Iranian Immigrants in Los Angeles: The Role of Networks and Economic Integration (The New Americans: Recent Immigration and American Society) (The New ... Recent Immigration and American Society)

The New Americans Recent Immigration and American Society

Edited by Steven J. Gold and Rubén G. Rumbaut

A Series from LFB Scholarly

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Iranian Immigrants in Los Angeles The Role of Networks and Economic Integration

Claudia Der-Martirosian

LFB Scholarly Publishing LLC New York 2008

Copyright © 2008 by LFB Scholarly Publishing LLC All rights reserved. Library of Congress Cataloging-in-Publication Data Der-Martirosian, Claudia. Iranian immigrants in Los Angeles : the role of networks and economic integration / Claudia Der-Martirosian. p. cm. -- (The new Americans : recent immigration and American society) Includes bibliographical references and index. ISBN 978-1-59332-240-3 (alk. paper) 1. Iranians--California--Los Angeles--Economic conditions--20th century. 2. Iranians--Employment--California--Los Angeles--History-20th century. 3. Iranians--Kinship--California--Los Angeles--History-20th century. 4. Iranians--Social networks--California--Los Angeles-History--20th century. 5. Iranians--California--Los Angeles--Ethnic identity--History--20th century. 6. Immigrants--California--Los Angeles--Economic conditions--20th century. I. Title. F869.L89I54 2008 304.8'7949405509048--dc22 2007043128

ISBN 978-1-59332-240-3 Printed on acid-free 250-year-life paper. Manufactured in the United States of America.

To the loving memory of my parents, Stella and George

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Table of Contents

List of Tables …………………………………………... vii Acknowledgements .…………………………………... xi Preface …………………………………………...……. xiii Chapter 1: Economic Capital, Human Capital or Social Capital?……………………………………... 1 Chapter 2: The Iranian Survey .…………………….…. 17 Chapter 3: Timing of First Job ………………………… 33 Chapter 4: Shift in Occupational Status …..…………… 55 Chapter 5: Determinants of Income …………………… 71 Chapter 6: Self-Employment …………..……………… 87 Chapter 7: Ethno-Religious Groups ………………..… 105 Appendix A: Iranian Survey, Household Roster ….….. 123 Appendix B: Iranian Survey, Migration Experience….. 124 Appendix C: Iranian Survey, First Employment in U.S. 125 Appendix D: Iranian Survey, Current Employment……126 vii

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Appendix E: Iranian Survey, Network Ties …………..127 Appendix F: Iranian Survey, Employment in Iran ….. 128 Appendix G: Iranian Survey, Spouse’s Employment ...129 Appendix H: Iranian Survey, Study Questionnaire …...130 References ………………………………………..….. 145 Index ………………………………………….…..….. 159

List of Tables

Table 2.1: Socioeconomic Characteristics of Iranian Males, LA County …………………………….…… 28 Table 2.2: Socioeconomic Characteristics of Iranian Females, LA County …………………..….… 29 Table 2.3: Socioeconomic Characteristics of Armenian Iranian Males, LA County ...…………..……. 30 Table 2.4: Socioeconomic Characteristics of Armenian Iranian Females, LA County ………..………. 31 Table 3.1: Sample Characteristics, Iranian Males, LA County, 1987-88 …………………..……….. 48 Table 3.2: Network Measures by Timing of First Job, Iranian Males, LA County 1987-88…….….... 49 Table 3.3: Logistic Regression, Finding First Job in the U.S., Iranian Males, LA County 1987-88 ...... 50 Table 3.4: Logistic Regression, Finding First Job in the U.S., Iranian Males, LA County 1987-88 ...… 51 Table 3.5: Logistic Regression, Finding First Job in the U.S., Iranian Males, LA County 1987-88 ...… 52 Table 3.6: Period of Immigration by Network Measures Iranian Males, LA County 1987-88 ……...… 53 . ix

