Advances in Taxation, Volume 18

ADVANCES IN TAXATION Series Editor: Suzanne Luttman Recent Volumes: Volumes 1–3: Volumes 4 and 5: Edited by Sally M. Jo...

Author: Suzanne M. Luttman

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ADVANCES IN TAXATION Series Editor: Suzanne Luttman Recent Volumes: Volumes 1–3: Volumes 4 and 5:

Edited by Sally M. Jones Edited by Jerold J. Stern

Volumes 6–13: Volume 14: Volumes 15 and 16: Volume 17:

Edited by Thomas M. Porcano Edited by Thomas M. Porcano Edited by Thomas M. Porcano Edited by Suzanne Luttman

ADVANCES IN TAXATION VOLUME 18

ADVANCES IN TAXATION EDITED BY

SUZANNE LUTTMAN Department of Accounting, Santa Clara University, CA, USA

United Kingdom – North America – Japan India – Malaysia – China

EDITORIAL BOARD Suzanne M. Luttman Santa Clara University

Kenneth E. Anderson University of Tennessee Caroline K. Craig Illinois State University

Gary McGill University of Florida

Anthony P. Curatola Drexel University

Janet A. Meade University of Houston

Ted D. Englebrecht Louisiana Tech University

Michael L. Roberts University of Colorado-Denver

Philip J. Harmelink University of New Orleans

David Ryan Temple University

D. John Hasseldine University of Nottingham

Dan L. Schisler East Carolina University

Peggy A. Hite Indiana University-Bloomington

Toby Stock Ohio University

Beth B. Kern Indiana University-South Bend

ix

LIST OF CONTRIBUTORS Steven Balsam

Department of Accounting, Fox School of Business, Temple University, Philadelphia, PA, USA

Richard Cummings

Department of Accounting, University of Wisconsin-Whitewater, Whitewater, WI, USA

Jennifer L. Fecowycz

–

Tonya K. Flesher

Patterson School of Accountancy, University of Mississippi, University, MS, USA

Ernest R. Larkins

Georgia State University, School of Accountancy, J. Mack Robinson College of Business, Tucker, GA, USA

Teresa Lightner

Rawls College of Business, Texas Tech University, Lubbock, TX, USA

Gary A. McGill

Fisher School of Accounting, University of Florida, Gainesville, FL, USA

Karen C. Miller

McAfee School of Business Administration, Union University, Jackson, TN, USA

Thomas M. Porcano

Department of Accountancy, Farmer School of Business, Miami University, Oxford, OH, USA

Robert Ricketts

Frank M. Burke Chair in Taxation, Texas Tech University, Lubbock, TX, USA

David Ryan

Department of Accounting, Fox School of Business, Temple University, Philadelphia, PA, USA vii

viii

LIST OF CONTRIBUTORS

J. Riley Shaw

Patterson School of Accountancy, University of Mississippi, University, MS, USA

Peter J. Westort

College of Business, University of Wisconsin-Oshkosh, Oshkosh, WI, USA

Brett R. Wilkinson

Hankamer School of Business, Baylor University, Waco, TX, USA

JAI Press is an imprint of Emerald Group Publishing Limited Howard House, Wagon Lane, Bingley BD16 1WA, UK First edition 2008 Copyright r 2008 Emerald Group Publishing Limited Reprints and permission service Contact: [email protected] No part of this book may be reproduced, stored in a retrieval system, transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise without either the prior written permission of the publisher or a licence permitting restricted copying issued in the UK by The Copyright Licensing Agency and in the USA by The Copyright Clearance Center. No responsibility is accepted for the accuracy of information contained in the text, illustrations or advertisements. The opinions expressed in these chapters are not necessarily those of the Editor or the publisher. British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library ISBN: 978-1-84663-912-8 ISSN: 1058-7497 (Series)

Awarded in recognition of Emerald’s production department’s adherence to quality systems and processes when preparing scholarly journals for print

AD HOC REVIEWERS Jane Livingstone North Carolina at Greensboro

Bruce Busta St. Cloud State University Michael Calegari Santa Clara University

Steve Matsunaga University of Oregon

Anne Christensen Montana State University

David Monarchi University of Colorado, Boulder

Shirley Dennis-Escofﬁer University of Miami

Kevin Murphy Oklahoma State University

Peter Frischmann Idaho State Jeffrey Gramlich University of Southern Maine

Michael Schadewald University of Wisconsin, Milwaukee

Robert Halperin Hong Kong Polytechnic University

Dennis Schmidt Montana State University

Yongtae Kim Santa Clara University

Roxanne Spindle Virginia Commonwealth University

xi

THE EFFECT OF INTERNAL REVENUE CODE SECTION 162(m) ON THE ISSUANCE OF STOCK OPTIONS Steven Balsam and David Ryan ABSTRACT Internal Revenue Code section 162(m) limits tax deductibility of executive compensation to $1 million per covered executive, with an exception for performance-based compensation. Both stock options and annual bonuses can qualify as performance-based, but they vary in the difﬁculty of qualiﬁcation and the degree of additional compensation risk that qualiﬁcation imposes on the executive. Most stock-option grants easily qualify with little change in risk, but qualiﬁcation increases the risk associated with annual bonus compensation relative to what it was prior. The results of this study show that the propensity to issue stock options has increased for affected executives as a percentage of total compensation. Additional analysis suggests that this increase in stock-option compensation is substituting for lower increases in salary for affected executives, but not for annual cash bonuses. In fact, the results suggest that bonus compensation is also increasing as a percentage of total compensation. In summary, the results indicate that ﬁrms and their executives are acting in a way consistent with the incentives provided by section 162(m). Advances in Taxation, Volume 18, 3–28 Copyright r 2008 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 1058-7497/doi:10.1016/S1058-7497(08)18001-2

3

4

STEVEN BALSAM AND DAVID RYAN

INTRODUCTION The Revenue Reconciliation Act of 1993 added section 162(m) limiting the corporate tax deduction for executive compensation to $1 million per individual for the top ﬁve executives of a corporation and providing an exception for compensation in excess of $1 million if it qualiﬁes as ‘‘performance-based.’’ This chapter extends the prior research on the effect of section 162(m) on executive compensation by focusing on whether 162(m) is achieving its intended effect of increasing the use of such performance-based compensation as stock options in executive compensation. Using the population of ﬁrms available on Standard & Poor’s ExecuComp database, the results show that the propensity to issue stock options has increased for affected executives, not only in absolute terms, but also as a percentage of total compensation. Additional analysis shows that the increase in stock-option compensation may be substituting for lower increases in salary for affected executives. But, there is no evidence that stock-option compensation is substituting for annual cash bonuses. The Congressional intent of section 162(m) was to reduce excessive, non-performance-based executive compensation (U.S. Congress, House, 1993). The results indicate that ﬁrms and their executives are acting in a way consistent with the incentives provided by section 162(m). Under section 162(m), ﬁrms that wish to pay an executive more than $1 million either have to forfeit deductions or structure the compensation package so that the excess over $1 million qualiﬁes under the performancebased exception. While a variety of compensation forms can qualify as performance-based, they vary in the difﬁculty of qualiﬁcation, the risk qualiﬁcation imposes on the executive, etc. For example, for amounts paid under a bonus plan to qualify as performance-based, the payout must not exceed that determined using objective plan parameters set at the beginning of the year. In contrast, stock-option plans are relatively easy to qualify under section 162(m) and as long as the exercise price is set at or above the market price on the date of grant, are assumed to be performancebased. This study continues in Section 2 with a discussion of section 162(m), and a review of the relevant literature in Section 3. Section 4 develops our research question and models, while Section 5 discusses our sample selection. Section 6 presents the empirical results. The ﬁndings of the study are summarized in Section 7.

The Effect of Internal Revenue Code Section 162(m)

5

SECTION 162(m) Section 162(m) was a response to the concern about the perceived link between the international competitiveness of United States industry and the substantial salaries paid to United States executives (Brownstein & Panner, 1992). Corporate governance critics (e.g., Crystal, 1992; McCarroll, 1993) argued that executive compensation was excessive, both in comparison to that paid to lower level employees and that paid to overseas executives; and that executives were setting their own pay with no shareholder input. Section 162(m), which became effective for tax years beginning on or after January 1, 1994, places a $1 million cap on the annual deduction for nonperformance-based compensation to the top ﬁve executives (the chief executive ofﬁcer (CEO) and the next four highest compensated ofﬁcers). Executive compensation generally consists of salary, fringe beneﬁts, annual cash incentives, and long-term cash or stock-based incentives. The section 162(m) limit does not apply to (1) commissions, (2) non-taxable fringes and qualiﬁed retirement plan contributions, and (3) performance-based compensation. Prior to the imposition of section 162(m), most ﬁrms claimed to tie compensation to performance. However, compensation committees had substantial discretion in awarding executive bonuses. Speciﬁc goals and performance criteria were rarely set in advance and even more rarely made public. Under section 162(m), to qualify bonus plans for the performancebased exception, ﬁrms are required to adopt a performance-based plan that is based on the executive’s attainment of one or more performance goals that were established ex-ante by a compensation committee composed solely of independent directors. The performance goals must be based on objective formulae and the material terms of the plan must be disclosed to and approved by shareholders. The compensation committee, which has the discretion to award less, but not more than the objectively determined amount, must certify that the performance goals have been met before payment is made. Any compensation awarded by the committee based on discretionary assessments of performance that is in excess of the objectively determined amounts does not qualify for the performance-based exemption. By deﬁnition, salary will not qualify as performance-based since it is not contingent on the attainment of any criteria. Thus, any salary amounts earned in excess of $1 million are not deductible unless payment is deferred until after the executive’s retirement or unless paid under a contract

6

STEVEN BALSAM AND DAVID RYAN

executed prior to February 17, 1993. Annual bonuses will qualify under the performance-based exception as long as the ﬁrm adopts a bonus plan consistent with the section 162(m) requirements discussed earlier. On the other hand, employee stock options easily qualify. The regulations speciﬁcally state that employee stock options qualify as performance-based under section 162(m) if the grant or award is made by the compensation committee, the plan states the maximum number of shares that can be granted during a speciﬁed period, and the amount of compensation the employee could receive is based solely on an increase in the value of the stock after the date of the grant or award.

LITERATURE REVIEW There is a growing body of research that shows section 162(m) has had some impact on ﬁrms’ compensation practices, albeit perhaps not the intended impact. For example, Balsam and Ryan (1996) examined the propensity of ﬁrms to qualify their short-term bonus plans to meet the requirements of section 162(m), ﬁnding that many ﬁrms were sensitive to the potential tax and political costs of not qualifying. However, they showed that ﬁrms more likely to make the requisite modiﬁcations were those where compensation was most related to performance – a formalization of existing policy. Further, approximately half of the ﬁrms in their sample chose not to modify, and many of those that did, expressly reserved their right to pay nondeductible compensation. Reitenga, Buchheit, Yin, and Baker (2002) also observed that many ﬁrms elected not to qualify their compensation plans on the grounds that executive performance could not be evaluated using a ﬁxed formula and that reserving the use of discretion in determining executive pay was in the best interest of the ﬁrm. Prior research (see e.g., Balsam, 2002; Perry & Zenner, 2001) found that all components of the compensation package increased after 1993, with the largest increase coming in the form of stock-option grants. This ﬁnding that compensation increased post-section 162(m) is consistent with the theoretical predictions of Halperin, Kwon, and Rhoades-Catanach (2001). However, while prior research shows the increase post-section 162(m), it does not show that the increase in stock options is disproportionate to affected executives and ﬁrms. There is also research showing that section 162(m) affected ‘‘unaffected ﬁrms.’’ Harris and Livingstone (2002) examined ﬁrms whose CEOs earned less than $1 million and found it had the perverse effect of raising the compensation of those CEOs.

The Effect of Internal Revenue Code Section 162(m)

7

While research shows that section 162(m) has not led to a reduction in executive compensation, there is some limited (and mixed) evidence that compensation has become more responsive to ﬁrm performance. Examining the sensitivity of pay to performance, Johnson, Porter, and Shackell (2001), Perry and Zenner (2001), and Balsam and Ryan (2007) all found some evidence of an increased sensitivity of compensation to performance after 1993. While Johnson et al. (2001) did not attribute this increased sensitivity to section 162(m), Perry and Zenner (2001) did, ‘‘especially for ﬁrms with million-dollar pay packages.’’ Similarly, Rose and Wolfram (2000, p. 201) provided some evidence that the 162(m) limit ‘‘has led ﬁrms near the $1 million cap to restrain their salary increases and perhaps to increase the performance components of their pay packages.’’ However, in a later paper, Rose and Wolfram (2002, p. S138) concluded ‘‘There is little evidence that the policy signiﬁcantly increased the performance sensitivity of chief executive ofﬁcer (CEO) pay at affected ﬁrms. We conclude that corporate pay decisions have been relatively insulated from this policy intervention.’’ Balsam and Ryan (2007) focused on CEOs hired after 162(m) ﬁnding an increase in the sensitivity of pay to performance for those CEOs. A more recent trend is for researchers to examine the details of ﬁrms’ responses to section 162(m). Balsam and Yin (2005) examine the actual tax status of executive compensation, ﬁnding that almost 40 percent of their sample ﬁrms forfeit some tax deductions because of section 162(m). Interestingly, they found that in 90 percent of the ﬁrm years in which forfeiture occurred, the ﬁrm had at least one plan that met the requirements of section 162(m) and consistent with our research expectations, they had a qualiﬁed stock-option plan in the vast majority of cases.

RESEARCH QUESTION In ﬁrms where the CEO or other top ofﬁcers are earning in excess of $1 million in annual compensation, the after-tax cost of performance-based compensation such as bonuses and stock-option grants is reduced relative to other forms of compensation. As discussed earlier, the ﬁrm must take a number of steps and put compensation at risk for an annual cash bonus to qualify for the performance-based exception under section 162(m). In contrast, stock-option plans can be easily qualiﬁed with no change in compensation risk. Option grants are performance-based compensation if the options have exercise prices equal to or greater than the market price at

8

STEVEN BALSAM AND DAVID RYAN

the time the award is made and the plan states the maximum number of shares that can be granted during a speciﬁed period. Most ﬁrms already met these requirements when section 162(m) was imposed.1 Thus, unlike annual cash bonus plans, section 162(m) required minimal modiﬁcations to compensatory option plans. That being said, depending upon the ﬁrm, options may still be riskier than annual cash bonuses. However, as illustrated by Reitenga et al. (2002), qualifying a bonus plan increases the compensation risk for the executive. In contrast, qualifying a compensatory option plan has little effect on the executive’s compensation risk. Hence, section 162(m) increased the risk of annual cash bonuses relative to options. Consistent with Congressional intent to decrease non-performance-based compensation, the ﬁrm may ﬁnd it desirable and easier to shift compensation into options if the executive is subject to 162(m) and earns more than $1 million a year.

Model (1): Increased Use of Stock-Option Compensation The following pooled, cross-sectional Tobit regression model tests the hypothesis that section 162(m) has lead to the increased use of stock options in the compensation packages of affected individuals. Tobit is used in the analysis because the dependent variable in the primary analysis, the ratio of stock option to total compensation, is bounded by zero and one. The formal model is: PERCENTOPTit ¼ a0 þ a1 DUM1it þ a2 DUM2it þ a3 VALUEit1 þ a4 DIVYIELDit þ a5 SIZEit þ a6 TRSit þ a7 ROAit þ a8 VARROAit þ a9 RISKit þ a10 CONSTRAINTit X þ a11 FCFit þ a12 BKMit þ a YEAR X þa IND þ eit ð1Þ where the dependent variable is: PERCENTOPTit

the Black–Scholes value of option grants to executive i in year t divided by executive i’s total compensation, where both the Black–Scholes value and total compensation are provided by ExecuComp2

The Effect of Internal Revenue Code Section 162(m)

9

and the independent variables are : DUM1it

DUM2it

VALUEit1

DIVYIELDit SIZEit TRSit ROAit

VARROAit

RISKit

CONSTRAINTit

FCFit

BKMit YEAR IND

an indicator variable taking the value of 1 if cash compensation of executive i is greater than $900,000 in year t, 0 otherwise3 an indicator variable taking the value of 1 if cash compensation of executive i is greater than $900,000 in year t and year t is 1994 (1995 if non-December ﬁscal year end) or later, 0 otherwise value of executive i’s shares held plus the intrinsic value of exercisable and un-exercisable options deﬂated by total direct compensation, all measured at the end of year t1 the dividend yield of executive i’s ﬁrm in year t the log of assets of executive i’s ﬁrm in year t the return to shareholders of executive i’s ﬁrm in year t net income before extraordinary items and discontinued operations deﬂated by total assets for executive i’s ﬁrm for year t4 variance of net income before extraordinary items and discontinued operations, deﬂated by total assets for executive i’s ﬁrm for year t the volatility measure (60 month) used by ExecuComp to calculate the Black–Scholes values for executive i’s ﬁrm in year t indicator variable taking the value of 1 when retained earnings plus the value of cash dividends and stock repurchases in the current year divided by cash dividends and stock repurchases is less than two and 0 otherwise ratio of free cash ﬂow to total assets measured as common and preferred dividends less cash ﬂow from operating and investing activities deﬂated by total assets for executive i’s ﬁrm in year t the ratio of book value to market value for executive i’s ﬁrm in year t a series of indicator variables for each year in the sample, 1 in year t, 0 otherwise a series of indicator variables for two-digit SIC codes

10

STEVEN BALSAM AND DAVID RYAN

The test variable is DUM2. The coefﬁcient on DUM2 represents the incremental effect of section 162(m) on the percentage of stock options in the compensation package of individuals who are affected by the requirements of section 162(m). A positive coefﬁcient on this variable would be consistent with section 162(m) leading to an increase in stockoption compensation for this group.

Control Variables The previous literature shows that executive compensation is related to both executive and ﬁrm related factors. Consequently, the following control variables are included in the model: Executive Related Controls DUM1 is included because, independent of section 162(m), the composition of the compensation package may be more heavily weighted towards options for more highly paid individuals. Consequently, a positive coefﬁcient is expected for this variable. VALUE is included as a proxy for the pre-existing holdings of managers because there is an optimal level of equity holdings and compensation can be used to adjust for deviation from that optimum (Core & Guay, 1999). VALUE is measured as the value of the shares held plus the intrinsic value of both un-exercisable and exercisable options deﬂated by total compensation. A negative coefﬁcient is expected for this variable. Firm Related Controls DIVYIELD is included because the value of a ﬁrm’s stock options is less, all else equal, the higher the dividend yield. Thus, managers in ﬁrms with high dividend yields are less likely to prefer stock-option compensation (Lambert, Lanen, & Larcker, 1989). A negative coefﬁcient is expected for this variable. SIZE, measured as the log of assets, is included because prior research has shown that the portion of options in an executive compensation package increases with ﬁrm size (Balsam, 2002, Table 2.6). Thus, there should be a positive coefﬁcient on SIZE. TRS and ROA are included because performance may affect the composition of the compensation package. However, the direction of the effect is not clear. Poorly performing ﬁrms may choose to shift compensation to options to motivate executives. On the other hand, well-performing ﬁrms may also shift compensation to options. While Murphy (1985) ﬁnds a

The Effect of Internal Revenue Code Section 162(m)

11

negative association between stock-option compensation and ﬁrm performance, Liang and Weisbenner (2001) ﬁnd a positive association between stock-option compensation and stock price. Consequently, there is no prediction for the direction of the association between the proportion of stock-option compensation and ﬁrm performance. Variables to proxy for the relative risk of compensation tied to market and accounting measures are included also. RISK, measured as the 60month volatility measure used by ExecuComp in calculating the Black– Scholes values, controls for market related risk. Its effect on the compensation package is ambiguous. That is, while RISK increases the value of an option under the Black–Scholes model, implying a positive coefﬁcient, it also may make the option less desirable to an under-diversiﬁed executive. For example, Meulbroek (2001) estimates that for Internet ﬁrms, the estimated value of stock options to under-diversiﬁed managers is only 53 percent of their cost to the ﬁrm. However, a recent paper (Hodges, Rajgopal, & Shevlin, 2005) shows that managers overvalue options relative to the Black–Scholes model. Which effect predominates is an empirical question. VARROA, the variance of ROA, proxies for the risk associated with accounting measures of performance. VARROA is expected to be positively related to the dependent variable because the greater the volatility of a ﬁrm’s income, the greater the risk of compensation tied to accounting measures of performance.5 Prior research (Yermack, 1995; Dechow, Hutton, & Sloan, 1996; Core & Guay, 1999; Carter, Lynch, & Tuna, 2004) shows that ﬁrms with less free cash ﬂow are more likely to use equity instead of cash compensation since equity compensation requires no cash payment. Following Core and Guay (1999) and Carter et al. (2004), free cash ﬂow (FCF), is included as a control variable, constructed such that a larger value represents less FCF. Consequently, a positive coefﬁcient is expected on this variable. A proxy for a ﬁrm’s investment opportunity set, BKM is also included because ﬁrms with greater investment opportunities may be more likely to conserve cash and use stock-option compensation instead. (Core & Guay, 1999; Carter et al., 2004) This is measured as the ratio of ﬁrm book value to the market value of its equity. A negative coefﬁcient is expected because a greater value indicates a lesser opportunity set. Core and Guay (1999, p. 160) also argue that ‘‘ﬁrms that are constrained with respect to earnings will grant more stock options’’ because cash compensation is expensed while stock-option compensation has, until recently, only been required to be disclosed in footnotes to the ﬁnancial statements. Consequently, consistent with Core and Guay, the control

12

STEVEN BALSAM AND DAVID RYAN

variable, CONSTRAINT is included. There should be a positive coefﬁcient on this variable. To account for any macro-economic year-to-year or industrywide effects, indicator variables for each year and industry (2-digit SIC codes) in the sample are included. Tables 1 & 2 provide the sample distribution by year and industry (1-digit SIC codes). Table 1.

Industry Distribution. One-Digit SIC Code

Agriculture, mining, extraction, construction Consumer products Manufacturing Transportation, communication, and utilities Wholesale and retail sales Financial services Services Services and other Total

Table 2. Fiscal Year Ending 1993 1994 (Pre-December) 1994 (December) 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Number of Observations

Percentage of Observations

1

821

0.06

2 3 4

2,732 3,977 3,977

0.19 0.28 0.27

5 6 7 8 and 9

1,735 1,184 1,451 494 14,138

0.12 0.08 0.10 0.03 100.00

Year Distribution.

Number of Observations

Percentage of Observations

301 201 585 1,129 1,203 1,196 1,074 1,092 1,090 1,091 1,269 1,271 1,268 1,236 132

2.1 1.5 4.1 8.0 8.5 8.5 7.6 7.7 7.7 7.7 9.0 9.0 9.0 8.7 0.9

The Effect of Internal Revenue Code Section 162(m)

13

Model (2): Substitution Effect Model (1) tests whether affected executives are receiving a greater portion of their compensation in the form of stock options in the post-section 162(m) period. Increased stock-option compensation post-section 162(m) may have occurred for two reasons. The ﬁrst possibility is that stock options increased because section 162(m) gave it additional imprimatur and consequently, compensation committees simply added more options to compensation packages without any offsetting reduction elsewhere in the package. In fact, this theory is consistent with the pattern observed by Balsam (2002, Table 2.7), whereby stock-option grants increased over time, but so did the other components of the compensation package. The other alternative is that the increase in stock options was offset by reductions, or if not reductions, lesser increases in the other components of the compensation package than would have been observed in the absence of section 162(m). In effect, did ﬁrms substitute stock-option compensation for other forms of compensation in the pay packages of affected executives? Risk and taxes provide opposing incentives to substitute options for bonuses. If the annual bonus plan is non-qualiﬁed, then there is no change in the risk of the bonus. Hence, there is no reason from a risk perspective to shift from bonuses to options. However, the ﬁrm may shift compensation from bonuses to options to preserve deductions. Alternatively if the annual bonus plan is qualiﬁed, then the risk of the bonus has increased relative to the risk of the options (which may still be riskier). Consequently, for risk reasons, there may be a shift from bonuses to options. However, since both are deductible, there is no tax reason to expect a shift. The following modiﬁed versions of model (1), focusing on changes in the proportionate composition of compensation from the pre- to post-section 162(m) period, tests which of the two alternatives is more likely. Further we utilize a seemingly unrelated regression framework as changes in the proportionate share of one component are likely to be associated with changes in the proportionate share of the other components, that is, the dependent variables are likely to be related. DPERCENTOPTit ¼ a0 þ a1 DUM2i þ a2 DUM3i þ a3 DDIVYIELDi þ a4 DSIZEi þ a5 DTRSi þ a6 DROAi þ a7 VARROAi þ a8 DRISKi þ a9 DCONSTRAINTi þ a10 DFCFi þ a11 DBKMi þ a12 PERCENTOPTit1 þ aSIND þ eit

ð2Þ

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STEVEN BALSAM AND DAVID RYAN

DPSALARYit ¼ a0 þ a1 DUM2i þ a2 DUM3i þ a3 DDIVYIELDi þ a4 DSIZEi þ a5 DTRSi þ a6 DROAi þ a7 VARROAi þ a8 DRISKi þ a9 DCONSTRAINTi þ a10 DFCFi þ a11 DBKMi X þ a12 PSALARYit1 þ a IND þ eit

ð3Þ

DPBONUSit ¼ a0 þ a1 DUM2i þ a2 DUM3i þ a3 DDIVYIELDi þ a4 DSIZEi þ a5 DTRSi þ a6 DROAi þ a7 VARROAi þ a8 DRISKi þ a9 DCONSTRAINTi þ a10 DFCFi þ a11 DBKMi X þ a12 PBONUSit1 þ a IND þ eit

ð4Þ

where the symbol D in a variable name denotes a change in the value of the variable as deﬁned above from the last year pre-section 162(m) to the ﬁrst year post-section 162(m), and DPERCENTOPT is the change in the proportionate option compensation of executive i; DPSALARY the change in the proportionate salary compensation of executive i; and DPBONUS the change in the proportionate bonus compensation of executive i. The difference between model (1) and these models is that while model (1) is a levels regression, these models are changes in levels regressions. The dependent variables are the change in compensation proportions. For example, DPERCENTOPT is the year-to-year change in the proportion of compensation that is option compensation. Two additional independent variables are included: the lagged value of the compensation proportions, PERCENTOPT, PSALARY, and PBONUS and an indicator variable, DUM3. DUM3 takes the value of 1 if the ﬁrm is affected and qualiﬁed its short-term bonus plan. Thus, the DUM3 group represents a subset of the DUM2 group. We include the lagged value of the compensation proportions to control for the fact that a year-to-year change in a proportion will be related to the prior year’s amount. The coefﬁcients of interest are the indicator variables, DUM2 and DUM3. The coefﬁcients of these indicator variables represent the incremental effect of section 162(m) on the compensation proportions. A positive coefﬁcient on DUM2 implies an increase in proportionate compensation; a negative coefﬁcient implies a decrease in proportionate compensation. Thus, a positive coefﬁcient on DUM2 in the regression on DPERCENTOPT and corresponding negative coefﬁcients in the regressions on DPBONUS and DPSALARY provide evidence that ﬁrms substituted option compensation for bonus and/or salary compensation after the implementation of 162(m).

The Effect of Internal Revenue Code Section 162(m)

15

The coefﬁcient of DUM3 represents the incremental effect of section 162(m) on the compensation proportions given that the ﬁrm qualiﬁed its short-term bonus plan. The interpretation of the DUM3 coefﬁcient is similar to that of DUM2. This analysis is conducted using the change between the last year presection 162(m), and the ﬁrst year post-section 162(m). Section 162(m) applies to compensation that is otherwise deductible in any taxable year beginning on or after January 1, 1994. Consequently, for December ﬁscal year end companies, the last year prior to (ﬁrst year after) section 162(m) would be 1993 (1994), while for non-December ﬁscal year end companies, the last year prior to (ﬁrst year after) section 162(m) would be 1994 (1995).

SAMPLE SELECTION The source for the sample is Standard & Poor’s ExecuComp, which includes the ﬁrms in the S&P 500, Mid-Cap, and Small-Cap indexes. The data available on ExecuComp was augmented with ﬁnancial data from Standard & Poor’s Compustat. Data on bonus plan qualiﬁcation for the DUM3 indicator variable was hand collected from corporate proxy statements. As the sample ﬁrms are the largest publicly held U.S. corporations, they are the ones most likely to be affected by section 162(m). We restrict our data to the highest paid ofﬁcer for each ﬁrm (hereafter CEO). At the time of the analysis, ExecuComp had compensation data on 21,738 CEO executives over the period 1992–2006. The test sample is reduced to 14,138 observations due to missing data. In particular, more than 4,000 observations are lost due to the lagged data required for the VALUE variable.6 However, as shown in the right-hand column of Table 5, rerunning the analysis without the VALUE variable and hence on the larger data set does not affect the results. Table 3 provides some descriptive statistics about the sample.7 In roughly half (the mean of DUM1 is 0.54) of executive year observations, the individual earned more than $900,000 in cash compensation, making him/her affected (according to our deﬁnition) by section 162(m). Options were a signiﬁcant part of the compensation package (PERCENTOPT), as they comprised a mean (median) 33 (30) percent of total compensation and 117 (58) percent of cash compensation, with the mean (median) grant valued at $1,858,580 ($581,790). The mean (median) dividend yield is 1.29 percent (0.54 percent) and the mean (median) one-year return to shareholders is 17.83 (10.42) percent. The mean (median) income before extraordinary items and discontinued operations deﬂated by total assets (ROA) is 4 (5) percent,

Table 3. Variables PERCENTOPT PERCENTOPT2 BSVAL NUMGRT DUM1 DUM2 DIVYIELD SIZE TRS TRS3YR TRS5YR ROA PCROA LOSS LESS VARROA RISK CONSTRAINT FCF BKM VALUE

Descriptive Statistics.

Observations

Mean

Standard Deviation

1st Quartile

Median

3rd Quartile

14,138 14,138 14,138 14,138 14,138 14,138 14,138 14,138 14,138 14,138 14,138 14,138 14,138 14,138 14,136 14,138 14,138 14,138 14,138 14,138 14,138

0.33 1.17 1,858.58 158.34 0.54 0.52 1.29 7.34 17.83 9.48 7.44 0.04 0.00 0.16 0.36 0.02 0.42 0.44 0.00 0.49 22,537

0.28 1.56 3,433.50 251.24 0.50 0.50 2.07 1.58 65.76 26.96 20.05 0.09 0.09 0.36 0.48 0.08 0.19 0.50 0.10 0.67 1,078,488

0.00 0.00 0.00 0.00 0.00 0.00 0.00 6.19 13.24 3.92 4.01 0.02 0.01 0.00 0.00 0.00 0.27 0.00 0.05 0.26 1.65

0.30 0.58 581.79 68.00 1.00 1.00 0.54 7.21 10.42 9.68 8.97 0.05 0.01 0.00 0.00 0.00 0.37 1.00 0.00 0.43 4.43

0.54 1.56 1,945.52 188.32 1.00 1.00 1.97 8.41 35.99 23.89 19.83 0.08 0.02 0.00 1.00 0.00 0.51 1.00 0.05 0.64 14.58

Notes: PERCENTOPTit is the Black–Scholes value of option grants to executive i in year t divided by executive i’s total compensation, where both the Black–Scholes value and total compensation are provided by ExecuComp; PERCENTOPT2it the Black–Scholes value of option grants to executive i in year t divided by executive i’s total cash compensation, where both the Black–Scholes value and total cash compensation are provided by ExecuComp; BSVALit the Black–Scholes value of the options granted to executive i in year t as provided by ExecuComp; NUMGRTit the total number of options granted to executive i in year t; DUM1it an indicator variable taking the value of 1 if cash compensation of executive i is greater than $900,000 in year t, 0 otherwise; DUM2it an indicator variable taking the value of 1 if cash compensation of executive i is greater than $900,000 in year t and year t is 1994 or later, 0 otherwise; DIVYIELDit the dividend yield of executive i’s ﬁrm in year t; SIZEit the log of assets of executive i’s ﬁrm in year t; TRSit the return to shareholders of executive i’s ﬁrm in year t; TRS3YRit the return to shareholders of executive i’s ﬁrm for three years ending with year t; TRS5YRit the return to shareholders of executive i’s ﬁrm for three years ending with year t; ROAit net income before extraordinary items and discontinued operations deﬂated by total assets for executive i’s ﬁrm for year t; PCROAit change in net income before extraordinary items and discontinued operations deﬂated by total assets for executive i’s ﬁrm in year t; LESSit indicator variables taking the value of one if net income before extraordinary items and discontinued operations was less than prior year, and zero otherwise for executive i’s ﬁrm in year t; LOSSit indicator variables taking the value of one if net income before extraordinary items and discontinued operations was less than zero, and zero otherwise for executive i’s ﬁrm in year t; VARROAit variance of ROA using all available observations for company of executive i. Minimum number of observations is 6; RISKit the volatility measure (60 month) used by ExecuComp to calculate the Black–Scholes values for executive i’s ﬁrm in year t; CONSTRAINTit indicator variable taking the value of 1 when retained earnings plus the value of cash dividends and stock repurchases in the current year divided by cash dividends and stock repurchases in prior year is less than two and 0 otherwise; FCFit ratio of free cash ﬂow to total assets; BKMit book-to-market value of equity; and VALUEit value of shares owned, plus intrinsic value of options held, deﬂated by total direct compensation – all measured at end of previous year.

The Effect of Internal Revenue Code Section 162(m)

17

while the mean (median) change in ROA (income before extraordinary items and discontinued operations deﬂated by total assets) was 0 (1) percent. The mean (median) variance of ROA (VARROA) is 2 (0) percent. Sixteen percent of the ﬁrm year observations in the sample had a loss in the current year, 36 percent had income lower in the current year than in the prior year, and 44 percent of the sample is earnings constrained (the mean of CONSTRAINT). The mean and median ratio of FCF to total assets is less than 1 percent and the mean (median) value book to market value is 49 (43) percent. Table 4 provides Pearson correlation statistics for the independent variables. While most of the variables are signiﬁcantly correlated, the largest correlations (about negative .4) are between the risk variable (the 60-month volatility measure used in calculating the Black–Scholes values) and size, proﬁtability (ROA), and dividend yield. This is not surprising because more volatile companies tend to be smaller and less proﬁtable.

EMPIRICAL RESULTS Model (1): Increased Use of Stock Options Table 5 presents the results of the Tobit regression analysis for model (1), as well as a model which excludes VALUE and hence, allows the incorporation of 1992 into the analysis.8 In both regressions, the coefﬁcient on DUM2 is, as predicted, positive and signiﬁcant ( p-valueo.10 in a one-tailed test). This supports the hypothesis that there was a positive incremental effect of section 162(m) on the amount of stock options in the compensation package of affected individuals. The coefﬁcient on the indicator variable, DUM1 is signiﬁcant, but negative. The sign of this coefﬁcient may be driven by the deﬁnition of highly paid executives, which is based upon cash compensation – ceteris paribus, the higher cash compensation the lower stock-based compensation. The control variable, VALUE is negative as expected and signiﬁcant. All of the ﬁrm related control variables are signiﬁcant. The coefﬁcients on DIVYIELD and SIZE are negative and positive respectively, consistent with the proportion of stock in the compensation package being inversely related to dividend yield and positively associated with ﬁrm size. The coefﬁcients on the performance measures are mixed, as the coefﬁcient on ROA is positive and signiﬁcant, while the coefﬁcient on TRS is negative and signiﬁcant. The coefﬁcient on RISK is positive and signiﬁcant consistent with the positive effect of risk on the value of the option being associated with an increase in the proportion of stock in the compensation package.

BKM

FCF

Constraint

VarROA

Risk

ROA

TRS

Size

Divyield

Divyield

0.25 (o.0001)

Size

TRS

0.02 (0.01) 0.17 (o.0001)

0.08 (o.0001)

0.02 (0.004)

ROA

0.35 (o.0001)

0.06 (o.0001)

0.41 (o.0001)

0.41 (o.0001)

Risk

0.31 (o.0001)

0.34 (o.0001)

0.004 (0.65)

0.19 (o.0001)

0.09 (o.0001)

VarROA

0.18 (o.0001)

0.51 (o.0001)

0.23 (o.0001)

0.05 (o.0001)

0.29 (o.0001)

0.32 (o.0001)

Constraint

Pearson Correlation Coefﬁcients (p-value)

0.08 (o.0001)

0.14 (o.0001)

0.06 (o.0001)

0.32 (o.0001)

0.06 (o.0001)

0.03 (0.00)

0.03 (0.002)

FCF

Correlation Analysis of the Independent Variables.

0.11 (o.0001)

Table 4.

0.02 (0.03)

0.03 (0.0002)

0.04 (o.0001)

0.006 (0.49)

0.11 (o.0001)

0.12 (o.0001)

0.02 (0.01)

0.09 (o.0001)

BKM

0.005 (0.54)

0.005 (0.52)

0.02 (0.05)

0.003 (0.67)

0.003 (0.76)

0.002 (0.81)

0.008 (0.34)

0.03 (0.002)

0.00 (0.79)

Value

18 STEVEN BALSAM AND DAVID RYAN

19

The Effect of Internal Revenue Code Section 162(m)

Table 5.

Tobit Regression Results.

PERCENTOPTit ¼ a0 þ a1 DUM1it þ a2 DUM2it þ a3 DIVYIELDit þ a4 SIZEit þ a5 TRSit þ a6 ROAit þ a7 VARROAit þ a8 RISKit þ a9 CONSTRAINTit þ a10 FCFit þ a11 BKMit X X þ a12 VALUEit1 þ a YEAR þ a IND þ eit

Variable Name INTERCEPT DUM2 Executive related control variables DUM1 VALUE Firm related control variables DIVYIELD SIZE TRS ROA RISK VARROA CONSTRAINT FCF BKM YEAR IND N

Model With VALUE Coefﬁcient (w2)

Model Without VALUE Coefﬁcient (w2)

0.1437 (3.15) 0.0294 (1.98)

0.0350 (0.22) 0.0272 (4.82)

0.0702 (11.45) 0.0000 (20.64)

0.0699 (32.41)

0.0092 (155.05) 0.0538 (803.65) 0.0002 (31.42) 0.2959 (117.89) 0.3961 (512.21) 0.0470 (2.89) 0.0638 (167.07) 0.1400 (43.28) 0.0439 (111.81) NR NR 14,138

0.0115 (303.25) 0.0476 (780.01) 0.0000 (7.44) 0.2320 (119.78) 0.3655 (571.22) 0.0424 (7.60) 0.0586 (170.96) 0.1313 (49.69) 0.0021 (13.76) NR NR 18,306

Notes: Variable deﬁnitions are provided as the table footnote of Table 3. We omit the coefﬁcients for the year and industry dummies for brevity. Signiﬁcance at po0.001. Signiﬁcance at po0.01. Signiﬁcance at po0.1 (one-tail).

20

STEVEN BALSAM AND DAVID RYAN

The other risk related measure, the variability of income, VARROA is also positive and signiﬁcant, consistent with an increased use of options when accounting-based bonuses are more risky. Consistent with the ﬁndings of prior research, FCF and CONSTRAINT are positive and signiﬁcant. These results indicate that ﬁrms are more likely to use stock options to compensate managers when they have less FCF and are constrained with respect to earnings. The results of the BKM variable are surprising. The coefﬁcient on BKM is negative and signiﬁcant when the Value variable is included in the analysis which is consistent with the notion that ﬁrms are more likely to use stock-option compensation when they have greater investment opportunities. However, the coefﬁcient on BKM becomes positive and signiﬁcant when the Value variable is excluded from the analysis. The year and industry indicator variables provide a control for industrywide and macro-economic effects. While the coefﬁcients on these indicator variables are omitted for brevity, they are brieﬂy discussed here. As might be expected, the coefﬁcients associated with most of the years are positive and signiﬁcant. The coefﬁcients for the most recent years are generally insigniﬁcant. With a lag, these coefﬁcients seem to track overall market stock price movements. The results are consistent with overall market performance affecting the desirability of stock options to executives and their use by corporations. While most of the coefﬁcients on the industry controls are signiﬁcant and negative, consistent with expectations, those associated with high technology industries are signiﬁcant and positive.

Sensitivity Analysis Murphy (1998) notes that about 40 percent of ﬁrms grant a ﬁxed number of options each year, while another 40 percent of ﬁrms grant options with ﬁxed value each year. In the former situation, there would be a mechanical relation between the Black–Scholes value of option grants and share price. Consequently, in a rising market and independent of any other incentives, BLK_VALU and PERCENTOPT will increase, on average. For that reason, the analysis in Table 5 is rerun using three alternative dependent variables: PERCENTOPT2, BLK_VALU, and SOPTGRT (see Table 6). PERCENTOPT2 is the Black–Scholes value of options granted divided by the executive’s total cash compensation, while BLK_VALU is simply the Black–Scholes value of the options granted, and SOPTGRT is the total number of options granted to the executive. While the ﬁrst two alternative measures are subject to the same mechanical relation between option value

21

The Effect of Internal Revenue Code Section 162(m)

Table 6.

Tobit Regression Results for Alternative Dependent Variables.

