public two-year colleges. However, it is also important to think through strategies for delivering more programs that enable students to complete four-year degrees.

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By using the state indicators as measures of funding levels by states for student grant aid programs, it was possible to assess the effects of state funding. The findings were similar to other persistence research on college students enrolled in the 1990s (as reviewed in St. John, 2003). These analyses in this book add to prior studies that have found inequalities in persistence opportunities for students enrolled during this period, largely as a consequence of insufficient state investment in grants, controlling for federal aid (St. John, 1999, 2003). The other contribution of this study was the systematic analysis of the role and influence of state education reforms on academic preparation and college access. In the studies of state-level indicators it became apparent that the new policies—testing, requirements, and regulations—were associated with higher SAT scores, but these policies were negatively associated with high school graduation rates. The analyses of K–16 pathways indicate states used these policies to rationalize limited access to public four-year colleges, rather than to expand access. The analyses of student-level data further indicated that the association between education requirements and the completion of advanced math courses was modest for the entire population and nonexistent for low-income students. The new reforms benefited middle-income students rather than low-income students. The failure of states even to provide opportunities for low-income students to take advanced math courses created a barrier that tests and regulations could not overcome. It is abundantly apparent that a balanced approach to college access is needed, one that includes a focus on need-based student aid along with improving academic preparation for low-income students. The failure of states and the federal government to invest sufficiently in need-based grants has contributed to new inequalities in educational opportunity. The research that has ignored the role of finances and education policies demonstrates an attitude of rationalizing policy initiatives on beliefs rather than on evidence, using research to rationalize rather than to evaluate and assess. As we dug beneath the surface to examine the impact of school reform, it became evident that the role and effect of K–12 policies were nowhere near as simplistic as the preparation rationale assumed. Improving education is a complex process. Piling on new standards and requirements does not result in higher levels of student attainment. It is crucial that advanced courses be offered in schools serving low-income students. It may be more costly for school districts to offer these courses, a proposition that may explain why funding for instruction also made a difference in the attainment process. The process of reforming schools is far more complex than is assumed by many proponents of the preparation rationale. Raising standards and requiring tests may or may not be positive forces. What is remarkable is that these policies have persisted for over 20 years without evaluation.

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Unfortunately, the barriers to educational opportunity in minority-serving schools must be overcome before we will know whether the current course of education policy proved to be workable or to be an outright failure. The evidence in this study supports the argument that education reforms have failed in the goal of improving equal opportunity, especially in urban schools that serve many of the nation’s low-income students (Mirón and St. John, 2003). The next stage in academic reform must address the difficult challenge of enabling more urban and rural high school students to gain access to high quality, advanced courses and to encourage them to succeed in these courses. If these steps are not taken, then it will be necessary to set an entirely new agenda for education reform, one that emphasizes professional development of teachers and investment in students who have the greatest need for extra support. Such an agenda would be more similar to the now-abandoned direction of education reform of the 1960s and 1970s than to the current course of education reform.

Chapter 10 REDEFINING THE PUBLIC INTEREST

The global changes now under way in economics and education have profound implications for justice in democratic societies. The primary claimants for justice are the disenfranchised, those who lack access to basic educational opportunities, for if they do not have equal access, historical patterns of injustice are perpetuated. The majority also have a claim for just access to quality education. However, there are compelling reasons why efforts to improve educational quality for all students should not take precedence if historic inequalities have not been remedied. We should not overlook the tension, manifested in public finance and taxation, between the will of the majority and the interests of those who have been denied equal opportunity, especially in this period of sustained public resistance to taxation. The rights for equal access to quality education for the least advantaged must be balanced with claims for improving quality for the majority in a system of public finance. The movement toward globalization in economic development and education policy has complicated efforts to balance these competing interests in education policy and public finance. In the past decade, the progressive social and economic values in the U.S. and most other developed countries gave way to new global forces toward privatization of education, public accountability of education systems, and reduced public financial support for higher education systems. In this context, it is necessary to rethink the meaning of the public interest in education, given these new global forces, before reconsidering the globalization patterns. The studies of state indicators and student outcomes help inform a rethinking of the public interest in education policy and public finance. In this chapter, I reconsider the role of education in just societies and ways public policies should be adapted to maintain a balanced focus on justice with respect to the quality of and access to education.

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THE PUBLIC INTEREST AND EDUCATION

In democratic societies the development of educational opportunities is inextricably linked to citizenship. Not only is an educated citizenry essential for citizens to be informed voters, but education is increasingly necessary for economic citizenship—to secure employment sufficient to support families and to contribute to society. Over time the level of education necessary for employment has risen, from eighth grade in the early 20th century, to high school in midcentury, and to some college by the end of the century. There are fundamental differences between access to elementary and secondary education (i.e., K–12), often compulsory and fully supported by taxpayers, and access to higher education, earned by achievement in K–12 systems and at least partially paid for through student and family resources. The shift in the threshold of education required for economic citizenship calls into question issues related to both K–12 and higher education. For K– 12 education this means that basic education shifts from being a gateway to employment to being a preparatory step for the more advanced education needed for economic citizenship. And given the strong role of graduate and professional education as a gateway to higher earning jobs, undergraduate education itself—especially the classic liberal arts education—becomes preparatory for professional education (St. John and Wooden, 2006). As a first step toward rethinking the public interest in education, these changes in the role of education are appropriately examined through a just society framework. The conceptual basis for this rethinking is considered below before the implications of the studies of state indicators and student outcomes are reconsidered relative to the public interest.

1.1

Education in just societies

To the extent that education is treated in the literature on just societies, the right of equal access to K–12 education is usually the focal point. However, the shift in the role of education in economic citizenship means that the right to education should be reconsidered. The structures of education systems, along with the strong social reproduction forces of race and gender, provide the backdrop to the discourse in education and social justice. In Development as Freedom, Amartya Sen (1999) provides a compelling treatment of the role of education in economic development. He contrasts the readiness of India and China when they moved to open market systems. He points out that when China began opening its markets in 1979 the country was ready for takeoff because it had an educated citizenry, while India did not have this capacity when it made the move. He explained,