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Table 4.1: Last Occupation in Iran by First Occupation in the U.S., Iranian Males, LA County 1987-88 ..67 Table 4.2: Change in Occupational Status Between Last Job in Iran and First Job in the U.S., Iranian Male Wage & Salary Workers, LA County 1987-88..68 Table 4.3: Logistic Regression Predicting Positive Change in Occupational Status, Iranian Male Wage & Salary Workers, LA County 1987-88 ………. 69 Table 5.1: Sample Characteristics of Iranian Males, LA County 1987-88 ………………………… 82 Table 5.2: Mean Annual Income by Economic Embeddedness, Iranian Males, LA County 1987-88 ………….………………………..…. 83 Table 5.3: Multiple Regression Predicting Log (income) Iranian Males, LA County 1987-88 ………… 84 Table 5.4: Mean Annual Income by Strength of Tie, Iranian Males, LA County 1987-88 …………. 85 Table 5.5: Job Satisfaction by Strength of Tie, Iranian Males, LA County 1987-88 …………………. 86 Table 6.1: Sample Characteristics of Self-Employed, Iranian Males, LA County 1987-88 ………. 100 Table 6.2: Logistic Regression Predicting SelfEmployment, Iranian Males, LA County 1990 ……………………………………….....101 Table 6.3: Logistic Regression Predicting SelfEmployment, Iranian Males, LA County 1987-88 …………………………….…..….. 102

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Table 6.4: Economic Network Scale Items by Type of Tie, Iranian Males, LA County 1987-88 ………..103 Table 7.1: Characteristics of Ethno-Religious Group, Iranian Males, LA County 1987-88 ………. 119 Table 7.2: Top Occupational and Industrial Niches, Iranian Immigrants, LA County 1990 …………….. 120 Table 7.3: Mean Scores for Quality of Jobs, Iranian Occupational and Industrial Niches, LA County 1990 ………………………………….……. 121

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Acknowledgements

It gives me great pleasure to write this section and thank those who have been supportive throughout the process of writing this book. The 1987-88 Iranian Survey in Los Angeles gave me the opportunity to work with a group of talented scholars - Professors Georges Sabagh, Ivan Light, Roger Waldinger and Mehdi Bozorgmehr - in the fields of Sociology and Middle Eastern Studies. The project, funded by the National Science Foundation (Grant #SES-8512007), took place at UCLA under the leadership of Georges Sabagh and Ivan Light, as Principal Investigators. Mehdi Bozorgmehr was the Project Director. I joined the project in 1986 as the Armenian Iranian Project Coordinator. Georges Sabagh was the heart and soul of the study. His knowledge of the Middle East and his clear vision for the project made it all possible. Ivan Light’s expertise in the field of entrepreneurship and immigration provided the theoretical backbone to the Iranian study. The successful completion of the project was primarily due to Mehdi Bozorgmehr’s persistence and hard work. Roger Waldinger’s passion for the field of immigration and his wealth of knowledge about the economic integration of immigrants inspired me to study this topic. Without the support of these great scholars, I couldn’t have completed this manuscript. As Project Coordinator, I experienced every phase of the study – questionnaire construction, translation of the questionnaire, sampling design, drawing the study sample, xiii

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training the interviewers, screening respondents, conducting in-person interviews, coding the questionnaire, creating the data set, and analyzing the data. During the data collection, one interviewer, Mr. Seboo Hovanessian, stood out as he went above and beyond the call of duty. Without his constant perseverance and dedication, we wouldn’t have had the success rate in completing the interviews and finishing the study in a timely manner. Writing a book requires countless hours of editing and proof reading. This book wouldn’t have come together without the meticulous reading of my colleague, Dr. Kathryn Atchison, and my husband, Dr. Shant Barmak. Shant read each chapter several times giving me constructive feedback. I couldn’t have done this without his loving support. My sister, Anita, has always inspired me to set high goals and work hard to reach them. I am ever so grateful to have her in my life for her encouragement and praise. My journey started with the love and support of my parents, Stella and George. They instilled in me the love of learning and were a constant support for me. This book is dedicated to the loving memory of my parents.

Preface

Writing this book and being involved in the Iranian Study was a personal journey. In the summer of 1977, a year prior to the Iranian Revolution, my parents decided to move to Los Angeles so my sister and I could continue our education in the U.S. At the time, my sister was 16 years old, about to enter her senior year of high school and prepare for college the following year. I was only 12, and although I was fluent in Armenian and Farsi, I had no knowledge of English. Until the Iranian Revolution started in late 1978, we believed our stay in the U.S. would be temporary. After the Revolution, for an Armenian – a Christian minority in Iran – returning to the newly established Islamic regime was not an option. Permanent residency in the U.S. became our only choice, and soon our focus shifted to getting my grandparents and close relatives to the U.S. My grandparents finally arrived to Los Angeles as refugees in 1981. We were all transplanted in a new country facing new challenges. Adjusting was difficult. My father would always joke that he studied the wrong language at the university – French instead of English! My sister and I continued our studies. As years went by and more Iranians migrated to Los Angeles, the transition to the new environment became easier. My parents became more active in the Armenian Iranian community as they were able to re-connect with family and friends who had also migrated from Iran. The immigration to the U.S. was a major turning point for our family. It was not a coincidence that I decided to study immigration and participate in the Iranian project at xv