The dependent variables are PERCENTOPT2, BLK_VALU, and SOPTGRNT DEPENDit ¼ a0 þ a1 DUM1it þ a2 DUM2it þ a3 DIVYIELDit þ a4 SIZEit þ a5 TRSit þ a6 ROAit þ a7 VARROAit þ a8 RISKit þ a9 CONSTRAINTit þ a10 FCFit þ a11 BKMit X X þ a12 VALUEit1 þ a YEAR þ a IND þ eit Coefﬁcient (w2)

Coefﬁcient (w2)

Coefﬁcient (w2)

3.0400 (29.96) DUM2 0.5155 (13.09) Executive related control variables DUM1 0.9815 (48.07) VALUE 0.0000 (12.72) Firm related control variables DIVYIELD .0637 (158.93) SIZE 0.4872 (1425.82) TRS 0.0010 (21.73) ROA 2.3522 (163.84) RISK 3.1508 (679.29) VARROA 0.5320 (7.77) CONSTRAINT 0.5103 (227.44) FCF 0.8125 (31.08) BKM 0.3394 (161.77) YEAR NR IND NR N 14,138

10,026.50 (44.08) 2,597.89 (48.07)

744.70 (49.14) 122.77 (21.21)

1,917.17 (26.51) 0.0001 (6.71)

65.87 (6.19) 0.0000 (7.77)

108.70 (46.98) 1,839.50 (2,913.44) 1.1099 (3.03) 7,215.80 (215.45) 7,036.81 (469.33) 2,849.31 (26.77) 897.32 (94.99) 1,338.51 (11.29) 512.40 (52.14) NR NR 14,138

2.64 (4.61) 112.03 (2,222.50) 0.1443 (10.54) 9.08 (0.07) 547.44 (570.17) 500.92 (162.54) 54.68 (70.13) 109.61 (15.50) 36.50 (62.90) NR NR 14,138

Variable Name INTERCEPT

Notes: Variable deﬁnitions are provided in the Table footnote of Table 3. We omit the coefﬁcients for the year and industry dummies for brevity. Signiﬁcance at po0.001. Signiﬁcance at po0.01. Signiﬁcance at po0.1 (one-tail).

22

STEVEN BALSAM AND DAVID RYAN

and share price, the last is unaffected by it. That is, any changes observed in SOPTGRT are the result of a conscious decision by the compensation committee to increase or decrease the number of options granted. The results for all three alternative dependent variables are consistent with the original model. In all of the analyses, the coefﬁcient on DUM2 is positive and signiﬁcant (po0.001). Most of the control variables remain signiﬁcant in the expected direction; although some signs change in the SOPTGRNT model. The effect of some alternative performance measures, reported in Table 7, are also examined. For accounting-based performance measures, ROA is replaced ﬁrst with LESS (an indicator variable equal to one if net income before extraordinary items and discontinued operations was less than in the prior year) and then with LOSS (an indicator variable taking the value of one if net income before extraordinary items and discontinued operations was less than zero). For stock-based performance measures, the total return to shareholders over one year is replaced with total returns to shareholders over three (TRS3YR) and ﬁve (TRS5YR) year periods. In all permutations, the coefﬁcient on DUM2 is positive and signiﬁcant (one-tail tests). The major difference when the accounting performance variable is varied is the coefﬁcient on that variable. That is, in the base model, the coefﬁcient on ROA is positive and signiﬁcant, as it is when LESS is used as the performance measure. But when LOSS is used, the coefﬁcient becomes negative and signiﬁcant. The major difference when the market variable is varied is that, while in the one-year window, the coefﬁcient on TRS is negative and signiﬁcant, in the longer windows, TRS3YR and TRS5YR, it is positive and signiﬁcant. Some ﬁrms do not grant stock options to their executives. Consequently, we reran (in an un-tabulated analysis) all of the Tobit regressions after deleting all ﬁrms that did not grant any options during the sample period. The results are unchanged.

Model (2): Substitution Effect Table 8 presents the results of the seemingly unrelated regression analysis. The coefﬁcients on DUM2 in the DPERCENTOPT and DPBONUS regressions are positive and signiﬁcant, and negative and signiﬁcant in the DPSALARY regression. These results demonstrate that performance-based compensation, options and bonuses, increased proportionally more than salary after the implementation of section 162(m). This provides evidence that affected ﬁrms shifted executive pay to performance-based compensation.

23

The Effect of Internal Revenue Code Section 162(m)

Table 7. Tobit Regression Results with Alternative Performance Measures. The dependent variable is PERCENTOPT Coefﬁcient (w2)

Variable Name INTERCEPT

0.0888 (1.20) DUM2 0.0309 (2.18) Executive related control variables DUM1 0.0627 (9.08) VALUE 0.0000 (18.67) Firm related control variables DIVYIELD 0.0091 (152.83) SIZE 0.0507 (715.33) TRS 0.0001 (11.03) TRS 3 yr

0.0929 (1.31) 0.0303 (2.09)

0.1404 (3.00) 0.0290 (1.92)

0.1486 (3.36) 0.0304 (2.10)

0.0667 (10.27) 0.0000 (19.67)

0.0725 (12.12) 0.0000 (21.02)

0.0745 (12.80) 0.0000 (21.42)

0.0089 (146.06) 0.0517 (746.00) 0.0001 (18.79)

0.0091 (149.54) 0.0542 (814.06)

0.0091 (147.28) 0.0537 (799.33)

0.0001 (1.30)

TRS 5 yr ROA LESS

VARROA CONSTRAINT FCF BKM YEAR IND N

0.3868 (492.47) 0.0465 (2.82) 0.0627 (161.69) 0.1400 (42.70) 0.0380 (81.62) NR NR 14,138

0.3886 (498.25) 0.0500 (3.26) 0.0646 (170.25) 0.1354 (40.15) 0.0358 (73.99) NR NR 14,138

0.0180 (15.94)

LOSS RISK

0.2667 (89.84)

0.0005 (20.81) 0.2483 (80.68)

0.3473 (416.92) 0.0331 (1.60) 0.0617 (156.71) 0.0684 (11.09) 0.0528 (163.37) NR NR 14,136

0.0227 (12.01) 0.3664 (439.85) 0.0195 (0.54) 0.0623 (158.75) 0.0818 (15.74) 0.0493 (139.99) NR NR 14,138

Notes: Variable deﬁnitions are provided as Table footnote of Table 3. We omit the coefﬁcients for the year and industry dummies for brevity. Signiﬁcance at po0.001. Signiﬁcance at po0.01. Signiﬁcance at po0.1 (one-tail).

Table 8.

Seemingly Unrelated Regression (SUR) Results.

The dependent variables are the change in PERCENTOPT, PSALARY, and PBONUS DPERCENTOPTit ¼ a0 þ a1 DUM2i þ a2 DUM3i þ a3 DDIVYIELDi þ a4 DSIZEi þ a5 DTRSi þ a6 DROAi þ a7 VARROAi þ a8 DRISKi þ a9 DCONSTRAINTi þ a10 DFCFi X þ a11 DBKMi þ a12 PERCENTOPTit1 þ a IND þ eit DPSALARYit ¼ a0 þ a1 DUM2i þ a2 DUM3i þ a3 DDIVYIELDi þ a4 DSIZEi þ a5 DTRSi þ a6 DROAi þ a7 VARROAi þ a8 DRISKi þ a9 DCONSTRAINTi þ a10 DFCFi X þ a11 DBKMi þ a12 PSALARYit1 þ a IND þ eit DPBONUSit ¼ a0 þ a1 DUM2i þ a2 DUM3i þ a3 DDIVYIELDi þ a4 DSIZEi þ a5 DTRSi þ a6 DROAi þ a7 VARROAi þ a8 DRISKi þ a9 DCONSTRAINTi þ a10 DFCFi X þ a11 DBKMi þ a12 PBONUSit1 þ a IND þ eit DPERCENTOPTit Variable INTERCEPT DUM2 DUM3 DDIVYIELD DSIZE DTRS DROA DRISK VARROA DCONSTRAINT DFCF DBKM Lag(percentopt) Lag(psalary) Lag(pbonus)

DPSALARYit

DPBONUSit

Coefﬁcient

(t-Statistic)

Coefﬁcient

(t-Satistic)

Coefﬁcient

(t-Statistic)

0.2410 0.0455 0.0209 0.0023 0.0246 0.0000 0.2734 0.1729 1.2311 0.1016 0.3449 0.0669 0.4583

2.43 2.54

0.08758 0.0878 0.0229 0.0010 0.0606 0.0000 0.3719 0.0864 0.0652 0.0540 0.1250 0.1335

1.19 6.57 1.15 0.37 1.93 0.36 2.99 0.46 0.21 1.82 2.05 2.48

0.0711 0.0409 0.0187 0.0054 0.0674 0.0001 0.3510 0.0592 0.2651 0.0249 0.1829 0.0631

1.19 3.74 1.14 2.49 2.92 0.52 3.42 0.39 1.03 1.02 3.65 1.43

0.3608

14.88 0.4164

14.99

0.76 0.63 0.57 0.19 1.61 0.68 2.88 2.51 4.13 0.91 17.85

Notes: System weighted R2 0.39; the symbol D in a variable name denotes a change in the value of the variable from the last year pre-section 162(m) to the ﬁrst year post-section 162(m); DPERCENTOPT is the change in the proportionate option compensation of executive i; DPSALARY the change in the proportionate salary compensation of executive i; and DPBONUS the change in the proportionate bonus compensation of executive i. The independent variables are otherwise deﬁned as the table footnote of Table 3. For December ﬁscal year end companies, the last year prior to (ﬁrst year after) section 162(m) would be 1993 (1994), while for non-December ﬁscal year end companies, the last year prior to (ﬁrst year after) section 162(m) would be 1994 (1995). We omit the coefﬁcients for the industry dummies for brevity. Signiﬁcance at po0.001. Signiﬁcance at po0.01. Signiﬁcance at po0.1 (one-tail).

The Effect of Internal Revenue Code Section 162(m)

25

Interestingly, there is no evidence that ﬁrms shifted pay from bonuses to options. Rather, it appears that affected ﬁrms shifted from non-performancebased pay (i.e., salary) to performance-based pay (bonuses and/or options). The coefﬁcients on the DUM3 variable are not signiﬁcant in any of the regressions. This suggests that qualifying short-term bonus plans did not affect ﬁrm pay with respect to the composition of executive pay packages.

SUMMARY This chapter extends the prior research on the effect of section 162(m) on executive compensation by focusing on the use of stock options. The congressional intent of section 162(m) was to strengthen the relationship between executive compensation and ﬁrm performance. As a consequence, the section tends to favor stock-option compensation relative to other forms of compensation. Under 162(m), when compensation is in excess of the million-dollar limit, additional salary is not deductible and ﬁrms must put bonus compensation at risk for the bonus to qualify as deductible. This is a signiﬁcant change from most ﬁrms’ practices in previous years. In contrast, section 162(m) required little or no change in compensatory stock-option plans because such plans generally met the deﬁnition of ‘performance-based’ under section 162(m). Section 162(m) thus increased the risk of annual cash bonuses relative to option compensation. The results of this study provide evidence that section 162(m) has led to a shift towards performance-based compensation in executive pay. Consistent with Congressional intent, affected ﬁrms increased the use of options and bonuses relative to salary compensation, presumably to maximize deductions. While there is evidence of a substitution effect for salary increases in affected ﬁrms, there is no evidence of a substitution effect for annual cash bonuses. There is a growing recognition that section 162(m) spurred the increase of stock-option compensation, but also that such compensation has lead to some signiﬁcant unintended consequences. For example, recent empirical work demonstrates a linkage between earnings manipulation and the use of executive stock-option compensation (e.g., Burns & Kedia, 2006; Bergstresser & Philippon, 2006). The Financial Economists Roundtable (2003, p. 5) believes that section 162(m) ‘‘is a clumsy attempt to regulate the level and structure of executive compensation’’ and has called for its repeal. Thus, while ﬁrms and their executives are acting in a way consistent with the incentives of section 162(m), those actions may not be as originally intended by Congress.

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NOTES 1. Both Matsunaga (1995, note 6) and Murphy (1998) ﬁnd about 95 percent of corporations granting options with an exercise price equal to grant-date fair market value. 2. As noted in the sensitivity analysis, the dependent variable is alternatively deﬁned (1) as the Black-Scholes value of options granted divided by total cash compensation, (2) as the un-deﬂated Black–Scholes value of options granted, and (3) as the number of options granted, with no change in results. 3. Cash compensation is used rather than total compensation to deﬁne affected executives following the previous literature, e.g., Perry and Zenner (2001), Balsam and Ryan (2007), Balsam and Yin (2005). The cutoff of $900,000 avoids missing ﬁrms that reduced compensation because of section 162(m). The results are not affected by the use of other cutoffs, i.e., $950,000 or $1,000,000. In addition, the results are qualitatively the same if salary or total direct compensation is used to deﬁne affected executives. Consequently, while the choice of affected executives could potentially bias the results, the fact that the results are robust to alternative cutoffs and ways of measuring affected executives provides reassurance that this is not the case. 4. The conclusions remain when other performance measures are used instead of ROA. 5. While the bonus has to be based on objective performance measures, there is no requirement that these measures be accounting based. 6. Information on each executive’s prior year stock and option holdings is needed to compute the variable, VALUE. Consequently, observations are lost in those instances where prior year information on the executive’s holdings is not available because the executive was not a listed ofﬁcer in the company in the prior year. 7. Variables are winsorized at two standard deviations from the mean. 8. Recall that in the model, this period’s equity compensation is based in part, on the equity and option holdings at the end of the period; i.e., VALUE is lagged, causing the loss of the earliest year for which data is available. One advantage of running the model without VALUE is that it allows 1992 data to be incorporated into the analysis, thus increasing the percentage of the observations in the pre-section 162(m) period.

REFERENCES Balsam, S. (2002). An introduction to executive compensation. San Diego, CA: Academic Press. Balsam, S., & Ryan, D. (1996). Response to tax law changes involving the deductibility of executive compensation: A model explaining behavior. Journal of the American Taxation Association, 18, 1–12. Balsam, S., & Ryan, D. (2007). Limiting executive compensation: The case of CEOs hired after the imposition of section 162(m), the million-dollar cap on executive compensation. Journal of Accounting, Auditing and Finance, 22(4), 599–621.

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Balsam, S., & Yin, J. (2005). Explaining ﬁrm willingness to forfeit tax deductions under Internal Revenue Code section 162(m): The million-dollar cap. Journal of Accounting and Public Policy, 24, 300–324. Bergstresser, D., & Philippon, T. (2006). CEO incentives and earnings manipulation. Journal of Financial Economics, 80, 511–529. Brownstein, A., & Panner, M. (1992). Who should set CEO pay? The press? Congress? Shareholders?, The Harvard Business Review (May–June), 28–38. Burns, N., & Kedia, S. (2006). Impact of performance based compensation on misreporting. Journal of Financial Economics, 79, 35–67. Carter, M. E., Lynch, L. J., & Tuna, I. (2004). The role of incentives and accounting in the design of executive compensation packages. Working Paper. The University of Pennsylvania. Core, J., & Guay, W. (1999). The use of equity grants to manage optimal equity incentive levels. Journal of Accounting and Economics, 28, 151–184. Crystal, G. (1992). In search of excess: The overcompensation of the American executive. New York: W.W. Norton. Dechow, P., Hutton, A., & Sloan, R. (1996). The economic consequences of accounting for stock-based compensation. Journal of Accounting Research, 34, 1–20. Financial Economists Roundtable. (2003). Statement on the controversy over executive compensation. Available at http://www.luc.edu/orgs/ﬁnroundtable Halperin, R., Kwon, Y., & Rhoades-Catanach, S. (2001). The impact of deductibility limits on compensation contracts: A theoretical examination. Journal of American Taxation Association, 23(supplement), 52–65. Harris, D., & Livingstone, J. (2002). Federal tax legislation as a political cost benchmark. The Accounting Review, 77(October), 997–1018. Hodges, F., Rajgopal, S., & Shevlin, T. (2005). How do managers value stock options and restricted stock. Working Paper. University of Washington. Johnson, M., Porter, S., & Shackell, M. (2001). Stakeholder pressure and the structure of executive compensation. Working Paper. University of Michigan. Lambert, R. A., Lanen, W. N., & Larcker, D. F. (1989). Executive stock option plans and corporate dividend policy. Journal of Financial and Quantitative Analysis, 24, 409–425. Liang, N., & Weisbenner, S. J. (2001). Who beneﬁts from a bull market? An analysis of employee stock option grants and stock prices. FEDS Working Paper no. 2001–57. Washington, DC. Matsunaga, S. R. (1995). The effects of ﬁnancial reporting costs on the use of employee stock options. The Accounting Review, 70(1), 1–26. McCarroll, T. (1993). Rolling back executive pay. Time, March 1, pp. 49–50. Meulbroek, L. K. (2001). The efﬁciency of equity-linked compensation: Understanding the full cost of awarding executive stock options. Financial Management, 30(2), 5–44. Murphy, K. J. (1985). Corporate performance and management remuneration: An empirical analysis. Journal of Accounting and Economics, 7, 11–42. Murphy, K. J. (1998). Executive compensation. Working Paper. University of Southern California. Perry, T., & Zenner, M. (2001). Pay for performance? Government regulation and the structure of compensation contracts. Journal of Financial Economics, 62(3), 453–488. Reitenga, A., Buchheit, S., Yin, J., & Baker, T. (2002). CEO bonus pay, tax policy, and earnings management. Journal of American Taxation Association (supplement), 1–23.

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Rose, N., & Wolfram, C. (2000). Has the ‘‘million-dollar cap’’ affected CEO pay? The American Economic Review, 90(2), 197–202. Rose, N., & Wolfram, C. (2002). Regulating executive pay: Using the tax code to inﬂuence chief executive compensation. Journal of Labor Economics, 20(2), 138–175. U.S. Congress, House. (1993). Fiscal year budget reconciliation: Recommendations of the committee on ways and means, May 18. U.S. Washington, DC: Government Printing Ofﬁce. Yermack, D. (1995). Do corporations award CEO stock options effectively? Journal of Financial Economics, 39, 237–269.

AN ANALYSIS OF DIVIDEND AND CAPITAL GAINS TAX RATE DIFFERENTIALS AND THEIR EFFECT ON THE STRUCTURE OF CORPORATE PAYOUTS Teresa Lightner ABSTRACT This study investigates whether corporations consider shareholder-level taxes when setting corporate distribution policy. I investigate the relation between the tax-rate differential on dividend and capital gains income and its effect on ﬁrms’ distribution policies. I ﬁnd that ﬁrms consider shareholder-level taxes and that this association varies with the percentage of the ﬁrm owned by individual shareholders. Hence, ﬁrms increase share repurchases and decrease the percentage of total corporate payout in the form of a dividend as the tax-rate differential increases. Thus, an increased substitution effect occurs as capital gains become relatively more tax-advantaged compared to dividends. Furthermore, I ﬁnd a positive association between the percentage of the ﬁrm owned by individual investors and the percentage of total corporate payout distributed as a repurchase. These ﬁndings are consistent with personal

Advances in Taxation, Volume 18, 29–51 Copyright r 2008 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 1058-7497/doi:10.1016/S1058-7497(08)18002-4

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income taxes inﬂuencing managerial decisions regarding the payout of excess corporate funds.

INTRODUCTION This study investigates whether corporations consider shareholder-level taxes when setting corporate distribution policy. The two primary means by which a corporation distributes excess cash are dividends and share repurchases.1 During the time period of this study, dividends were taxed at ordinary rates for individual shareholders through 2002. From 2003 to 2005, dividends were taxed at the capital gains rate. Alternatively, for repurchases, the difference between the repurchase price and a shareholder’s basis was taxed at capital gains tax rates. From 1985 to the present, the differential between maximum tax rates on dividend and capital gains income ranged from 0 to 30 percent (Table 1).2 Thus, if corporations consider the tax implications of their distribution policy, ﬁrms with taxable investors should be more inclined to engage in share repurchases rather than dividend payments as the tax-rate differential widens favoring capital gains.3 In a related article, Lie and Lie (1999) examine four types of distribution methods to determine whether managers consider the tax situations of the ﬁrm’s investors when making distribution decisions. Lie and Lie ﬁnd that managers are more likely to announce an open-market share repurchase Table 1. Period

Maximum Tax-Rate Differentials 1985–2007.

Maximum Dividend Rate (%)

Maximum Capital Gains Rate (%)

Tax-Rate Differential (%)

Maximum Ordinary Rate Corporations (%)

Corporate Dividends Received Deduction (%)

1985–1986 1987 1988–1990

50 38.5 33

20 28 28

30 10.5 5

46 40 34

1991–1992 1993–05/06/97 05/07/97–2000 2001 2002 2003–2007

31 39.6 39.6 39.1 38.6 15

28 28 20 20 20 15

3 11.6 19.6 19.1 18.6 0

34 35 35 35 35 35

85 85 80 (1988) 70 (1989–1990) 70 70 70 70 70 70

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program than initiate a regular dividend increase if the ﬁrm has a low dividend yield and the repurchase occurred prior to 1986 (their proxies for the clientele of the ﬁrm and the shareholders’ relative tax rates). In this study, I use the tax-rate differential between the maximum rate on dividends and capital gains income to represent the difference in taxation shareholders face when ﬁrms distribute dividends or repurchase shares. This measure captures the difference in taxation between the two distribution methods and is less susceptible to alternative explanations than a binary pre/post TRA ’86 variable. Also, dividend yield may capture the ﬁrm’s or the shareholder’s preference for dividends irrespective of tax differences between dividends and capital gains.4 In addition, I use the dollar value of shares actually repurchased by the ﬁrm rather than the existence of a share repurchase announcement, which does not commit the ﬁrm to repurchase shares of stock and is sometimes used only to signal information without repurchasing shares.5 During the 1980s and 1990s, share repurchases were always taxadvantaged relative to dividends for individual shareholders. Nonetheless, dividends do provide important non-tax beneﬁts to both the corporation (e.g., signaling) and shareholders (e.g., steady cash ﬂows) and will continue to be paid regardless of the tax-rate differential. Research has shown that ﬁrms reduce or eliminate dividends only as a last resort because of the negative signal it sends to the market (DeAngelo & DeAngelo, 1990; DeAngelo, DeAngelo, & Skinner, 1992; Denis, Denis, & Sarin, 1994; Michaely, Thaler, & Womack, 1995).6 Furthermore, repurchase programs cannot replace dividends as a company’s ongoing proportionate payout plan because the Internal Revenue Service (IRS) has the power to recast the transaction as a dividend rather than as an exchange. Thus, as the taxrate differential increases, ﬁrms may slow the growth rate of dividends or completely replace dividend increases with share repurchases. This chapter investigates the relation between the tax-rate differential on dividend and capital gains income and its effect on ﬁrm’s distribution policies (i.e., payment of dividends and repurchases of stock). Speciﬁcally, the study examines whether corporations increase the proportion of total payout in the form of a repurchase as the tax-rate differential increases. The importance of the tax-rate differential should vary based upon the clientele of the ﬁrm because dividends and capital gains are differentially taxed to individual investors, corporate investors, and tax-exempt/tax-deferred entities. I test the association between distribution policy and the taxrate differential and whether this association varies with individual ownership. The results of this analysis will further expand our understanding of

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how taxation affects managerial decisions concerning corporate payout methods. Consistent with expectations, I ﬁnd that ﬁrms consider shareholder tax implications in the corporate payout decision and that this relation varies with the percentage of the ﬁrm owned by individual shareholders. Hence, ﬁrms increase share repurchases and decrease the percentage of total corporate payout in the form of a dividend as the tax-rate differential widens. Thus, an increased substitution effect occurs as capital gains become relatively more tax-advantaged compared to dividends. Furthermore, these ﬁndings are consistent with personal income taxes inﬂuencing managerial decisions regarding the payout of corporate funds. This chapter also contributes to our understanding of the effects of tax policy on corporate transactions. Many recent studies examine the effect of shareholder capital gains taxes on acquisition premiums, acquisition structure, and goodwill amortization deductions arising from acquisitions. Generally, these studies ﬁnd that target shareholder-level tax liabilities are associated with the price of an acquisition and the type of structure an acquisition takes and that, in an acquisition, a majority of the tax beneﬁts associated with goodwill amortization end up accruing to the target ﬁrm’s shareholders (e.g., Ayers, Lefanowicz, & Robinson, 2003; Landsman & Shackelford, 1995; Dhaliwal, Erickson, & Heitzman, 2004; Ayers, Lefanowicz, & Robinson, 2004; Erickson & Wang, 2000; Henning, Shaw, & Stock, 2000; Ayers, Lefanowicz, & Robinson, 2000). In these studies, the acquiring corporation most likely increases the acquisition price and considers shareholder tax implications to complete the acquisition in a timely and cost efﬁcient manner agreeable to all parties. In other words, the corporation beneﬁts from taking the target shareholders’ tax situations into consideration. This chapter also examines the effect of tax policy on corporate distribution policies and ﬁnds that shareholder tax implications are taken into consideration in the decision-making process. However, the beneﬁt to the corporation may not be quite so obvious. Most likely, the primary beneﬁt accrues to the ﬁrms’ shareholders who can reduce their personal income taxes. Nevertheless, an argument can be made that structuring distributions in the most taxadvantaged form for the corporation’s shareholders may make the ﬁrm more attractive to those shareholders. In addition, prior research ﬁnds that the market values repurchase announcements more when capital gains rates are low relative to dividends (Grullon & Michaely, 2002). Thus, when capital gains rates are low relative to the rate on dividends, share price increases more for ﬁrms that announce repurchase programs than for those that increase dividends.

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The remainder of this chapter is organized as follows. The next section describes the effect of income taxes on corporate distributions. This discussion is followed by a review of the related literature and development of the hypotheses to be tested. The next section describes the model speciﬁcation, research design, and sample selection. The ﬁnal three sections present results, limitations, and concluding remarks.

CORPORATE DISTRIBUTIONS While dividends give rise to an immediate income tax liability at dividend tax rates, the tax consequences of a share repurchase program can be quite different. If a repurchase qualiﬁes for exchange treatment under IRC Section 302(b), shareholders choosing to sell stock back to the company will be liable for tax on the excess of the sales price over the shareholder’s basis in the shares.7 The magnitude of the personal tax advantage from repurchases depends on each shareholder’s cost basis and his or her individual marginal tax rate. Section 302(b) states that share repurchases will not qualify for preferential capital gains treatment but will instead be treated as a dividend unless either the redemption of shares is ‘‘substantially disproportionate’’ and the shareholder owns less than 50 percent of the total voting power after the redemption or the distribution is ‘‘essentially not equivalent’’ to paying a dividend. A redemption is ‘‘substantially disproportionate’’ if, after the repurchase, the percentage ownership of the tendering shareholder is less than 80 percent of the percentage ownership position before the repurchase. These two provisions, in effect, rule out preferential capital gains treatment for pro-rata repurchases. While the IRS has the authority to declare prorata repurchases equivalent to dividend payments, thereby eliminating any tax advantage, it almost never imposes such a ruling on a large public corporation. Non-corporate taxpayers generally prefer to have a stock redemption treated as a sale or exchange rather than a dividend. This preference exists because such transactions result in the tax-free recovery of the redeemed stock’s basis. Moreover, the maximum capital gains tax rate applied to stock repurchases is usually lower than the rate applied to dividends.8 However, corporate shareholders will prefer a dividend to a share repurchase because of the favorable tax treatment they receive on dividends. Corporations can exclude some or all of the dividends they receive from taxable income through the dividends received deduction.9 Additionally, any preferential

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tax rates applicable to dividends or long-term capital gains income is not available to corporations. Consequently, corporate and non-corporate shareholders will have different distribution preferences. Types of Share Repurchase Programs A ﬁrm primarily uses four methods to execute a share repurchase program: (1) open-market repurchases, (2) tender-offer repurchases,10 (3) Dutchauction repurchases,11 and (4) privately negotiated transactions.12 According to the Securities Data Corporation, approximately 90 percent of all repurchases are open-market transactions. They generally involve buying back stock at market prices over a period of time, ranging from several months to years. The ﬁrm pays the normal commission, and the seller is generally not aware that he is selling to the corporation. Prior research suggests that open-market transactions are announced in hopes of signaling good news to investors. On average, the announcement of a repurchase program prompts positive abnormal stock price performance (see, e.g., Comment & Jarrell, 1991; Dann, 1981; Ikenberry, Lakonishok, & Vermaelen, 1995a; and Vermaelen, 1981). Ikenberry et al. (1995a) ﬁnd abnormal performance of approximately 3.5 percent in the days surrounding the announcement of a repurchase plan. Thus, shareholders who choose not to participate in a repurchase program may experience a large (unrealized) capital gain due to the positive signal associated with repurchases. As a result of a buyback, percentage ownership of the ﬁrm increases for non-participating shareholders as shares outstanding decline. Therefore, share repurchase programs seemingly offer an alternative, tax-advantaged method for ﬁrms to distribute cash to investors, while simultaneously beneﬁting non-participating shareholders through increased stock price and increased proportional ownership.

RELATED LITERATURE AND HYPOTHESIS DEVELOPMENT The Motivation to Distribute Corporate Cash Rozeff (1982) and Easterbrook (1984) argue that increased payouts to shareholders reduce the volume of funds over which management has discretionary control, thereby lessening managerial power and reducing

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agency costs. Jensen (1986) states further that the conﬂict of interest between shareholders and managers over payout policies is especially severe when the organization generates substantial free cash ﬂow (i.e., discretionary cash ﬂows). The desire to reduce agency costs motivates managers to distribute cash rather than invest it at below the cost of capital or waste it on organizational inefﬁciencies. This study questions whether ﬁrms consider the tax situations of its shareholders once management decides to distribute cash. Thus, as the taxrate differential increases, will the ﬁrm force a more heavily taxed dividend on the individual shareholders or will it distribute the cash as a share repurchase, which is taxed at lower capital gains rates or deferred altogether if shareholders choose not to participate? A shareholder’s ability to sell his or her shares on the market at any time and recognize a capital gain does not change the expectations of this analysis. I analyze only the ﬁrm’s distribution decision and whether the tax situation of the ﬁrm’s collective shareholder group affects this choice.

Related Literature and Theory Prior research suggests that tax policy affects corporate transactions such as distribution decisions. Blouin, Raedy Smith, and Shackelford (2004) analyze dividend declarations in the quarters surrounding passage of The Jobs and Growth Tax Relief Reconciliation Act of 2003 (JGTRRA). This Act reduced the maximum statutory personal tax rate on dividends from 38.6 to 15 percent. That study compares dividend declarations in the two quarters following passage of JGTRRA to the ﬁve quarters or year and three months preceding the Tax Act. In an additional analysis, they use a t-test to compare repurchase behavior in 2002 and 2003. They ﬁnd an increase in dividend declarations and total dividends after enactment and a decline in share repurchases. They conclude that dividends were substituted for repurchases following JGTRRA. These ﬁndings are consistent with manager-owners modifying the ﬁrm’s dividend policy in response to a tax rate change. Chetty and Saez (2004) also examine the effect of JGTRRA on dividend payments and ﬁnd that dividends increased in the quarters following the dividend tax cut. However, they do not ﬁnd that substitution occurred between repurchases and dividends but rather that total payouts increased for both repurchases and dividends.

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While both Blouin et al., and Chetty and Saez speciﬁcally examine JGTRRA and use observations from 2002 and 2003 for their research questions, my study examines payout policy over the time period 1985–2005. It covers multiple tax rate changes over a longer horizon rather than a tax rate change related to a speciﬁc event. In addition, their research questions do not examine the tax-rate differential nor do they look at the trade-off between repurchases and dividends simultaneously. In an earlier paper, Grullon and Michaely (2002) examine the trade-off between dividends and repurchases and conclude that substitution occurs in the sense that ﬁrms ﬁnance their repurchases with funds that otherwise would be used to increase dividends. They do not tie their result to the taxrate differential but do ﬁnd that abnormal returns are higher for repurchase announcements prior to TRA ’86 when repurchases were more tax favored. Perez-Gonzales (2002) examines only dividends and tests whether the preferences of the ﬁrm’s large shareholders determine dividend policy. He compares ﬁrms with large (greater than 5 percent) individual shareholders to a control group of ﬁrms with an institution as the largest shareholder to determine whether ﬁrms with large individual shareholders inﬂuence the dividend policy of the ﬁrm. Consistent with expectations, he ﬁnds dividend payouts increase as the tax disadvantage of dividends relative to capital gains lessens but only if the ﬁrm’s largest shareholder is an individual. Chetty and Saez (2004) and Blouin et al. (2004) also ﬁnd limited clientele effects. Chetty and Saez (2004) ﬁnd that ﬁrms whose top executives hold more of the ﬁrm’s shares and have fewer unexercised stock options are more likely to initiate dividend payments. Blouin et al. (2004) do not ﬁnd an overall clientele effect in the six months following passage of JGTRRA but do ﬁnd insider ownership encouraged dividend increases after tax rates were lowered. Not surprisingly, ﬁrms with inﬂuential shareholders were the ﬁrst to react to the lower dividend rates in the months following passage of JGTRRA. While it seems intuitive that the ﬁrm’s largest shareholder, corporate executives, or insiders will inﬂuence the distributions of the ﬁrm, I use a sample of publicly traded ﬁrms, with and without inﬂuential shareholders, to consider the collective tax situations of its shareholders. If a ﬁrm has ‘‘enough’’ individual shareholders and the tax-rate differential increases, it seems reasonable that the ﬁrm will take shareholder tax rates into consideration when formulating distribution policy. These prior studies have two primary implications for my analysis. First, the ﬁnding that ﬁrms substitute repurchases for dividends combined with the ﬁnding that the market values repurchase announcements more when

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capital gains rates are low relative to dividend tax rates provides support and motivation for my expectation that ﬁrms will substitute repurchases for dividends as the tax-rate differential widens. Second, the ﬁnding that ﬁrms are more likely to announce a repurchase plan if dividend yields are low, the ﬁrm’s largest shareholder is an individual, or as the percentage of the ﬁrm held by insiders or corporate executives increases suggests that clientele effects may have a large impact on the distribution decision. These observations lead to the ﬁrst and second hypotheses, stated in alternative form: H1. The tax-rate differential between tax rates on dividend and capital gains income is positively associated with the percentage of total corporate payout in the form of share repurchases. H2. The percentage of the ﬁrm owned by individual shareholders is positively associated with the percentage of total corporate payout in the form of share repurchases.

MODEL SPECIFICATION AND SAMPLE SELECTION Based on earlier analysis and discussion, I estimate the following regression model to examine whether corporate distributions are positively associated with the tax-rate differential and individual ownership. Table 2 deﬁnes the variables in the model (expected sign in parentheses). REPURCH%it ¼ g0 þ g1 RATEDIFFt þ g2 INDit1 þ g3 CASHit1 ðþÞ

ðþÞ

ðþÞ

þ g4 INDAMKBKit1 þ g5 NONOPit þ g6 YEARt ðþÞ

ðþÞ

ðþÞ

þ g7 LREPURCH% þ eit ðþÞ

The dependent variable, REPURCH%, measures the percentage of a ﬁrm’s total payout structured as a stock repurchase. I compute this variable by dividing the dollar value of share repurchases by the sum of the dollar value of share repurchases plus cash dividends. RATEDIFF and IND are the primary variables of interest. RATEDIFF measures the responsiveness of share repurchase activity to the difference between tax rates on capital gains and dividend income. Share repurchases become a more taxadvantaged form of corporate payout for non-corporate shareholders as

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Table 2. Variable Name Dependent variable REPURCH%it Independent variables RATEDIFFt INDit1 CASHit1 INDAMKBKit1

NONOPit YEARt LREPURCH%it1

Variable Deﬁnitions. Variable Deﬁnition

The dollar value of share repurchases in year t, divided by the sum of the dollar value of dividends and repurchases. The difference between tax rates on dividend and capital gains income during year t. One minus the percentage of common stock owned by institutional and corporate investors at year t1. The sum of cash, marketable securities, and short-term investments, scaled by total assets, all at time t1. The ratio of the average total market value of equity to total book value of equity for ﬁrm i’s industry, subtracted from the total market value of equity to total book value of equity for ﬁrm i, at year t1. Non-operating income for ﬁrm i, scaled by market value of equity. Indicator variable for each year of the sample (t ¼ 1 y 14). The lagged dollar value of share repurchases in year t, divided by the sum of the dollar value of dividends and repurchases.

the tax-rate differential increases.13 I expect a positive relation between the dependent variable and the tax-rate differential. Furthermore, I expect this relation to be stronger for ﬁrms with more individual investors because these shareholders are subject to the tax-rate differential. I cannot observe the exact makeup of investors in each ﬁrm because individual shareholdings are not public information and therefore must rely on another measure. Consistent with current research, I use the Thomson Financial CDA/Spectrum database to obtain the percentage of the ﬁrm held by institutional shareholders (e.g., Ayers et al., 2003, 2004; Blouin et al., 2004; and Dhaliwal, Li, & Trezevant, 2003). CDA/Spectrum’s institutional holdings data source is the actual 13-F form ﬁled with the SEC on a quarterly basis by money managers/management companies. This measure includes institutional owners that are either tax-exempt (e.g., retirement plans), taxable (e.g., corporations), or mutual funds that can have individuals, retirement plans, or corporations as investors. I measure the variable IND, a proxy for the percentage of the ﬁrm held by individual investors, as one less than the percentage of shares held by institutional investors. I compute this measure as of December 31st in the year preceding the observation. I predict IND will have a positive coefﬁcient.

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Bagwell and Shoven (1989) and Barth and Kasznik (1999) ﬁnd cash to be positively related to repurchase announcement likelihood. Thus, as the level of cash increases, ﬁrms will make distributions in the form of a repurchase rather than a dividend. I compute the variable CASH as the sum of year t1 ending cash balance, marketable securities, and short-term investments, scaled by total assets. The remaining variables in the model control for factors that affect the initiation and execution of share repurchase programs. Empirical evidence from a number of studies supports the theory of undervaluation as a reason for undertaking share repurchase programs (Dann, 1981; Vermaelen, 1981; Comment & Jarrell, 1991; Ikenberry et al., 1995a). Often, when ﬁrms undertake a share repurchase program, managers state that the prevailing market price of their ﬁrm is undervalued. For this reason, managers see their own stock as a good investment of the company’s free cash ﬂows (Dann, 1983; Wansley, Lane, & Sarker, 1989). Thus, the undervaluation hypothesis implies that a repurchase is an investment opportunity rather than a substitute payout method. I include the industry adjusted market-to-book value ratio (INDAMKBK), in the model to control for the prior ﬁnding that companies initiate repurchase programs because their stock is undervalued relative to others in their industry. Over the period 1985–1996, Jagannathan et al. (2000) ﬁnd that repurchasing ﬁrms have signiﬁcantly more non-operating income than do ﬁrms that only increase dividends. Consequently, their results suggest that dividends are paid out of ‘‘permanent’’ operating cash ﬂows while repurchases are paid out of ‘‘temporary’’ non-operating cash ﬂows. Accordingly, I include NONOP, non-operating income scaled by market value of equity, to control for this factor. I include the variable YEAR in the model to control for a positive trend in repurchase activity during the time period of this study. Thus, YEAR is a trend variable equal to 1 in 1985, the ﬁrst year of the sample, through 21 in 2005, the last year of the sample. The ﬁnal variable in the model is LREPURCH%. This variable is the lagged value of the dependent variable, REPURCH%. Dividends are considered to be a more permanent payout structure and are seldom reduced while repurchase programs are usually characterized as infrequent distributions with no expectation of future payout. However, repurchase programs can span several years for some ﬁrms. Thus, this variable is included in the model to capture those ﬁrms that are repurchasing shares on an ongoing basis or completing an earlier announced repurchase program.

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Sample The sample consists of all ﬁrms that disbursed cash through share repurchase programs and/or dividends and meet all data requirements during the time period 1985–2005. I begin the sample in 1985 when repurchase activity leveled off after the introduction of S.E.C. Rule 10b-18 in 1982. Prior to 1982, a repurchase announcement could be viewed as a violation of SEC rules that prohibited companies from manipulating their stock price. The adoption of SEC Rule 10b-18 in 1982 requires a ﬁrm to announce its intention to repurchase shares on the open market or through privately negotiated transactions to qualify the repurchase under the ‘‘safeharbor’’ rule of the Securities and Exchange Act. As a result, stock buybacks underwent tremendous growth during 1983 and 1984. Ikenberry et al. (1995a) conclude that the adoption of this rule caused an increase in the number of openmarket repurchase programs due to the resolution of the legal ambiguity. Grullon and Michaely (2002) state that just one year after the approval of Rule 10b-18, the aggregate amount of cash spent on share repurchase programs tripled. Thus, I begin the sample in 1985 after repurchase activity leveled off. I deﬁne the sample as follows. For the years 1985–2005, I identify all ﬁrmyears that report on a calendar year-end with data available on Compustat, CRSP, and CDA/Spectrum to estimate the variables in the model. Accordingly, I begin with 75,788 observations that meet these criteria. First, I remove 4,171 ﬁrms that are ﬁnancial institutions or utility companies. These ﬁrms are excluded because utilities and ﬁnancial institutions often have legal distribution requirements. Their dividend payments may therefore be determined by law rather than through management or shareholder decisions. Next, I remove any observation having one or more variables with missing information. This procedure reduces my sample by 47,256, leaving 24,361 ﬁrm-year observations. Large repurchases may be motivated by a desire to change a ﬁrm’s capital structure or fend off takeover attempts. Since I am interested in whether taxation affects corporate distributions, I eliminate all ﬁrm-years in which repurchases total more than 30 percent of a stock’s market value in a given year. As a result, I deleted 129 ﬁrm-years with repurchases exceeding 30 percent. The p-value for RATEDIFF has a higher level of signiﬁcance if these 129 ﬁrm-years are not deleted. The ﬁnal sample has 24,232 ﬁrm-year observations. Table 3 displays the number of observations by year in the ﬁnal sample.