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While pre-reform China was deeply skeptical of markets it was not skeptical of basic education and widely shared health care. When China turned to marketization in 1979, it already had a highly literate people, especially the young, with good schooling facilities across the country. In this respect, China was not very far from the basic educational situation in South Korea or Taiwan where, too, an educated population had played a major role in seizing economic opportunities offered by a supportive market system. In contrast, India had a half-literate adult population when it turned to marketization in 1991, and the situation is not much improved today. (p. 42) In some respects, the current education challenge facing the U.S. is similar to India’s situation described by Sen, but our society faces a different sort of economic transition that also requires education advancement. The basic level of education, noted by Sen, has been evident in the U.S. for most of the 20th century. But a new argument is now frequently made that citizens need at least some level of college education to achieve economic wellbeing. Is the next stage of economic development going to be tied to higher levels of attainment in the U.S. and other developed countries? Would the loss of the competitive edge in educational attainment leave the U.S. in a poor position to provide leadership in the next stage of global economic development? Has the U.S. policy of low taxes and low investment in education undermined its competitive position? To explore these questions, it is crucial to explore the role of human capabilities. Sen (1999) develops the concept of human capabilities in contrast to the narrower notion of human capital. He argues: There is, in fact, a crucial valuation difference between the human-capital focus and the concentration on human capabilities—a difference that relates to some extent to the distinction between means and ends. The acknowledgement of the role of human qualities in promoting and sustaining economic growth . . . tells us nothing about why economic growth is sought in the first place. If, instead, the focus is on the expansion of human freedom to live the kind of lives that people have reason to value, then the role of economic growth in expanding these opportunities has to be integrated into that more foundational understanding of the process of development as expansion of human capabilities to lead to more worthwhile and more free lives. (p. 293) The tension in the U.S. around globalization, at least in the late 20th century, focused on both the loss of manufacturing jobs and the need for highly educated labor to compete in the high technology marketplace. The economic takeoff in the U.S. during the early 21st century has centered around the loss of analytic jobs, including computer programming. Does the

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loss of competitive position have anything to do with the problems with education or the failure to equalize opportunity? Or is it really just a matter of cheaper labor outside the U.S.? Viewed through the human-capital lens, the answer to this question might focus on labor costs and economic returns to corporations. If it is purely economic costs, then changes in tax policy could solve part of the problem, as John Kerry argued in the U.S. presidential debates of 2004. However, if the employment problem relates to insufficient education in the population— as was widely argued in the boom of the 1990s—then the human capabilities rationale may provide a more compelling explanation of the challenge facing the U.S. Employment became a crucial issue in the 1990s, as new arguments were made about transforming welfare to put more people back to work. Welfare reform focused on employment and simple forms of technical education. However, more advanced forms of education may be needed for economic citizenship. It is little wonder that the academic preparation rationale, with its explicit focus on the pipeline to and through college, developed in the 1990s. Creating policies that channeled more citizens toward productive employment made sense, but the task proved more complicated than proponents of this rationale had conceived it to be. They advocated policies without addressing the underlying social problems that inhibited educational equality. The most widely accepted notion of equal access is meritocratic: Students who are equally prepared should have equal opportunity to enroll in college (Type 1). In the U.S. context, it is evident that need-based student grants play a substantial role in equalizing access to college for prepared students. In fact, one of the major lessons from this study has been that state investment in need-based grants is a crucial, often overlooked, element of the privatization process. However, in the international literature on college finance the argument has frequently been made that private capital can better be used to equalize opportunity through flexible loan repayment schemes. Private capital already provides a crucial resource for funding access in the U.S., as it does internationally. In the U.S. there are upper limits on borrowing for college, but there is a great deal of evidence that the loan burden for some professions is already too high (Grubb, 1996a, 1996b) and has contributed to the growing pattern of inequality (Price, 2004). The U.S. model lacks some of the flexible repayment features of newer loan schemes that are now widely advocated internationally. Another aspect of the access problem pertains to access to basic education (Type 2). It has been argued that Unequal access to primary and secondary education can be the greatest obstacle to offering equal access to tertiary education. As long as

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particular social groups lack access to an acceptable level of basic education, those same groups will be unlikely to join colleges and universities. (Lleras, 2004, p. 163) As was evident in the analyses of student outcomes (part III), there is unequal access to elementary and secondary education in the U.S. Further, school reform efforts have benefited advantaged groups (i.e., middle-income families) more than the groups who have a history of inequality discriminated against (i.e., low-income families). By failing to address equal access to basic education as a central focus of reform, a direction that could have resulted from the publication of A Nation At Risk (National Commission, 1983), the nation and the states did not sufficiently focus reforms on the historically disadvantaged, thus increasing disparities in educational opportunity and attainment. The U.S., of course, is not alone in facing this challenge. Race, gender, and social class differences in K–12 education opportunities limit access to both basic and higher education in many countries. For example, Sen (1999) has focused attention on literacy education of women in India’s state of Kerala in the context of human capabilities and economic development. While he has been critical of the country as a whole, especially with respect to access to basic literacy education for women, he has recognized differences across states in the country. Viewed from a human-capabilities perspective, the challenge of equalizing educational access for diverse groups is more crucial when the level of educational attainment necessary for economic productivity rises, as is the case in the U.S. For example, the failure of many developing countries to enable women to gain basic literacy inhibits economic development (Sen, 1999). Similarly, the competition for educated labor in the U.S. has motivated some states to focus on merit grants for college students rather than need-based grants. However, these programs appear to have worsened inequalities in attainment of secondary education within those states (chapter 3). These Type 2 disparities in access to education may require a rethinking of college admissions policies in the U.S., along with reform of K–12 education. It would be unjust to penalize another generation of low-income and minority students for the failures of public policy. There is growing evidence that if disparities in schools are taken into account, it is possible to develop more just admissions practices (St. John and Musoba, 2002; St. John, Simmons, and Musoba, 2002; Sedlacek, 2004) and that adapted selection procedures predict college success as well as standardized measures (St. John, Hu, Simmons, and Musoba, 2001). Specifically, students who have achieved competitively in their high schools—as measured by grades, class rank, or test scores indexed to the school mean—are

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academically successful in college as well. Adjusting college admissions in the U.S. to account for differences in the quality of schools may be an important step. At the very least, such adjustments could lessen the severity of the current disparity while the underlying problems are addressed. A balanced assessment of the new loan schemes is needed not only to inform decisions about public finance policies in developed democracies, but also in developing countries. A critical question in the U.S. and other developed countries is this: Who should be taxed? Is it fair to have higher tax rates for citizens who were originally from low-income families? In developing countries that lack the potential tax base for substantially expanding access, this extended monetary penalty for low-income families may be the best option. Most nations now face critical challenges financing the necessary expansion in higher education to compete in the 21st century global economy. Although America led the progressive movements of the 20th century, it is not leading the transformation in higher education in the new century, being caught instead in the midst of contentious debates over models and methods. The frequent claim that there is equal access to higher education for qualified students in the U.S. (Choy, 2002; King, 1999a; NCES, 1997a, 2001a, 2001c) simply does not stand up to the evidence from the analyses of state indicators (part II) or of student outcomes (part III). There are serious inequalities in access in the U.S., as there likely are in other nations that have adopted the privatization strategies for higher education finance. Addressing this underlying equity challenge is crucial for all nations to compete in the new global age.

2.