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UCLA. Ever since our move, I was curious why we had chosen Los Angeles and not Washington D.C. or New York as my parents had contemplated settling in those cities. I became curious about the migration process – how did other Iranian families reunite? How did they adjust socially and economically? Why did they choose Los Angeles? I had so many questions and no answers. I began this work to answer these questions. After the passing of my parents, writing this book became even more critical. It is about the experiences of my parent’s generation. As first generation immigrants, they left Iran during uncertain times and started new lives thousands of miles away to provide better opportunities for their children. Just like 1978, today’s political climate, especially after 9/11, is even more tenuous and brings more attention to Iranian immigration to the U.S. This book is timely since it gives a background on how first generation Iranian immigrants entered into the U.S. labor market and the gap between their employment status in Iran and the U.S. My father was a successful Civil Engineer in Iran who had his own business. Entrepreneurship was an important part of his life in Iran but not in the U.S. The life stories of 557 Iranian male heads of households, just like my father, have been accumulated in this study to illuminate the Iranian experience of emigration to the U.S. three decades ago.

CHAPTER 1

Economic Capital, Human Capital or Social Capital?

INTRODUCTION Iranian migration to the United States began in the 1950’s. After the Iranian Revolution in 1978-79 Iranian immigration to the U.S. exploded with Los Angeles as the primary place of destination. This study documents the economic integration of Iranian immigrants in Los Angeles and examines its determinants by focusing on the role of immigrant network ties. Economic performance, the outcome measure, is conceptualized in two stages: initial, settlement stage and long-term, settled stage. The manuscript is divided into four main chapters where each chapter has a different dependent variable: length of time spent finding the first job (ch.3), shift in occupational status (ch.4), income (ch.5) and self-employment (ch.6). Chapter 2 describes the study design and methods, chapters 3 and 4 concentrate on the initial settlement stage, and chapters 5 and 6 focus on the settled stage. The U.S. Census, which is the most widely used and the largest data source for the foreign-born, does not contain data on network ties. The 1987-88 probability sample survey of 671 Iranian immigrants living in Los Angeles is used to study the effect of network ties on economic performance. This in-person questionnaire survey contains pre- and post- migration socioeconomic data, information about the migration process, and questions regarding the 1

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types of help Iranian immigrants received and/or gave since their arrival to the U.S. This study borrows from the theory of social capital and social embeddedness, which was re-introduced to the field of economic sociology during the mid 1980’s (Bourdieu 1986; Granovetter 1985; Coleman 1988; Granovetter 1995) and applied to immigration studies in the mid 1990’s (Portes and Sensenbrenner 1993; Portes 1998; Portes 1995a, 1995b; Aguilera and Massey 2003; Waldinger and Lichter 2003; Giorgas 2000; Menjivar 2000). Having access to immigrant network ties does not necessarily mean that immigrants possess a high level of social capital. Social capital is a resource, which exists in the relations between individuals (Coleman 1988; Bourdieu 1986). As Portes (1998, 1995a) argued, "the resources themselves are not social capital, the concept refers instead to the individual's ability to mobilize them on demand" (Portes 1995a, p. 12). “The key concept is that social capital is not an individual characteristic or personality trait but a resource that resides in the networks and groups to which people belong” (Mouw 2006, p. 1). This study builds on this approach and measures social capital in terms of the extent to which immigrants have access to and are embedded in economic network ties. The general hypothesis of this study is that economic embeddedness affects how well immigrants perform in the labor market. HUMAN CAPITAL VS. SOCIAL CAPITAL According to the neoclassical economists, immigrants' economic success, which is often measured by their average earnings, is determined by their human capital characteristics, such as education, work experience and other labor-market related skills (Chiswick 1978, 1979; Borjas 1987). Human capital refers to skills and knowledge acquired by an individual. Chiswick (1978) who "pioneered the application of the concept of human capital to immigrant economic attainment" (see Portes 1995a, p. 23), argued that the longer immigrants stay in the U.S., the more likely they are to accumulate labor market related skills,