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An Analysis of Dividend and Capital Gains Tax Rate Differentials

Table 3. Sample Observations by Year. Year

Firm-Year Observations

1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Total

740 707 844 833 826 869 861 909 980 1,154 1,171 1,236 1,283 1,499 1,531 1,493 1,510 1,498 1,491 1,320 1,477 24,232

I obtain the data to form the variables REPURCH%, CASH, MKBK, NONOP, and LREPURCH% from the annual Compustat industrial and research ﬁles. I calculate the denominator of REPURCH% and LREPURCH% using the dollar value of dividends from CRSP. Next, I multiply per share information by the number of common shares outstanding from Compustat. Finally, I add this amount to the total dollar value of share repurchases to form the denominator $PAYOUT.

RESULTS Descriptive Statistics Table 4 provides descriptive statistics for each variable in the model. The variable REPURCH% ranges from 0 to 100 percent with a mean (median)

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Descriptive Statistics.

Table 4. Variablea REPURCH% RATEDIFF IND CASH MKBK NONOP YEAR LREPURCH% a

n

Mean

Standard Deviation

Median

Minimum

Maximum

24,232 24,232 24,232 24,232 24,232 24,232 24,232 24,232

.42 .12 .59 .11 .20 .01 12.55 .39

.43 .09 .27 .16 2.01 .12 5.97 .42

.26 .12 .58 .05 .08 .005 13.0 .22

0 0 .0003 0.01 37.50 8.38 1 0

1.00 .30 1.00 .99 230.00 5.44 22 1.00

For variable deﬁnitions refer Table 2.

of 42 percent (26 percent). The mean (median) ﬁrm is comprised of 59 percent (58 percent) individual shareholders. This percentage is similar to the mean (median) in Blouin et al. (2004) of 54 percent (53 percent) individual shareholders. Table 5 displays Pearson correlation coefﬁcients. The dependent variable REPURCH% has a strong positive correlation with the two hypothesized variables of interest, RATEDIFF ( po.0001) and IND ( po.0001). REPURCH% is also signiﬁcantly correlated with CASH, YEAR, and LREPURCH%. In addition to the dependent variable, RATEDIFF has a signiﬁcant positive correlation with IND ( po.0001), YEAR ( po.0001), and the lagged value of the dependent variable REPURCH%. IND has a signiﬁcant positive correlation with CASH ( po.0001), MKBK ( po.0001), and NONOP ( po.0001). The signiﬁcant negative correlation ( po.0001) between IND and YEAR is consistent with the widely held view that institutional ownership of stocks grew while individual ownership declined during the last two decades.

Regression Results Table 6 reports the coefﬁcients from estimating the regression equation. This model investigates the impact of the tax-rate differential on the tradeoff between repurchases and dividends. Consistent with expectations, the estimated coefﬁcient for RATEDIFF is positive and highly signiﬁcant with a t-statistic of 7.67 and a p-value o.0001. Thus, as the tax-rate differential widens favoring capital gains, ﬁrms increase the percentage of total payout in the form of share repurchases. This ﬁnding is consistent with ﬁrms

1.00

REPURCH%

1.00

0.04 o.0001

RATEDIFF

Note: For variable deﬁnitions refer Table 2.

LREPURCH%

YEAR

NONOP

MKBK

CASH

IND

RATEDIFF

REPURCH%

Correlation p-value

1.00

0.08 o.0001

0.05 o.0001

IND

1.00

0.08 o.0001

0.02 .0002

0.27 o.0001

CASH

1.00

0.03 o.0001

0.05 o.0001

0.00 0.39

0.00 0.43

MKBK

1.00

0.00 0.94

0.05 o.0001

0.04 o.0001

0.00 0.88

0.01 0.12

NONOP

Table 5. Pearson Correlation Coefﬁcients Probability W 7r7 under H0: r ¼ 0, NOBS ¼ 24,232.

1.00

0.03 o.0001

0.03 o.0001

0.09 o.0001

0.20 o.0001

0.22 o.0001

0.19 o.0001

YEAR

1.00

0.14 o.0001

0.00 0.54

0.01 0.16

0.07 o.0001

.05 o.0001

0.06 o.0001

0.62 o.0001

LREPURCH%

An Analysis of Dividend and Capital Gains Tax Rate Differentials 43

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Table 6. Multiple Regression Results. REPURCH%it ¼ b0 þ b1 RATEDIFFt þ b2 INDit1 þ b3 CASHit1 þ b4 INDAMKBKit1 þ b5 NONOPit þ b6 YEARt þ b7 LREPURCH%it1 þ eit Variablea INTERCEPT RATEDIFF IND CASH INDAMKBK NONOP YEAR LREPURCH%

Expected Sign

Parameter Estimate

t-Statistic

p-Value

(?) (þ) (þ) (þ) () (þ) (þ) (þ)

0.05762 0.21749 0.06247 0.38247 0.00681 0.01820 0.00714 0.57379

6.66 7.67 2.02 28.79 1.03 1.71 18.60 113.07

0.0001 0.0001 0.0435 0.0001 0.3038 0.0878 0.0001 0.0001

Model F-Statistic ¼ 2511.15 (p-value=o0.0001) Adjusted R2 ¼ 0.42, NOBS ¼ 24,232 Note: I calculate p-values using the Froot (1989) adjustment, which controls for cross-sectional dependence across time periods for sample observations. a For variable deﬁnitions refer Table 2.

considering shareholder’s tax situations when setting distribution policy. The model is highly signiﬁcant ( po.0001) and has an R2 of 42 percent. The other hypothesized variable of interest is IND, the percentage of the ﬁrm owned by individual shareholders. The estimated coefﬁcient on IND is positive and signiﬁcant with a p-value of .0435. This result suggests that ﬁrms will distribute a larger percentage of total payout in the form of a repurchase as the percentage of individuals owning the ﬁrm increases.

Control Variables Table 6 also presents the coefﬁcient estimates of the control variables in the model. I ﬁnd the coefﬁcients on CASH, YEAR, and LREPURCH% to be positive and highly signiﬁcant ( po.0001). Therefore, the level of cash, the general upward trend in repurchases activity over the past two decades, and the prior year’s repurchases are strong predictors of the percentage of total payout in the form of repurchases this year. In addition, NONOP is marginally signiﬁcant ( p ¼ .09) and, INDAMKBK is not signiﬁcant ( p ¼ .30).

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45

Sensitivity Analyses I calculate p-values for the regression model using the Froot (1989) adjustment, which controls for cross-sectional dependence across time periods for sample observations. The highest variance inﬂation factor for any variable is 1.11, which suggests the model does not suffer from multicollinearity. Variance inﬂation factors over 10 indicate that problems may exist within the sample. The Durbin–Watson statistic for my sample is 1.96. A Durbin–Watson statistic close to 2 suggests that the error terms are uncorrelated. Thus, I make no correction for serial correlation. Logistic Regression I also estimate a logistic regression to investigate whether the tax-rate differential inﬂuences the decision to repurchase shares or increase dividends. The dependent variable equals 1 if the ﬁrm repurchases shares and 0 if the ﬁrm pays only a dividend. The results of this regression are qualitatively and quantitatively unchanged from the results presented in Table 6. Model (1) tests whether repurchases become a larger percentage of total payout as the tax-rate differential increases while a logistic regression indicates a choice between the two payout methods. Because the dollar value of repurchases and dividends are continuous variables and since many ﬁrms use both dividends and repurchases during a year, regression Model (1) should better answer the hypotheses in this study. Stock Market Crash of 1987 During 1987, 507 of the 606 repurchase program announcements occurred after the stock market crash on October 19, with 400 of these announcements occurring between October 19 and October 31 (Jagannathan et al., 2000). I ran the model with share repurchases during the fourth quarter of 1987 excluded from the sample to avoid possible clustering associated with the large number of share repurchase announcements after the stock market crash of 1987. The results of the model are unchanged by excluding 1987 fourth-quarter repurchases.

LIMITATIONS The results of this study provide evidence that ﬁrms consider shareholderlevel taxes in the corporate payout decision. Because of data limitations, I cannot observe individuals’ tax rates and their shareholdings. Therefore,

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proxies must be used. Consistent with prior research, I use the highest marginal tax rate as a proxy for individuals’ tax rates and CDA/Spectrum’s institutional ownership data as a proxy for non-taxable investors. Thus, the study does not capture the true tax-rate differentials of all shareholders. However, using the maximum rate differential biases the study against ﬁnding results because shareholders would be less likely to repurchase shares if their differential is less than the maximum. Furthermore, the impact of other shareholder taxes, such as the alternative minimum tax and state-level taxes, is not factored into the analysis. Finally, it remains possible that changes in payout policy relate to non-tax factors that are not considered by this study.

CONCLUSIONS This study investigates whether corporations consider shareholder-level taxes when setting corporate distribution policy. I investigate the relation between the tax-rate differential on dividend and capital gains income and its effect on ﬁrm’s distribution decisions. The results suggest that ﬁrms consider shareholder-level taxes as the tax-rate differential increases. Hence, ﬁrms increase share repurchases and decrease the percentage of total corporate payout in the form of a dividend as the tax-rate differential increases. Thus, a substitution effect occurs as capital gains become more tax-advantaged relative to dividends. The importance of the tax-rate differential should vary based upon the clientele of the ﬁrm because dividends and capital gains are differentially taxed to individual investors, corporate investors, and tax-exempt/taxdeferred entities. I test the association between distribution policy and the tax-rate differential and whether this association varies with individual ownership. I ﬁnd a positive association between the tax-rate differential and the percentage of total payout as a repurchase and also a positive association between the percentage of the ﬁrm owned by individuals and the percentage of total payout as a repurchase. While ﬁrms have many competing objectives when deciding how to distribute excess cash, such as signaling with dividends versus minimizing tax exposure with repurchases, I ﬁnd that shareholder tax implications are a factor in the decision process. The results of this study suggest that, as Congress adjusts tax rates, ﬁrms adjust distributions. In addition, I ﬁnd ﬁrms distribute cash differently dependent on the tax clientele of the ﬁrm.

An Analysis of Dividend and Capital Gains Tax Rate Differentials

47

Future research on corporate payouts may further explore the timing and characteristics of ﬁrms that initiate or increase repurchases. First, an extension could examine whether ﬁrms are more likely to initiate paying dividends for the ﬁrst time when the tax-rate differential is low. If ﬁrms respond to shareholder tax rates, they should be more likely to initiate dividends when they are tax-advantaged. Second, in light of recent tax cuts, future research may want to examine whether ﬁrms respond to temporary tax cuts in the same manner they respond to permanent tax cuts. In other words, do temporary tax cuts have the same impact on the tax-rate differential and form of distribution method as permanent tax cuts. Firms may be reluctant to increase dividends when a temporary rate reduction occurs. The market penalizes ﬁrms for reducing dividends. Therefore, if ﬁrms know a tax cut will expire in the near future, they may choose to repurchase shares of stock or pay special dividends instead of undertaking a more permanent dividend increase.

NOTES 1. In this study, dividends refer to cash dividends. Share repurchases occur when a corporation buys back stock from its shareholders, thereby reducing the number of shares of common stock outstanding. 2. Since I am unable to observe individuals within a ﬁrm or the marginal tax rates they are subject to I use the highest marginal tax rates to compute the tax-rate differential. Because this study spans 21 years and many tax-rate differentials, I am able to observe how ﬁrms change their distribution policies at a wide range of taxrate differentials. 3. From a tax perspective, individual investors with a tax rate on dividends that exceeds the capital gains rate will beneﬁt from a share repurchase program rather than receipt of a dividend while corporate shareholders will usually prefer a dividend to a repurchase since they can take the dividends received deduction. Alternatively, institutional investors should be indifferent between a share repurchase and a dividend. 4. Additionally, their ﬁnding that a ﬁrm is more likely to repurchase shares when a ﬁrm has a low dividend yield may be true by construction. For example, until recently, Microsoft, like many growth ﬁrms, did not pay dividends by choice but did disburse cash through repurchases when it had excess cash available. Thus, regardless of their shareholder’s tax rates, these ﬁrms had a low dividend yield and were not making a choice between repurchasing shares and increasing dividends. 5. Lie and Lie (1999) use the announcement of a share repurchase program rather than actual shares repurchased as the dependent variable in their regressions. SEC Rule 10b-18 requires a ﬁrm to announce its intention to repurchase shares on the open market to qualify the repurchase under the ‘‘safe harbor’’ rule that protects ﬁrms from stock price manipulation charges. However, Jagannathan, Stephens, and

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Weisbach (2000) ﬁnd that open-market share repurchase announcements are in no way a ﬁrm commitment to buy back shares of stock. Rather, the announcement offers the ﬁrm the ﬂexibility to undertake a repurchase program if it so desires. Ikenberry, Lakonishok, & Vermaelen (1995b) ﬁnd that, during 1989–1995, ﬁrms buy back, on average, only 28 percent of the shares authorized and that a signiﬁcant number of ﬁrms choose to repurchase no shares at all. 6. Megginson (1997, p. 357) notes that, if ﬁrms cut or eliminate dividends, they are severely punished by the stock market, sometimes witnessing stock price declines of up to 50 percent. Furthermore, Michaely et al. (1995) document that a dividend omission elicits a much larger negative stock price reaction than the positive reaction experienced by its mirror image, a dividend increase. Alternatively, the failure to renew a repurchase program does not elicit the negative response associated with a dividend omission. Therefore, initiating or increasing a buyback program offers a level of ﬂexibility not available with dividends. 7. While a disproportionate redemption under IRC Section 302(b)(2) is the most common way for a large publicly traded corporation to qualify a stock redemption for capital gains treatment, there are other types of redemptions that will also qualify for sale or exchange treatment (redemptions not essentially equivalent to a dividend, complete termination redemptions, partial liquidations, and redemptions to pay death taxes). 8. Current tax law taxes dividends and capital gains at the same rates. The JGTRRA of 2003 reduced the tax rate on dividend income to be equivalent to the rate on capital gains income. Therefore, the tax-rate differential from 2003 to the present is zero. 9. Table 1 includes the lowest dividends received deduction for corporations for the years 1985–2007. 10. Cash tender offers generally are made to investors by the ﬁrm at an abovemarket price. Tender-offer repurchases, unlike open market repurchases, are regulated by the SEC Speciﬁcally, tender-offer repurchases must comply with the anti-manipulation and anti-fraud provisions of the Securities and Exchange Act of 1934, as amended. With a cash tender offer, the ﬁrm usually advertises in The Wall Street Journal their willingness to purchase a given number of shares for a speciﬁed price during a set time period. Investors who sell their shares receive a premium price and pay capital gains taxes rather than ordinary income taxes on the realized gain. 11. In a Dutch-auction repurchase, a range of prices is stipulated by the corporation, within which each tendering shareholder chooses his or her minimum acceptable selling price. The repurchasing ﬁrm then orders the offers by the shareholders’ minimum acceptable price. Next, the ﬁrm determines the minimum price that will garner the pre-speciﬁed number of shares the ﬁrm is seeking to repurchase. This price is then paid to all shareholders that tender shares at an askprice equal to, or lower than, this endogenously determined price. Typically, the minimum price set by the ﬁrm is only slightly higher than the market price, while the maximum price represents a premium similar to tender-offer repurchases. 12. Although they are infrequent, a privately negotiated repurchase entails buying back shares of stock from a shareholder (usually a shareholder with a large holding) through direct negotiation with the ﬁrm. Either the ﬁrm or the shareholder may initiate the repurchase negotiations.

An Analysis of Dividend and Capital Gains Tax Rate Differentials

49

13. Because of data unavailability, the study assumes a zero basis. The variable RATEDIFF assumes that the entire amount of cash paid for repurchases is taxed as a capital gain. In reality, the taxable amount of a repurchase is the difference between the buyback price and the shareholder’s basis. Because information on investor’s shareholdings is conﬁdential, I am unable to determine the basis and sales prices of each individual share of stock bought in a repurchase program. For this reason, the variable RATEDIFF is measured with error. As a result, RATEDIFF is a conservative measure of the tax-rate differential faced by an investor.

ACKNOWLEDGEMENTS This chapter is adapted from my dissertation completed at the University of Oklahoma. I am indebted to Fran Ayres, my dissertation chair, and committee members Robert Lipe, Terry Crain, Bill Megginson, and Cindy Rogers. I would also like to thank Ben Ayers, Steve Buchheit, Andy Cuccia, Marlys Lipe, Robert Ricketts, and seminar participants at the AAA Annual Meeting, Auburn University, Oklahoma State University, Texas Tech University, University of Arkansas, University of Georgia, University of Missouri, University of Notre Dame, and University of Oklahoma.

REFERENCES Ayers, B., Lefanowicz, C., & Robinson, J. (2000). The effects of goodwill tax deductions on the market for corporate acquisitions. The Journal of the American Taxation Association, 22(Supplement), 34–50. Ayers, B., Lefanowicz, C., & Robinson, J. (2003). Shareholder taxes in acquisition premiums: The effect of capital gains taxation. The Journal of Finance, 58, 2783–2801. Ayers, B., Lefanowicz, C., & Robinson, J. (2004). The effect of shareholder-level capital gains taxes on acquisition structure. The Accounting Review, 79, 859–887. Bagwell, L., & Shoven, J. (1989). Cash distributions to shareholders. Journal of Economic Perspectives, 3, 129–140. Barth, M., & Kasznik, R. (1999). Share repurchases and intangible assets. Stanford Working Paper no. 1402R3, Stanford University. Blouin, J., Raedy Smith, J., & Shackelford, D. (2004). The initial impact of the 2003 reduction in the dividend tax rate. Working Paper, University of North Carolina. Chetty, N., & Saez, E. (2004). Dividend taxes and corporate behavior: Evidence from the 2003 dividend tax cut. NBER Working Paper no. W10841, NBER. Comment, R., & Jarrell, G. (1991). The relative signaling power of Dutch auction and ﬁxedprice self-tender offers and open-market share repurchases. The Journal of Finance, 46, 1243–1271.

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Dann, L. (1981). The effects of common stock repurchase on security holders’ returns. Journal of Financial Economics, 9, 101–138. Dann, L. (1983). Is your common stock really worth buying back? Directors & Boards, 7(4), 23–29. DeAngelo, H., & DeAngelo, L. (1990). Dividend policy and ﬁnancial distress: An empirical investigation of troubled NYSE ﬁrms. The Journal of Finance, 45, 1415–1431. DeAngelo, H., DeAngelo, L., & Skinner, D. (1992). Dividends and losses. The Journal of Finance, 47, 1837–1863. Denis, D., Denis, D., & Sarin, A. (1994). Information content of dividend changes: Cash ﬂow, signaling, overinvestment and dividend clienteles. Journal of Financial and Quantitative Analysis, 29, 567–587. Dhaliwal, D., Erickson, M., & Heitzman, S. (2004). The effect of seller income taxes on acquisition price: Evidence from purchases of taxable and tax-exempt hospitals. Journal of the American Taxation Association, 26, 1–21. Dhaliwal, D., Li, O., & Trezevant, R. (2003). Is a dividend tax penalty incorporated into the return on a ﬁrm’s common stock. Journal of Accounting and Economics, 35, 155–178. Easterbrook, F. (1984). Two agency cost explanation of dividends. American Economic Review, 74, 650–659. Erickson, M., & Wang, S. (2000). The effect of transaction structure on price: Evidence from subsidiary sales. Journal of Accounting and Economics, 30, 59–97. Froot, K. (1989). Consistent covariance matrix estimation with cross-sectional dependence and heteroskedasticity in ﬁnancial data. Journal of Financial and Quantitative Analysis, 24, 333–355. Grullon, G., & Michaely, R. (2002). Dividends, share repurchases, and the substitution hypothesis. The Journal of Finance, 57, 1649–1684. Henning, S., Shaw, W., & Stock, T. (2000). The effect of taxes on acquisition prices and transaction structure. The Journal of the American Taxation Association, 22(Supplement), 1–17. Ikenberry, D., Lakonishok, J., & Vermaelen, T. (1995a). Market underreaction to open market share repurchases. Journal of Financial Economics, 39, 181–208. Ikenberry, D., Lakonishok, J., & Vermaelen, T. (1995b). Market underreaction to open market share repurchases. NBER Working Paper No. W4965. Jagannathan, M., Stephens, C., & Weisbach, M. (2000). Financial ﬂexibility and the choice between dividends and stock repurchases. Journal of Financial Economics, 57(3), 355–384. Jensen, M. (1986). Agency costs of free cash ﬂow, corporate ﬁnance, and takeovers. American Economic Review, 76(2), 323–329. Landsman, W., & Shackelford, D. (1995). The lock-in effect of capital gains taxes: Evidence from the RJR Nabisco leveraged buyout. National Tax Journal, 48, 245–259. Lie, E., & Lie, H. (1999). The role of personal taxes in corporate decisions: An empirical analysis of share repurchases and dividends. Journal of Financial and Quantitative Analysis, 34(4), 533–552. Megginson, W. L. (1997). Corporate Finance Theory. Reading, MA: Addison-Wesley Educational Publishers Inc. Michaely, R., Thaler, R., & Womack, K. (1995). Price reactions to dividend initiations and omissions: Overreaction or drift? NBER Working Paper no. W4778, NBER.

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Perez-Gonzalez, F. (2002). Large shareholders and dividends: Evidence from U.S. tax reforms. Columbia University Working Paper, Columbia University. Rozeff, M. (1982). Growth, beta, and agency costs as determinants of dividend payout ratios. Journal of Financial Research, 5, 249–259. Vermaelen, T. (1981). Common stock repurchases and market signaling. Journal of Financial Economics, 9, 139–183. Wansley, J., Lane, W., & Sarker, S. (1989). Management’s view of share repurchase and tender premiums. Financial Management, 18, 97–110.

DIVIDEND TAXES AND SECURITY PRICES: THE REACTION OF DIVIDEND-PAYING STOCKS TO THE JOBS AND GROWTH TAX RELIEF RECONCILIATION ACT OF 2003 Teresa Lightner, Robert Ricketts and Brett R. Wilkinson ABSTRACT We analyze cumulative abnormal returns (CARs) around key events leading up to the passage of JGTRRA to determine whether a reduction in the individual tax rate on dividend income affects stock prices, and if so, whether that effect differs for different groups of ﬁrms. In general, we ﬁnd that dividend yield is positive and signiﬁcantly related to CARs around both the December and January announcements that legislation might be enacted to reduce or eliminate the dividend tax. Consistent with this observation, when Congress subsequently passed the ﬁnal Senate vote to reduce but not eliminate dividend taxes, we observe positive and statistically signiﬁcant returns for high-yield dividend ﬁrms, but not for

Advances in Taxation, Volume 18, 53–72 Copyright r 2008 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 1058-7497/doi:10.1016/S1058-7497(08)18003-6

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other ﬁrms. Additionally, we analyze the role of institutional ownership in the relation between ﬁrm yield and price reaction. The incentive to buy dividend-paying stocks should not be inﬂuenced by the degree to which a ﬁrm’s stock is held by institutional investors but rather by the ﬁrm’s dividend yield. Our results suggest that this distinction is important – institutional ownership appears to be signiﬁcant for tax changes that induce seller-initiated market reactions, but not for changes that increase buyer-initiated reactions.

1. INTRODUCTION Despite decades of research, the academic community has yet to reach a consensus on the relationship between the shareholder-level tax assessed on corporate dividends and stock prices. In May of 2003, Congress passed the Jobs & Growth Tax Relief Reconciliation Act of 2003 (JGTRRA) which reduced the maximum individual tax rate applicable to dividend income from 38.6% to 15%, the lowest rate on dividend income in U.S. history. The Act was preceded, however, by an announcement in December in the New York Times that JGTRRA would include a provision that reduced the dividend tax rate by 50%, followed by an announcement in January by the administration that individual dividend taxes were going to be eliminated altogether. After some concern that the Act might not be passed, it narrowly cleared the Senate in a 51-50 vote. Because these signals were received by the market in a relatively short-time period (approximately 5 months), they offer a unique setting in which to evaluate how stock prices react to tax rate decreases on dividend income received by individual investors. We analyze cumulative abnormal returns (CARs) around key event dates leading up to the passage of JGTRRA to determine whether a reduction in the individual tax rate on dividend income affects stock prices, and if so, whether that effect differs for different groups of ﬁrms. Moreover, we examine whether the impact of institutional ownership differs for seller and buyer-initiated stock market reactions. In general, we ﬁnd that dividend yield is positive and signiﬁcantly related to CARs around both the December and January announcements that legislation might be enacted to reduce or eliminate the dividend tax. Consistent with this observation, when Congress subsequently passed the ﬁnal Senate vote to reduce but not eliminate dividend taxes, we observe positive and statistically signiﬁcant returns for high-yield dividend ﬁrms, but not for other ﬁrms. We observe no signiﬁcant reaction around the effective date of the act.

Dividend Taxes and Security Prices

55

Additionally, we analyze the role of institutional ownership in the relation between ﬁrm yield and price reaction. Ayers, Cloyd, and Robinson (2002) ﬁnd that the market reaction to the 1993 increase in the dividend tax was ﬁltered by institutional ownership. Because institutional owners were not affected by the 1993 tax increase on dividends, ﬁrms with higher levels of institutional ownership exhibited less severe price reactions to the tax change. In effect, there was less selling pressure on these ﬁrms, and their stock prices reacted accordingly. By reducing the tax rate on dividends, however, JGTRRA increased the incentive to buy, rather than sell, dividend-paying stocks. The incentive to buy dividend-paying stocks should not be inﬂuenced by the degree to which a ﬁrm’s stock is held by institutional investors but rather by the ﬁrm’s dividend yield. Our results suggest that this distinction is important – institutional ownership appears to be signiﬁcant for tax changes that induce seller-initiated market reactions, but not for changes that increase buyer-initiated reactions. Finally, we examine the manner in which overall institutional ownership levels in individual stocks changed in reaction to announcements surrounding JGTRRA. We investigate the change in institutional ownership between the third and fourth quarters of 2002 when the tax rate reduction was ﬁrst announced and the ﬁrst and second quarters of 2003 when investors learned that dividend income would not receive a full exclusion but instead would be taxed at capital gains rates. We ﬁnd that taxed investors moved into highyield stocks at the end of the fourth quarter of 2002 when tax rates were expected to decline and out of high-yield stocks between the ﬁrst and second quarters of 2003 when investors learned that tax rates would not fall as much as previously anticipated. The remainder of this paper is organized as follows. In the next section we summarize the literature on stock price and dividend taxes, focusing primarily on recent work in this area, and use this discussion to formulate our research questions. Section 3 explains our research method with details of the sample in Section 4. This discussion is followed by a review of our results. Finally, we offer conclusions and suggestions for future research.

2. PRIOR LITERATURE AND RESEARCH QUESTIONS The traditional view holds that the dividend tax penalizes investors in dividend-paying ﬁrms and that these ﬁrms thus provide lower after tax returns than their non-dividend-paying counterparts of comparable risk.

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This perspective has led researchers to examine whether returns differ for dividend-paying vs. non-dividend-paying ﬁrms around identiﬁable events such as ex-dividend days, changes in tax legislation, etc. (e.g., Ayers et al., 2002; and Lightner, Morrow, Ricketts, & Riley, 2008). Similarly, the higher tax cost associated with dividend payments relative to capital gains has been analyzed as a factor in the choice in the form of corporate distribution and the growth of stock repurchases over the past decade or so (Blouin, Raedy Smith, & Shackelford, 2004; Chetty & Saez, 2004; Jagannathan, Stephens, & Weisbach, 2000; Lie & Lie, 1999; Lightner et al., 2008; Moser, 2007). In contrast to the traditional view, the new or capitalization view implies that ﬁrm dividend policy is irrelevant. Under this view, dividend taxes are capitalized into stock prices regardless of ﬁrms’ actual dividend payouts (see Zodrow, 1991). Despite extensive research, the debate remains unresolved. Recent work by Ayers et al. (2002) and Dhaliwal, Krull, Li, and Moser (2003) suggests that a ﬁrm’s dividend policies are relevant in determining stock market prices. Ayers et al. (2002) ﬁnd that stock prices reacted negatively for high-dividend-yield ﬁrms upon passage of the Revenue Reconciliation Act of 1993, which increased the maximum ordinary tax rate (and thus, the maximum dividend tax) from 31.0% to 39.6%. Also of interest, their results indicate that this reaction is positively correlated (in absolute value) with ﬁrm dividend yields and negatively correlated (again in absolute value terms) with the degree of institutional ownership of the ﬁrm. Both of these ﬁndings support the traditional view that dividend taxes impact share values. Lightner et al. (2008) investigate the existence of a clientele shift associated with the JGTRRA by examining trading volume in dividendpaying stocks surrounding passage of the Tax Act. They examine changes in shareholder composition of ﬁrms over the period between announcement in the press and ﬁnal passage of the plan. They ﬁnd dividend yield to be a signiﬁcant predictor of abnormal trading volume around key dates. Furthermore, they ﬁnd a statistically signiﬁcant negative relationship between dividend yield and the change in institutional ownership. Similarly, Dhaliwal et al. (2003) analyze the implied cost of capital over periods of differing tax rates (and thus differing tax penalties for dividends). Their results indicate that during periods when the dividend tax penalty is relatively high (30–40%), the cost of capital is notably higher for highdividend paying ﬁrms. No dividend premium was observable during periods of relatively low-dividend tax penalties (0–20%). Two papers examine clientele effects related to dividend increases and initiations surrounding the JGTRRA (Blouin et al., 2004; and Chetty &

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57

Saez, 2004). Both papers examine the tax act and ask whether ﬁrms reacted to JGTRRA by increasing or initiating dividends. In contrast, we examine JGTRRA from the shareholder perspective and investigate whether investors bid up the prices of dividend-paying stocks as information came to the market that tax rates on dividends were going to be reduced or eliminated. Neither Blouin et al. (2004) nor Chetty and Saez (2004) ﬁnd an overall effect of the act on ﬁrm dividend payouts. Rather, they ﬁnd a limited effect in ﬁrms with self-interested principals whose tax incentives changed as a result of the JGTRRA. Speciﬁcally, Blouin et al. (2004) ﬁnd that ﬁrms with higher levels of insider ownership were more likely to increase dividends after tax rates were lowered. Similarly, Chetty and Saez (2004) ﬁnd that ﬁrms whose top executives held more of the shares of the ﬁrm and have fewer unexercised stock options were more likely to initiate dividend payments than were ﬁrms with the presence of large taxable institutional owners or independent directors with large shareholdings. Thus, these studies ﬁnd that the ﬁrms most likely to have increased dividends following the reduction in dividend taxes were those ﬁrms with inﬂuential investors who would beneﬁt from the change. Similarly, Perez-Gonzalez (2002) examines dividend payout and tests whether the preferences of the ﬁrms’ large shareholders determine dividend policy. He compares ﬁrms with large (W5%) individual shareholders to a control group of ﬁrms whose largest shareholder is an institution to determine whether large individual shareholders inﬂuence the dividend policy of the ﬁrm. Consistent with expectations, he ﬁnds dividend payouts increase as the tax disadvantage of dividends relative to capital gains lessens, but only if the ﬁrm’s largest shareholder is an individual. We extend this literature by analyzing the extent to which the tax legislation enacted in 2003 impacts stock prices. The Jobs & Growth Tax Relief Reconciliation Act of 2003 reversed the sharp increase in dividend taxes enacted in 1993, ultimately reducing the dividend tax rate to 15% (5% for taxpayers whose taxable incomes fall in the 10% or 15% brackets), and equalizing the tax on dividends and capital gains. While the act signiﬁcantly reduced the dividend tax, it did not go as far as the administration’s initial proposal, which would have eliminated the dividend tax altogether. As a result, shareholder expectations concerning future tax rates on dividends were continually changing during this period of legislation. Thus, the 2003 legislation provides a unique opportunity to analyze the relationship between stock prices and the taxation of dividends. Extending the analysis of Ayers et al. (2002), we anticipate positive stock market reactions to both the December 25, 2002, New York Times article

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reporting that the administration was going to announce a 50% reduction in the tax rate on dividend income and the January 6, 2003, Wall Street Journal article announcing that President Bush would unveil his new tax cut plan the following day and that it would completely exclude dividend income from taxation. Moreover, because this tax change affected investors in dividendpaying ﬁrms more than other ﬁrms, we surmise that this reaction will be stronger as dividend yield increases. Our ﬁrst research question concerns the relation between dividend yield and share price reaction to the announced reduction in dividend tax rates. We anticipate a positive relation. We are also interested in the effects of institutional ownership on individual stocks’ reaction to announcements surrounding JGTRRA. As noted previously, Ayers et al. (2002) ﬁnd that institutional ownership muted to some degree the stock market reaction to the 1993 increase in the maximum tax rate applicable to dividends. Because institutional owners are not subject to individual taxes on dividend income, the 1993 increase in individual tax rates did not affect them. Accordingly, they faced no incentive to sell dividend-paying shares in the face of increased individual taxes, and ﬁrms with higher levels of institutional ownership exhibited lower share price reactions to the 1993 Act. Different circumstances existed in 2003. The initial announcement that dividend taxes were to be eliminated would be expected to stimulate increased buyer demand for dividend-paying stocks, regardless of the prechange clientele for those stocks. Thus, we do not expect institutional ownership to ﬁlter this reaction; institutional owners are just as likely as individual owners to sell shares in the face of higher prices. Therefore, our second research question examines the previously identiﬁed moderating effect of institutional ownership on the relation between share price reaction and dividend yield. In contrast to prior research which investigated a tax increase, we anticipate no such moderating effect from institutional ownership related to the tax rate decrease on dividends from JGTRRA.

3. RESEARCH METHOD We estimate the following regression model to test our hypotheses: CARit ¼ a0 þ a1 EVENTt þ a2 YIELDi þ a3 INSTSHit þ a4 EVENTt YIELDi þ a5 YIELDi INSTSHi þ a6 EVENTt YIELDi INSTSHi þ ak Xki þ eit

ð1Þ

Dividend Taxes and Security Prices

59

where: CARit

EVENTt YIELDi INSTSHi Xki

cumulative abnormal return for sample ﬁrm i cumulated over a 3-day period beginning 1 day prior to the event date and ending 1 day after the event date an indicator equal to 1 if period t is the JGTRRA event period and 0 for the control periods surrounding JGTRRA common stock dividends divided by market value of equity for ﬁrm i as of December 31, 2002 percent of outstanding shares held by institutions for ﬁrm i as of the end of the quarter prior to announcement a vector of variables that control for ﬁrm attributes, such as ﬁrm size, proﬁtability, book to market ratio, risk, and leverage that may be associated with stock returns during the sample period

We use an event study methodology to investigate whether the reduction in dividend tax rates associated with JGTRRA affected stock prices. First, we extract price-level data from CRSP and use a standard market model to estimate daily abnormal returns for each sample ﬁrm. Next, we cumulate daily returns using a three-day window beginning one day prior to the event date and ending one day after the event date. For an observation to be included in our sample, the ﬁrm must have price information on the CRSP database to calculate returns during the 255-day market model estimation period ending 46 trading days before our ﬁrst event date. We use December 25, 2002, as our ﬁrst date of interest. On this date, an article appeared in the New York Times announcing that President Bush would soon unveil his new tax cut plan that would reduce the tax rate on dividend income by 50%. Shortly thereafter, on January 6, 2003, an article appeared in the Wall Street Journal stating that President Bush would announce a tax cut package on the following day which would include the administration’s plans to push for the complete elimination of the dividend tax. Over the next few months as Congress debated the tax cut and budgetary issues, Republicans imposed a $350 billion dollar cap on the total tax cut package. This cap required some elements of Bush’s original $725 billion dollar plan to be eliminated. On April 30, 2003, a Wall Street Journal article made ﬁrst mention that dividends would be taxed at capital gains rates instead of the full exclusion originally proposed so Congress could stay within the $350 billion cap. Thus, we use April 30, 2003, as our next event date to test

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whether the market reversed its initial reaction to the January 6, 2003, announcement that the dividend tax was to be eliminated altogether. On May 23, 2003, both the House and Senate passed the Conference Committee version of the JGTRRA. This date became the effective date of the reduction in dividend tax rates. We conducted a media search to determine whether there were any confounding events during our event period. The December New York Times article was found through a web search and the January event period was found through a Lexis/Nexis search. We searched the JGTRRA legislation and the important announcement dates for confounding tax law changes or announcements that might impact our results. While the reduction in the tax rates on dividends for individual shareholders is the centerpiece of the legislation included in the JGTRRA, the Act also included the IRC section 199 deduction for businesses, an acceleration of tax rate reductions, a small reduction in the capital gains tax rate, and increases in the child tax credit, bonus depreciation, and AMT exemption amount, among other changes. These are all important pieces of legislation but their effect on corporations should not be related to dividend yield. For example, ﬁrms should not have CARs based on dividend yield due to the new 199 manufacturing deduction. The primary variable of interest in the model is the EVENT YIELD variable. Consistent with the traditional view of dividend taxation, we anticipate a positive coefﬁcient in response to a decrease in the dividend tax rate and a negative coefﬁcient in response to an increase in the tax rate. Moreover, to the extent dividend policy inﬂuences stock price response resulting from a change in the tax rate, there will be a signiﬁcant relation between abnormal return and dividend yield. In addition, we are interested in the interaction of the institutional ownership variable with dividend yield. If our expectations regarding the inﬂuence of institutional ownership on stock market reaction to changes in the tax burden on dividend income are accurate, this interaction will not be signiﬁcant as tax rates are expected to decline. In contrast, the interaction should be signiﬁcant for expected tax rate increases. The variable EVENT is an indicator variable equal to 1 if CAR is measured over a JGTRRA event window and 0 if CAR is measured over a control period. We measure our event CARs over a three-day period surrounding each event date and the control period CARs over three, threeday control periods on each side of the event date. Thus, we cumulate our control period CARs over the following windows, expressed as days relative to the event date: (8, 10), (5, 7), (2, 4), (þ2, þ4), (þ5, þ7), and

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61

(þ8, þ10). We include EVENT in our research design to control for differences in abnormal returns that are unrelated to tax rate changes resulting from JGTRRA. We compute each ﬁrm’s dividend yield, YIELD, by dividing common stock dividends by the ﬁrm’s market value of equity on December 31, 2002. If individual investors’ demands for shares of dividend-paying ﬁrms increased based on news concerning proposed or approved reductions in tax rates on dividends, we expect the coefﬁcient associated with YIELD, to be positive and statistically signiﬁcant. We cannot observe the exact makeup of investors in each ﬁrm because individual shareholdings are not public information and therefore must rely on another measure. Consistent with recent research (e.g., Ayers, Lefanowicz, & Robinson, 2002, 2003, 2004; Blouin et al., 2004, and Dhaliwal et al., 2003), we use the Thomson Financial CDA/Spectrum database to obtain the percentage of the ﬁrm’s shares held by institutional shareholders. CDA/Spectrum’s institutional holdings data is drawn from the actual 13-F forms ﬁled with the SEC on a quarterly basis by money managers/management companies. This measure includes institutional owners that are either tax-exempt (e.g., retirement plans), taxable (e.g., corporations), or mutual funds that can have individuals, retirement plans, or corporations as investors. This variable is expressed as the percentage of the ﬁrm held by institutional investors. We compute this measure as of the end of the quarter preceding each event date. We include several control variables in our model that may be associated with stock returns during the sample period. Ayers et al. (2002) ﬁnd proﬁtability, ﬁrm book to market ratio, and size are characteristics that impact abnormal returns surrounding their event date.1 They also include leverage, although they do not ﬁnd it to be signiﬁcant. We include each of these variables in our model. We measure ﬁrm proﬁtability as the ﬁrm’s return on value (ROV), deﬁned as net income before extraordinary items deﬂated by market value of equity as of December 31, 2002. We include ROV in the model to capture differences in proﬁtability across ﬁrms. We also include each ﬁrm’s book to market value ratio (BV/MV) in the model to control for differences in unrecognized assets, growth prospects, and risk across ﬁrms. BV/MV is measured as book value of equity divided by market value of equity as of year end. We include a proxy for size in our research design to ensure that the results are not caused by smaller, non-dividend-paying ﬁrms outperforming larger, dividend-paying ﬁrms. SIZE is deﬁned as log of market value of equity as of December 31, 2002. Firm leverage (LEV) is measured

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as total liabilities deﬂated by market value of equity. We include this variable to control for differences in leverage across ﬁrms. Finally, because BV/MV is often considered a proxy for size, we include BETA as an alternate measure of risk (Dhaliwal & Li, 2006; Michaely & Vila, 1995, 1996). We use beta from the Capital Asset pricing Model measured over a 255-day period ending 46 days before our ﬁrst event date.