EQUAL ACCESS TO HIGHER EDUCATION

In Investing in Human Capital: A Capital Markets Approach to Student Funding, Miguel Lleras develops a cogent argument for human capital contracts (HCCs), an income-contingent loan strategy that would reduce government subsidies. Given that Australia is using a similar loan scheme as a central part of its strategy for higher education, it is important to consider this important international experiment. Australia implemented a program that uses private capital and income-contingent repayment in part because of taxpayer resistance to older financial schemes for expanding higher education access. Lleras rationalizes his scheme as a means of moving toward equal access. He argues that HCCs can be “accessible to all, independent of their background . . . [and] contribute decisively to create equal access to education” (2004, p. 163). Lleras does acknowledge that unequal access to

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quality K–12 programs can undermine equal access because of differences in preparation, but argues that this scheme equalizes access for students who prepare. Future evaluations of Australia’s loan scheme should consider whether this claim holds up. If equal access improves after this scheme is implemented, then perhaps this rationale is credible. However, a long history of government efforts to equalize opportunity was overlooked by Lleras. Like other international proponents of loan schemes, Lleras does not pay adequate attention to the role of need-based grants. While Lleras traces the history of school vouchers in the U.S., he fails to consider the history of student need-based grants. The middle and late 1970s, after Pell grants were implemented, was a brief period of equal college access across racial/ethnic groups in the U.S. After 1978, loans expanded, need-based grants contracted, and a substantial gap in enrollment rates developed between African Americans and Hispanics compared to Whites, and low-income students compared to high-income students (Ellwood and Kane, 2000; St. John, 2003; see also part III). The major difference between the U.S. scheme and HCCs, at least from the borrower’s perspective, is in the emphasis on income-contingent repayment. In Lleras’s scheme, higher education access would be paid through progressive taxation of students as loan recipients. He argues that graduates who secure jobs in the middle-income range should pay about 5 percent of income for subsidy. Compared to the progressive income tax scheme in the U.S. in the 1970s, the new loan schemes shift the burden of repayment from the public in general to the individual. Yet there are reasons related to financial inequality to consider need-based grants as a means of equalizing opportunity within generations, through taxation. The older model of promoting equal opportunity consistent with Rawls’s difference principle (Rawls, 2001) and other conceptions of just societies (e.g., Nussbaum, 1999, 2001; Sen, 1999; Walzer, 1983) is lost in these new schemes, a reality that might help explain the growing inequalities in access in the U.S. Can these newer market schemes truly replace the equalizing hand of government? An evaluation of comparative national studies of loan schemes may be overdue but is not the purpose of the current study. Instead, the states studied in this book were subject to the same national loan and grant schemes. Since federal need-based grants had declined in the 1980s and 1990s while national loan schemes expanded, the studies included in this volume provide a balanced assessment of the roles of state-level pricing strategies—tuition charges and grant schemes—in the context of a national loan program. The studies indicate a need for need-based grants, relative to the goal of equalizing opportunity, even in a national system with an expansive loan scheme in place.

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Access to “basic” education

Given the importance in the 21st century of collegiate education to economic citizenship in the U.S. and other developed countries, it is little wonder that there is a push to reform high school education. Rather than resist this new policy direction because of the inequalities being recreated, it is important to understand the implications of this shift and to redesign school reform strategies to ensure that all capable citizens have the opportunity for a basic education sufficient to function as citizens, to build the human capabilities in the basic education system for access to postsecondary training or entry to the workforce. The U.S. has differed from Western Europe and most other nations in the way it has structured high school education. The American comprehensive high school was designed to provide multiple avenues through secondary education: college preparation, general education, and vocational education. Most other countries did not combine vocational education with college preparatory education in large comprehensive high schools, but instead created multiple types of high schools, some for vocational students, others for students preparing for college, and so forth. In these other nations, testing schemes were set up to assess student progress and were the primary means of sorting across schools. However, regardless of the type of national system used for high school education, raising the standard for the basic education required for economic citizenship can accentuate inequalities across class, gender, and/or racial boundaries. Therefore, we need a more up-to-date conception of what the right to a basic education is and should be within nations. While Walzer’s (1983) concern about Rawls’s concepts of liberties and rights was that they were not sufficiently grounded in real tradeoffs of social decision making (see chapter 2), Martha Nussbaum, another advocate of the capabilities approach, argues, “In short, the Rawlsian approach does not probe deeply enough to show us how resources do or do not go to work in making peoples able to function” (1999, p. 34). In her analysis of social justice for women in the developing world she defines education as a right: “Nothing is more important to women’s life chance than education. With literacy, a woman may choose her options and to some larger extent shape her future” (p. 100). The capabilities approach, however, is not defined just by basic literacy, but by what it takes to function in society (i.e., with full economic citizenship). Consider her analysis of education of women in Jerusalem: And one must ask, well, what is being taught when girls are taught. In the ultraorthodox communities of Jerusalem, all children attending statesupported schools are permitted to follow a curriculum that contains

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absolutely no information about world history or about the life of the world outside (just as at home television and radio are entirely forbidden). They do learn modern math and science but women are carefully shielded from any image of a woman’s proper role that is not that of the ultraorthodox community. They will not be in a position to choose their own way of life as the result of their very own reflection. (1999, p. 101) The standard of raising basic education to a level that enables economic citizenship is relevant to the debate about school reform in the U.S. Applied to the U.S. context of education reform, it is apparent that the standard for the basic level of high school education is in a period of redefinition. Through most of the 20th century a general high school education was expected for economic citizenship, and attendance of both elementary and secondary education was compulsory. In the middle of the 20th century, comprehensive high schools emerged as the primary strategy for high school education. Small high school districts consolidated so they could afford to offer three types of curriculum: general, vocational, and college preparatory. The requirements for diplomas often varied within schools, consistent with state policies. In this old model, high school graduation was the general—or basic—standard and there was substantial variation in the level of courses required. There was a history of local control of schools and community board standards for high school graduation, and there was substantial variation in the number of courses of different types required for graduation. The new emphasis on improving high school preparation raises the standard for a basic high school education to the level equivalent to college preparatory. While this new standard is widely advocated (Adelman, 2004; Kazis, Vargas, and Hoffman, 2004; NCES, 1997a, 2001c; Pathways to College Network, 2004), there is a great deal of variability in the rate of implementation of the new standard across states in the U.S. By using fixed effects models to examine the effects of change over time in the 1990s (part II) and using multilevel models to examine the effects of state requirements for the 1992 student cohort (part III), it was possible to assess the effects of these policy changes, as summarized in the prior chapter. Based on this assessment, it is possible to ponder the implications of shifting the standard for a basic education for a perspective of human capabilities. One aspect of the problems facing the U.S. is that not all high schools had the advanced courses available to all students. The analyses illustrated that the positive effects of the new policies were not evident probably because schools serving low-income students lacked the resources— financial and/or human—to offer quality preparatory curricula. The inequality in the opportunity to attain the emerging standard of academic preparation is reflected in the gaps in test scores, college enrollment rates,