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become fluent in English, and succeed economically (Chiswick 1978). "In an analysis of the earnings of immigrants, the number of years since migration is an important variable, and ignoring it would mask important differences between the native and the foreign born and among the foreign-born" (Chiswick 1978, p. 918). According to Chiswick (1979), the transferability of skills to the country of destination and "the self-selection of immigrants on the basis of innate ability and motivation for economic advancement" (1979, p. 358) are two important determinants of economic progress. When immigrants first arrive they have lower earnings than the native born with similar demographic characteristics because of the less than perfect transferability of skills. The disadvantage is greatest for refugees from countries with a different language and economy and least for economic migrants from countries with a language and economy similar to the destination. With the passage of time, however, immigrants acquire knowledge of the language and customs of the country of destination and adjust their skills and credentials to the new environment (Chiswick 1979, p. 358). Borjas (1987) built on this argument: "Recently, the focus has shifted from analyses of single cross-section data sets to studies of cohort or longitudinal data. The departure from these studies is the well-known fact that the analysis of a single cross-section of data cannot separately identify aging and cohort effects" (Borjas 1987, p. 531). For Borjas, aging effect refers to immigrants' acquisition of labor market related skills over time, whereas cohort effect refers to cohort differences in quality "caused by non-random return migration propensities and/or secular shifts in the skill mix of immigrants admitted to the United States" (Borjas 1987, p. 531). In both instances, aging and cohort effects refer to immigrants' individual characteristics. Sociologists have

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been dissatisfied with this approach (for a review, see Portes and Bach 1985, ch. 1). "Clearly, education, knowledge of English, and work experience are important factors affecting newcomers' employment prospects, but they do not suffice to fully explain occupational mobility and earnings (Portes 1995a, p. 23). Economists concentrate solely on the effects of individual skills and ignore the effects of the broader social structures. Sociologists view immigrants not simply as individuals, but "as members of groups and participants in broader social structures that affect in multiple ways their economic mobility" (Portes 1995a, p. 24). Every aspect of migration, decision to migrate, place of destination, and the settlement process, is network driven (Massey and Espinosa 1996; Portes 1995a; Tilly 1990; Boyd 1989; Light, Bhachu and Karageorgis 1993). Immigrants usually migrate into and settle in areas where co-ethnics have already established a community (see Portes and Rumbaut 1990, ch. 3). Moreover, "ethnic networks provide sources of information about outside employment, sources of jobs inside the community, and sources of credit and support for entrepreneurial ventures" (Portes and Rumbaut 1990, p. 88). To put it simply: networks migrate; ... By and large, the effective units of migration were (and are) neither individuals nor households but sets of people linked by acquaintance, kinship, and work experience who somehow incorporated American destinations into the mobility alternatives they considered when they reached critical decision points in their individual or collective lives. Longdistance migration entails many risks: to personal security, to comfort, to income, to the possibility of satisfying social relations. Where kinsmen, friends, neighbors, and work associates already have good contacts with possible destinations, reliance on established interpersonal networks for information minimizes and spreads the risks (Tilly 1990, p. 84).

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"The subject of social networks is not new in international migration research" (Boyd 1989, p. 639). The field of economic sociology reintroduced the concepts of embeddedness and social capital (Granovetter 1985, 1990; Bourdieu 1986; Coleman 1988). Embeddedness questions the individualistic approaches to economic action and presumes that economic action is embedded in social relations. This theoretical framework has allowed immigration researchers to expand immigrant network theory and better explain how immigrant network ties affect immigrants' economic life (Portes and Sensenbrenner 1993; Portes 1995a; Portes 1998; Waldinger and Lichter 2003; Waldinger 1996; Aguilera and Massey 2003; Giorgas 2000). Social networks are among the most important types of structures in which economic transactions are embedded. These are sets of recurrent associations between groups of people linked by occupational, familial, cultural, or affective ties. Networks are important in economic life because they are sources for the acquisition of scarce means, such as capital and information, and because they simultaneously impose effective constraints on the unrestricted pursuit of personal gain (Portes 1995a, p. 8). EMBEDDEDNESS AND SOCIAL CAPITAL Most economists who follow the "new institutional economics" position argue that "behaviors and institutions previously interpreted as embedded in earlier societies, as well as in our own, can be better understood as resulting from the pursuit of self-interest by rational, more or less atomized individuals" (reviewed by Granovetter 1985, p. 482; see Williamson 1975). During the 1920s, some anthropologists, called the "formalists", agreed with this position and claimed, "even in tribal societies, economic behavior was sufficiently independent of social relations" (Granovetter 1985, p. 482).