4. SAMPLE SELECTION AND DATA We estimate our model using a sample comprising all calendar year-end ﬁrms with available data on the Research Insight, CDA/Spectrum, and CRSP databases. All data except institutional ownership and stock market returns are extracted from the Research Insight database. To enhance the comparability of our results, we include only ﬁrms with data for the December 2002, January 2003, and May 2003 periods.2 This yields an initial sample of 3,445 ﬁrms. Following Ayers et al. (2002) we eliminate 870 ﬁrms with both negative net income and negative retained earnings because no dividend tax will apply to distributions from these ﬁrms.3 We also delete an additional 26 ﬁrms with a negative book value, two ﬁrms with extreme yield results (yield W0.35), 156 ﬁrms with extreme leverage results (total liabilities/market value W10) and three ﬁrms with unusually high price (price W$500). This results in a ﬁnal sample of 2,388 ﬁrms. Descriptive statistics for the sample are reported in Table 1 and Pearson correlation coefﬁcients are reported in Table 2. There are no correlations among the independent variables that are high enough to be of concern. Additionally, the variance inﬂation factors for all variables are all less than 4, suggesting that the model does not suffer from multicollinearity. In addition, the overall sample is broadly comparable with that used in Ayers et al. (2002).

5. RESULTS Our ﬁrst research question asks whether market reactions to the December 25 (50% reduction of the dividend tax) and January 6 (elimination of the dividend tax) announcements differ across ﬁrms with differing dividend yields. The results, summarized in Table 3, are consistent with expectations. On December 25 the New York Times, citing ofﬁcials close to the White House, reported that the administration planned a 50% reduction in the tax

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Dividend Taxes and Security Prices

Table 1. Variable N ¼ 2,388 ﬁrms YIELD ROV BK/MK SIZE LEV BETA INSTSH (Q3, 2002) INSTSH (Q4, 2002) INSTSH (Q1, 2003) INSTSH (Q2, 2003) (n ¼ 2,376)

Sample Descriptive Statistics.

Mean

Standard Deviation

Lower Quartile

Median

Upper Quartile

0.02 0.05 0.77 6.05 2.14 0.95 0.41

0.03 0.12 0.57 2.04 2.49 0.66 0.29

0 0.03 0.60 4.56 0.36 0.48 0.12

0.00 0.06 0.63 6.00 1.00 0.91 0.39

0.03 0.08 0.92 7.43 3.22 1.32 0.66

0.40

0.29

0.12

0.39

0.66

0.42

0.30

0.13

0.40

0.68

0.42

0.30

0.13

0.41

0.69

Notes: YIELDi, common stock dividends divided by market value of equity for ﬁrm i as of December 31, 2002; ROVi, net income before extraordinary items divided by market value of equity for ﬁrm i as of December 31, 2002; BV/MVi, book value of equity divided by market value of equity for ﬁrm i as of December 31, 2002; SIZEi, log of market value of equity for ﬁrm i as of December 31, 2002; LEVi, total liabilities divided by market value of equity for ﬁrm i as of December 31, 2002; BETAi, CAPM beta from a standard market model measured over a 255day period ending 46 days prior to the ﬁrst event date; INSTSHi, percentage of institutional ownership in relevant quarter.

rate on corporate dividends received by individual investors. We examine the market’s response to this announcement by estimating our model using a 3-day CAR (December 24, 26, and 27) as our dependent variable. Results of the analysis are summarized in column A of Table 3. As expected, we ﬁnd a statistically signiﬁcant positive coefﬁcient on the EVENT YIELD variable. Therefore, during the event period that covered the initial announcement of a tax rate decrease on dividends, the cumulative abnormal market return increased as ﬁrm dividend yield increased. This ﬁnding is consistent with the traditional view that ﬁrm dividend policies impact the price effect of a decrease in dividend tax rates. On January 6, 2003, the market received news that a tax cut package, which included the administration’s plans to push for the complete elimination of the dividend tax, would be announced on January 7. We re-estimate the model and again, consistent with our expectations, ﬁnd a positive and signiﬁcant coefﬁcient for EVENT YIELD. These results

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TERESA LIGHTNER ET AL.

Table 2. Pearson Correlations for Independent Variables (p-value).

YIELD ROV BK/MK SIZE LEV INSTSH BETA

YIELD

ROV

BK/MK

SIZE

LEV

1.00

0.04 (0.07) 1.00

0.003 (0.87) 0.19 (0.00) 1.00

0.15 (0.00) 0.04 (0.07) 0.44 (0.00) 1.00

0.18 (0.00) 0.07 (0.00) 0.21 (0.00) 0.16 (0.00) 1.00

INSTSH

BETA

0.09 (0.00) 0.04 (0.04) 0.19 (0.00) 0.43 (0.00) 0.26 (0.00) 1.00

0.16 (0.00) 0.13 (0.00) 0.14 (0.00) 0.39 (0.00) 0.24 (0.00) 0.44 (0.00) 1.00

Notes: RE/BVi, retained earnings divided by book value of equity for ﬁrm i as of December 31, 2002; YIELDi, common stock dividends divided by market value of equity for ﬁrm i as of December 31, 2002; ROVi, net income before extraordinary items divided by market value of equity for ﬁrm i as of December 31, 2002; BV/MVi, book value of equity divided by market value of equity for ﬁrm i as of December 31, 2002; SIZEi, log of market value of equity for ﬁrm i as of December 31, 2002; LEVi, total liabilities divided by market value of equity for ﬁrm i as of December 31, 2002; INSTSHi, percentage of institutional ownership in quarter 3, 2002; BETAi, CAPM beta from a standard market model measured over a 255-day period ending 46 days prior to the ﬁrst event date.

suggest that dividend policy does matter: investors responded differently in their valuation of ﬁrms following the announcement of the tax reduction, dependent on dividend policy. As noted earlier, the role of institutional ownership needs to be considered as a possible mitigating factor in the relation between ﬁrm yield and price reaction. In their analysis of the 1993 Act, Ayers et al. (2002) ﬁnd that institutional ownership mitigates the negative relation between yield and a dividend tax increase. In other words, stocks with higher yields reacted more negatively to the tax increase but only inasmuch as the stockholders are likely impacted by the tax increase. In contrast, we anticipate that when the dividend tax is decreased, the interaction of institutional ownership and yield will not inﬂuence the positive price reaction. We examine the effect reported by Ayers et al. (2002) by interacting institutional ownership with dividend yield. Our variable of interest is then EVENT YIELD INSTSH. As shown in Table 3, consistent with our expectations, we do not ﬁnd a signiﬁcant relation between abnormal returns and this interaction term. Thus, we document that institutional ownership

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Dividend Taxes and Security Prices

Table 3. Cross-sectional OLS Regressions of Cumulative Abnormal Returns on Independent and Control Variables around Key Announcement Dates Leading up to the JGTRRA 2003. Variable

Predicted Sign

A. First Event December 26, 2002 (n ¼ 14,328)

B. Second Event January 6, 2003 (n ¼ 14,328)

Estimated coefﬁcient (t-statistic) Intercept

?

EVENT

?

YIELD

?

INSTSH

?

INSTSH YIELD

?

EVENT YIELD

þ

EVENT INSTSH YIELD

?

ROV

?

BKMK

?

SIZE

?

LEV

?

BETA

?

Adjusted R2 F-statistic

0.001 (0.33) 0.008 (6.62) 0.027 (1.07) 0.000 (0.09) 0.060 (1.08) 0.092 (1.65) 0.103 (0.86) 0.008 (2.42) 0.000 (0.58) 0.001 (3.00) 0.000 (0.95) 0.004 (6.69) 0.01 10.14 (.0001)

0.001 (0.81) 0.006 (5.14) 0.048 (1.82) 0.005 (2.76) 0.050 (0.85) 0.101 (1.76) 0.072 (0.56) 0.010 (3.02) 0.003 (4.58) 0.000 (0.32) 0.000 (0.78) 0.004 (6.55) 0.01 16.24 (0.0001)

Notes: RE/BVi, retained earnings divided by book value of equity for ﬁrm i as of December 31, 2002; YIELDi, common stock dividends divided by market value of equity for ﬁrm i as of December 31, 2002; ROVi, net income before extraordinary items divided by market value of equity for ﬁrm i as of December 31, 2002; BV/MVi, book value of equity divided by market value of equity for ﬁrm i as of December 31, 2002; SIZEi, log of market value of equity for ﬁrm i as of December 31, 2002; LEVi, total liabilities divided by market value of equity for ﬁrm i as of December 31, 2002; INSTSHi, percentage of institutional ownership in relevant quarter (Q3 2002 for December model, Q4 2002 for January model).Signiﬁcant at the 5% level.Signiﬁcant at the 1% level; tests are two tailed except where sign is predicted.

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TERESA LIGHTNER ET AL.

does not mitigate the market reaction to the tax cut announcement. This result is logically consistent because the incentive to purchase shares of dividend-paying ﬁrms following a tax decrease on dividends is the same for individual investors regardless of the percentage of target shares held by institutions, and suggests that the marginal investor is a taxed individual. With regard to the institutional ownership variable (INSTSH), in contrast to Ayers et al. (2002) we ﬁnd the coefﬁcient is either not signiﬁcant or is negative. Ayers et al. suggest that the INSTSH variable reﬂects non-tax effects and thus we do avoid placing emphasis on the interpretation of the term. Our next date of interest is April 30, 2003. On this date the market learned that dividends would not be subject to full exclusion but rather would be taxed at the lower capital gains rates. Given that the market was anticipating a complete exclusion, this announcement effectively equates to an increase in the expected future tax on dividends. To the extent that dividend policy inﬂuences the market response to the announcement of a tax increase, we predict a negative relation between abnormal return and yield. We again use a 3-day CAR as our dependent variable but fail to ﬁnd any signiﬁcant relation between yield and abnormal return during this window (results not reported here). One possibility is that the market became aware of this change (perhaps over an extended time period) prior to the date that we identiﬁed. During May 2003, several crucial events in the life of the legislation occurred. On May 9, the bill providing for reduction in the dividend tax passed the House of Representatives. Uncertainty remained as to whether the legislation would win the support of the Senate. On May 15, the bill passed the Senate (51-50) with Vice President Cheney casting the deciding vote. The Act was passed by Congress on May 23. We examine the market reaction around each of these dates. We ﬁnd no signiﬁcant relation between yield and abnormal return around the May 9 date (results not reported here).4 This may be consistent with concerns that the Senate would not support the legislation and that investors remained uncertain of the likely outcome. In contrast, we ﬁnd a positive and signiﬁcant relation between EVENT YIELD and abnormal return (t ¼ 3.42) around May 15, the date of the Senate vote, and May 23, the date the bill passed the Senate.5 Results are reported in Table 4. Again, consistent with our earlier results, we ﬁnd that institutional ownership plays no mitigating role. In our initial analyses, we use dividend yield for the year prior to the tax change to test whether yield helps us predict abnormal return on key dates. As a sensitivity test, we also estimate the model using dividend yield for the

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Dividend Taxes and Security Prices

Table 4. Cross-sectional OLS Regressions of Cumulative Abnormal Returns on Independent and Control Variables around May 15 and 23 Announcement Dates. Variable

Predicted Sign

May 15 n ¼ 14,328

May 23 n ¼ 14,310

Estimated coefﬁcient (t-statistic) Intercept

?

EVENT

?

YIELD

?

INSTSH

?

INSTSH YIELD

?

EVENT YIELD

þ

EVENT YIELD INSTSH

?

ROV

?

BKMK

?

SIZE

?

LEV

?

BETA

?

Adjusted R2 F-statistic

0.002 (1.22) 0.008 (6.32) 0.084 (3.08) 0.000 (0.19) 0.044 (0.74) 0.203 (3.42) 0.062 (0.49) 0.004 (1.22) 0.008 (10.92) 0.000 (0.15) 0.000 (2.88) 0.012 (17.06) 0.04 58.25 (.0001)

0.004 (2.02) 0.002 (1.67) 0.047 (1.77) 0.004 (2.20) 0.055 (0.95) 0.141 (2.44) 0.018 (0.15) 0.008 (2.30) 0.007 (9.63) 0.000 (1.98) 0.001 (3.32) 0.009 (12.93) 0.03 46.87 (.0001)

Notes: RE/BVi, retained earnings divided by book value of equity for ﬁrm i as of December 31, 2002; YIELDi, common stock dividends divided by market value of equity for ﬁrm i as of December 31, 2002; ROVi, net income before extraordinary items divided by market value of equity for ﬁrm i as of December 31, 2002; BV/MVi, book value of equity divided by market value of equity for ﬁrm i as of December 31, 2002; SIZEi, log of market value of equity for ﬁrm i as of December 31, 2002; LEVi, total liabilities divided by market value of equity for ﬁrm i as of December 31, 2002; INSTSHi, percentage of institutional ownership in quarter 1, 2003.Signiﬁcant at the 5% level.Signiﬁcant at the 1% level; tests are two tailed except where sign is predicted.

68

TERESA LIGHTNER ET AL.

year following the tax change. In this test we are examining whether investors used future dividend yield rather than the yield prior to the Tax Act when bidding up shares of stock. To some extent this could be problematic because the tax changes induced some large one-time dividend payments during 2003 by some ﬁrms. We avoid this problem in part by using the 2004 yield rather than the yield from 2003. For all event dates except one, our results are qualitatively unchanged from those using prior year dividend yield. For the December 26 event date, we ﬁnd no signiﬁcant relation between abnormal return and EVENT YIELD when using 2004 dividend yield. In addition to examining the relation between dividend yield and stock price reaction to the announced dividend tax cuts, we examine the manner in which institutional ownership levels changed over this time. Our second research question predicts a negative relation between dividend yield and changes in the levels of institutional ownership from the end of the third quarter of 2002 to the end of the second quarter of 2003. This period encompasses the entire JGTRRA policy-making process. During this time, we expect changes in institutional ownership to be inﬂuenced by the impending legislation. If institutional ownership proxies for tax-exempt investors, we anticipate that high-yield stocks will attract a higher percentage of taxed investors (lower percentage of institutional investors) following the tax cut. We partition the non-zero-yield ﬁrms into quintiles based on yield and examine the change in institutional ownership in each quintile as information about the tax cut reached the market. Results are reported in Table 5. Comparing institutional ownership levels between quarter 3 and quarter 4 of 2002, we ﬁnd a signiﬁcant decline in institutional ownership for ﬁrms in all but the lowest yield quintile. This is consistent with taxed investors moving into these higher yield stocks due to an expectation of a dividend tax cut. During April and May the market began receiving information that dividends would not be subject to a full exclusion but rather would be taxable at capital gains rates. We expect that a reversal in institutional ownership levels will occur as investor expectations change. That is, the increased interest in high-yield stocks by taxed investors likely decreased once it was known that dividends would not enjoy the full exclusion reported in January 2003. We compare the levels of institutional ownership between the end of quarter 1 and quarter 2 of 2003. By June 30, 2003, the Act had passed and investors knew that dividends would be taxed at capital gains rates. At March 31, 2003, there was likely still an expectation that dividends would not be subject to tax.

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Dividend Taxes and Security Prices

Table 5. Quintile

Comparison of Changes in Institutional Ownership by Yield Quintiles. Mean Yield (N)

Change in Institutional Ownership between Quarters 3 and 4 of 2002

Change in Institutional Ownership between Quarters 1 and 2 of 2003

1 (lowest)

0.007 (250)

2

0.017 (250)

3

0.025 (251)

4

0.035 (250)

5 (highest)

0.075 (250)

0.004 (1.24) 0.006 (2.58) 0.006 (3.22) 0.006 (2.69) 0.01 (3.61)

0.01 (2.44) 0.001 (0.65) 0.007 (4.04) 0.007 (3.45) 0.01 (4.14)

Signiﬁcant at the 5% level.Signiﬁcant at the 1% level; tests are two tailed. The mean yield is

the average yield for the relevant quintile of dividend yield.

As reported in Table 5, our results are consistent with expectations. As per our expectations, we ﬁnd the percentage of institutional ownership increased signiﬁcantly for ﬁrms in quartiles 3, 4, and 5. This is consistent with taxed investors choosing to move out of these high-yield stocks and into lower yield stocks. However, we also ﬁnd that the level of institutional ownership increased in the lowest yield quintile as well, although at a lower signiﬁcance level. As a sensitivity test, we examine changes in institutional ownership over the same quarters but in the period prior to any tax change being discussed. We complete this analysis to gain conﬁdence that the results do not simply reﬂect a pattern of behavior unrelated to the changes we are examining. Thus, we examine the change from quarter 3 to quarter 4 of 2000 and from quarter 1 to quarter 2 in 2001. To control for ﬁrm-speciﬁc effects, we use the same ﬁrms and yield quintile groups as in our original analysis. Consistent with our previous restrictions, ﬁrms with missing institutional shareholder data or with reported institutional ownership greater than one are deleted, leaving a sample of 2,020 ﬁrms. In contrast to our earlier analysis where we observe a decrease in institutional ownership between quarter 3 and quarter 4 of 2002, we ﬁnd evidence of a signiﬁcant increase in institutional ownership from quarter 3 to quarter 4 for 2000 for the middle three yield quintiles. We ﬁnd no signiﬁcant change in institutional ownership for the highest-yield quintile and a marginally signiﬁcant increase in the lowest

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quintile. When we compare the difference in ownership between quarters 3 and 4 of 2000 with the differences between quarters 3 and 4 of 2002, we ﬁnd a signiﬁcantly negative change in ownership across all yield quintiles (i.e., the decline in institutional ownership in 2002 differs signiﬁcantly from the increase in institutional ownership in 2000 for the same ﬁrms). We next compare the change from quarter 1 to quarter 2 of 2001. Recall that over these same quarters in 2003, we ﬁnd a signiﬁcant increase in four of our ﬁve yield quintile groups. For 2001, we ﬁnd a signiﬁcant increase in institutional ownership in only the lowest quintile. In all other quintiles, the change is not signiﬁcantly different from zero. We then compare the increases observed in 2003 against the changes from quarter-to-quarter in 2001. Our results suggest that only for quintiles 3 and 5 is the difference from quarter 1 to quarter 2 in 2003 signiﬁcantly greater (5%, one-tailed test) than the comparable change from quarter 1 to 2 in 2001. The results of this sensitivity analysis imply that the changes in institutional ownership that we observe between quarters 3 and 4 of 2002 are different from changes between quarters prior to the tax change announcements. Although the sensitivity results are weaker, we also ﬁnd some similar support for the changes between quarters 1 and 2 of 2003 being different from earlier quarter-to-quarter changes.

6. CONCLUSIONS Questions regarding the implications of dividend taxation continue to be of enduring interest to the academic community. Despite decades of research, a consensus has yet to be reached on the relationship between the shareholderlevel tax assessed on corporate dividends and stock prices. Recent research by Ayers et al. (2002) ﬁnd support for the traditional view by looking at the inﬂuence of dividend policy on share price reaction to a change in the dividend tax rate. Although Ayers et al. ﬁnd a signiﬁcant relation between increasing tax rates and share price, they note that research in this area is inconclusive. In this study, we examine the stock price reaction of the announcement of a reduction in the dividend tax rate associated with the Jobs and Growth Tax Relief Reconciliation Act of 2003. The 2003 Tax Act and events leading up to it create unique opportunities to analyze whether share prices are sensitive to dividend policy in response to changes in the rate at which dividends are taxed to individual shareholders. Both the traditional and the capitalization (or new) views of dividend taxation suggest that stock prices will respond positively to a reduction in

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dividend taxation. The traditional view, however, suggests that the reaction will be conditioned upon the ﬁrm’s dividend policy, while the capitalization view allows no role for ﬁrm dividend policy. Consistent with Ayers et al. (2002), we ﬁnd support for a role for dividend policy in inﬂuencing the stock price reaction to announcements in dividend tax changes. Speciﬁcally, we ﬁnd that high-yield dividend ﬁrms exhibited signiﬁcant positive CARs around the December (January) announcement that legislation might be enacted to reduce (eliminate) the dividend tax. Similarly, we ﬁnd a positive reaction at the time of the Senate vote (May 15) and the ﬁnal passing of the Act by Congress on May 23. We also ﬁnd that the level of institutional ownership has no impact when tax rates are decreasing, unlike the case of rate increases documented by Ayers et al. (2002). Our results suggest that this distinction is important – institutional ownership appears to be signiﬁcant for tax changes that induce seller-initiated market reactions, but not for changes that increase buyer-initiated reactions. Collectively, our results add additional support to the traditional view with respect to dividend taxation.

NOTES 1. Please see Table 1 for variable deﬁnitions. 2. The CDA/Spectrum institutional holdings database classiﬁes a small number of ﬁrms as having greater than 100% institutional ownership. We exclude these ﬁrms from our sample for our analysis. This impacts a small number of ﬁrms (e.g., 32 ﬁrms in quarter one of 2003). 3. We include any ﬁrm with negative net income and missing retained earnings data in this number. Firms with missing net income are already deleted from the sample. 4. We estimate the regression with all control period CARS, and without the May 15 and May 23 CAR control periods. In neither case do we ﬁnd signiﬁcant results. 5. As discussed earlier, when conducting the May 23 analysis, we eliminate the control period CAR that encompasses the May 15 event date. Likewise, when we estimate the model for the May 15 period, we eliminate the control period encompassing May 23. Given that we found no signiﬁcant effect for the May 9 date, we do not eliminate the control period encompassing May 9 for the analysis reported in Table 4. However, as a sensitivity test we also eliminate the May 9 control CAR and re-estimate the regression models. We ﬁnd that the results are qualitatively unchanged.

REFERENCES Ayers, B. C., Cloyd, C. B., & Robinson, J. R. (2002). The effect of shareholder-level dividend taxes on stock prices: Evidence from the Revenue Reconcilliation Act of 1993. The Accounting Review, 77(4), 933–947.

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Ayers, B. C., Lefanowicz, C., & Robinson, J. R. (2003). Shareholder taxes in acquisition premiums: The effect of capital gains taxation. The Journal of Finance, 58, 2783–2801. Ayers, B. C., Lefanowicz, C., & Robinson, J. R. (2004). The effect of shareholder-level capital gains taxes on acquisition structure. The Accounting Review, 79, 859–887. Blouin, J., Raedy Smith, J., & Shackelford, D. (2004). The initial impact of the 2003 reduction in the dividend tax rate. Working Paper, University of North Carolina. Chetty, N., & Saez, E. (2004). Dividend taxes and corporate behavior: Evidence from the 2003 dividend tax cut. NBER Working Paper no. W10841. Dhaliwal, D., Krull, L., Li, O., & Moser, W. (2003). Dividend taxes and implied cost of equity capital. Working Paper, University of Arizona. Dhaliwal, D., & Li, O. (2006). Investor tax heterogeneity and ex-dividend day trading volume. The Journal of Finance, 64(1): 463–490. Jagannathan, M., Stephens, C., & Weisbach, M. (2000). Financial ﬂexibility and the choice between dividends and stock repurchases. Journal of Financial Economics, 57(3), 355–384. Lie, E., & Lie, H. (1999). The role of personal taxes in corporate decisions: An empirical analysis of share repurchases and dividends. Journal of Financial and Quantitative Analysis, 34(4), 533–552. Lightner, T., Morrow, M., Ricketts, R., & Riley, M. (2008). Investor response to a reduction in the dividend tax rate: Evidence from the Jobs & Growth Tax Relief Reconciliation Act of 2003. Journal of the American Taxation Association, forthcoming. Michaely, R., & Vila, J. (1995). Investors’ heterogeneity, prices, and volume around the ex-dividend day. Journal of Financial and Quantitative Analysis, 30, 171–198. Michaely, R., & Vila, J. (1996). Trading volume with private valuation: Evidence from the ex-dividend day. Review of Financial Studies, 9, 471–509. Moser, W. (2007). The effect of shareholder taxes on corporate payout choice. Journal of Financial and Quantitative Analysis, 42(4), 991–1019. Perez-Gonzalez, F. (2002). Large shareholders and dividends: Evidence from U.S. tax reforms. Working Paper, Columbia University. Zodrow, G. R. (1991). On the ‘‘traditional’’ and ‘‘new’’ views of dividend taxation. National Tax Journal, 44, 497–509.

BONUS DEPRECIATION INCENTIVES: THE IMPACT ON GENERAL AVIATION AIRCRAFT Karen C. Miller, J. Riley Shaw and Tonya K. Flesher ABSTRACT The use of corporate aircraft has increased as businesses place more value on ease of mobility. The bonus depreciation incentives of 2002 and 2003 provided growth opportunities for the general aviation market by allowing accelerated depreciation deductions for the purchase of new corporate aircraft. These incentives allowed more than twice the traditional MACRS allowance for depreciation for the ﬁrst year of operation of an asset, but the present value of the tax savings after the full depreciable life of the corporate aircraft only generated a 3.25 percent reduction in the after-tax-cost. This study documents that the bonus depreciation incentives did not generate signiﬁcant growth in the general aviation aircraft market via increased production of aircraft. These incentives may have simply slowed the recession that might have taken place in this industry otherwise. However, the incentives in this study did play a signiﬁcant role in determining which type of aircraft to purchase, piston or turbine.

Advances in Taxation, Volume 18, 73–101 Copyright r 2008 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 1058-7497/doi:10.1016/S1058-7497(08)18004-8

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INTRODUCTION This research studies the impact of the bonus depreciation incentives introduced in 2002 and 2003 upon capital expenditures in the general aviation industry, which includes all aviation other than commercial and military aircraft. The Job Creation and Worker Assistance Act of 2002 (JCWAA) and the Growth Tax Relief Reconciliation Act of 2003 (GTRRA) both provided accelerated or bonus depreciation incentives in the taxable year in which long-lived assets are placed-in-service. The House Committee believed that ‘‘increasing and extending the additional ﬁrst-year depreciation would accelerate purchases of equipment, promote capital investment, modernization, and growth, and would help to spur an economic recovery’’ (House Committee Report, 2003). Ironically, the commercial airline security issues following the 9/11 tragedy had a positive impact on business aviation growth (Miller & Flesher, 2003).1 According to the International Business Aviation Council, in a 2002 report, corporate ofﬁcers and directors used company aircraft twice as much as before 9/11. In the wake of these two tax acts, the General Aviation Manufacturers Association (GAMA) surveyed recent aircraft purchasers and determined that bonus depreciation had been ‘‘directly responsible for generating over $2 billion worth of new airplane orders since its enactment’’ (GAMA, 2004b, p. 1). GAMA claimed that increasing bonus depreciation for aircraft would directly and positively affect the number of aircraft sold. GAMA made this claim in efforts to persuade Congress to extend bonus depreciation for the general aviation industry. Although companies may have reported $2 billion worth of new orders, no empirical data exists to conﬁrm or refute the role of bonus depreciation on these new orders.2 Accordingly, the general aviation industry commended Congress for allowing and extending bonus depreciation in its sector of the economy (GAMA, 2004a, p. 1). This research attempts to quantify the impact of the bonus depreciation incentives upon the manufacture and delivery of general aviation aircraft in the United States. Limiting this study to the impact of bonus depreciation on the general aviation industry from 1987 to 2005 speciﬁcally minimizes some of the econometric estimation problems that generate conﬂicting results in prior research when studying the impact of tax laws on capital investments.3 First, limiting the industry to general aviation mitigates some of the difﬁculties associated with differing depreciation rates by analyzing one type of asset and one consistent asset life. Prior research by Clark (1993) indicates that using aggregated assets from different classes may confound the effect of tax

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incentives. Further, this research uses an asset with a relatively short class life. Clark (1993) found an acceleration of the economic stimulus for shortlived assets. If bonus depreciation does stimulate capital investment, as proposed by GAMA and other lobbying organizations, the effect should occur in general aviation because of the relatively short ﬁve-year MACRS life. In addition, the JCWAA of 2002 and the GTRRA of 2003 isolate bonus depreciation from other related capital investment enhancements. Accordingly, this research does not have to consider estimations of the investment tax credit (ITC) (repealed after 1986), delayed investments due to anticipation of the incentives, differences due to the alternative minimum tax, or limitations due to business size. Additionally, the American Jobs Creation Act of 2004 (AJCA) extended the placed-in-service dates for bonus depreciation speciﬁcally for qualifying non-commercial aircraft through the end of 2005. This extension denotes an isolated importance of the impact on general aviation aircraft. The JCWAA of 2002 also extended the carryback period for net operating losses from two to ﬁve years, allowing more time to take advantage of the new tax deductions. There are several reasons why prior research concerning the effect of tax incentives is unlikely to generalize to the business aviation setting. Many of the prior tax incentive studies focus on the ITC, which provided a credit in the year of purchase for a portion of the asset cost (Goolsbee, 1998, 2004; Kaufman & Gitman, 1988). Recent research on the effects of bonus depreciation provides mixed results. Desai and Goolsbee (2004) ﬁnd modest increases in investment as a result of the bonus depreciation incentives, but Hulse and Livingstone (2003) ﬁnd no evidence that bonus depreciation increased investment. Further, none of the recent studies focuses on a single industry. This study has the advantage that all business-use aircraft utilize the same tax life and that bonus depreciation extensions were speciﬁc to general aviation, which helps to control for some of the econometric estimation problems noted earlier. This study utilizes sample data from 1987 to 2005 in an ordinary least squares time series regression. The results reveal that, in contrast to GAMA claims, the bonus depreciation incentives did not have a statistically signiﬁcant relationship with the shipment of general aviation aircraft in the United States. However, the results indicate that the incentives contributed to a signiﬁcant shift in the sales mix of general aviation aircraft manufactured from piston to turbine aircraft.4 This study does not imply that bonus depreciation was not beneﬁcial to the general aviation industry. The decline in shipments after 9/11 might have

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fallen even lower without the incentives, and the delayed recovery of the U. S. economy prior to and after 9/11 may have conﬂicted with the shortterm nature of the incentives. However, the results of this study may be beneﬁcial to policy makers when determining whether short- or long-term beneﬁts are the best incentive for the general aviation industry.

BACKGROUND Federal tax policies often involve incentives to boost investment. By shortening the recovery periods of depreciable assets, annual depreciation deductions increase, providing businesses with accelerated tax deductions that actually lower (through the time value of money) the present value of the cash outlay initially needed to purchase new investments. The House Committee relied on this theory when enacting the 2002 and 2003 tax acts after the tragedy of 9/11. The Committee felt that bonus depreciation incentives would stimulate equipment purchases and foster economic recovery by increasing employment and expanding business opportunities (2003). As a result, the JCWAA of 2002 implemented an additional ﬁrst-year depreciation deduction, and the GTRRA of 2003 increased this deduction.5

The Job Creation and Worker Assistance Act of 2002 The JCWAA of 2002 provided for additional ﬁrst-year depreciation of 30 percent for qualifying property.6 In most cases, general aviation aircraft qualify for MACRS depreciation over a ﬁve-year period.7 The acquisition and placed-in-service dates created deadlines of less than four years for those considering the purchase of new general aviation aircraft and motivated professional organizations to lobby for extensions of the law in order to take advantage of the incentives. Although bonus depreciation does not provide additional deductions for general aviation aircraft, the incentives do accelerate the amount of depreciation that a business aircraft owner can take in the ﬁrst year of use, and this reduces the present value of the after-tax cost of the aircraft. Prior to the bonus depreciation incentives, businesses could deduct 20 percent in the ﬁrst year. With the additional 30 percent ﬁrst-year bonus depreciation, businesses may deduct 30 percent of the original purchase price plus 20 percent of the remaining adjusted cost basis of the aircraft in the acquisition year. Therefore, the business deducts 44 percent of the cost of the airplane in the ﬁrst year.8

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The Job Growth Tax Relief Reconciliation Act of 2003 The JGTRRA of 2003 extended the 30 percent bonus depreciation deduction to property acquired after May 3, 2003, and placed-in-service prior to January 1, 2005. In addition, the 2003 Act introduced an additional ﬁrst-year depreciation deduction of 50 percent. The business receives a 50 percent deduction from the bonus depreciation plus an additional 20 percent of the remaining 50 percent adjusted basis for the ﬁrst year of operation. This results in an overall ﬁrst-year depreciation deduction of 60 percent of the original price of the business aircraft, as compared to the 20 percent ﬁrst-year deduction without the depreciation bonus. Table 1 shows the present value of the tax savings generated from the additional ﬁrst-year bonus depreciation incentives. The purchase of a $2 million aircraft utilizing the additional 50 percent deduction results in depreciation in the ﬁrst year of $1.2 million for a tax savings of $420,000 (assuming a 35 percent tax rate) compared to depreciation of $400,000 and Table 1. Year

1 2 3 4 5 6 Total

Comparison of the After-Tax Cost of a $2,000,000 Aircraft with and without the Bonus Depreciation Incentives.

Depreciation Depreciation Tax Savings Tax Savings Present Value Present Value Per Year with without Per Year of Tax with Bonus of Tax Bonus Bonus without Savings with Depreciationb Savings Depreciationa Depreciationb Bonus Bonus without ($) Depreciationa Depreciationc Bonus ($) ($) Depreciationc ($) ($) ($) 1,200,000 320,000 192,000 115,200 115,200 57,600

400,000 640,000 384,000 230,400 230,400 115,200

420,000 112,000 67,200 40,320 40,320 20,160

140,000 224,000 134,400 80,640 80,640 40,320

Difference of Present Value Preand Post Tax Savings ($)

381,818 92,562 50,488 27,539 25,036 11,380

127,273 185,124 100,977 55,078 50,071 22,760

254,545 (92,562) (50,489) (27,539) (25,035) (11,380)

588,823

541,282

47,540

Notes: After-tax cost: With bonus depreciation, $1,411,177 ($2,000,000 cost$588,823 PV of tax savings); Without bonus depreciation, $1,458,718 ($2,000,000 cost$541,282 PV of tax savings); Savings with bonus depreciation, $47,540 ($1,458,718 without$1,411,177 with); Percentage savings, 3.25 percent ($47,540 savings/$1,458,718 after-tax cost). a Depreciation in year one with bonus depreciation is the sum of the bonus depreciation of 50 percent and regular MACRS depreciation of 20 percent for a ﬁve-year asset. b Tax savings represent the decrease in taxable income resulting from the depreciation deduction assuming the maximum corporate tax rate of 35 percent. c The present value of the tax savings from depreciation is based on a discount rate of 10 percent and a six-year depreciation period.

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a tax savings of $140,000 using MACRS with no additional incentives. The difference in the present value (using six years and 10 percent) of the tax savings, generated by bonus depreciation over MACRS alone, generates savings the ﬁrst year of more than $250,000. This is, in essence, a discount of 13 percent of the purchase price the ﬁrst year. However, the discount is much smaller after ﬁve full years of depreciation. Table 1 shows that the after-tax cost of a $2 million aircraft with bonus depreciation is $1,411,177 compared to the after-tax cost of that same aircraft without bonus depreciation of $1,458,718. Bonus depreciation results in a reduction in the after-tax cost of a $2 million aircraft of $47,541, which generates a 3.25 percent reduction in the after-tax cost. The after-tax cost depends on the discount rate used by managers making the purchase decision and decreases if managers use a lower discount rate in their purchase decision. The American Jobs Creation Act of 2004 According to GAMA, the time between placing an order and delivery of general aviation aircraft averages 12–18 months (GAMA, 2005). Since the manufacture, delivery, and placement into service of such equipment exceeds the period allowed by the initial bonus depreciation laws, lobbyists for the general aviation industry pushed Congress for an extension beyond the placed-in-service deadline of January 1, 2005. Congress responded, and the AJCA of 2004 extended the placed-in-service date for bonus depreciation for qualifying non-commercial aircraft through the end of 2005. The extension included additional requirements for qualifying aircraft.9

LITERATURE REVIEW AND HYPOTHESIS DEVELOPMENT Empirical studies disagree over the effectiveness of depreciation and depreciation-related incentives to spur economic growth through capital investments. Although general aviation manufacturers and professional organizations have generated surveys that boast of ‘‘market stimulation’’ and strengthened airplane sales due to bonus depreciation, no empirical evidence exists to conﬁrm or refute these claims (GAMA, 2005, p. 9). The ability of bonus depreciation incentives to spur investment depends on whether managers focus on the ﬁrst-year tax savings or whether they instead focus on the overall present value of net tax savings. Prior literature

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provides some industry-speciﬁc studies in the areas of utilities, agriculture, and even commercial airline equipment, but there are no empirical studies in the ﬁeld of general aviation.10 This research utilizes variables and theories tested and disputed in the prior macro- and micro-level empirical studies summarized in the following literature review. When Congress introduced accelerated depreciation in 1954, the justiﬁcation for using tax policy to stimulate investment was simply the argument that businesses would buy more when investments cost less (Hall & Jorgenson, 1967). Presumably, increased investment will result as long as manufacturers do not increase prices to recoup the tax beneﬁts of the lower cost (Goolsbee, 1998). Hall and Jorgenson (1967) compare the effects of accelerated depreciation, the ITC of 1962, the revised depreciation guidelines of 1962, and a hypothetical ﬁrst-year write-off in 1954 without the advantage of accelerated depreciation. Their study ﬁnds a substantial increase in investment because of accelerated depreciation. Rennie (1977) follows the lead of Hall and Jorgenson and examines how the cost of capital inﬂuenced investment expenditures in privately owned class A and B electric utilities. Rennie (1977) ﬁnds that accelerated depreciation from 1954 resulted in a reduction of the cost of capital, which generated increases in production plant expenditures from 1957 to 1969.11 Halvorsen (1991) also ﬁnds increased investments resulting from the more generous depreciation rules during 1955–1978 in the ﬁeld of agriculture. All of these studies conclude that tax policy was indeed effective in changing the amount and timing of investment expenditures. Goolsbee (1998) studies depreciation tax policies from 1962 to 1998 and argues: ‘‘Investment demand is actually very responsive to investment tax policy, but in the short run the increased demand for investment mainly increases capital goods prices rather than quantities’’ (p. 122). In a similar study, Goolsbee (2004) indicates that ﬁrms would also shift toward higher quality investments in response to tax subsidies. Davis and Swenson (1993) conﬁrm these results in a laboratory experiment where tax shifting provided beneﬁts to suppliers in the form of higher prices.12 The bonus depreciation incentives of 2002 and 2003 provide changes and new data for the continuation of this line of research. Cohen, Hansen, and Hassett (2002) examine the impact of the 30 percent bonus depreciation incentive on the marginal cost of equipment. Their research ﬁnds a signiﬁcant difference in the impact of the law and claims to ﬁnd a large stimulus due to the race to invest prior to expiration of the beneﬁt. Overall, the research indicates that the large cost reduction could create a signiﬁcant impact on investment, strengthened by the temporary nature of the law.13

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Another study of the 2002 and 2003 tax acts by Desai and Goolsbee (2004) provides evidence of small investment increases.14 Desai and Goolsbee (2004) believe these minimal increases are not evidence that tax policy is an ineffective stimulus. Rather, the short-run stimulus effect was simply too small to counteract the double-digit declines in aggregate investment that occurred in the 2000s.15 Hulse and Livingstone (2003) also analyze a capital expenditures model focusing only on the 30 percent bonus depreciation incentives and ﬁnd reduced capital expenditures in the pre- and post-depreciation eras surrounding the 2002 incentives. The present study isolates the impact of the tax incentives on capital asset purchases in the general aviation sector of the economy and attempts to identify those statistically signiﬁcant variables that predict anticipated changes of capital asset purchases in the general aviation industry. Additionally, this research examines whether these tax incentives differentially inﬂuence speciﬁc types of manufacturers in the general aviation sector by providing incentives for buyers to purchase higher performance aircraft that are more expensive. The ﬁrst research question examines the relationship between bonus depreciation and the quantity of general aviation aircraft manufactured in the United States. A relative reduction in the price of general aviation aircraft due to bonus depreciation should, other things being equal, result in an increased quantity of general aviation aircraft manufactured and sold to businesses. This research question addresses the following hypothesis: H1. Bonus depreciation signiﬁcantly increased the quantity of general aviation aircraft manufactured in the United States. The second research question looks at the relationship between bonus depreciation and a possible decrease in the proportion of piston to turbine aircraft. Studies by Goolsbee (2004) cite large bodies of literature that emphasize the importance of heterogeneity for understanding investment as a part of the macroeconomic growth theory. Goolsbee (2004) empirically studies the patterns of investment in highly aggregated capital machinery surrounding tax subsidies offered by the ITC. Goolsbee (2004) ﬁnds that ﬁrms shift investment toward higher quality capital goods when they receive investment subsidies. That is, shipments of higher priced capital goods increase more than the shipments of lower priced goods because the tax incentives alter the relative price of the higher quality capital goods versus the lower quality goods. The general aviation market allows investors to choose between heavily differentiated products (piston and turbine) within a relatively narrow market of assets (aircraft), which changes the

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routine investment decision from how much to what type. In accordance with the results in Goolsbee (2004), the after-tax cost of higher performance turbine aircraft has decreased relative to the after-tax cost of lower performance piston aircraft. The addition of a ﬁxed cost to the pricing of two grades of the same goods reduces the price of the higher quality goods relative to the lower quality goods (Bertonazzi, Maloney, & McCormick, 1993). Accordingly, cost of capital reductions, through bonus depreciation, should result in a lower relative price for more expensive turbine aircraft. Accelerated depreciation should provide a larger tax beneﬁt to someone buying the more expensive turbine aircraft relative to the less expensive aircraft. The related shift in response to the economic decision to buy larger, more expensive aircraft would lower the ratio of piston to turbine aircraft sold in the general aviation sector of the economy. Research question two examines whether taxpayers alter their investment choices between piston and turbine aircraft in response to the bonus depreciation incentives. The second research question addresses the following hypothesis: H2. Bonus depreciation signiﬁcantly decreased the ratio of piston to turbine general aviation aircraft manufactured in the United States.