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and postsecondary attainment rates. This unequal situation is made worse by college admission standards that use test scores (e.g., SAT and ACT). There is a relatively tight link between advanced high school courses and achievement on SAT tests, controlling for background and other achievement indicators (St. John, Musoba, and Chung, 2004a). Thus the use of standardized test scores in college admissions, without adjusting for high school contexts—especially whether students had the opportunity to take advanced courses—adds to the inequality in access in the U.S. High schools in U.S. towns and suburbs benefited very substantially from the formation of comprehensive high schools, where communities had the will to fund advanced courses and provide supplementary courses in music, drama, and other options. Urban school districts often maintain vocational and regular high schools, rationalizing access to preparatory high schools with entrance examinations. Urban communities with the structured and segmented system—like New Orleans (Mirón, 2003)—have not been able to adjust to the new educational requirements. This pattern raises several questions about the abandonment of comprehensive high schools. Indeed, the failure of urban school reform (Mirón and St. John, 2003) may be related to the failure to fully implement comprehensive high schools. Another aspect of the problem relates to the apparent relationship between the new standard for basic education—higher requirements, higher test scores, and so forth—and high school graduation rates. The implementation of these policies in the 1990s was associated with reductions in the high school graduation rates. This is not an argument that the new educational standards are inappropriate. Rather, my concern relates to variation in capacity to implement quality instruction in advanced topics, instruction that is relevant to the learning styles of high school students and that are offered by teachers with content knowledge. But not all students have access to advanced math courses in their high schools. Thus the transition to using “college preparation” as the standard for basic education poses enormous problems for K–16 education systems in most U.S. states. Other nations, too—European countries, for example, where high schools are differentiated into vocational high schools and preparatory high schools—have provided limited college preparatory opportunities. Whether the teachers and programs are at separate school sites or at the same sites, the problems of transition are largely similar. In fact, it is possible that American high schools have some advantage in making the transition because most comprehensive high schools have had some college preparatory courses even if they do not have all of the advanced courses now considered necessary.

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245

Public finance

In prior analyses using the justice framework, I have focused on spending per student enrolled (summing public expenditures on institutions and student aid, on a per-student basis) as an indictor of taxpayer costs (e.g., St. John, 2003; St. John, Tuttle, and Musoba, 2006). I have argued that policy makers should balance the basic right for an education with equity considerations when making policy decisions about the use of tax dollars for education. I have emphasized savings per student assuming a constant level of quality. This approach provides one perspective, one that may still apply to issues of equal access to higher education for prepared students. However, given the apparent disparities in access to basic education, there is a need to reconsider this position. A broader perspective must also consider investment in human capabilities, with an understanding of public finance informed by the human capabilities position and given the inequalities in access to the new standard of basic education. Rather than accepting conservative rationales about human capital, a more compassionate perspective is needed. Nussbaum (2001) addresses this type of moral challenge in policy: The insights of the compassionate imagination may be embroiled in laws and institutions at many different levels and in many different ways. As with Rawls’s imagining of the human need for primary goods in the Original Position [1971], they may be involved in the construction of the basic structure of society and choice of its most basic distributional principles. They may also be involved in legislation at a more concrete level: in the creation of tax code and a welfare system, in the creation of levels of offence and punishment in criminal law, in democratic deliberation about human inequality at many different levels . . . . Since all of these areas of compassion by itself supplies nothing concrete until it is tethered to a view about basic goods, we must return to this issue . . . when we will have a definite conception of basic goods to work with. (p. 103) We should rethink basic structures of education and finance in the U.S., and perhaps internationally, with respect to the new incongruity between the expectations around taxation and the new requirements for and expectation of K–16 education systems. With the underfunding of mandated school reforms, the full funding of need-based grants has not been a priority for available tax revenue (or for borrowed revenue). States face similar constraints on taxation—the voters in states resent taxes, just as they do when voting for federal officeholders. In the U.S. there has been a rapid movement toward financing defense and other national policies with

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borrowed dollars. Much of the funding for the war machine and other enterprises is borrowed from international corporations—rather than taxing the wealthy and corporations for the benefits of being able to function in a peaceful, developed, democratic society with an educated workforce. To make matters worse, there are extensive targeted tax reductions for middleincome families with children in college (Kane, 1999; Wolanin, 2001). This strategy may make it easier for middle-income families to pay the costs of attending four-year colleges, but it does nothing to alleviate more basic inequalities, evident in state analyses (part II) and the examination of the 1992 cohort (part III), a group that entered college before the 1997 tax law providing tax credits. In addition to the problems with the tax system, there are serious problems with the way privatization has been implemented across the states. There has been limited access to public four-year colleges because of the failure of states to expand these systems to meet demand. The capacity of private colleges to expand to meet the new demand is linked to state investment in grants, but most states have failed to make this investment. There has also been a dominant belief that school improvement is not related to funding (Finn, 1990, 2001; Paige, 2003), yet the evidence is that funding need-based grants and schools makes a difference in preparation. Ensuring students they will have an opportunity to enroll if they prepare is crucial, especially for minorities (Musoba, 2004a, 2004b). Improvement of K–12 systems requires the better education of teachers in advanced subjects, as well as salaries that motivate individuals who are prepared in advanced math to choose this career path rather than more lucrative careers in engineering, business, and other fields that use quantitative skills. There is a clear need to rethink public finance strategies in light of the real human needs in the U.S. The current belief that money does not matter and that schools and colleges can improve without public support is flawed. Nothing short of a fundamental rethinking of educational accountability and privatization is needed, given the challenges facing the K–16 education system, the apparent shift in the standard for basic education, and the need for increased investment in education at a time when the traditional revenue source—tax dollars—is apparently constrained.

3.

GLOBALIZATION

The severity of the current challenges facing K–16 educational systems—in the U.S. as well as internationally—leads us to ideological assumptions commonly made about education and tax systems. It is necessary to consider new ways of addressing the challenges that face

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education reform and public finance. Economic globalization is well under way. While it may be possible to rethink strategies used to tax individuals and corporations, as well as to fund and regulate education, it probably is not possible to reverse the trend toward globalization, whether or not such a shift is desirable. As a conclusion, I reconsider the issues of privatization and accountability.