METHODOLOGY The Sample GAMA (2006), an international trade association representing 56 of the world’s leading manufacturers of ﬁxed-wing general aviation aircraft, supplied the general aviation shipments for the period 1987–2005.16 This research examines quarterly shipments (76 observations) of general aviation aircraft for the ﬁrst research question and annual shipments (19 observations) for the second research question, including the shipments of both piston and turbine aircraft.17 The 2002 and 2003 tax acts implemented the bonus depreciation incentives as the ﬁrst major change inﬂuencing the depreciation laws for general aviation aircraft since the repeal of the ITC in 1986. Therefore, restricting the data to this period allows the study to focus on the targeted impact of the bonus depreciation incentives separately from other provisions that affect capital investments. This sample represents general aviation aircraft manufactured in the United States, and the study cannot segregate this sample between the

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shipments of general aviation aircraft to businesses or individuals or between domestic and foreign purchasers.18 From 1979 to 2005, exports accounted for an average 27 percent of the total production of general aviation aircraft. The average percentage of exports declines to almost 17 percent for the period 2001–2005 (GAMA, 2005). Bonus depreciation should not inﬂuence shipments to individuals and exports to foreign countries and could mask some of the impact of the incentives identiﬁed in this study.

The First Model The ﬁrst research question tests the signiﬁcance of bonus depreciation as a valid predictor of the quantity of general aviation aircraft manufactured in the United States. This model utilizes quarterly data from 1987 to 2005 (76 observations) in a generalized least squares regression model. The model is as follows: SHPMTS ¼ a1 þ a2 ðDEPÞ þ a3 ðINTÞ þ a4 ðGDPÞ þ a5 ðFUELÞ þ a6 ðCOSTÞ þ a7 ðFRACTÞ where: SHPMTS DEP INT GDP FUEL COST FRACT

number of general aviation aircraft shipments manufactured in the United States ﬁrst-year tax savings from depreciation in deﬂated dollars ﬁrst-year tax savings from real interest in deﬂated dollars gross domestic product chained weighted-average price of aviation gas and jet fuel per gallon in deﬂated dollars average price of general aviation aircraft in deﬂated dollars19 number of fractional ownership shares of general aviation aircraft

This model deﬂates all monetary variables using a gross domestic product deﬂator (nominal divided by chained) to reﬂect current replacement costs.20 Independent Variable The ﬁrst-year tax savings from accelerated depreciation (DEP) should have a positive relationship with the manufacture of general aviation aircraft.

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The DEP variable is a product of the deﬂated average cost, ﬁrst-year depreciation rate, and the highest corporate income tax rate.21

Control Variables This model uses ﬁrst-year tax savings from interest (INT) as a cost of capital variable related to borrowing costs.22 Real interest rates equal the average of monthly prime interest rates from 1987 to 2005.23 The INT variable includes the product of the deﬂated average cost of aircraft, the real interest rates, and the highest corporate tax rates. Since higher interest rates increase the cost of capital, the manufacture of general aviation aircraft should have an inverse relationship with INT. Accordingly, the manufacture of general aviation aircraft should rise as interest rates fall. According to GAMA (2005), industry billings are highly correlated with gross domestic product. This research utilizes quarterly ‘‘real’’ gross domestic product (chained) (from the United States Department of Commerce, Bureau of Economic Analysis (2005) for the period 1987– 2005) to correct for the effects of inﬂation. The purchases of general aviation aircraft should increase in direct relation to a thriving economy, as measured by increased gross domestic product. One of the largest costs of operating general aviation aircraft is the cost of aviation gas and jet fuel. Therefore, the model includes these deﬂated costs to control for the inﬂuence of operating costs on the decision to purchase an aircraft. This variable includes the average cost of aviation gas (consumed by pistons) and jet fuel (consumed by turbines) weighted by the actual consumption of aviation gas by pistons and consumption of jet fuel by turbines to determine the quarterly weighted-average cost of aviation gas and jet fuel.24 Additionally, the model includes the deﬂated average market price of purchasing general aviation aircraft (COST) to control for the inﬂuence of price on the decision to purchase an aircraft. GAMA provides the market value, or cost, of purchasing general aviation aircraft in the form of billings for each manufacturer in the association. COST equals the deﬂated value of all general aviation aircraft manufactured by members of GAMA. Decreases in the cost of aircraft through the bonus depreciation incentives should increase the shipments of general aviation aircraft. Accordingly, increased operating costs through increased fuel prices should result in decreased shipments. Therefore, this study anticipates an inverse relationship between these cost variables (FUEL and COST) and the dependent variable.

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The business use of aircraft has slowly evolved from a lavish and extravagant luxury to an ordinary and necessary business expense (Miller & Flesher, 2003). To help in this transition, the industry has marketed aircraft to fractional owners. Increasing the number of owners of an individual aircraft eases the individual cash ﬂow needed to step into a general aviation aircraft investment. Manufacturers claim that fractional ownership is a key component driving the industry demand for general aviation aircraft (Reavis, 2003). Therefore, increased ownership through the FRACT variable should increase the units of general aviation aircraft manufactured and shipped in the United States.25

The Second Model The second research question measures bonus depreciation as a signiﬁcant predictor of the mix of piston and turbine general aviation aircraft manufactured in the United States. Data regarding the ratio of piston to turbine aircraft is only available on an annual basis. Therefore, this model utilizes annual data from 1987 to 2005 (19 observations) in an ordinary least squares regression model. The model is as follows: RPT ¼ a1 þ a2 ðDEPÞ þ a3 ðINTÞ þ a4 ðFUELÞ þ a5 ðFRACTÞ where: RPT DEP INT FUEL FRACT

ratio of piston to turbine general aviation aircraft shipments manufactured in the United States ﬁrst-year tax savings from depreciation in deﬂated dollars ﬁrst-year tax savings from interest in deﬂated dollars ratio of weighted-average price of aviation gas and jet fuel per gallon deﬂated number of fractional ownership shares of general aviation aircraft

The second model also deﬂates all monetary variables using the gross domestic product deﬂator to measure current replacement costs. Independent Variable Although bonus depreciation allows the same overall depreciation as MACRS for general aviation aircraft, more depreciation occurs during

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85

the initial year of purchase, thereby reducing the cost of capital through the time value of money. Accordingly, bonus depreciation could shift general aviation purchasing incentives to the larger and more expensive aircraft by effectively reducing their relative price.26 The dependent variable for this model is the ratio of the units of piston aircraft manufactured annually to the units of turbine aircraft manufactured annually. The ﬁrst-year tax savings from the bonus depreciation (DEP) incentives should decrease the quantity of piston aircraft to turbine aircraft, since more expensive turbine aircraft should tend to be manufactured and sold when the relative price can be decreased. Control Variables An acquiring company must trade off signiﬁcant increases in the real interest rate against other cost factors to determine whether the company can actually generate a sufﬁcient return on assets when considering a move to a more expensive turbine aircraft. Signiﬁcant increases in interest rates increase the cost of more expensive turbine aircraft relative to piston aircraft. Increases in interest should generate an increase in the ratio of piston to turbine aircraft. This model utilizes an average of the monthly prime interest rates derived from the Federal Reserve. The INT variable includes the product of the deﬂated average cost of aircraft, the average real interest rates, and the highest corporate tax rates. The second model implements the ratio of the weighted-average price of aviation gas to jet fuel as an operating cost variable.27 The price of aviation gas used by piston aircraft exceeds the price of jet fuel used by turbine aircraft. However, turbine aircraft consume more gallons of fuel per hour than pistons. Increases in the weighted-average price per gallon of aviation gas compared to jet fuel (FUEL) should generate a decrease in the ratio of piston to turbine aircraft. If the cost to operate piston aircraft rises through increasing fuel prices, investors might purchase the turbine aircraft as a result. In addition, the intangible beneﬁts of time-savings and the luxury of the corporate turbine could also support a shift from piston to turbine aircraft. This model includes the FRACT variable to represent the potential shift from piston to turbine aircraft when multiple owners pool their cash ﬂows for such a large investment. However, if fractional owners tend to represent ﬁrst-time buyers in a market for piston aircraft, the ratio could increase with additional piston purchases. Therefore, this study does not anticipate whether the ratio of pistons to turbines will increase or decrease as a result of the FRACT variable.

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RESULTS Model One Descriptive Statistics and Correlation Table 2 provides descriptive statistics for this model. The quarterly shipment variable equals the number of units of general aviation aircraft shipped quarterly. The depreciation and interest variables show the deﬂated dollar amounts of tax savings generated in the year of purchase of a general aviation aircraft. Gross domestic product is in billion dollars. The fuel amounts include the deﬂated weighted-average price of aviation gas and jet fuel, weighted by the consumption of each by piston and turbine aircraft. The result is a deﬂated weighted-average price for fuel per gallon. The cost variable measures the average price of all general aviation aircraft shipped each quarter in deﬂated dollars. Fractional shares represent the number of individual fractional owners. The correlation matrix in Table 3 reveals that all of the independent predictor variables exhibit signiﬁcant correlation (two-tailed) with the dependent variable. Two variables, GDP and FRACT, do exhibit Table 2.

Model One – Quarterly Descriptive Statistics, 1987–2005. Minimum Maximum

Number of general aviation aircraft shipments (actual) First-year tax savings from depreciation (deﬂated dollars actual) First-year tax savings from real interest (dollars actual) Gross domestic product chained (billions) Weighted-average price of avgas and jet fuel per gallon (deﬂated dollars actual) Average price of general aviation aircraft (deﬂated dollars actual) Fractional shares of ownership (actual)

Median

Mean

Standard Deviation

170

888

339

411

185

93,504

865,633

235,967

332,148

236,139

47,570

167,927

78,561

81,980

23,260

6,365

11,248

8,338

8,562

1,445

0.52

1.83

0.89

0.92

0.23

375,063

5,613,949

3,363,886

3,237,017

867,199

1

1,603

137

527

630

Notes: Dependent variable, number of general aviation aircraft shipments; control variable, ﬁrst-year tax savings from depreciation deﬂated.

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Table 3.

SHPMT DEP INT GDP FUEL COST FRACT

Model One – Quarterly Correlation Matrix, 1987–2005.

SHPMT

DEP

INT

GDP

FUEL

COST

FRACT

1.000 0.577 0.237 0.823 0.362 0.493 0.792

0.577 1.000 0.148 0.829 0.489 0.647 0.910

0.237 0.148 1.000 0.191 0.140 0.525 0.007

0.823 0.829 0.191 1.000 0.350 0.755 0.933

0.362 0.489 0.140 0.350 1.000 0.049 0.442

0.493 0.647 0.525 0.755 0.049 1.000 0.685

0.792 0.910 0.007 0.933 0.442 0.685 1.000

Notes: SHPMTS, general aviation aircraft shipments manufactured in the United States (dependent variable); DEP, ﬁrst-year tax savings from depreciation deﬂated (control variable); INT, ﬁrst-year tax savings from real interest; GDP, gross domestic product chained; FUEL, weighted-average price of aviation gas and jet fuel per gallon deﬂated; COST, average price of general aviation aircraft deﬂated; FRACT, fractional ownership shares of general aviation aircraft.

correlations with the primary variable of interest, DEP, above the 0.80 level. As a result, each variable was entered into a separate simple regression equation with the dependent variable, and both equations generated the same coefﬁcient signs for these variables as the multiple regression equation, thus avoiding issues of collinearity. None of the remaining variables has tolerance values less than the common cutoff threshold of 0.10.28 Thus, the possible collinearity issues with DEP are not counterintuitive to this model. Regression Results Generalized Durbin–Watson tests (score of 1.52 with a one-tailed p-value of 0.006) indicated problems with autocorrelation in the original model. The autocorrelation in this model has been corrected using autoregressive error correction techniques.29 According to Table 4, the ﬁrst model generates an F-value of 11.92, signiﬁcant with a one-tailed p-value less than 0.001. The model generates an adjusted R2 of 0.922.30 Table 4 provides each of the autoregressive coefﬁcients, the related t-values, and the level of signiﬁcance for the variables reported in this model. The overall regression model indicates that ﬁve of the six variables entered into the regression equation are signiﬁcant predictors of the number of general aviation shipments manufactured in the United States. Independent Variable There is no signiﬁcant relationship between the primary variable of interest, DEP, and the dependent variable. This variable has an insigniﬁcant negative

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Table 4. Model One – Quarterly Regression Results, 1987–2005 (Test of the Effect of Depreciation Incentives on Shipments of General Aviation Aircraft). SHPMTS ¼ a1þa2(DEP)þa3(INT)þa4(GDP)þa5(FUEL) þa6(COST)þa7(FRACT) Predicted Sign

(Constant) DEP INT GDP FUEL COST FRACT

þ þ þ

Unstandardized Coefﬁcients

Standardized Coefﬁcients

B

Standard Error

Beta

53.44 0.00013 0.0032 0.0588 102.1542 0.00013 0.2936

165.85 0.000 0.001 0.022 47.354 0.000 0.0592

0.161 0.402 0.459 0.129 0.618 0.999

t

One-tailed Sig.

0.32 1.28 4.43 2.68 2.16 5.13 4.96

0.7483 0.2053 o0.0001 0.0092 0.0347 o0.0001 o0.0001

Notes: SHPMTS, general aviation aircraft shipments manufactured in the United States (dependent variable); DEP, ﬁrst-year tax savings from depreciation deﬂated (control variable); INT, ﬁrst-year tax savings from real interest; GDP, gross domestic product chained; FUEL, weighted-average price of aviation gas and jet fuel per gallon deﬂated; COST, average price of general aviation aircraft deﬂated; FRACT, fractional ownership shares of general aviation aircraft. Model signiﬁcance: F=11.92 (po0.001). Adjusted R2 ¼ 0.922. , , signiﬁcant at less than 0.01, 0.05, and 0.10 level, respectively.

coefﬁcient (0.00013) with a one-tailed p-value of 0.2053, which indicates no signiﬁcant relationship between quarterly shipments of general aviation aircraft as the cost savings from DEP increase. This lack of an effect conﬁrms prior studies of bonus depreciation and could reﬂect the short-term nature of the bonus depreciation incentives.31 Although the bonus depreciation incentives created potentially large ﬁrstyear tax savings for general aviation aircraft, this industry did not generate shipments equal to or in excess of the pre 9/11 numbers until almost 2005 when the tax incentives expired. Other studies of the bonus depreciation era after 9/11 claim that the investment recovery from 2001 to 2004 was slower than average and that the short-term bonus depreciation incentives were just too small to counteract the declines (Desai & Goolsbee, 2004). While the incentives appeared to provide large tax savings initially, the changes in the present value of the after-tax cost were less enticing after ﬁve full years of depreciation. If managers focus more on the overall after-tax

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89

cost instead of the ﬁrst-year tax savings, the small 3 percent reduction in the after-tax cost savings, as shown in Table 1, would only generate minimal purchase incentives for corporations planning to purchase and maintain an aircraft for the full ﬁve-year depreciation period. The decreasing trend of shipments during a period of increasing depreciation deductions as implied by this model could also be the result of increased pricing by the manufacturers. According to Goolsbee (1998), almost 40 percent of investment subsidies accrue to the manufacturer. This type of bid-up pricing, if applicable to the general aviation industry, would counter the reduced cost of capital anticipated by lawmakers for the consumers. The average cost of general aviation aircraft exceeded $5.5 million the ﬁrst quarter of 2001 and remained above prior averages until the ﬁrst quarter of 2003 when it fell to a little more than $4 million. For the remaining bonus depreciation period (2003–2005), the prices continued to average more than $4 million. According to Goolsbee (1998), one of the primary areas for price increases includes large transportation equipment where the import competition is low and where there are high levels of backlogged orders. Both characteristics noted by Goolsbee apply to the general aviation industry. Although the increased prices began prior to the enactment of the bonus depreciation incentives in the fourth quarter of 2001, the increases were sustained through 2003, which also supports ﬁndings by Goolsbee (1998) where the increases seem to last at least two or three years. If general aviation manufacturers attempted to capture some of the beneﬁts of the incentives through bid-up pricing, these efforts could diminish the impact of bonus depreciation upon the shipments of general aviation aircraft. Control Variables The ﬁrst-year tax savings from real interest rates generates a positive coefﬁcient (0.0032) with a one-tailed p-value less than 0.0001. The research design suggests that interest increases the cost of capital for ﬁnanced ﬁxed asset purchases. According to Hassett and Hubbard (1997), optimistic sales prospects and increased investment purchases often lead to higher interest rates that can cause a sign opposite of that predicted by theory between investments and interest rates. As the economy recovers, along with gross domestic product, interest rates also begin to rise. If the purchase of general aviation aircraft does indeed increase with increasing gross domestic product, one might also note increased purchases when the interest rate is increasing as well. Shipments of general aviation aircraft could also increase as interest rates rise if investors anticipate even higher interest rates and accelerate their purchase decisions to avoid the anticipated increases.

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As predicted, the GDP variable also produces a signiﬁcant positive coefﬁcient (0.0588) with a one-tailed p-value of 0.0092. In a thriving economy, corporations are more likely to acquire additional capital investments. According to GAMA (2005), the general aviation industry billings are highly correlated with changes in gross domestic product and seem to perform the best at gross domestic product changes equal to or in excess of 3 percent. The signiﬁcantly positive coefﬁcient for GDP indicates that general aviation shipments increase as the GDP variable increases. As anticipated, the COST variable has an inverse relationship with the dependent variable. With a negative coefﬁcient (0.00013) signiﬁcant with a one-tailed p-value of less than 0.0001, this control variable indicates that the shipments of general aviation aircraft should decrease as the COST variable increases. In addition, this ﬁnding follows results reported by Auerbach and Hassett (1992) where the coefﬁcient for the cost of capital was relatively small in a model that found signiﬁcant effects of the cost of capital on investment. The FRACT variable also generates a signiﬁcant relationship with the dependent variable. With a statistically signiﬁcant and positive coefﬁcient (0.2936) with a one-tailed p-value less than 0.0001, increases in the FRACT variable result in increased shipments of aircraft. FUEL generates a signiﬁcant negative coefﬁcient (102.1542) with a one-tailed p-value of 0.0347, indicating that operating costs of general aviation aircraft can negatively inﬂuence the decision of whether to purchase.

Standardized Beta Coefﬁcients The standardized beta coefﬁcients help to determine which independent variables had the most impact on the overall regression equation. Table 4 provides the standardized beta coefﬁcient for each of the independent variables in the ﬁrst model. With the largest beta coefﬁcient (0.999) in the table, FRACT appears to make the largest impact on the dependent variable. Therefore, continued growth in the fractional ownership market could signiﬁcantly increase the number of general aviation aircraft manufactured quarterly. The next largest impact comes from COST and GDP with beta coefﬁcients of 0.618 and 0.459, respectively. This interesting combination reveals that such major acquisitions typically occur in a growing or thriving economy, and not surprisingly, that potential investors are more likely to make these acquisitions when the actual price of the aircraft is decreasing.

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Measuring Lagged Data Since prior research indicates lags in both the manufacturing and nonmanufacturing segments of the economy, the ﬁnal stage of analysis for this model includes the lag of the dependent variable.32 Investors may not have responded immediately to the bonus depreciation incentives (especially if prices were increasing). Comparing the changes in lagged shipments to the prior quarter’s bonus depreciation incentives helps to identify changes in investments that lagged behind the initial enactment of the depreciation incentives. None of the lags from one to eight quarters generates a statistically signiﬁcant positive relationship between the DEP variable and the number of general aviation aircraft shipments. In fact, lags for quarters ﬁve through six indicate statistically signiﬁcant but negative coefﬁcients for the DEP variable. A combination of factors may have contributed to the lack of signiﬁcant positive ﬁndings in the lagged shipments analysis including the possible order-to-delivery periods of two years for general aviation aircraft (Hennig, 2006), the short-term nature of bonus depreciation incentives, and the delayed announcement of extensions.

Research Question Two Descriptive Statistics and Correlation Table 5 provides descriptive statistics for the second model. Aircraft builders generally manufacture and ship approximately two piston aircraft for every turbine aircraft manufactured and shipped. The ﬁrst-year tax savings from Table 5.

Model Two – Annual Descriptive Statistics, 1987–2005. Minimum Maximum Median

Ratio of piston to turbine shipments First-year tax savings from depreciation (deﬂated dollars actual) First-year tax savings from real interest (actual) Ratio of deﬂated weighted-average fuel costs Fractional ownership shares

Mean

Standard Deviation

1.15 101,561

2.94 594,767

1.82 200,547

1.94 273,598

0.57 171,148

39,027

95,857

67,611

66,455

17,434

0.32

1.45

0.88

0.89

0.37

5

6,411

548

2,110

2,574

Notes: Dependent variable, ratio of piston to turbine shipments; control variable, ﬁrst-year tax savings from depreciation in deﬂated dollars.

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KAREN C. MILLER ET AL.

Table 6. Model Two-Annual Correlation Matrix, 1987–2005. Ratio of First-Year Tax First-Year Tax Ratio Fractional Piston to Savings from Savings from Weighted Ownership Interest Average Fuel Shares Turbine Depreciation Costs Ratio of piston to 1.000 turbine shipments First-year tax savings 0.634 from depreciation in deﬂated dollars First-year tax savings 0.404 from real interest 0.678 Ratio of deﬂated weighted-average fuel costs Fractional ownership 0.746 shares

0.634

0.404

0.678

0.746

1.000

0.387

0.833

0.949

0.387

1.000

0.040

0.263

0.833

0.040

1.000

0.921

0.949

0.263

0.921

1.000

Notes: Dependent variable, ratio of piston to turbine shipments; control variable, ﬁrst-year tax savings from depreciation deﬂated.

depreciation and interest in deﬂated dollar amounts represent the average cost of all general aviation aircraft manufactured and shipped over a 12-month period. These two variables once again assume that all shipments are depreciable assets ﬁnanced in the initial year of purchase. The ratio of the deﬂated average price of aviation gas consumed to jet fuel consumed represents the price per gallon in deﬂated dollars. The fractional ownership shares simply represent the number of fractional owners of general aviation aircraft. Table 6 reveals signiﬁcant correlations of each independent variable with the related dependent variable at one-tailed p-values less than 0.05. Correlations for FRACT and FUEL do exhibit high levels of collinearity (above 0.80) with depreciation. However, the variables have tolerance levels below the common threshold level of 0.10. Once again, collinearity does not appear to be an issue with these two variables in the model. Although these variables may mask some of the predictive ability of the other variables, they remain in the second model for explanatory purposes. Regression Results With an F-value of 13.16, signiﬁcant at less than 0.0001 level, the ANOVA, which forms a basis for signiﬁcance in a regression model, indicates the

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Table 7. Model Two-Annual Regression Results, 1987–2005 (Test of the Effect of Depreciation Incentives on Ratio of Piston to Turbine Aircraft Shipments). RPT ¼ a1þa2(DEP)þa3(INT)þa4(FUEL)þa5(FRACT) Predicted Sign

(Constant) DEP INT FUEL FRACT

þ

Unstandardized Coefﬁcients B

Standard Error

5.28338 0.000004 0.000022 1.2979 0.000234

1.274 0.000 0.000 0.728 0.000

Standardized Coefﬁcients

t

One-tailed Sig.

4.148 3.080 3.331 1.783 1.700

0.001 0.008 0.005 0.096 0.111

Beta

1.343 0.678 0.846 1.063

Notes: RPT, ratio of piston to turbine general aviation aircraft shipments manufactured in the United States (dependent variable); DEP, ﬁrst-year tax savings from depreciation deﬂated (control variable); INT, ﬁrst-year tax savings from real interest; FUEL, ratio of weightedaverage price of aviation gas and jet fuel per gallon deﬂated; FRACT, fractional ownership shares of general aviation aircraft; dependent variable, ratio of piston to turbine shipments. Model signiﬁcance: F ¼ 13.16 (po0.0001). Adjusted R2 ¼ 0.73. , , signiﬁcant at less than 0.01, 0.05, and 0.10 level, respectively.

overall model is a signiﬁcant predictor of the dependent variable. The model generates an adjusted R2 of 0.730 and a Durbin–Watson score of 1.880.33 This Durbin–Watson score indicates no ﬁrst-order serial correlation in the model based on the lower and upper limits of dL ¼ 0.859 and dU ¼ 1.848, calculated using 19 data periods and ﬁve variables (including the intercept) at the 0.05 signiﬁcance level. According to the generalized Durbin–Watson tests to assess autocorrelation, this model does not exhibit problems with autocorrelation. Table 7 provides the unstandardized coefﬁcients, the beta coefﬁcients, their t-values, and their related signiﬁcance in this regression equation.34 Independent Variable The primary variable of interest, DEP, generates a signiﬁcant negative coefﬁcient (0.000004) with a one-tailed p-value of 0.008. This inverse relationship supports the second hypothesis, which indicates that bonus depreciation did inﬂuence the mix of piston and turbine general aviation aircraft manufactured in the United States. This ﬁnding also supports prior

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KAREN C. MILLER ET AL.

research of composition shifting by Goolsbee (2004), which claims that investment tax policy can alter the incentives to buy different qualities of capital, even within the same categories where tax treatment is fairly identical. Based on the coefﬁcient estimate of 0.000004 and the average change in the DEP variable for the years 2002–2005, approximately 50 purchasers chose turbine aircraft over piston aircraft during the tax incentive period from 2002 to 2005. Control Variables Only one of the control variables has a signiﬁcant relationship with the dependent variable. The INT variable yields a signiﬁcant negative coefﬁcient (0.000022) with a one-tailed p-value of 0.005. This inverse relationship indicates that the ratio of piston to turbine shipments decreases as INT increases. Therefore, this variable does not seem to prohibit potential investors from stepping up from the piston to the turbine purchase. The control variables for FUEL and FRACT generate insigniﬁcant coefﬁcients. Standardized Beta Coefﬁcients An analysis of the standardized beta coefﬁcients helps to determine the actual impact or importance of each variable in the overall model. As noted in Table 7 earlier, the model contains two variables signiﬁcant at the less than 0.05 level. The beta coefﬁcients for these two variables indicate that the DEP variable actually seems to have the most impact on the ratio of piston to turbine aircraft with a beta coefﬁcient of 1.343. The INT variable then follows in importance with a beta coefﬁcient of 0.678. Although DEP may have been an insigniﬁcant variable in the initial decision to purchase general aviation aircraft, this variable seems to have a much larger impact on which aircraft to actually purchase, piston or turbine. The impact of depreciation in this phase of the research follows the ﬁndings of Goolsbee (2004), which provided evidence that sales of higher quality, higher performance, more expensive capital will disproportionately increase when the cost of capital falls. Measuring Lagged Data The ﬁnal analysis of the second research question measures the lagged dependent variable. After lagging the data for one year, the variables in the model are no longer signiﬁcant. This may be a result of the actual relationships among the variables, or the insigniﬁcance could result from the limited observations available with the analysis of annual data in this model.

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Bonus Depreciation Incentives

CONCLUSION Based on this study, the bonus depreciation incentives did not generate a signiﬁcant change in the number of general aviation aircraft manufactured in the United States. While the tax incentives did not generate signiﬁcant increases in the units of general aviation aircraft manufactured in the United States, the incentives may have slowed the rapid decline in shipments due to the recessed economy after 9/11. These results could indicate that temporary tax incentives may not be the best type of cost reduction incentive for the general aviation industry, especially in a recessed economy. The depreciation tax incentives in this study played a signiﬁcant role in the explanation of the types of aircraft shipped. The ratio of piston to turbine aircraft manufactured does decrease as the ﬁrst-year tax savings from depreciation increases. Although piston shipments routinely outnumber turbine shipments, the results of this study indicate the potential of the tax incentives to narrow this gap between the amount of piston and turbine aircraft manufactured. The importance of the impact of the bonus depreciation incentives on the general aviation sector of the economy motivated this timely research. Prior research tends to place more emphasis on assets actually utilized in the production phase versus those assets that increase the intangible cost beneﬁts of the corporation, such as corporate aircraft. Manufacturers in this industry need empirical data for future forecasts and projections, and these laws provided direct incentives for the general aviation industry. However, the short-term nature of the incentives restricts the data and limits the time available to identify a statistically signiﬁcant impact. As a result, readers should generalize from this study with caution. In addition, assumptions regarding the data could mask the ability of the models to isolate the impact of the independent variables. Also, the general aviation industry itself poses measurement problems due to the uniqueness of the industry along with the unique costs associated with each type of aircraft. Future research might attempt to segregate the data and examine the impact of bonus depreciation on the turbine segment separately from the piston segment to better identify the impact directly attributed by the business use of the turbine aircraft.

NOTES 1. Most of the federal security mandates for commercial airlines did not apply to private corporate airplanes under general aviation guidance.

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2. As a lobbying organization for general aviation manufacturers, GAMA needed to convince Congress that the bonus depreciation incentives were working to support the lobby for extensions. 3. Davis and Swenson (1993), Chirinko (1986), and Chirinko and Eisner (1983) identify some of these difﬁculties. 4. Substantial differences exist between the performance and price of piston versus turbine aircraft. Single- and multi-engine piston aircraft are typically smaller with a shorter range. The average price of a single-engine piston aircraft ranges between $150,000 and $350,000. Turbine aircraft, which includes both turboprops and jets, provide longer ranges, ﬂy at higher speeds, and are larger allowing for more passengers. The price range for the turboprop piston aircraft can vary from $1 million to almost $5 million, while the turbine, or jet, can range from $4 million to over $40 million. In addition, the turbine aircraft require signiﬁcantly higher operating costs (GAMA, 2007). 5. See the Joint Committee Summary, P.L. 107-147, (2002) and the House Committee Report, No. 108-94, P.L. 108-27, (2003) for details of the reasoning behind implementation of these two Acts. IRC Section 168(k) addresses the new bonus depreciation incentives for 2002 and 2003, respectively. 6. Purchase and original use must begin with the taxpayer on or after September 11, 2001, with the aircraft in service before January 1, 2005 (extended from before September 11, 2004). 7. See Asset Class 00.21, Revenue Procedure 87-56, 1987-2 CB 674 for details. Qualifying general aviation aircraft must meet the listed property requirements of IRC Section 280F. 8. A business reduces the basis of the aircraft by 30 percent bonus depreciation and continues to depreciate the remaining basis over the ﬁve-year recovery period (six years including the half-year convention). For example, a business aircraft with an initial purchase price of $1,000,000 receives a 30 percent bonus depreciation deduction in year one of $300,000, making the adjusted basis equal to $700,000. Another 20 percent of the $700,000, or $140,000, is also deducted as depreciation in the ﬁrst year allowing a $440,000 deduction for depreciation in the ﬁrst year of use as opposed to the former MACRS depreciation of 20 percent, or $200,000. This results in an actual ﬁrst-year deprecation deduction of 44 percent, more than double the deduction without bonus depreciation. 9. See House Committee Report (2005) 108-548 and IRC (1986) 168(k)(2)(C). IRC 168(k)(2)(C) speciﬁcally excludes aircraft used for transporting persons that would have otherwise qualiﬁed under 168(k)(2)(B)(iii). A deposit of the lesser of $100,000 or 10 percent of the purchase price from the purchaser must be made, and the aircraft must have an estimated production period exceeding four months. 10. For utilities see Rennie (1977), for agriculture see Halvorsen (1991), and for commercial airline equipment, see Goolsbee and Gross (1997) and Smith (2005). 11. Rennie (1977) provides sources and data to determine the three- and eightyear lags incorporated in his study. 12. For additional studies on tax shifting, see Kachelmeier, Limberg, and Schadewald (1991). 13. Cohen et al. (2002) also note the distortion that could occur across different types of capital as a result of the 2002 depreciation incentives.

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14. Comments by Hassett are contrary to the claims of Desai and Goolsbee (2004). Hassett indicates the evidence actually supports the view that the tax cuts likely had a signiﬁcant impact on investment. Hassett’s comments are included in Desai and Goolsbee’s (2004) work in the comment section. 15. Desai and Goolsbee (2004) report changes in manufacturing investments for 2000–2002 that were almost 22 percent lower than investments from 1994–1999. 16. The data published by GAMA in 2005 represented the following percentages of the market: 100 percent of the business jets, 98 percent of the turboprops, and 90 percent of the pistons (Hennig, 2006). 17. The second question utilizes annual data because GAMA does not report the separate shipment of piston and turbine aircraft quarterly. Currently GAMA only reports total shipments quarterly. 18. Complete and reliable data to segregate business and personal use for 1987–2005 is not currently available. However, the Federal Aviation Administration (FAA, 2004) reports that almost 90 percent of the hours ﬂown by turbines represent business use while roughly 50 percent of the hours ﬂown by piston represent business use. Accordingly, most turbine use is business related (which should qualify for a depreciable purchase), and half of the piston use is business related. Since this study assumes all aircraft are business use, this assumption actually weakens the study’s ability to ﬁnd signiﬁcance. 19. The ﬁrst model originally considered a terrorism variable. However, this variable was insigniﬁcant and was removed from the model to eliminate serial correlation and collinearity issues. 20. Chained GDP, as reported by the United States Department of Commerce, Bureau of Economic Analysis (2005), weights adjacent years to account for substitutions in response to price changes. The gross domestic product deﬂator uses nominal gross domestic product divided by chained gross domestic product. 21. GAMA (2004b) provides total shipments and total costs to determine the average cost. 22. Since both depreciation and interest represent tax savings variables, and the bonus depreciation incentives are limited to the year of purchase, both variables measure tax savings in the initial year of purchase. 23. To determine the real interest rate, the prime (or nominal) interest rates approximate the nominal rate less the rate of inﬂation. The Federal Reserve Board (2005) provides the interest rates. 24. The per gallon fuel costs are reported by the Energy Information Administration (EIA), an independent statistical and analytical agency within the United States Department of Energy (2005). The FAA determines the annual fuel consumption values (FAA, 2004). The Aviation Policy and Plans (APO) division of the FAA online reports the 1991–2005 fuel consumption portion of the GAATA survey (FAA, 2006). 25. The National Business Aviation Association (2004) provided the number of fractional shareowners. 26. As noted earlier, the present value savings of the overall decrease in the relative price decreases substantially in the initial year of depreciation but only approximates a 3 percent after-tax cost savings after ﬁve full years of depreciation.

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27. Using the COST and GDP variables in the second model with annual data and fewer observations creates problems with collinearity and serial correlation. These variables are insigniﬁcant when entered into the model, and removing them does not affect the coefﬁcients of the remaining variables. Therefore, the second model excludes these variables to remove collinearity and serial correlation. 28. See Hair, Anderson, Tatham, and Black (1998) for the common thresholds of tolerance and the variance inﬂation factor. 29. The autoregressive error techniques use Yule–Walker estimates. 30. Both models meet the assumptions required for ordinary least squares regression, and no outliers were identiﬁed that affect the results of the equations. None of the linearity plots indicates curvilinear patterns. No sign of heteroscedasticity appears through a constant pattern in the residuals. In addition, no consistent patterns related to independence of the error term appear. The histogram distribution approximates a normal distribution, and the normal probability plot only shows minimal deviation from the normal diagonal. Assuming balanced error risks and moderate effect size (Faul, Erdfelder, Lang, & Buchner, 2007), compromise power for model one approximates 0.85. 31. For prior research that also ﬁnds minimal or even negative results, see Hulse and Livingstone (2003), Clark (1993), Kaufman and Gitman (1988), Shapiro (1986), Bosworth (1985), and Coen (1968). 32. See Hall and Jorgenson (1967), Rennie (1977), Shapiro (1986), and Raytheon’s 2003 Annual Report for additional information concerning the impact of lags. 33. With a limited sample size for this model, compromise power still approximates 0.65, assuming balanced type one and two errors and a moderate effect size (Faul et al., 2007). 34. The lower limit of the dependent variable, RPT, is bounded by zero. To alleviate concerns of the lower bound on the dependent variable, we performed a limited dependent variable estimation. The results of the limited dependent variable estimation approach were very similar to those obtained in the linear approximation reported in the paper. The lowest observation of RPT is 1.15, which is greater than one and close to the mean of 1.94. The lower bound of RPT is unlikely to have inﬂuenced the results.

ACKNOWLEDGMENTS The authors express their thanks for the comments and suggestions provided by the two blind reviewers and for contributions by Dr. Dale Flesher, Dr. William Shughart II, and Dr. Steve Miller.

REFERENCES Auerbach, A. J., & Hassett, K. (1992). Tax policy and business ﬁxed investment in the United States. Journal of Public Economics, 47(2), 141–170.

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Bertonazzi, E., Maloney, M. T., & McCormick, R. E. (1993). Some evidence on the Alchian and Allen theorem: The third law of demand? Economic Inquiry, 31(3), 383–393. Bosworth, B. P. (1985). Taxes and the investment recovery. Brookings Papers on Economic Activity, 1, 317–348. Chirinko, R. S. (1986). Business investment and tax policy: A perspective on existing models. National Tax Journal, 39(2), 137–155. Chirinko, R. S., & Eisner, R. (1983). Tax policy and investment in major U.S. macroeconomic econometric models. Journal of Public Economics, 20(March), 139–166. Clark, P. K. (1993). Tax incentives and equipment investment. Brookings Papers on Economic Activity, 1, 317–348. Coen, R. M. (1968). Effect of tax policy on investment in manufacturing. The American Economic Review, 58(2), 200–211. Cohen, D. S., Hansen, D. P., & Hassett, K. A. (2002). The effects of temporary partial expensing on investment incentives in the United States. National Tax Journal, 55(3), 457–466. Davis, J. S., & Swenson, C. W. (1993). Experimental evidence on tax incentives and the demand for capital investments. The Accounting Review, 68(3), 482–514. Desai, M. A., & Goolsbee, A. D. (2004). Investment, overhang, and tax policy. Brookings Papers on Economic Activity, 2, 285–355. Faul, F., Erdfelder, E., Lang, A. G., & Buchner, A. (2007). GPower 3: A ﬂexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39, 175–191. Federal Aviation Administration (FAA), Ofﬁce of Aviation Policy and Plans (2004). 2002 General Aviation and Air Taxi Activity (GAATA) Survey. FAA.Gov General Aviation and Air Taxi Activity Survey, May. Available at http://apo.faa.gov/gasurvey2002/ GA%20survey%202002.htm. Accessed on 11 November, 2004. Federal Aviation Administration (FAA), Ofﬁce of Aviation Policy and Plans (2006). FAA Aerospace Forecasts Fiscal Years 2000–2017. FAA.Gov Aviation/Aerospace Forecasts, March. Available at http://www.faa.gov/data_statistics/aviation/aerospace_forecasts. Accessed on 14 May, 2006. General Aviation Manufacturers Association (GAMA). (2004a). Pistons power general aviation shipments in 2003. GAMA News, 11 February. Available at http:// www.gama.aero/mediaCenter/pr.php?id ¼ 44. Accessed on 20 April, 2004. General Aviation Manufacturers Association (GAMA). (2004b). Create jobs by extending bonus depreciation and eliminating the placed-in-service requirement. Legislative, 29, March. Available at http://www.gama.aero/resources/legislative/bonusDepreciation. php. Accessed on 29 March, 2004. General Aviation Manufacturers Association (GAMA). (2005). Annual industry review and 2005 outlook. Washington, DC: GAMA. General Aviation Manufacturers Association (GAMA). (2006). General aviation statistical databook 2005 (13 February, 2006). Washington, DC: GAMA. General Aviation Manufacturers Association (GAMA). (2007). Media guide: FAQ about GA (8 May, 2007). Washington, DC: GAMA. Goolsbee, A. (1998). Investment tax incentives, prices and the supply of capital goods. The Quarterly Journal of Economics, 113(1 February), 121–148. Goolsbee, A. (2004). Taxes and the quality of capital. Journal of Public Economics, 88, 519–543. Goolsbee, A., & Gross, D. B. (1997). Estimating adjustment costs with data on heterogeneous capital goods. Working Paper. National Bureau of Economic Research, Cambridge, MA.

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Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis. New Jersey: Prentice Hall. Hall, R. E., & Jorgenson, D. W. (1967). Tax policy and investment behavior. The American Economic Review, 57(3), 391–414. Halvorsen, R. (1991). The effects of tax policy on investment in agriculture. The Review of Economics and Statistics, 73(3), 393–400. Hassett, K. A., & Hubbard, R. G. (1997). Tax policy and investment. In: A. J. Auerbach (Ed.), Fiscal policy: Lessons from economic research (pp. 339–385). Cambridge, MA: MIT Press. Hennig, J. (2006). General Aviation Manufacturer Association, Manager of Operations, Bonus Depreciation. E-mail to the author. 10 May, 2006. Hulse D. S., & Livingstone, J. R. (2003). Incentive effects of bonus depreciation. Working Paper. University of Kentucky, Lexington, KY. Internal Revenue Code of 1986 Section 168(k). International Business Aviation Council, General Aviation Forecast Conference. (2002). Business aviation outlook 2002 – time to put our thinking cap on. Montreal, Quebec: IBAC. Kachelmeier, S., Limberg, S., & Schadewald, M. (1991). A laboratory market examination of the consumer price response to information about producers’ costs and proﬁts. The Accounting Review, 66(October), 694–717. Kaufman, D. J., Jr., & Gitman, L. J. (1988). The Tax Reform Act of 1986 and corporate investment decisions. The Engineering Economist, 33(2), 95–108. Miller, K. C., & Flesher, T. K. (2003). Deductibility of business aircraft. Journal of Accountancy, 196(1), 57–60. National Business Aviation Association. (2004). NBAA business aviation fact book. Washington, DC: NBAA. Reavis, B. (2003). Honeywell aerospace forecasts $115 billion sales for new business jet aircraft through 2013, 23 May, 2006. Available at http://web.nbaa.org/public/news/stats/ honeywell/honeywell2003.doc Rennie, H. G. (1977). Federal tax effects on electric utility investment. The Engineering Economist, 22(2), 97–118. Rev. Proc. 87-56, 1987-2 CB 674. Shapiro, M. D. (1986). Investment, output, and the cost of capital. Brookings Papers on Economic Activity, 1, 111–164. Smith, L. (2005). Dynamics and equilibrium in a structural model of commercial aircraft ownership. Federal Trade Commission Bureau of Economics Working Papers No. 280, 1–50. The Federal Reserve Board (2005). Statistics: Releases and historical data. FRB, 27 February. Available at http://www.federalreserve.gov/releases/h15/data/m/prime.txt. Accessed on 10 May, 2006. United States Congress, Committee Report (2005). House Committee Report. H.R. Report No. 108–548. United States Congress, House of Representatives (2003). House Committee Report. H.R. Report No. 108-94. P.L. No. 108-27. United States Congress, Joint Committee (2002). Joint Committee Summary. Joint Committee Summary of P.L. 107-147. United States Department of Commerce, Bureau of Economic Analysis (2005). Current dollar and ‘‘real’’ gross domestic product. BEA National Economic Accounts,

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29 October. Available at http://www.bea.doc.gov/bea/dn1.htm. Accessed on 28 February, 2006. United States Department of Energy, Energy Information Administration, Ofﬁce of Oil and Gas (2005). Petroleum marketing annual 2005. EIA Ofﬁcial Energy Statistics from the U.S. Government, 14 May 2006. Available at http://www.eia.doe.gov/pub/oil_ gas/petroleum/data_publications/petroleum_marketing_annual/current/pdf/pmaall.pdf. Accessed on 14 May, 2006.

THE ASSOCIATION OF PAID INCOME TAX RETURN PREPARERS WITH HORIZONTAL EQUITY Peter J. Westort and Richard Cummings ABSTRACT The impact of paid tax return preparers on the horizontal equity (HE) of the federal tax system has signiﬁcance for regulatory and tax policy reasons. Using multiple analytical techniques to consider data from the Statistics of Income Division’s 2000 Individual Model File (IMF), this study shows that the HE measure is generally greater (implying less HE) for the paid-preparer returns than for the self-prepared returns, even after controlling for complexity and other variables that may differ systematically by tax preparation mode.

INTRODUCTION A recent study by the Government Accountability Ofﬁce (GAO) observes that about 56% of all individual federal income tax returns ﬁled in 2002 were signed by a paid preparer (GAO, 2006). While that particular study focuses on chain preparers, it represents the concern, in general, about the ability of Advances in Taxation, Volume 18, 105–140 Copyright r 2008 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 1058-7497/doi:10.1016/S1058-7497(08)18005-X

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all paid preparers to accurately determine tax liability (TL). Paid preparers are already regulated by the federal government and various preparer penalties may be assessed for noncompliance (Internal Revenue Code sections 6694 and 6695). This regulation reﬂects a concern over the potential impact of paid preparers on the fairness of the federal income tax system. One measure of fairness is horizontal equity (HE). Conceptually, HE refers to the extent to which taxpayers with the same amount of income bear the same tax burden. Because some taxpayers prepare their own tax returns and others employ paid preparers, an important question is how HE is impacted by the use of paid income tax return preparers. Does the use of paid preparers increase or decrease the equity of the system, or is there no systematic effect? This study examines the impact of paid income tax return preparers on the HE of the federal income tax system. While a number of prior studies address the impact of paid preparers on various characteristics of the income tax system, Allan and Iglarsh (1999) is the only study we are aware of that examines HE and preparer usage directly. They use a cluster analysis to deﬁne equals and then compute the coefﬁcient of variation (CV) within each cluster. In general, they observe that paid preparers are associated with greater CV measures or less HE. We add to the literature in the following ways. First we use multiple techniques to measure the difference in HE between self-prepared and paidprepared returns. The use of CV ratio (CVR) (Westort & Wagner, 2002) is an improvement over the use of CV analysis (Madeo & Madeo, 1981; Anderson, 1985, 1988) in that it allows for comparisons across income groups. Additionally, a regression approach (Grasso & Frischmann, 1992) allows us to control for variables that may differ by preparer mode. Finally, we use the Atkinson–Plotnick (AP) index (Iyer & Seetharaman, 2000), which is a rank-reversal measure of fairness rather than a TL dispersion measure. While this is an overall measure, rather than an income level measure, it provides another technique for studying the effect of preparer mode. In general, we observe that mean TL is lower for paid-prepared returns in 17 of the 30 income ranges, but the difference in mean TL is statistically signiﬁcant in only three of the income groups. Our measure of complexity is uniformly greater for the paid-prepared returns in all income groups and is statistically signiﬁcant. The difference in standard deviation of the expanded income amount for the two preparer modes is not statistically signiﬁcant in 27 of the 30 income ranges. Of the remaining three, it is greater for paidprepared returns in only two of the income ranges. The standard deviation of TL, however, is greater for the paid-prepared returns in all but 6 of the 30 income groups. The difference in standard deviation is statistically signiﬁcant

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for all but two of the income groups. Finally, we observe that the HE measure is generally greater (implying less HE) for the paid-prepared returns than for the self-prepared returns, even after controlling for complexity and other variables that may differ systematically by tax preparation mode. The authors add a caveat about interpreting the results. Typically, HE studies using archival data will compute TL or effective tax rate (ETR) before and after the tax phenomenon being studied. The difference in TL, however measured, therefore, is clearly attributable to the phenomenon since that is the only difference between the two measures. That is, each observation is measured twice, once before and once after the phenomenon. In the current study, however, we cannot observe what the TL would be for paid-prepared returns if they did not use a paid preparer. Similarly, we cannot observe what TL would have been if the self-prepared returns had been prepared by a paid preparer. By controlling for factors that differ systematically between the two groups, we infer that any difference must be due to preparer usage. Obviously, however, there exists the possibility that the differences are due, at least in part, to unknown factors for which we have not controlled. The next section is a review of prior research and a discussion of how this chapter ﬁts into the literature. The third section describes the research design followed by a discussion of the results and our conclusions.

PREVIOUS RESEARCH Several prior studies using archival data investigate the determinants and effects of preparer usage. Long and Caudill (1987, 1993) ﬁnd that paidpreparer usage is associated with the elderly, amount of income, selfemployment, complexity, and marginal tax rate. They observe that preparer usage tends to decrease TL. Christian, Gupta, and Lin (1993) detect a small, but statistically signiﬁcant, association of time-cost with preparer usage.1 They also ﬁnd an association with self-employment and complexity. They do not, however, observe an association with marginal tax rate. Frischmann and Frees (1999) study determinants of tax preparation fees, but they also model preparer choice (self-prepared or paid-prepared). They note that preparer usage is associated with time savings (computed in a manner similar to Christian et al., 1993) and uncertainty, but not with tax savings.2 None of these archival studies directly addresses the issue of HE. To the extent preparer usage (or mode) affects TL, it may well affect HE. These studies, however, do not examine preparer mode by a narrow enough income level to be considered HE studies, nor do they develop measures of equity.3

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Their ﬁndings are important for the current study in that they identify characteristics that may differ by preparer mode for which we, therefore, control. The difference in mean tax liabilities (or ETR) between groups of selfprepared and paid-prepared tax returns may be due to differences in complexity, age, or type of income; the same may be true for differences in dispersion of TL for the two groups. Thus, these factors are controlled for before ascribing differences to preparer usage. To some extent, Allan and Iglarsh (1999) control for these factors by clustering observations. They divide their sample into quintiles and then use cluster analysis to form groups of similarly situated taxpayers based on income proﬁle. They compare mean ETR for self-prepared and paid-prepared returns within each cluster. The difference in mean ETR is statistically signiﬁcant for only 7 of the 34 clusters. Mean ETR is lower for paid-prepared returns in four of the seven clusters and higher in the other three. They also use the CV of ETR to measure the difference in dispersion of self-prepared and paid-prepared returns by cluster. The CV for paid-prepared returns is less than for self-prepared returns in only 6 of the 34 clusters. Thus, the dispersion of ETRs is generally greater for paid-prepared returns. This is interpreted as being associated with a decrease in HE. This clustering, however, involves trade-offs; it only controls for income-related differences and does not allow for comparisons between clusters within the income quintiles. Westort and Wagner (2002) do not address preparer mode, but, in addition to introducing the CVR as a measure of HE, they point out the necessity of using income groups of equal width. Similarly, Grasso and Frischmann (1992) do not address preparer mode, but they introduce the use of the coefﬁcient of residual variation (CRV) in measuring HE. Iyer and Seetharaman (2000) evaluate other measures of HE, typically based on aftertax income. Among the measures they use is the AP index, which is a rankreversal-based measure. While this is an overall measure, it nonetheless provides an additional technique for analyzing differences in HE.

RESEARCH DESIGN Overview In this study, four techniques are used to measure HE. Three of these techniques use measures of TL dispersion to proxy for HE: CV, CVR, and CRV. In an HE context, these measures are provided for each of a

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predetermined number of income levels. They are not overall measures, although a weighted average CVR measure can be computed. The fourth technique is the AP index, an overall measure of rank reversals caused by the tax system, which we use to supplement the income level dispersion-based analysis of the prior three techniques. Consistent with many prior HE studies using archival data, the sample is sorted into income groups using an expanded income amount (Anderson, 1985, 1988; Grasso & Frischmann, 1992). This sorting by income is intended to form groups of economic equals. In the current study, each income group in the sample is further divided into two subgroups. One subgroup consists of those tax returns that were self-prepared. The other subgroup consists of those that were prepared by a paid preparer. HE measures are then computed for each subgroup by income range and then the differences in the HE measures are compared. Economic equals are here deﬁned as taxpayers with the same ﬁling status in the same income range. For ﬁling status, we limit our study to married taxpayers ﬁling joint returns because that is the ﬁling status with the largest number of taxpayers as well as the sole ﬁling status used in earlier HE studies (see Madeo & Madeo, 1981; Anderson, 1985). Expanded total income (ETI) is the measure used to construct income ranges. ETI is deﬁned as adjusted gross income (AGI) plus the following adjustments: Individual Retirement Arrangement (IRA) deduction for taxpayer and spouse, Keogh and Simpliﬁed Employee Pension (SEP) deductions, tax-exempt interest, tax-exempt Social Security beneﬁts, student loan interest, and prior year losses (capital and passive), reduced by current-year capital and excessive passive losses disallowed. Income range intervals of $10,000 are computed for ETI between $0 and $300,000, similar to Westort and Wagner (2002). This grouping is a trade-off between having a reasonable number of income ranges to analyze (with a sufﬁcient sample size in each range) and narrow enough ranges so that taxpayers can be considered economic equals.

Data This study uses the year 2000 Individual Model File (IMF) tax return data. This data set is provided by the Internal Revenue Service Statistics of Income

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(SOI) Division. It is a stratiﬁed random sample of actual individual tax returns ﬁled during 2000 and sanitized to prevent personal information from being disclosed. Our sample consists of 37,267 observations. An additional beneﬁt of this data set is that it is more recent than that used in prior studies and is thus more reﬂective of the current tax regime and taxpayers’ preparer usage patterns. Of the most recent studies cited here, Westort and Wagner (2002) use 1992 SOI data, Iyer and Seetharaman (2000) use panel data covering 1987–1990, and Allan and Iglarsh (1999) use 1988 SOI data.

Horizontal Equity Measures Measures of HE are computed for TL, which is the amount of income tax including the alternative minimum tax, less credits including the amount of earned income credit used to offset tax. This value will never be less than zero. Some prior studies use ETR as the variable of interest. We choose to use TL because the regression ﬁt, as measured by adjusted R2, is better for TL, although results are essentially the same. See Appendix A for a comparison of adjusted R2 values. Coefﬁcient of Variation The CV is the standard deviation of a sample divided by its mean and expressed as a percent. Thus the CV of TL is SDðTLÞ 100 MEANðTLÞ

(1)

SDðETIÞ 100 MEANðETIÞ

(2)

Likewise, CV of ETI is

Coefﬁcient of Variation Ratio The CVR is deﬁned as CVR ¼

CVðTLÞ CVðETIÞ

(3)

Since the CV as well as the CVR is computed for each subgroup at each income level, the CV for the self-prepared returns will be referred to as CVs

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and the CV for the paid-prepared returns will be referred to as CVp. Similarly, CVR will be referred to as CVRs and CVRp. Our use of the CVR is slightly different than that proposed by Westort and Wagner (2002) as explained below. Using CVR analysis requires measuring the difference between the CVR before the phenomenon being observed (CVRB) and the CVR afterwards (CVRA), or CVRB CVRA

(4)

Since the CVR is the ratio of CV(TL) over CV(ETI) Eq. (4) expands to CVðTLÞB CVðTLÞA CVðETIÞB CVðETIÞA

(5)

However, Westort and Wagner (2002) examine the change in TL, and the expanded income amount (ETI) remains unchanged. Thus, CVðETIÞB ¼ CVðETIÞA

(6)

Therefore, Eq. (5) reduces to CVðTLÞB CVðTLÞA CVðETIÞ

(7)

In our study, however, there is no before and after, just two subgroups in the same income range. Further, we cannot assume that CV(ETI)s ¼ CV(ETI)p and so our analysis is similar to that of Eq. (5), but not necessarily similar to that of Eq. (7). This means that the difference in CVR could be due to the differing dispersions in income as well as the differing dispersions in TL. For this reason we compare the mean and standard deviation of ETI for each subgroup and income range. To the extent there is no statistically signiﬁcant difference between these statistics for ETI, then differences in the CVR are due to differences in TL. Coefﬁcient of Residual Variation The CRV is described as an estimate of the standard deviation of the regression’s error term, divided by the mean of the dependent variable, expressed as a percent. Thus, in a regression with TL as the dependent variable CRV ¼

ROOT MSEðTLÞ 100 MEANðTLÞ

(8)

Results are referred to as CRVs and CRVp to distinguish return preparation mode.

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Regression Models Prior research consistently observes an association between complexity and preparer mode. This result is both theoretically and intuitively appealing if it is the complexity of tax returns that drives paid-preparer usage. Thus, differences in HE may well be, at least in part, due to complexity, not the effect of paid-preparer usage. We measure complexity (COMPLEX) as the amount of time (in minutes) the Internal Revenue Service (IRS) estimates for taxpayers to complete and ﬁle the Form 1040 and related schedules and forms. See Appendix B for a list of forms and schedules and associated times. Prior studies (Christian et al., 1993; Frischmann & Frees, 1999) use a similar estimate in hours and multiply it by an estimated wage rate to arrive at an estimate of the value of time saved. However, they are studying preparer choice and estimating fees, we are studying HE. Further, the aforementioned studies measure complexity as either a dummy variable for the existence of various forms and schedules, or the sum of the number of forms and schedules used. A disadvantage of these techniques is that they give equal weight to each form. The value of time in the current study is simply as a measure of complexity; it seems to be a reasonable proxy for complexity.4 Using the total of minutes weights each form differently, recognizing a different contribution to the level of complexity. Using a regression approach allows us to control for complexity as well as other variables found to be signiﬁcant in preparer usage studies. Basic Regression Model In our initial regression model, we also add variables for age and number of exemptions. Thus, we ﬁt the following regression model: TL ¼ F½ETI COMPLEX AGE XTOT

(9)

where ETI and COMPLEX are as described earlier. AGE is a categorical variable for whether or not either taxpayer or spouse is aged 65 or older. AGE is equal to 1 if either the taxpayer or spouse is aged 65 or older, it is zero otherwise. XTOT is the sum of the number of personal and dependency exemptions claimed on the tax return. We choose these variables because they were found to be signiﬁcant in prior studies (e.g., Long & Caudill, 1987, 1993). There is no variable for marginal tax rate since TL and marginal tax rate are both functions of the same process and TL is the dependent variable. Self-employment also differs by preparer mode in prior studies, but this measure is included in the complexity measure in the current study.

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Combined Regression Model An alternative regression approach is to combine both self-prepared and paid-prepared returns into one regression for each income range. Then, following Westort (2001), we use an iterative process where we include a categorical variable for preparer mode in one form of the regression and omit the preparer variable in a second form of the regression. The difference in CRV between the two regressions is the amount explained by the omitted variable. Thus, we also ﬁt the following regression model: TL ¼ F½ETI COMPLEX AGE XTOTðPREPÞ

(10)

where PREP is a categorical variable set equal to zero for self-prepared returns and one for paid-prepared returns. To the extent a variable has explanatory power, it will decrease the CRV. Complexity-Based Regression Models Another technique to control for complexity is to sort observations into groups with the same level of complexity and then observe the effect of paid preparers. Given the sample size and the desire to still analyze by income range, we, somewhat arbitrarily, divide the data into ﬁve equal-sized subgroups using the COMPLEXITY value. Whether or not the taxpayers in each quintile are the same with respect to complexity is subjective. Our goal is to observe the impact of paid preparers in groups that differ much less in complexity. Thus, we ﬁt the regression models again for the highest and lowest complexity quintiles. Ordinary Income Regression Model One effect of paid-prepared returns being more complex is that they may include income taxed at different rates. More complex returns may include more capital gains, more exempt income, or higher alternative minimum tax. To explore the effect of these potential differences on the CRV, we repeat the regression analysis using only ordinary income. We delete observations with exempt income, long-term capital gains, and alternative minimum tax. The remaining sample consists of 18,490 observations. Expanded Regression Model The preceding regression models are consistent with prior research. We next use an expanded model to, as accurately as possible, estimate TL using all variables that might affect TL. Any difference between the two models in CRV is then attributed to the effect of the paid preparers. Our expanded

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regression includes the following variables: ETI COMPLEX EXMPTINC FTI AGIADJ ITEM EXEMAMT

AGE CREDIT EIC

As deﬁned earlier As deﬁned earlier Exempt income is the total of exempt interest income and nontaxable Social Security Favorably taxed income is income taxed at long-term capital gain rates (10%, 20%, 25%, or 28%) The AGIADJ variable includes all the AGI adjustments claimed on the return This is the total standard or itemized deduction claimed on the return This is the exemption amount. It replaces XTOT, the number of exemptions claimed. This variable reﬂects the amount phased out As deﬁned earlier Total of credits used to offset the income tax Total of the earned income credit used to offset income tax after credits

Thus, we ﬁt the following regression model: TL ¼ ETI COMPLEX EXMPTINC FTI AGIADJ EXEMAMT AGE CREDIT EIC

(11)

Rank Preservation Method Iyer and Seetharaman (2000) review other methods of measuring HE in empirical studies. To explore the effect of paid preparers by using a different type of measure of HE, one of the rank-preservation methods is chosen to reanalyze the data in this study. Speciﬁcally, the AP index is used because that technique focuses on income to measure any effect on HE. The basic concept of the rank-preservation methods is that an equitable tax system is one which preserves the rank order of taxpayers in the distribution of income. Thus, the focus is the after-tax distribution of income, not the TL itself. These techniques, therefore, measure HE as a function of the extent to which the tax system causes a re-ranking of taxpayer income. The index approach (whether this or another index measure) generally provides an overall measure; it does not provide data by income groups. The AP index as implemented here is described in Iyer and Seetharaman (2000, pp. 92–93). It is based on a Lorenz curve where the cumulative

Association of Paid Income Tax Return Preparers with Horizontal Equity

115

percentage of income is measured on the vertical axis and the cumulative percent of recipients is measured on the horizontal axis arrayed by the size of income. The numerator of the index is the difference between two such curves; one where the vertical axis is after-tax income and the horizontal axis represents recipients arrayed by after-tax income. The second curve still reﬂects after-tax incomes on the vertical axis, but the horizontal axis represents taxpayers arrayed by pre-tax incomes. Thus, it is a measure of the rank reversals caused by the tax system. The denominator measures the maximum possible distance between the two curves, which would be the result with 100% rank reversal. The index, therefore, is a value between 0 (no re-ranking) and 1 (complete rank reversal). Computationally, the index is N P

API ¼

jM ni M i j

i¼1 N P

2

(12) jM i Mj

i¼1

where API is the Atkinson–Plotnick index, M ni the level of income when ranked by before-tax income (the rank-preserving level), Mi the level of income when ranked by after-tax income, M the average after-tax income, and N the total number of taxpayers. The index is computed for both the self-prepared and paid-prepared subgroups.

RESULTS Descriptive Statistics We ﬁrst present the means of the ETI and TL variables by income range and preparation mode along with t-tests for whether the differences are signiﬁcantly different from zero. These means and test results are presented in Table 1. Mean ETI is less for the paid-preparer group in 15 of the 30 ranges. The difference in mean ETI is statistically signiﬁcant in only three of the income groups and is lower for paid-prepared returns in one of those three. The mean for ETI does not appear, therefore, to differ systematically by preparation mode. While there is no theoretical reason for expecting income to differ within ﬁxed ranges, this observation is important for interpreting the CVR result below. Mean TL is, on average, greater for paidprepared returns in the lower income ranges and lesser in the higher income ranges. Mean TL is less for paid-prepared returns in 17 of the 30 ranges.

116

PETER J. WESTORT AND RICHARD CUMMINGS

Table 1. Means for ETI and TL by ETI Range. ETI Range

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Total

ETI

TL

n

Self

n

Paid

p-value

Self

Paid

p-value

338 668 789 878 1,115 1,039 924 711 571 453 367 366 302 293 411 351 245 215 187 182 144 144 96 79 75 74 73 81 96 107

5,710 15,411 25,054 35,151 44,845 54,721 64,724 75,022 84,742 94,805 104,937 115,036 124,931 135,215 144,880 154,862 165,028 174,864 184,740 194,900 204,772 215,037 224,865 234,503 244,203 255,434 265,083 275,137 284,680 295,179

697 1,578 1,709 1,729 2,293 2,063 1,782 1,366 1,076 965 829 711 738 660 809 728 604 584 512 467 485 483 428 396 384 357 330 342 367 421

5,961 15,377 24,857 35,291 45,021 54,940 64,622 74,837 84,758 94,916 104,849 114,954 125,002 135,108 144,822 154,861 164,829 174,753 185,057 194,977 204,905 214,916 224,979 234,975 244,816 254,810 265,129 275,131 285,333 295,064

0.212 0.798 0.112 0.239 0.101 0.046 0.378 0.154 0.915 0.501 0.634 0.662 0.720 0.606 0.743 0.994 0.361 0.624 0.194 0.757 0.642 0.667 0.742 0.205 0.100 0.097 0.905 0.986 0.050 0.703

73 250 946 1,788 3,197 4,471 6,217 8,193 10,704 12,721 14,931 17,374 19,609 23,511 24,918 28,196 29,306 33,678 34,675 36,860 37,003 40,073 45,085 46,642 48,313 57,303 55,955 55,694 60,516 66,432

308 423 1,545 1,924 2,938 4,521 6,069 8,283 10,949 12,860 14,619 16,347 19,635 21,708 24,480 26,679 29,327 30,846 34,000 36,594 38,106 39,580 41,158 44,002 50,321 55,513 52,516 55,981 58,545 65,703

0.019 0.259 0.216 0.312 0.170 0.733 0.463 0.712 0.553 0.762 0.510 0.029 0.964 0.200 0.569 0.172 0.974 0.200 0.431 0.791 0.451 0.698 0.026 0.193 0.411 0.646 0.149 0.912 0.465 0.720

11,374

25,893

Note: The p-value is from the t-test for whether the differences in means are signiﬁcantly different from zero.

Previous research relating paid preparers to mean TL is mixed. Long and Caudill (1987) ﬁnd that paid preparers are associated with lower TL in six of their seven income categories. Frischmann and Frees (1999) ﬁnd average TL greater for paid-prepared returns although their data are in aggregate, not by income groups.

Association of Paid Income Tax Return Preparers with Horizontal Equity

117

Because CV and CVR are inﬂuenced by the standard deviation of TL and ETI, we present those measures in Table 2. The dispersion of ETI is less for paid-prepared returns in 18 of the 30 income ranges. The associated F-test for difference in standard deviations is signiﬁcant for only two of the income ranges. We conclude that the dispersion of income is about the same for each preparer type and, therefore, is not likely the cause of differences in

Table 2. Standard Deviation for ETI and TL by ETI Range. ETI Range

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

ETI

TL

Self

Paid

p-value

Self

Paid

p-value

3,112 2,872 2,874 2,885 2,993 2,883 2,855 2,801 2,998 2,874 2,965 2,974 2,899 2,963 2,956 2,713 2,861 2,819 2,826 2,784 3,044 3,003 3,089 3,022 2,934 2,936 2,981 2,968 2,898 2,767

2,836 2,847 2,883 2,831 2,817 2,885 2,862 2,825 2,846 2,943 2,864 2,785 2,960 2,957 2,830 2,876 2,911 2,892 2,932 2,977 2,887 2,827 2,962 2,877 2,921 2,842 2,859 2,924 2,806 2,805

0.044 0.781 0.921 0.514 0.018 0.986 0.939 0.804 0.151 0.569 0.425 0.143 0.680 0.955 0.305 0.212 0.758 0.664 0.558 0.294 0.412 0.353 0.576 0.545 0.930 0.689 0.618 0.835 0.667 0.884

649 914 2,970 1,358 5,748 1,858 2,797 3,637 4,252 5,820 6,959 7,841 7,733 23,216 6,323 19,172 6,617 31,633 9,148 8,752 13,020 12,269 14,723 15,634 17,715 14,702 17,337 20,379 23,605 18,025

2,475 5,918 19,539 5,241 3,642 6,131 7,583 7,533 12,196 11,425 8,729 6,043 10,917 9,357 19,963 11,625 12,223 10,616 12,074 16,515 21,626 16,461 18,294 19,739 25,976 66,018 22,239 22,856 22,875 21,471

0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0109 0.0131 0.0001 0.0001 0.0117 0.2162 0.6749 0.0305

Note: The p-value is from the F-test for whether differences in standard deviations are signiﬁcantly different from zero.

118

PETER J. WESTORT AND RICHARD CUMMINGS

dispersion of TL. Tables 1 and 2 combined imply that the denominator in the CVR analysis (CV(ETI)) is not signiﬁcantly different for the two preparation modes. Dispersion of TL, however, does differ by preparer type. The standard deviation of TL is greater for paid-prepared returns than for self-prepared in 24 of the 30 income ranges and the difference in standard deviation is statistically signiﬁcant for all but one of those income ranges. Thus, the dispersion of TL tends to be greater for paid-prepared returns indicating that the numerator in the CVR analysis (CV(TL)) does differ by preparer mode. Further, this dispersion in TL is not associated with the dispersion of ETI. Table 3 provides descriptive statistics for the other variables in the basic regression model. The mean complexity measure (COMPLEX) is higher for paid-prepared returns in all income ranges and the difference is statistically signiﬁcant. It appears that paid preparers are associated with more complex tax returns. The AGE variable is greater for the self-prepared returns in the ﬁrst ﬁve income ranges, but lower in all but two of the remaining income ranges. But for these seven occurrences the difference is statistically signiﬁcant in only one of the ranges. For the 23 income ranges in which the AGE variable is greater for the paid-prepared returns, the difference is statistically signiﬁcant in 16 of those ranges. In general, then, those using paid preparers are older than those who prepare their own returns. The variable for number of dependents claimed (XTOT) is greater for the paid-prepared returns in the ﬁrst ﬁve income ranges and is statistically signiﬁcant in four of them. In 21 of the remaining 25 income ranges, however, self-prepared returns contain the greater number of dependents, but the difference is signiﬁcant in only six of them. There appears to be no pattern in the number of dependents claimed.

CV and CVR Analysis The results of the CV analysis are presented in Table 4. To the extent CV is a measure of HE, the results indicate less equity (larger CV) in 24 of the 30 income ranges for paid-prepared returns. This analysis is provided primarily as a baseline for comparison of the CVR and the CRV analysis. The CVR analysis is provided in Table 5. Because the denominator is CV of ETI, which is different for each preparer mode, we provide that measure along with CV of TL. The CVR is provided for each preparation mode in columns 5 and 9, respectively. To the extent the denominators are the same,

119

Association of Paid Income Tax Return Preparers with Horizontal Equity

Table 3. Means for COMPLEX, AGE, and XTOT by ETI Range. ETI Range 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Total

COMPLEX

AGE

XTOT

n

Self

n

Paid

p-value

Self

Paid

p-value

Self

Paid

p-value

338 668 789 878 1,115 1,039 924 711 571 453 367 366 302 293 411 351 245 215 187 182 144 144 96 79 75 74 73 81 96 107

1,302 1,262 1,301 1,296 1,343 1,380 1,499 1,679 1,710 1,897 1,953 2,102 2,136 2,190 2,103 2,094 2,176 2,239 2,305 2,288 2,542 2,382 2,387 2,606 2,687 2,433 2,684 2,601 2,580 2,482

697 1,578 1,709 1,729 2,293 2,063 1,782 1,366 1,076 965 829 711 738 660 809 728 604 584 512 467 485 483 428 396 384 357 330 342 367 421

1,730 1,624 1,731 1,789 1,811 1,925 2,028 2,201 2,328 2,462 2,527 2,579 2,652 2,750 2,630 2,645 2,721 2,792 2,781 2,855 2,939 3,013 2,990 3,051 3,223 3,124 3,020 3,171 3,122 2,963

0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0022 0.0001 0.0001 0.0076 0.0022 0.0001 0.0265 0.0001 0.0001 0.0002

0.024 0.058 0.145 0.218 0.322 0.223 0.222 0.155 0.172 0.137 0.172 0.156 0.149 0.164 0.134 0.154 0.159 0.177 0.128 0.220 0.167 0.097 0.135 0.190 0.147 0.108 0.260 0.148 0.115 0.168

0.019 0.042 0.113 0.203 0.315 0.292 0.255 0.230 0.205 0.220 0.238 0.219 0.226 0.205 0.223 0.223 0.232 0.212 0.211 0.197 0.233 0.263 0.248 0.255 0.240 0.165 0.221 0.222 0.199 0.185

0.6067 0.1111 0.0318 0.3917 0.6767 0.0001 0.0550 0.0001 0.1012 0.0001 0.0076 0.0096 0.0027 0.1286 0.0001 0.0055 0.0127 0.2531 0.0070 0.5259 0.0712 0.0001 0.0067 0.1907 0.0482 0.1693 0.4913 0.1070 0.0309 0.6779

2.778 2.744 2.923 2.977 2.874 3.006 3.021 3.110 3.096 3.243 3.188 3.104 3.023 3.109 3.114 3.103 3.090 2.991 3.219 3.115 3.139 3.313 2.958 3.101 3.040 3.257 2.699 2.988 3.083 3.234

2.849 2.946 3.129 3.109 2.987 2.923 2.946 2.964 2.960 2.988 2.925 2.994 2.938 3.017 3.022 3.004 2.998 3.027 3.088 3.073 2.967 2.882 2.998 2.982 2.875 3.098 2.973 3.003 3.065 3.076

0.3666 0.0002 0.0002 0.0096 0.0084 0.0699 0.1286 0.0124 0.0309 0.0003 0.0009 0.1719 0.2603 0.2811 0.2159 0.2221 0.3182 0.6855 0.1921 0.7022 0.1401 0.0001 0.7476 0.4254 0.2516 0.2708 0.0515 0.9139 0.8885 0.2162

11,374

25,893

Note: The p-value is from the t-test for whether the differences in means are signiﬁcantly different from zero.

any difference in the ratios will be attributable to the numerator (CV of TL). Therefore, the differences in CV of ETI (columns 3 and 7) are provided in column 10 and the percent change is in column 11. The percentage increase for income range one is 12.7%, for the remaining income ranges, however, the percentage change is between 6.9% and 6.3%. While there is no statistical test for the percentage change that we are aware of, we know from the results presented in Tables 1 and 2 that neither the means nor the

120

PETER J. WESTORT AND RICHARD CUMMINGS

Table 4. ETI Range 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Coefﬁcient of Variation (CV) of TL by Preparer Type.

Self-Prepared CV (TL)

Paid-Prepared CV (TL)

Percent Increase/Decrease

894.41 364.91 314.07 75.92 179.82 41.55 44.99 44.40 39.73 45.75 46.60 45.13 39.44 98.75 25.37 67.99 22.58 93.93 26.38 23.74 35.18 30.62 32.66 33.52 36.67 25.66 30.98 36.59 39.01 27.13

803.96 1,398.83 1,264.50 272.43 123.94 135.62 124.94 90.94 111.39 88.84 59.71 36.97 55.60 43.11 81.55 43.58 41.68 34.42 35.51 45.13 56.75 41.59 44.45 44.86 51.62 118.92 42.35 40.83 39.07 32.68

11.25 73.91 75.16 72.13 45.08 69.36 63.99 51.18 64.34 48.50 21.95 22.08 29.07 129.08 68.88 56.04 45.83 172.92 25.71 47.39 38.00 26.38 26.53 25.28 28.97 78.43 26.84 10.38 0.17 16.97

standard deviations of ETI are signiﬁcantly different from each other in 25 of the 30 income ranges. The differences in CVRs are presented in column 12 and the percentage change in column 13. It appears that the difference in TL is responsible for the difference in the CVRs. The CVR for paid-prepared returns is greater than that for self-prepared returns for 25 of the income ranges. This implies a detrimental effect on HE. This is the same result as for the CV analysis with the sole exception of the ﬁrst income range. CVR analysis will not have different signs from CV analysis when the denominator is the same. It appears that in ETI range one, the difference in CVR is at least partially attributable to differences in income (ETI) rather than solely to

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

ETI range

1

Table 5.

3

54.50 18.64 11.47 8.21 6.67 5.27 4.41 3.73 3.54 3.03 2.83 2.59 2.32 2.19 2.04 1.75 1.73

CV (ETI)

4

894.41 364.91 314.07 75.92 179.82 41.55 44.99 44.40 39.73 45.75 46.60 45.13 39.44 98.75 25.37 67.99 22.58

CV (TL)

Self-prepared

338 668 789 878 1,115 1,039 924 711 571 453 367 366 302 293 411 351 245

n

2

16.41 19.58 27.38 9.25 26.94 7.89 10.20 11.89 11.23 15.09 16.49 17.45 16.99 45.06 12.44 38.82 13.02

CVR selfprepared

5

697 1,578 1,709 1,729 2,293 2,063 1,782 1,366 1,076 965 829 711 738 660 809 728 604

n

6

47.57 18.51 11.60 8.02 6.26 5.25 4.43 3.77 3.36 3.10 2.73 2.42 2.37 2.19 1.95 1.86 1.77

CV (ETI)

CV (TL)

8

803.96 1,398.83 1,264.50 272.43 123.94 135.62 124.94 90.94 111.39 88.84 59.71 36.97 55.60 43.11 81.55 43.58 41.68

Paid-prepared

7

16.90 75.55 109.01 33.97 19.81 25.83 28.22 24.09 33.18 28.66 21.86 15.26 23.48 19.70 41.73 23.47 23.60

CVR paidprepared

9

11

6.93 0.12 0.13 0.19 0.42 0.02 0.02 0.04 0.18 0.07 0.09 0.16 0.05 0.00 0.09 0.11 0.03

Column 3 column 7

12.71 0.66 1.12 2.26 6.27 0.34 0.40 1.09 5.09 2.25 3.33 6.31 2.03 0.14 4.21 6.02 1.88

Percent increase/ decrease

Difference in CV(ETI)

10

2.98 285.87 298.15 267.14 26.46 227.48 176.62 102.63 195.44 89.90 32.54 12.57 38.19 56.29 235.50 39.55 81.20

Percent increase/ decrease

CVRp less CVRs

0.49 55.97 81.63 24.71 7.13 17.94 18.02 12.20 21.95 13.57 5.37 2.19 6.49 25.36 29.29 15.35 10.57

13

12

CVR of Self-Prepared Returns Compared to the CVR of Paid-Prepared Returns by ETI Range.

Association of Paid Income Tax Return Preparers with Horizontal Equity 121

Total Weighted average

18 19 20 21 22 23 24 25 26 27 28 29 30

ETI range

1

3

11,374

1.61 1.53 1.43 1.49 1.40 1.37 1.29 1.20 1.15 1.12 1.08 1.02 0.94

CV (ETI)

4

93.93 26.38 23.74 35.18 30.62 32.66 33.52 36.67 25.66 30.98 36.59 39.01 27.13

CV (TL)

Self-prepared

215 187 182 144 144 96 79 75 74 73 81 96 107

n

2

18.95

58.27 17.24 16.62 23.67 21.93 23.77 26.01 30.52 22.32 27.55 33.92 38.31 28.95

CVR selfprepared

5

584 512 467 485 483 428 396 384 357 330 342 367 421

n

6

25,893

1.65 1.58 1.53 1.41 1.32 1.32 1.22 1.19 1.12 1.08 1.06 0.98 0.95

CV (ETI)

Paid-prepared

7

36.95

34.42 35.51 45.13 56.75 41.59 44.45 44.86 51.62 118.92 42.35 40.83 39.07 32.68

CV (TL)

8

9

20.80 22.41 29.56 40.29 31.62 33.76 36.64 43.26 106.64 39.28 38.42 39.73 34.38

CVR paidprepared

Table 5. (Continued ) 11

0.04 0.05 0.10 0.08 0.08 0.06 0.06 0.01 0.03 0.05 0.02 0.03 0.01

Column 3 column 7

2.67 3.57 6.88 5.23 5.80 4.15 4.98 0.68 2.97 4.12 1.50 3.40 1.41

Percent increase/ decrease

Difference in CV(ETI)

10

64.31 29.97 77.85 70.20 44.20 42.00 40.84 41.75 377.70 42.55 13.28 3.70 18.76

Percent increase/ decrease

CVRp less CVRs

37.48 5.17 12.94 16.62 9.69 9.98 10.63 12.74 84.32 11.72 4.50 1.42 5.43

13

12

122 PETER J. WESTORT AND RICHARD CUMMINGS

Association of Paid Income Tax Return Preparers with Horizontal Equity

123

differences in TL. One of the beneﬁts of the CVR analysis is that comparisons can be made across income ranges. An inspection of columns 5 and 9 indicates that there does not appear to be any systematic difference in CVRs by ETI range. That is, the effect of paid preparers does not seem to change by income range. However, the weighted average CVR is also provided for both self-prepared and paid-prepared returns and clearly indicates that overall, the usage of paid preparers is associated with larger ratios implying less HE.