3.1

Privatization

Regardless of whether tax rates are raised once again, there is a need for more revenue to invest in education. Although the process of privatization is well under way, it is crucial to think about the differences in the meaning of privatization in the K–12 and higher education systems. Privatization in the K–12 systems in the U.S. may eventually be limited to introducing market mechanisms into public schools (e.g., charters) rather than to using vouchers that open the market to both public and private schools. With respect to creating markets, the two approaches have some similarities, but are quite different in the way funds follow students. In most countries, public funding is provided to religious schools as well as to secular schools. However, about 10 to 15 percent of the population that attends private schools in the U.S. does not have state and local tax support. Since the federal government provides some funding to private schools, including Catholic schools, through Chapter I and other programs, there is more flexibility in federal funding to shift funds to vouchers. There has been some limited experimentation with vouchers for Title I in Cleveland, Ohio (Metcalf and Paul, 2006). Most other experiments are funded by foundations (Peterson, 1998). Both types of schemes only pay a portion of the cost of attendance, requiring private schools to reduce tuition to capture marginal costs of expansion (St. John and Ridenour, 2001). There are fewer efforts to create open markets with state and local funds, largely because it would increase the percentage of students Thus, the financial implications of shifts to market strategies are fundamentally different for K–12 education in the U.S. than in other countries. The introduction of market forces through implementation of open school choice strategies is well under way and has become a common strategy in school desegregation (Orfield and Eaton, 1996). Therefore, the issue of market adaptation in the U.S. is largely related to accountability systems—the types of standards and requirements implemented, along with testing procedures—and whether they are extended to all schools. In contrast, privatization of higher education—the large-scale shift from public subsidies and low tuition in public colleges to the wide-scale use of loans and high tuition—in the U.S. is now under way, as it is internationally.

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The rising demand for higher education is coupled with the limited tax revenue capacity of national systems. Unless the tax systems are fundamentally changed, it will not be possible to expand substantially public higher education in the U.S. and return to low tuition. Many states in the western U.S.—the region with the country’s fastest population growth in the 1990s—now have severely limited capacity in public colleges and, as a consequence, have had reductions in their college enrollment rates. Now, not only is there an expectation that more students will go to college, but the size of the college-age cohort is increasing nationally. Thus, states will probably need more tax dollars for public higher education, even if the college enrollment rates remain static. Thus privatization, defined as shifting more of the burden of college costs from taxpayers to students and their families, may be the only way to address the access challenge in the U.S. However, as the shift is made, it is crucial to consider three issues: equal access to higher education for prepared students, adjustments in higher education admissions for unequal opportunity for preparation, and taxpayer burden. First, equal access for equally prepared students represents the minimum standard of equity for higher education systems. It is only a minimum standard because it assumes there is equal opportunity to prepare—to obtain the new basic education for college preparation, a standard that is not met in the U.S. However, since adaptation in admissions remains the best possible way to adjust for this pre-existing inequality, we can consider the financing of equal opportunity independent of preparation. The analyses in this book clearly confirm that public investment in need-based grants should be linked to tuition increases. Second, adaptations are necessary in admissions because of unequal opportunity to prepare. This problem is related to, but distinct from, the historical legacy of segregation. As a consequence of the de jure and de facto segregation in systems of education in the U.S., there were inequalities in education: Separate was not equal and inequality remains in present racial isolation. College admissions practices had adjusted to consider race in admissions, a process known as affirmative action. But at least four states have moved away from affirmative action—California, Florida, Texas, and Washington—and other public systems modified their practices to avoid lawsuits before the U.S. Supreme Court’s Grutter v. Bollinger (2003) decision. In this decision, the Court indicated that some type of adjustment was needed for the next quarter century but that race could not be an explicit criterion. Since the problem of unequal K–12 education continues in the U.S., especially with respect to the speed of adjustment to new basic standards for high school completion, there is also a need to adjust college admissions.

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Two approaches to the admissions problem have proven workable and merit further adaptation. One involves indexing admission standards to the high school context, the merit-aware approach (Goggin, 1999; St. John, Simmons, and Musoba, 2002). This can involve using class rank, as has been done in California and Texas, or adjusting test scores to the school context. The analyses in this book provide additional support for this approach. An alternative is to use noncognitive criteria—such as leadership and self concept—in the admissions process (Sedlacek, 2004). Other countries may face similar circumstances as they make adjustments in their K–16 systems. As college preparation becomes the standard for high school graduation, rather than the privilege of some students, there may be a need to adjust college admissions. The shift toward generally available preparatory high schools has implications that should not be overlooked in democratic systems of education. Third, coordination of public finance schemes to minimize additional taxpayer costs merits consideration. Previously, I have suggested two mechanisms: the coordination of state finance strategies and an expanded state-federal grant partnership (St. John, 2003, 2005; St. John, Chung, Musoba, Simmons, Wooden, and Mendez, 2004; see also chapter 4). Both arguments relate to challenges facing the public financing of higher education evident from the studies in this book. As the tuition charges go up for public colleges and universities, it is crucial to make a sufficient investment in state grants to equalize enrollment opportunity. An appropriate equity standard, given trends in higher education finance (St. John, 2003, 2005), would be to fund state need-based grants on a per-FTE basis for resident students in the state system of higher education at a level equaling one-quarter of the average public-sector tuition charge (chapter 4). Only four states met this standard, on average, in the 1990s, and not all of these states maintained the level throughout the decade. The analyses in this book further confirm the importance of making sufficient state investments in grants consistently over time. The argument for expanding the state-federal partnership—as part of the Leveraging Educational Assistance Program (LEAP)—is that this program provides incentives to states for reaching the equity standard. Funded with federal dollars, this option would be no more costly than loan subsidies. The major federal loan program costs taxpayers 25 cents to 50 cents per dollar borrowed, given interest subsidies, reinsurance, and other program costs. McPherson and Schapiro (1998) used 50 cents as a proxy measure of federal costs for one dollar of loans. Price (2004) used a lower cost ratio for subsidized loans in the U.S. It depends on interest rates. Since interest rates have dropped, the costs of these loans should be lower. Increasing funding of

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states’ grants—raising spending to the equity level—would have increased enrollment by an estimated 1.2 million students in the 1990s (chapter 4). However, proposing new strategies does not deal with the underlying theory problem that is a consequence of the shift toward privatization of public higher education. Human capital theory was once widely used to argue for public funding of higher education (Slaughter, 1991), but these arguments have been reinterpreted with the changing priorities of legislators (Trammell, 2004). Human capital theory proposes that both individuals and governments making investment decisions about education receive benefits. Proponents of privatization have used the notion of high individual returns from education to rationalize shifting the burden of paying for college from society to individuals—the low-tax rationale. Economic theory largely lacks a basis for discerning the relative values of the social and individual returns on investment in education. The strategies used for privatization simply have not worked with respect to social equity. A just society framework provides a fair basis for illuminating consequences of changes in finance. The human capabilities argument is based on the notion that there is a threshold of educational attainment necessary for full economic citizenship and that denial to some groups of education to that threshold accentuates inequalities and constrains economic development. In the U.S. there is a shift under way in the commonly accepted standard for basic high school education, the level of education expected for all citizens. Adjustments to the finance system are necessary to create an opportunity for all students to reach this new basic standard. If this standard for college access has been reached, it is a social injustice if college access is denied for financial reasons, as is the case in most U.S. states and perhaps also in other countries.