Regression Results Basic Regression Model Initial regression results are reported in Table 6. The adjusted R2 values are small in most income ranges for both self-prepared and paid-prepared returns. For self-prepared returns, the adjusted R2 values range from 0.0411 to 0.5245 with 22 of the income ranges reporting values of less than 0.10. The adjusted R2 values for the paid-prepared returns range from 0.002 to 0.118. Grasso and Frischmann (1992) report adjusted R2 values ranging from 0.13 to 0.54 for their ﬁrst nine deciles when they regress on TL. They report an R2 of over 0.9 for their 10th decile. In general, they observe larger adjusted R2 values than the current study. However, it is important to note that they use deciles which (except for the ﬁrst decile) cover a wider range of income. It appears that the ability of the income variable to explain variation in TL increases as the size of the income range increases. However, as the size of the income range increases, it becomes more difﬁcult to consider the taxpayers within the range economic equals. Westort (2001), using 1992 SOI data and income ranges similar to the current study, reports R2 values ranging from 0.003 to 0.120 for the simple model, which uses only ETI as an explanatory variable. However, when the regression model is expanded to include several explanatory variables, the adjusted R2 values range from 0.107 to 0.931. In comparison, the adjusted R2 values for the current study are approximately the same as for the simple model, but not as great as for the expanded model. The additional variables in Westort (2001) evidently have additional explanatory power. This addition of more explanatory variables is addressed below in an expanded regression. The intent of this study, however, is not to explain the variation in TL, but rather to observe the extent to which that variation may be attributed to paid preparers. The comparison of the adjusted R2 values to those of prior studies,

124

PETER J. WESTORT AND RICHARD CUMMINGS

Table 6. Coefﬁcient of Residual Variation (CRV) by Preparer Type from Regression Model: TL ¼ F[ETI COMPLEX AGE XTOT]. ETI Range

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Self-Prepared Returns

Paid-Prepared Returns

n

Adjusted R2

p-value

CRVs

338 668 789 878 1,115 1,039 924 711 571 453 367 366 302 293 411 351 245 215 187 182 144 144 96 79 75 74 73 81 96 107

0.0541 0.0660 0.0502 0.5245 0.0161 0.4226 0.2558 0.1820 0.1567 0.0739 0.0889 0.0151 0.0240 0.0029 0.0807 0.0021 0.1361 0.0114 0.0188 0.0538 0.0038 0.0230 0.0464 0.0184 0.0384 0.0411 0.0353 0.0122 0.0450 0.0313

0.0002 0.0001 0.0001 0.0001 0.0002 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0501 0.0241 0.3071 0.0001 0.3167 0.0001 0.1706 0.1137 0.0079 0.4867 0.1243 0.0804 0.6304 0.8665 0.8898 0.1698 0.2982 0.0848 0.1237

869.86 352.67 306.08 52.351 178.36 31.57 38.81 40.16 36.48 44.03 44.49 44.79 38.96 98.61 24.33 67.92 20.99 93.39 26.13 23.09 35.25 30.26 31.89 33.83 37.36 26.18 30.43 36.37 38.12 26.70

Adjusted R2

p-value

CRVp

CRVs less CRVp

697 0.0541 1,578 0.0017 1,709 0.0150 1,729 0.0464 2,293 0.0740 2,063 0.0314 1,782 0.0300 1,366 0.0230 1,076 0.0319 965 0.0220 829 0.0148 711 0.1180 738 0.0269 660 0.0121 809 0.0091 728 0.0297 604 0.0020 584 0.0118 512 0.0102 467 0.0336 485 0.0076 483 0.0249 428 0.0455 396 0.0361 384 0.0008 357 0.0089 330 0.1005 342 0.0288 367 0.0163 421 0.0393

0.0001 0.1537 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0027 0.0001 0.0001 0.0176 0.0231 0.0001 0.5944 0.028 0.056 0.0005 0.1056 0.0029 0.0001 0.001 0.3694 0.1285 0.0001 0.0078 0.0413 0.0004

781.89 1,397.64 1,254.97 266.03 119.27 133.48 123.06 89.89 109.60 87.86 59.27 34.72 54.85 42.845 81.18 42.92 41.72 34.21 35.33 44.36 56.54 41.07 43.43 44.04 51.60 118.39 40.16 40.24 38.75 32.03

87.97 1,044.97 948.90 213.68 59.10 101.90 84.25 49.74 73.12 43.83 14.79 10.07 15.89 55.76 56.85 25.00 20.74 59.18 9.20 21.27 21.28 10.80 11.53 10.22 14.24 92.21 9.73 3.87 0.64 5.33

n

Percent Increase/ Decrease

10.11 296.31 310.02 408.17 33.13 322.74 217.06 123.86 200.41 99.55 33.24 22.49 40.78 56.55 233.65 36.80 98.82 63.37 35.20 92.10 60.38 35.70 36.17 30.20 38.10 352.25 31.98 10.64 1.67 19.94

Note: The p-value is from the F-test for whether all the regression coefﬁcients are signiﬁcantly different from zero.

however, is an indication that these data are similar to the data in prior studies with respect to the relationship between income and TL. The initial regression model in the current study does best at explaining TL (as measured by adjusted R2) in the lower income ranges for self-prepared returns. Statistical signiﬁcance is also greater in the lower income ranges. The CRV is greater for paid-prepared returns in 24 of the income ranges. The percentage increase or decrease from the CRV of self-prepared returns to

Association of Paid Income Tax Return Preparers with Horizontal Equity

125

the CRV of paid-prepared returns is provided in the last column of Table 6. The percentage difference is greater than 100% in many instances. The CRV, itself, is not comparable across income ranges for the same reason that CV is not; the denominator is mean TL, which tends to increase as income range increases. However, the percentage change is comparable. The advantage of a regression approach is that it allows us to control for complexity (COMPLEX) as well as age (AGE) and number of exemptions (XTOT), which have been found to be related to preparer mode in prior studies. The result of the regression analysis, however, is similar to the result for the CV and CVR analysis. It appears that paid-preparer usage is associated with greater dispersion of TL, and thus, less HE. Combined Regression Model Results of the combined regression (with a categorical variable for preparation mode) are presented in Table 7. To the extent a variable has explanatory power, it will decrease the CRV. In an HE framework, this decrease in CRV is interpreted as the extent to which the variable improves the equity of the income tax system. Thus, it is not the statistical signiﬁcance of the parameter estimate that we measure, but the effect on dispersion as measured by the CRV (although the two values are clearly related). However, as Table 7 illustrates, the CRV actually increases in 24 of the 30 income ranges with the addition of PREP to the regression. This increase in CRV occurs when the decrease in the sum of the squared error terms caused by the addition of a variable is more than offset by the increase in degrees of freedom.5 This same phenomenon is observed in Westort (2001, p. 197). Nonetheless, the difference in the CRV (increase or decrease) that results from the addition of PREP to the regression is less than 1% in all income ranges and less than one-tenth of 1% in most of them. Thus, PREP does little to explain variation in TL in a combined regression model. Low Complexity Model Regression results for the low complexity quintile are presented in Table 8. For the ﬁrst ETI range, there is no variation in the dependent variable for the self-prepared returns because the TL for all observations is zero. For ranges above the 11th there are too few observations (ﬁve or fewer) to provide reliable results. Thus, comparison can only be made for ranges 2–11. The adjusted R2 values are relatively large compared to those in Tables 6 and 7; ranging from a low of 0.4678 to a high of 0.9486 for self-prepared returns and somewhat lower for paid-prepared returns. The CRVs are correspondingly low. We also note that, in aggregate, the mean for COMPLEX is 672

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PETER J. WESTORT AND RICHARD CUMMINGS

Table 7.

Difference in CRV Caused by Preparer Mode from Regression Model: TL ¼ F[ETI COMPLEX AGE XTOT (PREP)].

ETI Model 1: TL ¼ ETI COMPLEX AGE Range XTOT

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Model 2: TL ¼ ETI COMPLEX AGE XTOT PREP

n

Adjusted R2

p-value

CRV1

n

Adjusted R2

p-value

CRV2

1,035 2,246 2,498 2,607 3,408 3,102 2,706 2,077 1,647 1,418 1,196 1,077 1,040 953 1,220 1,079 849 799 699 649 629 627 524 475 459 431 403 423 463 528

0.0493 0.0032 0.0139 0.0579 0.0432 0.0467 0.0428 0.0386 0.0387 0.0269 0.0295 0.0707 0.0214 0.0058 0.0144 0.0041 0.0059 0.0046 0.0103 0.0317 0.0047 0.0293 0.0556 0.0367 0.0018 0.0073 0.0875 0.0280 0.0260 0.0409

0.0001 0.0258 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0493 0.0002 0.0787 0.0621 0.1057 0.0247 0.0001 0.1401 0.0002 0.0001 0.0002 0.3097 0.1298 0.0001 0.0032 0.0029 0.0001

872.43 1,339.18 1,190.03 224.30 143.77 110.85 101.78 76.86 91.78 76.82 55.06 38.81 50.87 67.42 67.17 53.35 37.07 59.24 33.07 39.58 52.66 38.70 41.18 42.24 49.58 107.77 38.57 39.45 38.53 30.94

1,035 2,246 2,498 2,607 3,408 3,102 2,706 2,077 1,647 1,418 1,196 1,077 1,040 953 1,220 1,079 849 799 699 649 629 627 524 475 459 431 403 423 463 528

0.0490 0.0028 0.0135 0.0576 0.0433 0.0464 0.0428 0.0382 0.0385 0.0263 0.0290 0.0722 0.0204 0.0070 0.0136 0.0049 0.0049 0.0078 0.0091 0.0304 0.0038 0.0277 0.0550 0.0355 0.0011 0.0050 0.0885 0.0261 0.0239 0.0392

0.0001 0.0477 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0394 0.0006 0.0684 0.1021 0.0469 0.0452 0.0001 0.1944 0.0004 0.0001 0.0005 0.3600 0.2124 0.0001 0.0067 0.0066 0.0001

872.60 1,339.44 1,190.26 224.34 143.76 110.87 101.78 76.87 91.79 76.85 55.08 38.78 50.89 67.38 67.20 53.33 37.09 59.15 33.09 39.61 52.68 38.73 41.19 42.27 49.60 107.89 38.55 39.49 38.57 30.97

CRV1 less CRV2

Percent Increase/ Decrease

0.164 0.262 0.237 0.043 0.007 0.017 0.000 0.018 0.011 0.026 0.014 0.032 0.024 0.041 0.027 0.022 0.017 0.097 0.020 0.025 0.023 0.030 0.011 0.028 0.017 0.126 0.021 0.038 0.042 0.028

0.019 0.020 0.020 0.019 0.005 0.015 0.000 0.023 0.012 0.034 0.026 0.083 0.048 0.061 0.040 0.041 0.046 0.163 0.060 0.064 0.044 0.078 0.028 0.066 0.033 0.117 0.054 0.097 0.108 0.091

Note: The p-value is from the F-test for whether all the regression coefﬁcients are signiﬁcantly different from zero.

for the self-prepared returns and 966 for paid-prepared; the difference is signiﬁcant at the 0.0001 level. Thus, we control for complexity by ﬁrst limiting the sample to the lowest complexity quintile and secondly by including a complexity variable in the regression. Nonetheless, the CRV is larger for the paid-prepared returns in all of the 10 income ranges for which data are available. This result again indicates that paid-preparer usage is associated with greater dispersion of TL or less HE.

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Table 8. CRV by Preparer Type from Regression Model: TL ¼ F[ETI COMPLEX AGE XTOT] – Low Complexity Quintile. ETI Range

Self-Prepared Returns n

2 3 4 5 6 7 8 9 10 11 Total

304 355 352 417 304 196 79 55 20 18 2,100

Paid-Prepared Returns

Adjusted R2

p-value

CRVs

n

0.5512 0.7335 0.7801 0.6855 0.4678 0.7966 0.7071 0.9445 0.9486 0.8505

0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001

98.81 50.39 29.18 20.41 19.33 8.76 11.68 3.22 2.31 2.71

678 657 631 808 642 468 263 148 97 89

Adjusted R2

p-value

CRVp

CRVs less CRVp

0.4224 0.6302 0.6972 0.5701 0.5813 0.5819 0.6442 0.4685 0.4124 0.2589

0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001

136.08 71.91 37.88 28.44 19.37 16.27 12.18 16.03 13.61 17.96

37.27 21.52 8.69 8.04 0.04 7.51 0.51 12.81 11.30 15.25

Percent Increase/ Decrease

37.71 42.70 29.79 39.39 0.20 85.64 4.36 398.46 488.28 563.44

4,481

Note: The p-value is from the F-test for whether all the regression coefﬁcients are signiﬁcantly different from zero.

High Complexity Model The results of the high complexity quintile are presented in Table 9. The adjusted R2 values for this subsample are small, which is markedly different from the low quintile result. An immediate observation, then, is that the model explains much less of the dispersion in TL when the returns are more complex. The overall result is similar to initial ﬁndings; the CRV for paidprepared returns is larger in 24 of the 30 income groups. Thus, while complexity may differ for self-prepared and paid-prepared returns, it does not seem to be the cause of the increased dispersion of TL.

Ordinary Income Regression Model Table 10 provides the results when the regression data are restricted to ordinary income. The adjusted R2 values are relatively low, similar to the initial regression results in Table 5. The CRV results, however, are more mixed. The CRV for paid-prepared returns is smaller in 11 of the 30 income groups. Thus, HE is improved in these income groups. However, there are 19 income groups for which the CRV is greater for paid-prepared returns and most of these are lower income groups which have larger sample sizes. For the lower income groups, the F-statistic and associated p-value indicate statistical signiﬁcance. For the higher income groups, as sample size

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Table 9. CRV by Preparer Type from Regression Model: TL ¼ F[ETI COMPLEX AGE XTOT] – High Complexity Quintile. ETI Range

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Total

Self-Prepared Returns

Paid-Prepared Returns

CRVs less CRVp

n

Adjusted R2

p-value

CRVs

n

Adjusted R2

p-value

CRVp

20 42 66 63 98 83 102 101 92 109 103 109 97 105 142 109 93 80 86 75 61 76 38 44 43 30 46 51 54 56

0.0188 0.0472 0.0052 0.1091 0.0131 0.3182 0.0350 0.0720 0.0990 0.0048 0.0398 0.0185 0.0064 0.0241 0.0154 0.0518 0.1542 0.0316 0.0466 0.0393 0.0137 0.0016 0.1056 0.0336 0.0960 0.1334 0.0510 0.0350 0.0224 0.1090

0.4820 0.2200 0.9884 0.0295 0.2672 0.0001 0.1139 0.0243 0.0107 0.3462 0.0922 0.7296 0.4986 0.8169 0.1906 0.0488 0.0008 0.1720 0.0966 0.1472 0.5322 0.4291 0.1041 0.6301 0.9879 0.1087 0.7686 0.6802 0.5895 0.0417

250.27 333.27 391.70 121.24 412.07 63.72 82.05 59.99 76.04 83.58 81.32 73.34 38.56 151.76 33.82 66.56 24.62 138.25 29.63 26.54 41.08 30.34 40.24 38.86 43.16 20.48 39.59 38.47 46.26 28.88

68 124 160 167 208 220 209 201 192 208 198 177 204 198 208 203 169 169 164 151 179 179 158 150 148 155 132 152 169 160

0.1087 0.0131 0.0088 0.0128 0.0026 0.0084 0.0011 0.0035 0.0281 0.0046 0.0127 0.0174 0.0210 0.0032 0.0110 0.0473 0.0034 0.0112 0.0014 0.0020 0.0006 0.0280 0.0250 0.0137 0.0087 0.0173 0.0927 0.0151 0.0171 0.0165

0.0234 0.6624 0.6244 0.7532 0.3402 0.2142 0.3794 0.5112 0.0533 0.5521 0.8227 0.1352 0.0836 0.3317 0.7811 0.0086 0.3378 0.7115 0.3787 0.4521 0.4233 0.0624 0.0965 0.7384 0.6042 0.1581 0.0025 0.1825 0.1459 0.1608

300.00 49.73 926.93 593.67 644.57 252.87 384.42 263.18 260.44 151.63 301.16 237.44 317.95 235.90 187.57 127.58 202.95 126.91 165.52 81.93 109.09 27.76 59.00 14.33 93.20 54.64 68.20 83.57 54.15 20.32 41.08 25.48 70.56 45.94 48.79 89.45 47.79 18.17 49.13 22.60 77.68 36.60 40.09 9.75 57.14 16.90 44.26 5.41 52.65 9.49 162.51 142.02 39.71 0.11 45.88 7.41 35.43 10.84 38.99 10.11

2,274

Percent Increase/ Decrease

19.87 178.13 64.56 217.08 36.80 372.63 287.51 212.66 166.90 98.03 34.14 19.55 141.68 55.06 60.08 38.28 186.62 64.71 61.31 85.15 89.10 32.15 42.00 13.91 21.99 693.41 0.28 19.26 23.43 35.03

5,180

Note: The p-value is from the F-test for whether all the regression coefﬁcients are signiﬁcantly different from zero.

decreases, the results are not statistically signiﬁcant. In general, with respect to returns with only ordinary income, paid preparers are associated with less HE when ETI is $100,000 or less. For ETI above $100,000 results are mixed. There is some evidence of increased HE in some income groups, but small sample sizes and lack of statistical signiﬁcance make the results difﬁcult to interpret.

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Table 10. CRV by Preparer Mode Restricted to Observations with only Ordinary Income: TL ¼ ETI COMPLEX AGE XTOT. ETI Range

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Total

Self-Prepared Returns

Paid-Prepared Returns

n

Adjusted R2

p-value

CRVs

n

Adjusted R2

p-value

300 584 675 699 642 656 553 454 318 235 191 170 127 113 158 129 86 76 58 44 31 48 30 21 17 18 9 22 27 26

0.0072 0.1075 0.1715 0.6337 0.1715 0.5936 0.3507 0.1560 0.3843 0.1310 0.2053 0.0117 0.0054 0.0400 0.1261 0.2523 0.3860 0.0258 0.0379 0.0572 0.0519 0.0086 0.0328 0.0991 0.0634 0.1149 0.5779 0.0615 0.0479 0.0176

0.1895 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.1767 0.5116 0.0591 0.0001 0.0001 0.0001 0.1827 0.8211 0.1502 0.6813 0.4659 0.5647 0.1981 0.2981 0.7444 0.9946 0.6267 0.6190 0.3514

1,109.02 234.18 152.89 45.61 74.88 24.07 34.23 40.67 21.54 33.32 49.07 29.58 52.00 134.87 17.14 48.45 19.50 19.70 30.12 28.94 39.81 31.35 26.19 39.00 30.31 34.70 30.99 37.49 35.46 15.44

569 1,264 1,355 1,202 1,200 1,054 870 635 472 413 316 252 240 200 239 220 185 171 129 114 97 106 97 88 74 100 67 67 81 96

0.0542 0.0162 0.0231 0.0454 0.2271 0.1391 0.3866 0.0698 0.1027 0.0374 0.0005 0.2555 0.1216 0.0995 0.0024 0.1654 0.0029 0.0690 0.0146 0.0251 0.0261 0.0561 0.0018 0.0252 0.0031 0.0002 0.0175 0.0149 0.0139 0.0046

0.0001 1,221.63 112.62 0.0001 599.42 365.24 0.0001 609.34 456.46 0.0001 273.50 227.89 0.0001 71.40 3.47 0.0001 69.45 45.38 0.0001 31.95 2.28 0.0001 73.07 32.40 0.0001 58.46 36.92 0.0003 60.91 27.59 0.4158 79.26 30.19 0.0001 30.047 0.46 0.0001 28.94 23.06 0.0001 23.22 111.65 0.3125 129.03 111.89 0.0001 24.92 23.53 0.4841 31.04 11.55 0.0019 21.75 2.05 0.7633 39.38 9.26 0.1230 40.34 11.40 0.9064 36.88 2.92 0.0308 33.95 2.60 0.3705 31.23 5.04 0.1631 27.19 11.81 0.3653 52.02 21.71 0.3935 34.27 0.43 0.6034 25.85 5.14 0.5697 29.04 8.46 0.5948 29.42 6.04 0.4673 21.67 6.23

6,517

CRVp

CRVs less CRVp

Percent Increase/ Decrease

10.15 155.96 298.56 499.59 4.64 188.51 6.66 79.67 171.43 82.79 61.52 1.56 44.35 82.78 652.66 48.56 59.23 10.42 30.73 39.38 7.35 8.30 19.25 30.28 71.63 1.24 16.59 22.56 17.02 40.38

11,973

Note: The p-value is from the F-test for whether all the regression coefﬁcients are signiﬁcantly different from zero.

Expanded Regression Model The results of the expanded regression model are presented in Table 11. The adjusted R2 values for both self-prepared and paid-prepared returns are relatively high compared to the results in Tables 6 and 7. Thus, TL is better estimated compared to simpler models. Also, the results are statistically signiﬁcant in all income groups for both self-prepared and paid-prepared

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Table 11. CRV by Preparer Mode: TL ¼ ETI COMPLEX EXMPTINC FTI AGIADJ EXEMAMT AGE CREDIT EIC. ETI Range

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Self-Prepared Returns

Paid-Prepared Returns

n

Adjusted R2

p-value

CRVs

n

Adjusted R2

p-value

CRVp

338 668 789 878 1,115 1,039 924 711 571 453 367 366 302 293 411 351 245 215 187 182 144 144 96 79 75 74 73 81 96 107

0.7116 0.1199 0.4183 0.7033 0.0826 0.6666 0.3971 0.3715 0.3590 0.2512 0.2995 0.1900 0.2842 0.1643 0.5560 0.0804 0.7748 0.2212 0.4951 0.7989 0.5183 0.8533 0.8524 0.8724 0.7959 0.6647 0.7429 0.9049 0.4892 0.6542

0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001

480.32 342.33 239.53 41.36 172.23 23.99 34.93 35.20 31.81 39.59 39.01 40.62 33.36 90.27 16.91 65.20 10.71 82.89 18.75 10.65 24.42 11.73 12.55 11.97 16.57 14.86 15.71 11.28 27.88 15.96

697 1,578 1,709 1,729 2,293 2,063 1,782 1,366 1,076 965 829 711 738 660 809 728 604 584 512 467 485 483 428 396 384 357 330 342 367 421

0.2442 0.0647 0.0290 0.0979 0.1322 0.0705 0.8590 0.1169 0.4042 0.1106 0.0853 0.3292 0.5035 0.1254 0.0454 0.1486 0.1570 0.2967 0.3482 0.2909 0.2127 0.5485 0.5406 0.4220 0.3494 0.1690 0.6722 0.5856 0.6160 0.6989

0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001

698.95 1,352.79 1,246.02 258.75 115.46 130.75 46.917 85.46 85.98 83.78 57.12 30.23 39.18 40.31 79.68 40.21 38.27 28.86 28.67 38.00 50.36 27.95 30.13 34.10 41.4 108.41 24.25 26.28 24.21 17.93

CRVs less Percent CRVp Increase/ Decrease

218.64 1,010.46 1,006.49 217.40 56.77 106.76 11.98 50.27 54.18 44.19 18.10 10.34 5.81 49.96 62.77 25.00 27.55 54.03 9.92 27.36 25.93 16.22 17.58 22.13 25.07 93.55 8.54 15.00 3.67 1.97

45.52 295.17 420.19 525.67 32.96 444.93 34.30 142.81 170.34 111.62 46.41 25.46 17.42 55.34 371.25 38.34 257.17 65.18 52.94 256.95 106.20 138.34 140.12 184.82 151.36 629.70 54.34 132.96 13.15 12.38

Note: The p-value is from the F-test for whether all the regression coefﬁcients are signiﬁcantly different from zero.

returns. Nonetheless, the overall result is similar to previous analysis; the CRV for paid-prepared returns is greater in 24 of the 30 income ranges.

Comparison of CV, CVR, and CRV Results We use a variety of techniques to measure HE and several versions of a regression model. To simplify comparison we provide Table 12, which

131

Association of Paid Income Tax Return Preparers with Horizontal Equity

Table 12. ETI Range

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Comparison of Percent Changes from Tables 4–11.

Table 4 Table 5 CV percent CVR increase/ percent decrease increase/ decrease

11.25 73.91 75.16 72.13 45.08 69.36 63.99 51.18 64.34 48.50 21.95 22.08 29.07 129.08 68.88 56.04 45.83 172.92 25.71 47.39 38.00 26.38 26.53 25.28 28.97 78.43 26.84 10.38 0.17 16.97

2.98 285.87 298.15 267.14 26.46 227.48 176.62 102.63 195.44 89.90 32.54 12.57 38.19 56.29 235.50 39.55 81.20 64.31 29.97 77.85 70.20 44.20 42.00 40.84 41.75 377.70 42.55 13.28 3.70 18.76

Table 6 CRV percent increase/ decrease

10.11 296.31 310.02 408.17 33.13 322.74 217.06 123.86 200.41 99.55 33.24 22.49 40.78 56.55 233.65 36.80 98.82 63.37 35.20 92.10 60.38 35.70 36.17 30.20 38.10 352.25 31.98 10.64 1.67 19.94

Table 7 Table 8 Table 9 Table 10 Table 11 Combined Low High Ordinary Expanded percent complexity complexity income percent increase/ percent percent percent increase/ decrease increase/ increase/ increase/ decrease decrease decrease decrease 0.02 0.02 0.02 0.02 0.00 0.02 0.00 0.02 0.01 0.03 0.03 0.08 0.05 0.06 0.04 0.04 0.05 0.16 0.06 0.06 0.04 0.08 0.03 0.07 0.03 0.12 0.05 0.10 0.11 0.09

37.71 42.70 29.79 39.39 0.20 85.64 4.36 398.46 488.28 563.44

19.87 178.13 64.56 217.08 36.80 372.63 287.51 212.66 166.90 98.03 34.14 19.55 141.68 55.06 60.08 38.28 186.62 64.71 61.31 85.15 89.10 32.15 42.00 13.91 21.99 693.41 0.28 19.26 23.43 35.03

10.15 155.96 298.56 499.59 4.64 188.51 6.66 79.67 171.43 82.79 61.52 1.56 44.35 82.78 652.66 48.56 59.23 10.42 30.73 39.38 7.35 8.30 19.25 30.28 71.63 1.24 16.59 22.56 17.02 40.38

45.52 295.17 420.19 525.67 32.96 444.93 34.30 142.81 170.34 111.62 46.41 25.46 17.42 55.34 371.25 38.34 257.17 65.18 52.94 256.95 106.20 138.34 140.12 184.82 151.36 629.70 54.34 132.96 13.15 12.38

presents the results of the CV and CVR analysis along with the results of the CRV analysis of the various regression models. Each column represents the percent increase or decrease of the dispersion measure from the selfprepared returns to the paid-prepared returns. We ﬁnd that the CRV analysis for the simple regression (the Table 6 result) is very similar to the CVR analysis (the Table 5 result) even though this latter analysis does not control for complexity. Combining the subsamples into one regression and using a categorical variable for preparer mode (Table 7) explains, in most cases, less than one-tenth of 1% of the variation in CRV.

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We conclude that this is not a useful technique for studying the impact of paid preparers. Their impact, if any, cannot be captured by a simple categorical variable. For the low complexity quintile (Table 8), we ﬁnd that paid preparers are associated with more variation (less equity) but there is only sufﬁcient data to study 10 of the 30 income ranges in the sample. For the high complexity quintile (Table 9), the result is similar to the basic Table 6 result. The positive increases are in the same income ranges and decreases are in the same income ranges except for two ranges. The ﬁrst range is negative in Table 6 but positive in Table 9. The 29th income range is positive in Table 6, although close to zero, but negative in Table 9. Overall, however, the high complexity result is not substantially different. When we restrict the analysis to ordinary income (Table 10), the results are more mixed. Nonetheless, use of a paid preparer is associated with more HE in only 11 of the income groups and less HE in the remaining 19. Finally, when we add several additional explanatory variables (Table 11), the result is similar to that in Tables 6 and 9. Atkinson–Plotnick Index Finally, the AP index, computed over the same zero to 300,000 ETI range provides the following values: Self-prepared returns Paid-prepared returns

0.02945 0.04002

Given that values closer to one imply less HE, then this technique also implies that paid-prepared returns are associated with less HE. This result is consistent with the weighted average CVR values reported in Table 5. Overall, after controlling for known factors that may distinguish paidprepared returns from self-prepared returns, we observe that, in a majority of income groups and in aggregate, paid preparers are associated with less HE. Limitations One of the limitations of any HE study is the ability of the metric used to adequately proxy for differences in equity. While this study also suffers from that same limitation, the use of several measures of dispersion supplemented by a rank-reversal measure helps, to some extent, to ameliorate that concern.

Association of Paid Income Tax Return Preparers with Horizontal Equity

133

A related limitation involves the use of the CVR measure as explained earlier. Implicit in this study is the assumption that the CV of ETI is the same for both self-prepared and paid-prepared returns. Statistical tests in Tables 1 and 2 indicate that they are approximately equal. Nonetheless, to the extent they are not equal, then difference in dispersion of TL may be due to differences in dispersion of income and not due to preparer mode. This study attempts to control for any variables that may differ systematically by preparer mode. Nonetheless, to the extent there are other variables that affect TL and for which we have not controlled, the results will be inappropriately attributed to prepare mode.

CONCLUSIONS The issue that we address is the effect of paid preparers on the HE of the federal income tax system. One possible effect is that HE is increased because paid preparers aid in the efﬁcient application of the tax law. Another possible effect, as explained by Allan and Iglarsh (1999), is that paid preparers magnify ambiguities in the tax law and, therefore, decrease HE. A third possibility is that paid-preparer usage has no systematic effect on HE. One confounding factor in this research is that paid preparers are also associated with more complex tax returns. Thus, controlling for complexity may allow the observation of its impact, if any, on HE. CV analysis allows comparison of our results to prior research. The CVR analysis permits us to compare results across income ranges. Using regression models and analyzing the CRV enables us to control for complexity and other variables that may affect HE. The CRV analysis for the simple regression is very similar to the CVR analysis, even though this latter analysis does not control for complexity. Along with the CV analysis, the CVR and CRV for the simple regression all indicate an overall association of paid preparers with less HE. Combining the subsamples into one regression and using a categorical variable for preparer mode explains, in most cases, less than one-tenth of 1% of the variation in CRV. We conclude that this is not a useful technique for studying the impact of paid preparers. Their impact, if any, cannot be captured by a simple categorical variable. For the low complexity quintile, paid preparers are associated with more variation (less equity), but there is only sufﬁcient data to study the lower income taxpayers. For the high complexity quintile, the result is similar to the CVR analysis and the basic regression model’s CRV analysis. Thus, complexity does not appear to be associated with differences in HE. When we restrict the analysis to ordinary income, the results are more

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mixed. Nonetheless, use of a paid preparer is more frequently associated with less HE. Finally, when we add several additional explanatory variables, the result is similar to the CVR analysis and the basic regression model’s CRV analysis. Comparing the dispersion-based measures to a rank-reversal measure, the AP index also indicates that use of a paid preparer is associated with less HE over the same total income range. Overall, then, we conclude that there is no evidence that paid preparers systematically improve the HE of the income tax system. Even after controlling for all factors that may distinguish paid-prepared returns from self-prepared returns, we observe that in a majority of income groups, paid preparers are associated with less HE. This study contributes to the literature in three ways. First, multiple techniques are used with the same dataset to assess the effects of paid-preparer usage on HE. Second, the study controls for complexity and other variables that, in prior research, have been found to vary systematically with preparer mode. Third, we deﬁne equally situated taxpayers using groups of equal width based on an expanded income amount; this is a necessity if the measure of HE is some measure of dispersion of TL. This improves upon prior research where the width of the income ranges used raises some uncertainty about whether the resultant groups of taxpayers can be considered economic equals.

NOTES 1. Their time-cost variable is calculated as the yearly IRS estimate of the total time associated with ﬁling each tax form (in hours) multiplied by an after-tax wage rate. The after-tax wage rate is [(TPI 10,000)/2,000] (1–MTR), which is the after-tax income scaled by the number of hours worked in a year (where TPI is total positive income and MTR is marginal tax rate). TPI is the sum of all positive income line items on the return, divided by $10,000 and stated in 1983 dollars. They use standard dollars because they use panel data. 2. Their uncertainty measure is the residual standard deviation of a tax liability model computed over a ﬁve-year period using panel data. 3. Long and Caudill (1987) use seven income ranges of varying widths. This grouping, especially in the upper income ranges, is generally not sufﬁciently narrow for the taxpayers to be considered economic equals. See Anderson (1985) and Westort and Wagner (2002) regarding economic equals. 4. Further, wage rate estimates were simply total positive income reported on the return divided by number of hours in a standard work year (about 2000). Given that some of that income represents return to capital not labor, we have questions about what it really is a proxy for.

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5. The CRV is deﬁned as p ðROOT MSEðTLÞ=MEANðTLÞÞ 100 (Eq. (8)). The ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ ﬃ numerator (ROOT MSE) is SSE=ðn k 1Þ, where SSE is the sum of the squared error terms in a regression, n the number of observations, and k the number of explanatory variables. Adding an explanatory variable reduces the SSE. This generally reduces the value of the CRV given no change in the mean of the dependent variable. However, an additional variable also increases k by 1, which means SSE is divided by a smaller number. When the decrease in SSE is small, the effect of dividing it by a smaller denominator (within the radical) is to increase the root mean squared error value and thus also increase the CRV value.

ACKNOWLEDGMENTS The authors would like to thank their respective institutions for funding the cost of data acquisition. We would also like to thank the participants at the 2005 Western Regional Meeting of the American Accounting Association and the 2005 Global Awareness Society International Annual Conference for their helpful comments on earlier versions of this manuscript. Finally, we would like to thank the editor and the two anonymous reviewers for their insightful suggestions and recommendations.

REFERENCES Allan, R. G., & Iglarsh, H. J. (1999). Tax preparers and horizontal inequity. Advances in Taxation, 11, 1–25. Anderson, K. E. (1985). A horizontal equity analysis of the minimum tax provisions: An empirical study. The Accounting Review, 60(July), 357–371. Anderson, K. E. (1988). A horizontal equity analysis of the minimum tax provisions: 1976–1968 tax acts. The Journal of the American Taxation Association, 10(Fall), 6–25. Christian, C., Gupta, S., & Lin, S. (1993). Determinants of tax return preparer usage: Evidence from panel data. National Tax Journal, 46(4), 487–503. Frischmann, P. J., & Frees, E. W. (1999). Demand for services: Determinants of tax preparation fees. The Journal of the American Taxation Association, 21, 1–23. Grasso, L. P., & Frischmann, P. J. (1992). Measuring horizontal equity: A regression approach. The Journal of the American Taxation Association, 14(Fall), 123–133. Iyer, G. S., & Seetharaman, A. (2000). An evaluation of alternative procedures for measuring horizontal equity. The Journal of the American Taxation Association, 22(Spring), 89–110. Long, J., & Caudill, S. (1987). The usage and beneﬁts of paid tax return preparation. National Tax Journal, 40(1), 35–46. Long, J., & Caudill, S. (1993). Tax rates and professional tax return preparation: Reexamination and new evidence. National Tax Journal, 46(4), 511–517. Madeo, S. A., & Madeo, L. A. (1981). Some evidence of the equity effects of the minimum tax on individual taxpayers. National Tax Journal, 34(4), 457–466.

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U. S. Government Accountability Ofﬁce (GAO). (2006). Paid tax return preparers: In a limited study, chain preparers make serious errors. Testimony before the Committee on Finance, U.S. Senate. Westort, P. J. (2001). Measuring variation in tax liability among economic equals. Advances in Taxation, 13, 169–203. Westort, P. J., & Wagner, J. M. (2002). Toward a better measure of horizontal equity. The Journal of the American Taxation Association, 24(Spring), 17–28.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

ETI Range

338 668 789 878 1,115 1,039 924 711 571 453 367 366 302 293 411

n

0.0541 0.0660 0.0502 0.5245 0.0161 0.4226 0.2558 0.1820 0.1567 0.0739 0.0889 0.0151 0.0240 0.0029 0.0807

0.0555 0.0528 0.0442 0.5001 0.0123 0.3873 0.2342 0.1625 0.1463 0.0659 0.0801 0.0183 0.0265 0.0013 0.0747

TL ETR Adjusted R2 Adjusted R2

Self-Prepared

0.0014 0.0132 0.0060 0.0244 0.0038 0.0353 0.0216 0.0195 0.0104 0.0080 0.0088 0.0032 0.0025 0.0042 0.0060

Difference 697 1,578 1,709 1,729 2,293 2,063 1,782 1,366 1,076 965 829 711 738 660 809

n

Dependent variable

0.0541 0.0017 0.0150 0.0464 0.0740 0.0314 0.0300 0.0230 0.0319 0.0220 0.0148 0.1180 0.0269 0.0121 0.0091

TL Adjusted R2 0.0298 0.0015 0.0152 0.0438 0.0626 0.0265 0.0262 0.0185 0.0294 0.0191 0.0131 0.1118 0.0233 0.0094 0.0079

ETR Adjusted R2

Paid-Prepared

0.0243 0.0002 0.0002 0.0026 0.0114 0.0049 0.0038 0.0045 0.0025 0.0029 0.0017 0.0062 0.0036 0.0027 0.0012

Difference

APPENDIX A. COMPARISON OF ADJUSTED R2 FOR REGRESSIONS ON TL AND ETR

Association of Paid Income Tax Return Preparers with Horizontal Equity 137

Total

16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

ETI Range

11,374

351 245 215 187 182 144 144 96 79 75 74 73 81 96 107

n

0.0021 0.1361 0.0114 0.0188 0.0538 0.0038 0.0230 0.0464 0.0184 0.0384 0.0411 0.0353 0.0122 0.0450 0.0313

0.0014 0.1337 0.0092 0.0039 0.0432 0.0032 0.0169 0.0455 0.0207 0.0397 0.0381 0.0355 0.0004 0.0448 0.0333

TL ETR Adjusted R2 Adjusted R2

Self-Prepared

0.0007 0.0024 0.0022 0.0149 0.0106 0.0006 0.0061 0.0009 0.0023 0.0013 0.0030 0.0002 0.0126 0.0002 0.0020

Difference

25,893

728 604 584 512 467 485 483 428 396 384 357 330 342 367 421

n

Dependent variable

0.0297 0.0020 0.0118 0.0102 0.0336 0.0076 0.0249 0.0455 0.0361 0.0008 0.0089 0.1005 0.0288 0.0163 0.0393

0.0244 0.0016 0.0090 0.0144 0.0350 0.0093 0.0213 0.0472 0.0361 0.0003 0.0070 0.0972 0.0278 0.0145 0.0402

ETR Adjusted R2

Paid-Prepared

TL Adjusted R2

APPENDIX A. (Continued )

0.0053 0.0004 0.0028 0.0042 0.0014 0.0017 0.0036 0.0017 0.0000 0.0005 0.0019 0.0033 0.0010 0.0018 0.0009

Difference

138 PETER J. WESTORT AND RICHARD CUMMINGS

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139

APPENDIX B. ESTIMATED TIME (IN MINUTES) FOR RECORD KEEPING, PREPARING, AND FILING FEDERAL INCOME TAX FORMS AND SCHEDULES Form/Schedule

Time (in Minutes)

1040 1040A 1040EZ A B C C-EZ D E EIC F J R SE short SE long 1040A Sch 1

781 586 233 337 86 623 103 418 363 34 350 120 98 52 99 56

1040A Sch 2

126

1040A Sch 3

89

1116

427

2106

266

2210

209

2210-F

101

2441 3468 3800

143 1,227 1,218

Description of Form

Individual Income Tax Return Short Form Form EZ Itemized Deductions Interest and Dividend Income Proﬁt or Loss from Business Proﬁt or Loss from Business, Short Form Capital Gains and Losses Supplemental Income and Loss Earned Income Credit Schedule Proﬁt or Loss from Farming Farm Income Averaging Credit for the Elderly or the Disabled Self-Employment Tax Self-Employment Tax Interest and Ordinary Dividends for Form 1040A Filers Child and Dependent Care Expenses for Form 1040A Filers Credit for the Elderly or the Disabled for Form 1040A Filers Foreign Tax Credit (Individual, Estate, or Trust) Employee Business Expenses (includes 2106EZ) Underpayment of Estimated Tax by Individuals, Estates, and Trusts Underpayment of Estimated Tax by Farmers and Fishermen Child and Dependent Care Expenses Investment Credit General Business Credit

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APPENDIX B. (Continued ) Form/Schedule

Time (in Minutes)

4137

71

4684 4797

244 2,964

4835 4952 5329 5884 6251 6478 6765 8582 8586 8606 8615

264 60 299 573 360 826 1,356 279 808 382 99

8801

352

8812 8814

50 77

8863

91

Description of Form

Social Security and Medicare Tax on Unreported Tip Income Casualties and Thefts Sales of Business Property (Form 1040, line 14, Other G/L) Farm Rental Income and Expenses Investment Interest Expense Deduction Additional Taxes on Qualiﬁed Plans Work Opportunity Credit Alternative Minimum Tax – Individuals Credit for Alcohol Used as Fuel Credit for Increasing Research Activities Passive Activity Loss Limitations Low-Income Housing Credit Nondeductible IRAs and Coverdell ESAs Tax for Children Under Age 14 Who Have Investment Income of More Than $1,400 Credit for Prior Year Minimum Tax – Individuals, Estates, and Trusts Additional Child Tax Credit Parent’s Election to Report Child’s Interest and Dividends Education Credits (Hope and Lifetime Learning Credits) (not initialized)

ACQUIRING INTERNATIONAL TAX KNOWLEDGE Jennifer L. Fecowycz, Ernest R. Larkins, Gary A. McGill and Thomas M. Porcano ABSTRACT Accounting programs and tax course offerings have been evolving in recent years, and one concern is the coverage of international tax topics. Although international tax is of prime importance to multinational corporations and Congress, little research has addressed the extent to which accounting programs cover international tax topics and whether demand for such coverage exists. This chapter presents the results of surveys about how students desiring a career in international tax services (ITS) can obtain international tax knowledge and what topical areas are most important. Many graduate accounting and taxation programs offer stand-alone international tax courses. Recruiters and professors characterize foreign tax credits, transfer pricing and treaties as the most important areas to emphasize in these courses. Though not essential to a career in ITS, taking an international tax course while in school exposes the student to this career opportunity, and a signiﬁcant percentage of new hires come from programs offering such a course. Our results provide accounting educators with information to evaluate their coverage of international tax topics, and to make changes if needed.

Advances in Taxation, Volume 18, 143–169 Copyright r 2008 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 1058-7497/doi:10.1016/S1058-7497(08)18006-1

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INTRODUCTION Accounting programs have been evolving in response to numerous recommendations from the Accounting Education Change Commission, the Association to Advance Collegiate Schools of Business International (AACSBI), formerly known as the American Assembly of Collegiate Schools of Business (AACSB), and various other accrediting bodies. These recommendations consistently include recommendations for how business schools should internationalize their curriculum. During the same period, tax course offerings also have changed in response to these organizational calls as well as the recommendations of the American Institute of Certiﬁed Public Accountants (AICPA) regarding its Model Tax Curriculum. Numerous studies have analyzed tax programs. However, only a few somewhat-dated studies speciﬁcally address international tax issues, and these deal with either their overall coverage in accounting programs or, from the profession’s viewpoint, their need for coverage and how ﬁrms handle international tax matters. Little current guidance exists for accounting academicians who are designing or revising courses and programs. International tax continues to be a very important consideration for multinational companies (MNCs) and for the U.S. Congress. To maximize the present value of after-tax cash ﬂows, MNCs must consider all tax effects on international transactions, including tax payments under foreign laws, reduced tax rates and exemptions available via treaties and incentive programs, and residual taxes the U.S. imposes on foreign proﬁt. From a policy perspective, Congress has established some capital-import neutral provisions that make U.S. MNCs more competitive with foreign ﬁrms, and thus provide beneﬁts to the U.S. economy. In the late 1990s and early 2000s, other countries challenged the legality of some of these provisions, speciﬁcally those excluding certain extraterritorial income, before the World Trade Organization (WTO) on the grounds that they provide unfair export subsidies to U.S. ﬁrms. The WTO upheld these challenges, and through its ability to permit countries to levy large duties on U.S. goods effectively forced Congress to repeal the offending provisions in 2004. This one example of international tax policy (with the prolonged and vigorous involvement of several foreign countries, the U.S. government, and the WTO in this controversy) highlights the economic and political dynamics of international tax issues. Given the lack of recent studies about international tax coverage in accounting programs, the signiﬁcant impact of international tax provisions on MNCs’ effective tax rates (considering both cash outlays and amounts

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reported for ﬁnancial accounting purposes), and the importance of international tax policy on the U.S. economy, this study provides information about the extent to which accounting academicians and large accounting ﬁrms provide international tax knowledge to students and new hires as well as speciﬁc topics they cover in their courses or training. We ﬁnd that better than 80% of surveyed Master of Tax (MST) programs offer stand-alone international tax courses, and MST programs provide more new hires to Big 4 International Tax Services (ITS) than any other source, satisfying about one-third of the demand. Beyond understanding basic concepts, Big 4 recruiters want new hires in ITS to know primarily about foreign tax credits, transfer pricing, and treaties. Our survey results provide accounting educators with information to evaluate their coverage of international tax topics, and to make changes if needed. The remainder of this chapter is organized as follows. The next section discusses studies about accounting programs and tax courses. The research method then is discussed, followed by the results of the study. The implications and conclusions appear in the last section.