3.2

Accountability

A second feature of globalization has been the institutionalization of accountability schemes (Henry, Lingard, Rizvi, and Taylor, 2001; Rizvi, 2006). In the U.S. there is a consistent pattern of rating schools on their test scores, within their communities, rating states on achievement indicators, and comparing the U.S. to other nations. Such comparison schemes are used to rationalize the accountability apparatus. Accountability mechanisms include new educational standards, raising course requirements for graduation, aligning testing with standards, and requiring tests for graduation. The K–12 system in the U.S. has been the locus of the most substantial implementation of accountability schemes. The irony is that while there have been many studies that show correlations between advanced math courses

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and academic success indicators (e.g., Adelman, 1995, 2004; NCES, 1990, 1997a; Pelavin and Kane, 1988, 1990), these official studies have not examined the effects of the new policies, for example, the No Child Left Behind Act and prior legislation. However, the results of these analyses in parts II and III do not confirm the notion that underpins this rationale. As noted in the prior chapter, it is apparent that this policy regime has not worked well, especially with respect to the goal of improving high school graduation rates. Graduation exams and school funding were both positively associated with high school graduation rates, but these relationships were intertwined. School funding was not significantly associated with graduation rates when only finance variables were considered, and exit exams were significant only when school finance was considered. The combined analyses of state indicators and individual outcomes reveal that educational policies now widen inequalities in achievement rather than narrow them. The increasing gap in achievement appears to be the product of policies promoted to reduce the gap. The problem may be related to unequal implementation, along with the decline in the commitment to equal education opportunity, but these structural inequalities have been largely overlooked in the policy literature. The accountability process has been used to rationalize increasing investment in schools for high- and middle-income students and to deemphasize programs that provide supplemental opportunities for low-income students. In the past decade or so, education policies in the U.S. have shifted funding for compensatory education programs (i.e., Title I) from schools that serve low-income students to reform strategies that emphasize market mechanisms, emphasizing a new array of choice schemes (Wong, 2003). The consequences of linking accountability systems to market strategies—the new direction of federal education policy in the U.S.—are seldom adequately articulated or studied. These analyses show that the new accountability strategies have not worked as intended (chapter 3). Further, the evidence from other research on these new market strategies is not convincing. There have been modest educational gains from some of the voucher experiments (Metcalf and Paul, 2006) but not from charters (Eckes and Rapp, 2006). The claim that market systems will serve low-income and minority students better than the current system is difficult to untangle. In experiments with vouchers in U.S. cities, efforts by teachers to innovate in public schools were constrained by the accountability system, while there were no such restrictions in private schools (Ridenour and St. John, 2003). In other words, there was not fair competition between the two models in these “experiments.” What may have been tested was the accountability system

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itself—the accountability system simply may not compete well in local discretion and professional responsibility. Accountability schemes were slower to be implemented in higher education in the U.S. but are not widely advocated (Ewell, 2004; Hauptman, 2004; Longanecker, 2004). The current argument is to link incentives for funding to institutional success measures (Ewell, 2004), an approach that usually involves comparing persistence or completion rates. However, given the finding from the reanalysis of the National Education Longitudinal Study (part III), it appears that linking funding to persistence rates will probably reward colleges and universities that attract more high-income students (St. John, Kline, and Asker, 2001). In other words, like K–12 education accountability, the new schemes could undermine their intent. So what is the alternative? What is consistently overlooked in the advocacy for accountability schemes is the linkage between public policies and educational outcomes. It is abundantly clear (parts II and III) that the effects of implemented public finance policies and education policies are often the reverse of what is intended. School reforms did not increase college access in the 1990s, and failing to fund need-based grants has created inequalities in access. It is crucial to hold states accountable for the effects of the new policies they adopt and implement. The focus on school and college accountability is misplaced, the goal should be to seize responsibility for educational reform in an increasingly market driven system.

4.

CONCLUSION

The argument made by the political right that education has failed has been repeated for more than two decades. This argument was used to rationalize privatization and accountability in education—policies that have dominated education globally throughout this period. Now a similar rationale is being used to reorganize high schools to make up for the failure of this reform strategy. Rather than take responsibility for the failure of their policies—evident in high school dropout rates—they use these failures to push the same rationale for further reforms. The problem with the rationale that has evolved in the U.S. lies in its failure to recognize inequality, especially the unequal schools for children from low-income families. To the extent that the education reforms of the past decade have benefited students, it has been wealthy and middle-income students who have gained access to advanced courses in high school and who have gone on to elite colleges. Raising education standards and creating rigorous, compelling curriculum is crucial—but not just for the children of the wealthy and middle class.

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The newest calls for reform seek to double the numbers of low-income students who go on to college and attain college degrees (Bishop, 2004; Hauptman, 2004; Kazis, Vargas, and Hoffman, 2004). These are important goals, but the means being rationalized often are the opposite of what is needed to reform education for low-income students. What these reformers fail to grasp is that many low-income students think they cannot afford to enroll because many of their older peers faced excessive and work-loan burdens while in college. This reality does not escape the consciousness of motivated low-income students. Indeed, there is evidence that the education and public finance policies pushed over the past decade—like standarddriven reforms and merit aid—favor wealthy students and encourage dropout by low-income students. How much more evidence do neoconservatives and neoliberals need to be persuaded to engage in critical reflection on their beliefs and in a genuine rethinking of their favorite policy initiatives? The time has come to engage in critical reflection on the underlying causes of the persistent inequalities in educational opportunities. It has proven false to argue for policies that favor the wealthy—like more merit aid and higher academic standards—as means of motivating poor people. Lowincome people know when they are being deceived—their experience and history tell them so. The research evidence on the effects of education and finance reforms (parts II and III) helps explain this disillusionment of the poor and working classes. In spite of policies favoring the wealthy and the middle class, many low-income students take the steps to prepare for college, work to acquire resources for college, and make the decisions they must to attain education and find meaningful work in jobs sufficient to support their families. It is time to adapt the new market models to make them more caring and supportive of the education choices being made by the poor and working class children in democratic societies. This is not an argument for harkening back to an older liberal socialist “utopia,” which never really existed. Instead we need to need build an understanding of how to make this new economic system work better. To expand access to college, it probably will be necessary to continue the process of privatization. However, the wealthy and elite citizens in democratic societies should step beyond their narrow selfinterest to recognize the need for need-based student grants and funding of schools that serve the low- and middle-income majority. This may require more taxation. Tax dollars improve education, especially when wisely directly toward subsidizing those with real needs rather than toward supporting the wealthy who make merit claims. We should reintegrate education policy with policy on public finance. The separation of the two has deeply divided the classes in democratic societies and has fueled the disintegration of educational opportunity for

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low-income students. Educational opportunity for hard-working, low-income citizens remains the best investment that democratic societies can make to invigorate economic development. It is time policy makers quit their denial of the failure of their dearly loved reforms and reengaged in the integrative reconstruction of education and public finance policies that support improved educational opportunity for all students who challenge themselves and prepare for college. Social justice—and especially the balancing of equal opportunity with newer claims about achievement and constraining taxpayer costs of education—should be a paramount concern in efforts to restructure and reform education. My argument is that fair, equitable approaches to education improvement and public finance of education are possible. However, research on the path taken in the late 20th century reveals the need for a mid-course correction. The emphasis on excellence in education and market systems of finance can work if the goals related to equal opportunity are balanced with the approaches that are already well established.

References

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Index

B

A

Bakke, Regents of the University of California v., 16 Balanced access model, 27-29, 45-49, 65, 94, 139, 140, 161-162, 167 Brown v. Board of Education, 41

Academic preparation, 72-73, 84, 88, 90, 92, 99, 104, 118, 135, 143, 145, 164, 190-191, 193, 198-199, 202-208, 209-212, 218-220, 225-227 advanced courses, 17-18, 66, 83, 84, 135, 164-165 and encouragement programs, 48, 58 in policy context, 27, 48, 58, 72, 87 (note), 115-116, 135, 161-162, 163, 218, 221, 222 rationale, 11-14, 16-18, 57, 58, 137-138, 161-162, 163, 217, 218, 224, 227, 231-232, 238 Accountability, 8, 16, 38, 41, 53, 135, 235, 246, 247, 250-252, government, 1 Advanced placement (AP), 45, 58, 61, 62, 66, 68, 72, 73, 80 Advisory Committee on Student Financial Assistance (ACSFA), 43, 163 American Council on Education (ACE), 18, 136-139, 193, 211-212 Attainment (See also Equal educational opportunity), 136-137, 139, 142, 153, 155, 158, 161, 193-213, 225-226, 229-230, 232, 237, 239, 244, 250

C Cold War, 5-6 College access (See also Academic preparation; Balanced access model), 217-218, 230-231, 240-241 and affordability, 163 and enrollment, 22, 113, 238 and financial aid, 164, 179, 190-191, 252 and grants, 22, 181, 190 and preparation, 164, 165, 171, 173, 190-191, 207, 224, 238-239, 242, 244-246, 248 and public finance, 164, 167, 217, 236, 252 and research, 164-166 and tuition, 22 College choice (See Four-year colleges; Two-year colleges) Council of Chief State School Officers (CCSSO), 60-61

269

270

E Education pipeline, 45-47, 70, 84, 86, 115-116, 117, 124 Efficiency measures, 35 Elementary and Secondary Education Act of 1965 (ESEA), 34, 41 Encouragement programs, 138, 148, 151, 155, 208, 211-212, 217 Enrollment, 19, 22, 59-60, 64, 70, 80, 83, 86, 87, 88, 92-93, 94, 98, 101, 104, 111, 119-120, 127-131, 136-137, 169, 171-173, 177, 181, 187, 193, 203, 207, 211-212, 213, 222 across institution types, 120-124, 125-127, 129-130, 138-139, 173-175, 179, 181-185 and financial aid (See also Grants), 164, 166, 194, 197, 208, 227-229, 230, 231 and high school curriculum, 164-165 and high school graduation, 91-92, 218, 221, 225 and high school math courses, 57, 115, 135-137, 148, 161-162, 165, 167, 171, 173, 175, 177, 179, 185, 187, 190, 209, 222 influenced by state financial strategies, 22, 84-87, 90, 93, 94, 98, 99-104, 119, 125-126, 173, 175, 179, 181, 185, 187, 190-191, 207, 210 influenced by state K–12 policies, 70, 222-225 and parents’ education, 136, 137, 169, 171, 173, 175, 177, 179, 181, 185, 187 and preparation, 170-171, 177, 209, 224-225 and response to tuition, 20, 166, 173, 175, 179, 181, 185, 187, 190-191 and standardized tests, 181 Equal educational opportunity (See also Enrollment; Grants; Rationales, education), 38, 217, 219, 220, 226, 229, 230, 231-233, 239, 253-254 and financial need, 37 to prepare, 33, 226, 233 Equity standard, 99-103 European education systems, 121, 123, 128, 131, 242, 244

Education and the Public Interest

F Financing policies (See also Academic preparation) federal, 10-11 state, 11, 83-84, 86, 88, 92, 93, 94-104, 119 Financial aid (See Grants; Loans) Finn, Chester, 12 Fixed effects regression (See also Statistical methods), 52-53, 62-63, 89, 115, 120 Four-year colleges, 83, 103, 116, 119, 120-121, 123-124, 125, 129-130, 195, 199-202, 209-210, 211, 222-223, 225-226, 231, 232 and enrollment, 86, 120-121, 123-124, 125, 169, 171, 173, 179, 181-185, 187, 190-191, 224-225, 230

G GI Bill, 19 Global political economy, 237, 240 defined, 5 Globalization, 237, 246-252 defined, 5 driving privatization, 4, 6, 7-8, 18-23, 115, 130-131, 235 in higher education, 10-11 international debt, 6 and schools, 8-10 social welfare, 5 trade, 5 Grants, 83, 115, 119, 202, 203 and equalizing opportunity, 29-30, 48, 99-103, 138, 166, 170 influence on enrollment, 16-17, 29, 93, 94-104, 127, 166, 173, 175, 177, 181, 185, 187, 190-197 influence on high school graduation, 90-92 influence on preparation, 48, 84 low-income students, 83, 94, 211, 212, 228 need-based, 83, 86, 87, 91-92, 93, 94-104, 127, 128-130, 166, 173, 175, 177, 179, 181, 185, 187, 190-191,197, 198, 202, 203, 207, 208, 211-212,

Index 228-229, 230, 231-232, 238, 239, 241, 246, 248-249 non-need based, 83, 86, 87, 91-92, 93, 127, 128-130, 166, 173, 175, 177, 179, 181, 185, 187, 190-191, 197, 198, 202, 203, 206, 207, 208, 217, 225, 227-229, 239 Pell, 16, 39, 94, 99, 128 and persistence, 104, 194 state grant programs, 22, 92, 93, 94-104, 197, 206, 207, 212, 246