PREVIOUS RESEARCH Early surveys about international tax knowledge focus on course offerings and topical coverage within undergraduate and graduate accounting programs (i.e., the supply side). Later studies place more emphasis on what tax knowledge the profession demands as perceived by either educators or practitioners.

Supply of International Tax Knowledge Lubell and Broden (1975) analyzed graduate tax programs (i.e., programs that lead to a graduate tax degree) in 1974. They identiﬁed 15 schools with such programs and then provided data regarding each school, including course offerings. Ten of the 15 schools offered one international tax course in the graduate tax programs, and one (Golden Gate) offered a second international tax course. Tidwell and Wyndelts (1977) followed up on the Lubell and Broden study because they believed it was too restrictive in its exclusion of graduate programs with tax ‘‘specializations’’ (deﬁned as three or more graduate tax courses). Their study included 18 schools, of which ﬁve also appeared in the

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Lubell and Broden study. They provided data about the programs, including courses offered. Six of the 18 schools offered an international tax course in their graduate programs. However, four of the six also were included in the Lubell and Broden study. Combining the results of both studies and eliminating double counting revealed that approximately 46% (13 of 28 schools) offered at least one international tax course in their graduate programs. Heller et al. (1981) present the results of an American Taxation Association (ATA) committee survey on undergraduate tax courses offered at U.S. universities and colleges. The committee report provides information about various aspects of tax courses such as the number of tax courses offered, number of faculty teaching tax courses, number of tax course sections offered, number of students enrolled in each tax course, etc. The survey addressed the topical coverage of each tax course (through three tax courses), and although many topics are listed, international tax is not one of them. The authors note that the emphasis from the ﬁrst to second and third tax courses switches from individual taxation and general concepts to corporate, partnership, and estate and gift taxation, as well as tax research and planning. There is an ‘‘other’’ category, and perhaps international tax is included in this category, but on average it accounts for only 1% of the topics covered in each tax course. Fogg and Campbell (1982) surveyed 168 AACSB schools to obtain information about tax faculty and tax course offerings. None of the respondent undergraduate programs offered an international tax course, and only 4% of the respondents offered an international tax (‘‘foreign taxation’’) course at the graduate level. This 4% is signiﬁcantly lower than the 46% from combining the Lubell and Broden (1975) and Tidwell and Wyndelts (1977) studies. It appears that the difference in part is due to the research method used. The 1970s studies only looked at schools that had a graduate tax concentration or specialization, whereas the 1982 study surveyed 168 schools with AACSB accreditation (and received 100 responses). Schools with a graduate tax concentration/specialization are more likely to offer an international tax course. Schools with AACSB accreditation might not even offer one tax course at the graduate level (in the Fogg and Campbell (1982) study, 96 schools offered a graduate business degree; 9 of them did not offer a tax course and 64% only offered one tax course). O’Neil, Weber, and Harris (1999) present survey results from 307 AACSB schools regarding the impact of the AICPA’s Model Tax Curriculum. More than 80% of the respondent schools required only one tax course in their

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accounting major. They found schools were revising their tax curriculum and that the AICPA’s Model Tax Curriculum was driving many revisions. They identiﬁed topics covered in the ﬁrst and second tax courses as individual, business entities, tax concepts, and other. Approximately 25% and 50% of the schools cover other topics in the ﬁrst and second tax courses, respectively. Unfortunately, the survey provided no breakdown of topics included in the other category, so whether it included international tax and in what proportion to all other topics is unknown. Kern and Dennis-Escofﬁer (2004) report online survey results of ATA members regarding taxes in the accounting curriculum. Their results indicate that a majority (W50%) of accounting graduates took only one tax course. A review of the topics covered (listed in the article and at the website containing a breakdown of covered topics) in the tax classes indicates that international either is not covered or was not included in the survey.

Demand for International Tax Knowledge Schwartz and Stout (1987) surveyed tax educators and practitioners to obtain their perceptions about tax requirements for entry-level auditing/ accounting positions in public accounting. They provide information on a variety of issues such as how well students are prepared for entry-level positions, desired number of tax courses, and instructional techniques. They also ask respondents to indicate the percentage of time academicians should devote to a list of 13 tax topics in one, two, and three tax courses (if more than one tax course was desired or required). If only one tax course was required, the respondents on average believed that the course should devote only 1% of the time to international tax. If two tax courses were required, respondents on average believed that the ﬁrst tax class should not cover international tax and the second tax class should spend 2% of the time on international tax. Finally, if three tax courses were required, then respondents on average indicated that the ﬁrst course should not cover international tax, and the second and third tax courses should dedicate 1% and 3% of the time to this area, respectively. Hreha et al. (1990) present a committee report regarding graduate education in international tax. The report provides guidelines for faculty who are developing a new international tax course. The committee surveyed faculty currently teaching an international tax course and professionals with international tax experience. The primary purpose was to identify topics

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that should be covered in an international tax course and effectively rank them in order of importance. Both groups identiﬁed similar topics but their ranked-importance of these topics differed. The report provided helpful direction for faculty interested in developing an international tax course. However, it did not indicate the current status of international tax in accounting programs, either via the number of schools offering a standalone international tax course or the extent that other tax courses covered international tax. Stara, Shoemaker, and Brown (1991) surveyed AICPA members to determine if tax courses offered in graduate programs coincided with what practitioners believed are important to prepare new staff for simple and complex tax engagements in the ﬁrst two years of their careers. The survey contained 17 tax courses. Overall, respondents ranked the international tax course last (even below a tax public policy course) for simple tax engagements; only 7 of 171 respondents considered it important for simple engagements. Although practitioners considered the international tax course to be signiﬁcantly more important for a complex tax engagement than it was for a simple tax engagement (72 of the 171 considered it important), they only ranked it 16th among the 17 courses for complex engagements (ahead of the tax public policy course).1 Stara et al. (1991) also indicate that more than 60% of AACSB schools offered an international tax course. Leinicke, Ostrosky, and Fish (1992) surveyed practitioners to determine which of the 10 topics they thought should be emphasized in the ﬁfth year of an accounting program. They ranked federal taxation ﬁrst, additional accounting and auditing ﬁfth, and international business last. The low ranking of international business might be partly because only 6% of their respondents worked for large national ﬁrms. Also, the authors did not breakdown federal taxation further. Sage and Sage (1993) present results of a small survey of CPA ﬁrm recruiters regarding the number and type of tax courses undergraduate and graduate students should take. Recruiters who hired directly for the tax department did not list international tax as a needed or desired course or topic for undergraduate students. Approximately 13% (2/16) indicated that masters of accounting programs should cover international tax and 45% indicated that masters of taxation programs should include such coverage. As a follow-up to O’Neil et al. (1999) and Koplin, Porter, Sheriff and Totten (1999) gives practitioners’ responses to the survey reported in O’Neil et al. (1999). The practitioners stress the need to maintain technical components of tax courses (i.e., do not decrease the focus on technical skill) to meet the increase in focus on nontechnical skills and the need for some

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corporate tax coverage. As with O’Neil et al. (1999), this study does not mention international tax. Schadewald (1999) notes the increasing importance of multi-jurisdictional issues in tax practice but suggests that accounting professors face several barriers to introducing these issues: (1) many topics already load the introductory tax courses, leaving little room for additional ones and (2) few resources on international tax exist that are suited for introductory courses. As an aid to faculty, Schadewald does provide sample outlines and case materials and identiﬁes helpful publications. However, as with other studies, he provides no indication of the extent to which accounting programs cover international tax. Schnee (2002) reports the survey results from AICPA members on how public accounting ﬁrms view professionals, which their tax departments hire. Of the survey’s several objectives, one was to identify speciﬁc knowledge (about topics such as corporate tax, pass-through entities, and international tax) that ﬁrms believe new hires need to succeed and the level of knowledge new hires had in these topics. Non-Big 5 respondents ranked technical knowledge as the most important item, but Big 5 respondents did not rank technical knowledge ﬁrst. Overall, both groups expressed some dissatisfaction with new hires’ technical knowledge. Unfortunately, the article does not disclose the rating of international tax, either as needed knowledge or new hires knowledge of it.

Summary of Literature Of the reviewed literature, the earlier studies focused on the accessibility of international tax knowledge within accounting programs. In addition to being dated, these earlier studies provide an inconsistent picture about the coverage of international taxation. For example, the combined results from Lubell and Broden (1975) and Tidwell and Wyndelts (1977) indicate that nearly one-half of accounting graduate programs offered an international tax course. In contrast, Fogg and Campbell (1982) found that only 4% of graduate programs offered such a course. As noted earlier, this inconsistency in part is due to different study designs. Later studies tended to emphasize the demand for international tax knowledge, though several had little to say about this area. Again, the results are a bit dated and somewhat inconsistent. The most recent empirical paper to provide demand information about international tax knowledge is Sage and Sage (1993). Their study showed that approximately one in eight

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recruiters felt international tax should be covered in a master of accounting program, while nearly half thought a master of tax program should include such coverage. Seemingly in contrast, Stara et al. (1991) found that AICPA members believed the need for international tax knowledge in both simple and complex engagements was lower than the demand for tax knowledge in 15 other areas. Yet, more recently, Schadewald (1999) makes the case that demand for international tax knowledge is increasing. The present study provides detailed information about international tax coverage within both undergraduate and graduate programs as well as some insight into what topics large accounting ﬁrms value most. In short, this study provides recent data that academicians can use to design or revise accounting courses and programs. The next section provides the research method of the current study.

RESEARCH METHOD Two methods (a questionnaire and an Internet search) were used to collect information regarding the current status of international tax in accounting programs. A survey was sent to faculty members teaching tax at 295 colleges/universities listed in Hasselback (2004). The ﬁrst part of the questionnaire requested demographic information about the college/ university, the school/college of business, and the accounting department. The second part dealt with tax offerings in the accounting program. Section A helped to identify: (1) the number of tax courses and international tax courses offered in the accounting program, (2) the extent to which accounting students take tax courses, and (3) employment placement information regarding accounting graduates. Sections B and C dealt with the extent of international tax coverage depending on whether there was a stand-alone international tax course in the accounting program. Two other methods were used to obtain information about international tax course offerings: an Internet search and phone calls to each accounting department of the same 295 colleges/universities. The Internet search involved reviewing each school’s website listing of course offerings. The accounting department of each school was called and asked if international tax was offered at the undergraduate and/or graduate levels and the number of courses and credit hours per course. While this search does not provide information about tax courses students take and employment placement, it supplements information regarding international tax course offerings.

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In addition, international tax professionals at Big 4 ﬁrms were surveyed to obtain information about each ﬁrm’s ITS. The survey addressed: (1) general aspects about ITS, (2) hiring practices, (3) training, and (4) the respondents perceptions about international tax education during a student’s academic career. Either the contact international tax professional or a head recruiter, depending on the ﬁrm, distributed the questionnaire internally.

RESULTS Academic Responses Ninety-ﬁve usable responses were received. The 32% response rate compares favorably with similar studies (cited in the Previous Research section of this chapter). Table 1 provides demographic information for the respondent schools. Panel A contains enrollment information. Universities of all sizes are represented, as are small and large business schools at the undergraduate and graduate levels. Undergraduate accounting programs with 101–200 majors account for almost 40% of the respondents, and graduate accounting programs with less than 25 students account for almost 50% of the respondents. Panel B of Table 1 provides information about the business school and accounting programs. Ninety-two schools offer a bachelors degree, 69 offer a Masters of Accounting (MAcc), 18 offer a Masters of Tax (MST), and 84 offer an MBA. Eighty-nine schools offer accounting undergraduate degrees, 69 offer a one-year Macc, and 20 offer either a 3-2 or 3-1 program that leads to a masters degree in Accounting. Thirty-two of the schools offering an MBA have an accounting concentration. Nearly all of the business schools have AACSBI accreditation, and approximately two-thirds of the accounting programs are accredited at the undergraduate and graduate levels. Table 2 provides information about the schools’ tax programs and international tax offerings.2 Most programs offer at least two tax courses. Sixty-six percent ((50þ6þ4)/91) offer at least two tax courses in bachelors programs, 83% in one-year MAcc programs, 100% in 3-2/3-1 and MST programs, 63% in MBA programs with an accounting concentration, and 31% in MBA programs not offering an accounting concentration (per Panel A). A review of Panel B indicates that only 8% of the undergraduate programs (7/89) offer at least one international tax course.3 International tax courses are offered more frequently at the graduate level; 28% of the

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Table 1. Respondent Demographics. Panel A – Enrollment University

Number of students

Undergraduate Business School/College

Graduate Business School/ College

Response frequency

Number of students

Response frequency

o5,000 5,000–10,000 10,001–15,000 15,001–20,000 20,001–25.000 W25,000

7 18 22 15 17 16

o500 501–1,500 1,501–2,500 2,501–3,500 W3,500 Missing

7 25 21 16 24 3

o50 51–100 101–250 251–400 W400 Missing

6 14 24 15 32 4

Total

95

Total

95

Total

95

Undergraduate Accounting Majors Number of students

Number of students

Response frequency

Graduate Accounting Majors

Response frequency

Number of students

Response frequency

o50 51–100 101–200 201–300 W300

22 22 37 7 7

o25 25–50 51–100 101–150 W150 Missing

47 16 17 6 6 3

Total

95

Total

95

Panel B – Programs Offered Business Programs

Accounting Programs Frequency

Type

Frequencya

Bachelors (BS)

92

89

Masters of Accounting (MAcc) Three-two or threeone programb Masters of Tax (MST) Masters of Business Administration (MBA) Other masters degree programs Doctoral program

69

Four-year program (BS) One-Year Master of Accounting (MAcc) Three-two or threeone programb Masters of Tax (MST) MBA – accounting concentration

Type

a

20 18 84

46 40

Doctoral program

69 20 18 32

29

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Table 1. (Continued ) Business School/College Accredited Response Yes No, currently seeking it No, not currently seeking it Total

Accounting Undergraduate Program Accredited

Accounting Graduate Program Accredited

Frequency

Response

Frequency

Response

Frequency

88 6

Yes No, currently seeking it No, not currently seeking it

64 5

Yes No, currently seeking it No, not currently seeking it Missing

62 5

Total

95

1

95

Total

26

95

21 7

a

Out of 95. After junior year students either enter Masters of Accounting program for two years (3-2) or complete undergraduate program (3-1).

b

MAcc programs offer at least one stand-alone international tax course as do 35% of the 3-2/3-1 programs, 83% of the MST programs, 34% of the MBA programs offering an accounting concentration, and 17% of MBA programs not offering an accounting concentration.4 Panels C and D of Table 2 contain information about law school tax courses. In general, business school students are not permitted to take tax courses offered in the law school. In all but one program (MAcc), the percent permitted to take these courses is small. However, 70% of the law schools that permit business students to take tax courses offer a course in international tax. Panel E of Table 2 has placement information for students in each type of program; speciﬁcally, the average percent of students in each employment category (i.e., the average of the percentages for all responding school). Public accounting dominates all other employers for programs offering accounting degrees. Forty-nine percent of BS graduates accept positions with public accounting ﬁrms. In comparison, the percentage for public accounting placement of other graduates are 77% for MAcc, 71% for 3-2/3-1, 82% for MST, 43% for the MBA with an accounting concentration, and only 10% for the MBA without such a concentration. One would expect a relation between international tax coverage within programs and the placement of students from those programs; that is, those programs offering heavier coverage of international tax topics would place a larger percentage of their students with Big 4 ﬁrms, where supposedly the

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Table 2.

Tax Program Information.

Panel A – Number of Tax Courses Offered Number of Tax Courses Program

0

1

2

3

4

X5

Missing

BS MAcc 3-2 and 3-1 MST MBA – accounting concentration offered MBA – no accounting concentration offered Other masters degrees

0 1 0 0 3 18 36

31 11 0 0 9 18 3

50 13 6 0 7 5 1

6 7 1 0 4 2 1

4 11 2 0 2 1 5

0 26 11 14 7 6 0

1 0 0 4 0 2 0

Panel B – Number of Stand-Alone International Tax Courses Offered Number of International Tax Courses Program

0

1

2

X3

Missing

BS MAcc 3-2 and 3-1 MST MBA – accounting concentration offered MBA – no accounting concentration offered Other masters degrees

82 50 13 3 21 43 40

5 16 6 9 10 8 6

0 3 1 4 1 1 0

2 0 0 2 0 0 0

3 0 0 0 0 0 0

Panel C – Students Permitted to Take Tax Courses Offered in Law Schoola Program

Yes

No

N/A

Total

BS MAcc 3-2 and 3-1 MST MBA – accounting concentration offered MBA – no accounting concentration offered Other masters degrees

4 33 16 18 16 11 12

70 43 31 21 32 49 36

26 24 53 61 52 40 52

100 100 100 100 100 100 100

Panel D – Law School Offers International Tax Course(s)-Given that Students are Permitted to Take Law Classesa Percentage Yes No

70 30

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Table 2. (Continued ) Panel E – Placement of Graduates (Percentage in Each Employment Area)a Program

Percentage Big 4 Other public Industry Government Other Unsure Total accounting

BS (n ¼ 71) MAcc (n ¼ 56) 3-2 and 3-1 (n ¼ 18) MST (n ¼ 17) MBA – accounting concentration offered (n ¼ 22) MBA – no accounting concentration offered (n ¼ 40) Other masters degrees (n ¼ 34)

27 51 54 54 28

22 26 17 28 15

28 12 11 13 36

10 5 1 3 6

14 6 2 1 2

0 0 15 3 13

101 100 100 102 100

5

5

51

7

7

25

100

7

26

23

7

0

37

100

a

Panels B–E percentages are based on respondents (i.e., not counting missing responses), percentage totals less than or greater than 100% due to rounding. Also, Panel C examines only those programs offering more than one tax course. In Panel E, n ¼ number of schools responding to these questions, and the panel presents the average percentage of students in each employment category; that is, the 27% under ‘‘BS-Big 4’’ is the average of the percentages of students at 71 schools responding to this question.

demand for international tax knowledge is greatest. To address this issue, the responses for Big 4 and other public accounting placement were further explored. All schools indicating placement in Big 4 and other public accounting were dichotomized based on number of tax offerings and whether international tax was offered. Table 3 presents the average of the percentages of students at respondent schools in each category, and a review of the table generally supports this relation. As shown in Table 3, bachelors, Macc, and 3-2 programs offering international tax courses placed a greater percentage of their students in Big 4 ﬁrms and public accounting (Big 4 plus other public accounting) than did those programs not offering international tax (programs with only one tax course and programs with more than one tax course but not an international tax course). Big 4 placement for bachelors programs with an international tax course is 58% vs. 26% ((26%þ25%)/2) for programs without international tax, and these percentages are 81% vs. 48%

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Table 3. Tax Course Structure and Public Accounting Placement. Public Accounting Course Structure

Bachelors (n ¼ 71)

1 tax course – no int. tax (n ¼ 26) W1 tax course (n ¼ 45) No int. tax (n ¼ 40) Int. tax (n ¼ 5)

Big 4

Other public accounting

Total

25

20

45

26 58

24 23

50 81

47

23

70

48 65

30 20

78 85

Masters of accountancy (n ¼ 56)

p1 tax course – no int. tax (n ¼ 8) W1 tax course (n ¼ 48) No int. tax (n ¼ 31) Int. tax (n ¼ 17)

3-2 program (n ¼ 18)

1 tax course – no int. tax (n ¼ 0) W1 tax course (n ¼ 18) No int. tax (n ¼ 11) Int. tax (n ¼ 7)

–

–

–

48 84

22 10

70 94

W1 tax course (n ¼ 17) No int. tax (n ¼ 3) Int. tax (n ¼ 14)

50 56

30 25

80 81

MST (n ¼ 17)

Notes: n denotes the number of responding schools in each category. Percentages based on respondents (i.e., not counting missing responses) and represent the average of the percentages of students at respondent schools (i.e., for schools offering a bachelors degree and only one course, the 25% under ‘‘Big 4’’ is the average of the percentages of students at all 26 schools).

((45%þ50%)/2) for all public accounting placement. At the MAcc level, placement in Big 4 is 65% vs. 48% and 85% vs. 74% in all public accounting. Big 4 placement in 3-2 programs is 84% vs. 48%, while it is 94% vs. 70% for all public accounting. Only at the MST level is the placement differential relatively small (56% vs. 50% for Big 4 and 81% vs. 80% for all public accounting). Table 4 contains information regarding international tax coverage in tax courses other than international tax for all programs. A review of Panel A indicates that when a stand-alone international tax course is not offered, other tax classes spend less than one contact hour on international tax topics in 52% of the undergraduate programs. Thirty-one percent of MAcc programs, 36% of 3-2 programs, 50% of MST programs, and 67% of MBA-Accounting concentration programs spend less than one contact hour on international tax topics. At the other extreme, the percentage of programs spending at least one week (X three contact hours) ranges from

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Table 4.

International Taxation Coverage.

Panel A – Number of Class Hours Spent on International Taxation Topics in Other Tax Classesa Percentage Number of hours

B/S

MAcc

3-2

MST

MBA – Acc concentration

MBA – no Acc concentration

Other masters

0

26 (63) 26 (15) 17 (7) 13 (15) 9 (0) 9 (0) 0 (0)

21 (50) 10 (13) 15 (13) 13 (21) 17 (0) 13 (0) 13 (4)

24 (58) 12 (8) 18 (17) 6 (17) 6 (0) 12 (0) 24 (0)

50 (29) 0 (0) 0 (14) 0 (14) 25 (21) 0 (0) 25 (21)

50 (57) 17 (14) 4 (0) 17 (7) 4 (0) 4 (0) 4 (21)

67 (73) 10 (7) 6 (7) 10 (7) 2 (0) 2 (0) 2 (7)

83 (62) 4 (8) 0 (8) 4 (15) 4 (0) 4 (0) 4 (8)

o1 1 2 3 4 W4

Panel B – International taxation topics covered in other tax course(s) offereda Percentage Indicating Topic Covered Introduction and background Policy issues Comparative tax systems Choice of entity Choice of location U.S. taxation of foreign entities and nationals Export incentives Income tax treaties Transfer pricing Foreign tax credit

67 (79) 32 (54) 26 (38) 21 (38) 26 (31) 47 (38) 14 (15) 42 (54) 40 (62) 77 (86)

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Table 4. (Continued ) Panel B – International taxation topics covered in other tax course(s) offereda Percentage Indicating Topic Covered U.S. individuals abroad Cross-border tax-deferred corporate transactions (Section 367) Tax havens Compliance issues Current events

67 (62) 2 (8) 21 (31) 12 (23) 33 (46)

a

Percentage based on respondents with such a program – percentage totals less than or greater than 100% due to rounding. Percentages when stand-alone international taxation course NOT offered shown ﬁrst; percentages when stand-alone international taxation course offered follows below in parentheses. Panels A and B’s percentages are based on respondents (i.e., not counting missing responses).

12% in MBA-Accounting concentration programs to 50% in MST programs. Overall, it appears that coverage is sparse in programs not offering an international tax course. Table 4 also provides information for programs offering at least one stand-alone international tax course. Per Panel A, the percent spending less than one hour on international tax topics in other tax courses increases in undergraduate programs (from 52% to 78%), MAcc programs (from 31% to 63%), 3-2 programs (36%–64%), and MBA-Accounting concentration programs (67%–71%). Having at least one course dedicated to the topic appears to reduce the need/desire to cover international tax topics in other tax courses. Panel B of Table 4 provides a breakdown of international tax topical coverage in other tax courses. When programs do not offer international tax as a stand-alone course, the most-common topic is the foreign tax credit, followed by an introduction to international tax and U.S. individuals abroad. The least common topics are cross-border tax-deferred corporate transactions and compliance issues. When programs offer an international tax course, the foreign tax credit is the most-common topic. An introduction to international tax, transfer pricing, and U.S. individuals abroad also are very common subjects.

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Table 5. International Taxation Coverage – Stand-Alone International Taxation Course Offered (Number of Contact Hours Covered in International Taxation Course(s) Offered). Average Number of Contact Hours Introduction and background Policy issues Comparative tax systems Choice of entity Choice of location U.S. taxation of foreign entities and nationals Controlled foreign corporations (Subpart F) Foreign currency Export incentives Income tax treaties Transfer pricing Foreign tax credit U.S. individuals abroad Cross-border tax-deferred corporate transactions (Section 367) Tax havens Compliance issues Current events

2.0 2.2 1.9 2.2 2.5 5.9 4.6 1.6 1.6 3.2 3.8 5.2 3.5 2.3 2.0 2.5 2.1

The least common topics are cross-border tax-deferred corporate transactions and export incentives. Table 5 contains information regarding topical coverage in international tax courses. The six areas given the most coverage (averaging three or more hours of class time) are U.S. taxation of foreign entities and nationals, foreign tax credit, controlled foreign corporations, transfer pricing, U.S. individuals abroad, and tax treaties. The topics given the least class time are foreign currency, export incentives, and comparative tax systems. The next section provides the practitioners’ viewpoint based on responses from international tax professionals at Big 4 public accounting ﬁrms.

Practitioner Responses The questionnaire distributed to international tax professionals covered four areas: general information about ITS, hiring practices, training, and their perceptions of international tax education in colleges and universities.

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A representative of each ﬁrm distributed the questionnaire internally and also collected returned questionnaires and then forwarded the results to us. Each of the Big 4 ﬁrms is represented, as are 21 ofﬁces and each ﬁrm’s ‘‘general’’ policies. Also, each response was from an ofﬁce with an international tax practice. Every ofﬁce does not have international tax specialist services, so when an ofﬁce needs assistance, 86% of the time it brings in the specialist from another ofﬁce (as opposed to 14% coming from the national ofﬁce). No ofﬁces outsource ITS. ITS specialists also work with groups in other areas (e.g., M&A) as needed. Only a small percentage (5%) of the ITS specialists has an appointment in a non-U.S. ofﬁce, and generally such appointments occur only for professionals at the manager level. Within ITS, all ﬁrms have specialty groups for transfer pricing, core ITS, customs and VAT, and other areas. This matches up with the major areas of work within ITS, of which the most common are transfer pricing, mergers and acquisitions, and cross-border ﬁnancing. ITS accounts for approximately 20% of local ofﬁces’ tax work and 22% of each ﬁrm’s entire tax work. Half the respondents expect these ITS percentages to increase, while the other half are uncertain. Twenty-two percent of the responses were unsure of the demand for international tax specialists. The other 78% indicate that the market for international tax specialists is strong but they also indicate that they are able to meet this demand with internal transfers and new hires (although 27% of these respondents indicate a strong unmet demand at the senior and manager levels). Table 6 provides information about Big 4 hiring practices. Ninetyfour percent of the respondents indicated they hire directly into ITS, with MST programs being the greatest contributor of new hires. Internal transfers to ITS are uncommon, but when they occur it usually is at the senior level. Specialization in ITS generally occurs at the senior or manager level. International students have neither preferential status nor are they disadvantaged in their pursuit of ITS positions. The market does not perceive their international status as an enhanced ability to work with personnel in non-U.S. ofﬁces. Further, these ofﬁces considered extra language skills helpful only for Asian clients (China and Japan speciﬁcally). Finally, the most-common advice suggested for students who want an ITS position was to have a strong tax background and an ability to work with others. Some respondents saw tax internships and a willingness to work with others as a plus.

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Table 6.

International Tax Services (ITS). Hiring Practices

1. Does the ﬁrm hire new staff directly into ITS?

Yes

2. If yes, then approximately what percentage is hired directly out of: Undergraduate accountancy programs Master of accountancy (MAcc) programs Master of tax (MST) programs Law school Unsure

8 7 34 19 33

3. Approximately what percentage is hired from: Internal transfers from other tax areas Internal transfers from non-tax areas External sources (e.g., other ﬁrms) Unsure

6 0 16 78

94%

No

6%

4. If internal transfers, then approximately what percentage of the ﬁrm’s employees in ITS is transferred at different experience levels? Senior 42 Manager 16 Senior manager 9 Partner 3 Unsure 30 5. Regarding internal transfers, when do employees begin to specialize in ITS? Most common Senior or manager Other Staff (within two years) 6. Is the market different for international students regarding the ability to specialize in international tax? Yes 50% No 50% a. Does this increase their ability to work in other ofﬁces around the Yes 0% No 100% world? b. Are their language skills helpful? Yes 13% No 87% 7. What advice would you give a student who wants to specialize in international tax? Most common Strong tax background, ability to work with others Other Tax internship, another language, willingness to work in large ofﬁces/ market

Table 7 contains information about ﬁrm training practices. All respondent ﬁrms have training programs. Generally, new hires receive two weeks of ITS training in the ﬁrst year, and over a ﬁve-year period will receive nine to ten weeks of specialized training. However, all levels of personnel (through partner) receive training. The training covers all areas of international tax and uses all types of delivery systems.

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Table 7.

International Tax Services (ITS). Training

1. What type of training is available for employees in ITS? All areas of international tax All types of training delivery methods (live, self-study, etc.) 2. How many days of training would a new ITS hire receive in the ﬁrst year?

2 weeks

3. How many days of training would a new ITS hire receive in the ﬁrst ﬁve years?

9–10 weeks

4. Is there ITS training available at each level (entry through partner)?

Yes

100%

No

0%

5. Approximately what percentage of the ITS training is done: Live – locally 13a Live – regionally 7 Live – nationally 41 Live – web-based 26 Self-study 11 Unsure 4 6. Are there ITS electives available at national training programs?

Yes

100%

No

0%

7. Is there any cross-country ITS training (i.e., a global approach to ITS where ﬁrm members from more than one country train together)?

Yes

75%

No

25%

a

Percentages total greater than 100% due to round offs.

The most-common delivery is live training at the national level, and numerous ITS electives exist at this level. Seventy-ﬁve percent of respondents use a global approach to ITS training (i.e., ITS personnel from two or more countries training together) at some point during the year. However, this approach occurs infrequently throughout the year because of cost concerns and perceived marginal beneﬁts. Respondents’ perceptions regarding the type of academic training useful for ITS personnel are presented in Table 8 and indicate that an international tax background is helpful but not absolutely essential for new hires. Internal training will provide the necessary background for personnel not receiving international tax knowledge through academic experiences. However, prior academic exposure to the foreign tax credit, transfer pricing, treaties, and basic international tax concepts would be helpful to a new hire. (As previously discussed, existing stand-alone courses generally spend substantial class time on each of these topics.)

Acquiring International Tax Knowledge

Table 8.

163

International Tax Services (ITS).

Perceptions on International Tax (Academic) Education 1. What areas of international tax do you prefer that your new hires have some exposure to during their academic careers? Most common Foreign tax credit, transfer pricing, basic concepts, and treaties Other Earnings and proﬁts, outbound property transfers, jurisdiction concepts, marginal tax rate analysis, and relation to ﬁnancial statements 2. What areas of international tax are not as important for new hires to have had some exposure to during their academic career? Most common Complex areas (e.g., Section 367, extraterritorial income exclusion) and specialty areas (e.g., expatriate) Other Inbound transactions and marginal tax rate analysis 3. Do you ﬁnd the international tax backgrounds of your new hires from accountancy programs – (bachelors, Macc, or MST) helpful or is this somewhat irrelevant because of the internal training they receive? Not important 100% 4. What other types of tax courses (other than international tax) are particularly important for an ITS specialist? Most common Corporate tax, partnership tax, and entities Other Mergers and acquisitions, tax research, and trusts 5. What other types of non-tax courses are particularly important for an ITS specialist? Most common Comparative economics and economics Other International accounting, international ﬁnance, international marketing, cross-cultural analysis, foreign language, ﬁnance, communication skills, and computer skills

Respondents indicate that corporate tax and partnership tax (taxation of various entities) would be very helpful for new hires. They also believe that a course in comparative economics (and economics in general) is helpful.

IMPLICATIONS AND CONCLUSIONS Few studies address the acquisition of international tax knowledge, and those that do are dated. The continued complexity in this area and increased importance of international tax policy suggest that accounting programs can beneﬁt from updated and expanded coverage. To assist tax academicians in developing and revising accounting courses and programs, the current study

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provides survey data obtained from accounting departments at 95 colleges and universities and tax professionals working for 21 ofﬁces of Big 4 ﬁrms. On the supply side, only about 20% of the surveyed colleges and universities offer MST programs but most MST programs (15 of 18) include stand-alone international tax courses. Combining MAcc and MST programs reveals that 39 percent of such graduate programs offer at least one international tax course ((16þ3þ9þ4þ2)/(69þ18) from Part B in Table 2). Though more graduate accounting and tax programs exist today, this percentage is less than the 46% (13/28) obtained from combining the Lubell and Broden (1975) and Tidwell and Wyndelts (1977) results. International tax courses, whether offered within MAcc, MST, or other programs, typically emphasize inbound transactions, the foreign tax credit, controlled foreign corporations, transfer pricing, the taxation of U.S. expatriates, and tax treaties. International tax topics receive little or no coverage in other undergraduate and graduate tax courses, but the likelihood and extent of coverage in these other classes increases when a stand-alone international tax course does not exist.5 On the demand side, the respondents indicated that about 20% of each ofﬁce’s tax practice involves ITS. Although difﬁcult to generalize to all Big 4 ﬁrms (because respondents came from only 21 different ofﬁces), at least for the surveyed ofﬁces, ITS seems to be a signiﬁcant portion of the practice. Ofﬁces without ITS personnel rely on specialists from their national ofﬁce or other in-ﬁrm locations rather than outsourcing the work. ITS specialists participate in teams with other industry or specialty groups, but most ITS groups have sub-specialty areas focusing on core international tax issues, transfer pricing, custom duties and VAT, and other areas. Most issues the ITS groups address involve transfer pricing, international acquisitions, and cross-border ﬁnancing. Consistent with Schadewald (1999), the respondents characterize the market for students wishing to join ITS as relatively strong. Their ofﬁces tend to hire new ITS staff from MST programs (34%), law schools (19%), and other accounting and law ﬁrms (16%). The tendency to hire new ITS staff from MST programs, nearly all of which offer at least one international tax course, suggests that prior academic exposure to international taxation may incline students to consider an international tax career and may make such students more attractive to ITS. We recommend that such students develop excellent interpersonal skills that enhance their contributions as team members and take courses dealing with entity taxation, comparative economics, and international tax. The latter course should cover the basics and also emphasize treaties, the foreign

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165

tax credit, and transfer pricing issues. However, lacking an international background does not preclude students from joining ITS because Big 4 ﬁrms often have excellent in-house training as an alternative route to acquiring international tax knowledge. Respondents generally indicated that an international tax background is not required; thus, international knowledge might make students more attractive to ITS but is not absolutely essential at the point of hire. The course is an added beneﬁt but not a requisite. This result may have implications for existing accounting and tax programs if resources to staff specialized courses are or become scarce. This study documents that a sizeable percentage (39%) of graduate programs offer a stand-alone international tax course for which two questions might be relevant. First, should the course continue to be offered? In light of tight academic budgets and limited resources, ongoing program assessments cause international tax courses (like other elective or specialized courses) to undergo regular scrutiny. Low enrollments or weak demand (as indicated in this study) for international tax knowledge during a student’s academic program might suggest discontinuing the stand-alone course and folding its contents into some other course. Second, assuming a sufﬁcient supply of interested students and demand from the profession, should the topical coverage in the course conform more to the coverage at other schools (see Table 5) or to the needs of the profession (see Table 6)? Signiﬁcant departures from these norms do not indicate that changes should necessarily occur, only that the rationale for topical coverage might be documented as part of the assessment process. At the same time, many schools do not offer a stand-alone course in international taxation. Any decision to create such a course should consider student interest, ﬁrm demand, and whether someone is available to teach the course. Big 4 ﬁrms meet part of their demand for personnel with international tax knowledge through in-house training. Thus, a decision to commit resources to a new course should be made in light of this and the speciﬁc demand from recruiters at a particular school. If a school decides to add an international tax course then we recommend consulting the ATA’s ‘‘Course Syllabus Exchange’’ (aaahq.org/ata/teaching/ default.htm) and contacting other professors who have taught such a course. Table 9 provides useful information when selecting a primary textbook. The number of chapters and pages give some indication about the text’s level of difﬁculty or detail. Generally, the listed textbooks adopt a narrative style of presentation, though two are case law texts, and one depends heavily on excerpts or readings from earlier law review articles and policy reports. Most emphasize almost exclusively the international aspects

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Table 9.

International Tax Textbooks Available for Adoption.

Texts

Chapters/ Pages

Primary Presentation

Primary Focus

Supplements/Coverage of Main Topicsa,b

7/185

Narrative

Global

10/574

Case law

U.S.

12/1,000þ

Narrative

U.S.

14/520 11/541

Narrative Readings

U.S. U.S.

Isenbergh (2005) Larkins (2004)

24/297 17/440

Narrative Narrative

U.S. U.S.

McDaniel et al. (2005) Moore et al. (2005) Postlewaite (1999)

12/232 22/910 15/349

Case law Narrative Narrative

U.S. U.S. U.S.

Riahi-Belkaoui (1998)

5/200

Narrative

Global

Rohatgi (2005) Schadewald and Misey (2005)

9/467 13/576

Narrative Narrative

Global U.S.

Not available/Little mention of expatriates Instructor’s manual/Little mention of expatriates Study problems, instructor’s manual/Adequate Not available/Adequate Not available/Little mention of expatriates Not available/Adequate PowerPoint lecture slides/ Adequate Instructor’s manual/Adequate Not Available/Adequate Not available/Not available to review Not available/Light coverage of several topics Not available/Adequate Not Available/Adequate

Arnold and McIntyre (2002) Avi-Yonah, Ring, and Brauner (2005) Bittker and Lokken (2004) Doernberg (2004) Graetz (2003)

Notes: Not available indicates that we did not ﬁnd any supplements listed on the publisher’s or Amazon’s website. a Stand-alone international tax courses devote signiﬁcant class time covering taxation of foreign individuals and entities, the foreign tax credit, controlled foreign corporations, taxation of expatriates, transfer pricing, and tax treaties (see Table 5). Big 4 recruiters believe students could beneﬁt most from academic instruction about the foreign tax credit, transfer pricing, and tax treaties (see Table 6). b In addition to listing available supplemental materials, this column notes any of these six topics the text does not adequately cover.

of U.S. tax law; only three adopt a global focus. The texts generally discuss topics most other professors teaching such a course cover, but few supplemental materials are usually available to adopters. Our ﬁndings are limited to the extent that the respondents do not represent all international tax ofﬁces of Big 4 ﬁrms. They also may be limited to the extent graduates of accounting programs do not accept employment with Big 4 ﬁrms. Regional and local ﬁrms providing ITS may lack the in-house training capabilities of larger CPA ﬁrms. As a result, they may place a higher premium on international tax knowledge available

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through academic accounting programs. Additionally, we did not explore corporate demand for such graduates. Further, the international tax topics that regional and local CPA ﬁrms and corporations deem most important may differ from those that Big 4 ﬁrms value, and their hiring practices into ITS may differ as well. A future survey might collect information from local and regional ﬁrms that offer ITS and corporate tax departments and compare and contrast the ﬁndings with the results we present. Academic accounting programs serving smaller markets or placing relatively few graduates with Big 4 ﬁrms could beneﬁt from such information.

NOTES 1. The low ranking for an international tax course might be due to their respondent proﬁle; only 23% were afﬁliated with ‘‘Big Six’’ ﬁrms and another 5% with ‘‘Top 20’’ ﬁrms. The remaining 72% came from other-sized ﬁrms which might not have had international tax engagements and therefore did not have (nor see) a need for international tax courses. 2. The survey did not request information on what courses (non-tax, tax, or within tax) are required in undergraduate or graduate programs. 3. Some schools have started to use module-type courses, whereby they cover selected topics in one-, one and one-half- or two-hour courses. The number of schools doing this is relatively small; however, to the extent that this is done, the number of tax courses listed in Panel B of Table 2 does not reﬂect this. The number of class hours presented in Tables 4 and 5 provides information for direct comparative purposes. 4. Results of the Internet search and phone calls indicated that seven schools offered at least one international tax at the undergraduate level (similar to the results from the mailed questionnaire) and 63 schools offered at least one graduate level international tax course. The questionnaire results indicated 67 programs offered such courses; however, some schools have more than one graduate program so some double counting exists in the questionnaire results. 5. In personal correspondence, Irvin N. Gleim, author and long-time publisher of CPA review materials, notes that historically the foreign tax credit topic is tested occasionally in the CPA examination. In addition, he notes that Part I of the Enrolled Agent exam includes questions on the special treatment of U.S. citizens and residents with foreign earned income, the foreign tax credit, and special ﬁling requirements for U.S. persons with foreign income.

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