H Hierarchical linear modeling (HLM) (See also Statistical methods; Two-level models), 54 High-income students, 158-161, 185-189, 190, 198, 202, 209, 210, 230 defined, 140 dropout, 158 enrollment, 171, 185-187, 229 high school math courses, 147, 148, 150, 158-162, 185, 187, 220, 222 preparation, 173, 185 High School and Beyond (HS&B), 164 High school math courses (See also Enrollment; High-income students; Lowincome students; Middle-income students), 115-116, 142, 143, 147, 148-162, 210, 218, 220-221 and college enrollment, 48, 57, 80, 123-125, 209, 222, 225-226 and degree attainment, 203, 220 High school exit exams (See High-stakes exams) High school graduation rates, 60, 62, 68-70, 73, 80-81, 88, 90-92, 98-99, 218-221, 222-224, 225, 227-228, 229, 232 High school tracking, 143-144 High-stakes exams, 14, 41, 68, 117, 124, 219-220 Higher Education Act (HEA), 20, 33, 39 Human capabilities approach, 237-239 Human capital theory (See Rationales for reform, economic)

I Integrity, 25-26, 53 in research, 35-36, 41, 53 and policy development, 25-26, 53

271

K K–12, 115, 120, 136, 139, 162, 236, 239 and alignment with higher education (See also Academic preparation, rationale), 11-12, 222-224 and reform to expand college access, 18, 162, 217, 224, 232, 239 and funding, 48-49, 61, 68, 73, 80, 117, 120, 124, 127, 224, 246, 247

L Lleras, Miguel, 240-241 Loans, 194, 212, 238, 240, 247, 253 and expansion of access, 11, 18 and expansion of choice, 30 international comparison, 10, 238, 240-241 and relationship to educational opportunity, 20 Low-income students, 40-42, 151-155, 175-180, 239, 251 ability to pay, 47, 138, 227-228, 253 access, 175-180, 211-212, 217-218 achievement, 151-155, 225 college choice, 179, 209 defined, 140 degree attainment, 198, 199, 206, 207, 208, 210, 212-213, 230 dropout, 147, 151-155 educational vouchers (See Vouchers) enrollment, 169, 171, 173, 175-178, 207, 209, 210, 211, 212, 229, 241 high school math courses, 147, 150-155, 221-222, 226, 232 tuition, 175-179 Lumina Foundation for Education, 87

M Market-based approaches in education, 11-14, 21-22, 23-24, 83, 119, 125-127, 128-131, 231 K–12 reform, 12, 128-131 Markets in higher education (See Fouryear colleges; Two-year colleges)

272 Mathematics graduation curriculum requirements, 61, 66, 68, 80, 137, 142, 155, 158, 161-162 Merit aid (See Grants, non-need-based) Middle Income Student Assistance Act of 1978 (MISAA), 16 Middle-income students, 155-158, 181-188, 190, 199, 202, 209, 212, 217, 229- 230, 232, 246, 251, 252, 253 college choice, 175, 181-188 defined, 140, 155 dropout, 148, 155-158, 206 enrollment, 171, 181, 190, 229, 231 high school math courses, 155-158, 181, 220-221, 223, 225-226 preparation, 155-158, 181, 226 standardized tests, 155

N Nation at Risk, A, 5 National Center for Education Statistics (NCES), 12, 32-33, 45-46, 48, 57, 58, 136-139, 140, 143, 144, 151, 193, 211-212, 223, 231 National Council of Teachers of Mathematics (NCTM), 13, 14, 58, 61, 62, 66, 68, 72, 80, 142, 148, 161, 163 National Defense Education Act of 1958, 31, 41 National Education Longitudinal Survey (NELS), 14-15, 18, 51, 193, 209-213 National Longitudinal Study of the High School Class of 1972 (NLS-72), 164, 165 National systems of education and social welfare (See also Cold War) academic achievement, 8-9 socialist v. capitalist, 5-7 No Child Left Behind Act (NCLB) of 2001, 11, 12-13, 14, 58 and research-based reform, 13-14 Nussbaum, Martha, 242, 245

O Organization for Economic Cooperation and Development (OECD), 8-9, 10-11

P Pathways to College Network, 43-44 Poverty (See Low-income students)

Education and the Public Interest Private colleges and enrollment, 93 Privatization, 247-250 of education, 3, 4, 7-8, 10, 235, 246, 252 of higher education, 19-24, 31, 37-38, 43, 83, 175, 238, 240, 246, 253 of social welfare, 3-4, 7-8 and political ideology, 7, 21 and taxes, 7-8 Public finance, 30-31, 119, 128-131, 193-194, 206, 208, 217, 219, 224, 226-230, 231, 235, 240, 245-246, 252, 253-254 influence on enrollment (See also Enrollment; Grants), 48, 85-86, 93-104, 128-130, 228-229 influence on preparation, 48, 208, 224, 227-228 and persistence, 49, 86, 193, 229-230 Public integrity (See Integrity) Public interest, 4, 36, 235, 236 defined, 32-35

R Race/ethnicity of student groups, 42, 140, 147, 199, 202, 218, 239 dropout, 148 enrollment patterns, 173, 177, 179, 181, 185, 206, 241 high school math courses, 148, 151, 153, 155-158 standardized tests, 65 Rationales for reform (See also Academic preparation, rationale) economic, 39-40, 241, 250 education, 41-45, 250-251, 252 Rawls, John, 32-38, 53, 241-242 Refinancing the College Dream, 27-28, 31, 35, 50

S School funding (See K–12, and funding) School vouchers (See Vouchers) Selection bias, 51-52, Selection effect, 14 Sen, Amartya, 236-239 Standardized exams, 135, 155, 212 ACT, 58, 64, 64 (note), 143, 150, 171, 177, 179, 185, 187

Index SAT, 58-59, 60, 62, 63, 64, 65-68, 72, 73, 80, 143, 150, 171, 177, 179, 181, 185, 187, 218, 220, 221, 225, 227, 232, 244 Statistical methods (See also Two-level models), 50-52, 89, 100, 102, 142-144, 169, 195, 197 Student aid (See Grants; Loans)

T Tax rate, 86, 88, 91, 93 Tuition (See also Enrollment; Privatization), 40, 48, 85-86, 88, 93, 99-103, 127, 128-130, 166, 173, 175, 177, 179, 181, 185, 187, 190-191 Twenty-first Century Scholars Program, 94 Two-level models (See also Hierarchical linear modeling; Statistical methods), 52, 139-144, 167-169, 195, 197, 220, 223 Two-year colleges, 85-86, 89, 93, 117- 199, 120-124, 173-175, 179, 181, 187, 195, 199, 210, 226, 231 and enrollment, 93, 117-119, 120-124, 173-175, 179, 181, 187, 229

V Vouchers, 37, 241, 247, 251

W Walzer, Michael, 35-37 World Bank, 10-11

273