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These two schools of thought exemplify a pure "markets" approach to human action. Granovetter (1985) disagreed with these approaches. He argued that since "most behavior is closely embedded in networks of interpersonal relations" (1985, p. 504), "economic action is (also) embedded in structure of social relations" (1985, p. 481). Granovetter borrowed this notion of embeddedness from the "substantivist" school of thought in anthropology, which was influenced by the works of Karl Polanyi (1944) and his associates (Polanyi, Arensberg and Pearson, 1957). Polanyi (1944) used the concept of embeddedness to illustrate the role of social forces in structuring precapitalist economies, but assumed that these forces would not operate in modern capitalist economies. By summarizing various findings, Granovetter (1985) illustrated that even in today's society, social expectations modify and even subvert the original intent of economic transactions. I assert that the level of embeddedness of economic behavior is lower in non-market societies than is claimed by substantivists and development theories, and it has changed less with "modernization" than they believe; but I argue also that this level has always been and continue to be more substantial then is allowed by formalists and economists (Granovetter 1985, pp. 482-483). According to Portes (1995a) "social capital is a product of embeddedness" (Portes 1995a, p. 13). Embeddedness is a general notion that questions the individualistic approaches to economic action, whereas social capital, with its distinct theoretical roots, is more specific. This concept was first introduced by Pierre Bourdieu (in French) and James Coleman (in English). Bourdieu (1986) differentiated between three types of capital: 1) Economic capital - "is immediately and directly convertible into money and may be institutionalized in the form of property rights" (Bourdieu 1986, p. 243). 2) Cultural capital - may be converted into economic capital under certain conditions and institutionalized in terms of educational qualifications.

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3) Social capital - is "made up of social obligations ("connections") which is convertible, in certain conditions, into economic capital and may be institutionalized in the form of a title of nobility" (Bourdieu 1986, p. 243). Bourdieu (1986) explained: Social capital is the aggregate of the actual and potential resources which are linked to possession of a durable network of more or less institutionalized relationships of mutual acquaintances and recognition - or in other words, to membership in a group - which provides each of its members with the backing of the collectivityowned capital, a "credential" which entitles them to credit, in the various senses of the word. The volume of the social capital possessed by a given agent thus depends on the size of the network of connections he can effectively mobilize and on the volume of the capital (economic, cultural or symbolic) possessed in his own right by each of those to whom he is connected (Bourdieu 1986, pp. 248-249). Like Bourdieu, Coleman (1988) defined social capital by its functions. For Coleman (1988) social capital differs from physical and human capital. Physical capital refers to tools, machines, other equipment and human capital refers to acquired skills and knowledge. According to Coleman, social capital "is not a single entity but a variety of different entities, with two elements in common: they all consist of some aspect of social structures, and they facilitate certain actions ... within the structure" (Coleman 1988, p. S98). Like physical and human capitals, social capital is a resource available to individuals to help them attain their economic or non-economic goals. But unlike human and physical capitals, social capital exists in the relations among individuals. For example, “the social capital of the family is the relations between children and parents” (Coleman 1988, p. S110). When explicating the concept of social capital, Coleman identified three forms:

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1) obligations and expectations, 2) information channels, and 3) social norms. He illustrated how these resources can be useful in attaining goals. In order to illustrate the usefulness of this concept, Coleman demonstrated how social capital in the family and in the community could create human capital in the example of high school graduation (see Coleman 1988). Influenced by Bourdieu and Coleman, Portes applied the concept of social capital to immigration studies. Following Bourdieu's definition of social capital, Portes (1995b) argued, "density of networks within an immigrant community increases social capital" (Portes 1995b, p. 258). According to Portes (1995a), social capital refers to the "capacity of individuals to command scarce resources by virtue of their membership in networks or broader social structures" (1995a, p. 13). These resources often include favors such as low- or free- interest loans, business or employment tips, price discounts, business advice, or other favors in economic transactions and social capital refers to the individual's ability to mobilize them (Portes 1995a). Even though Coleman believed that social capital is created "when the relations among persons change in ways that facilitate action" (Coleman 1990, p. 304), he did not over-emphasize the instrumental aspect of social capital. Coleman recognized that "a given form of social capital that is valuable in facilitating certain actions may be useless or even harmful for others" (Coleman 1988, p. S98). Portes (1995a) emphasized this point: It is important not to lose sight of the fact that the same social dynamics that produce altruistic gifts and concessionary favors can also constrain individual economic pursuits. Sociability is a twoway street and the resources gained from fellow community members and social network members, although in appearance "free" do carry hidden costs (Portes 1995a, p. 14). Portes and Sensenbrenner (1993), using empirical examples from the immigration literature, explored "the different forms in which social structures can affect

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economic action" (1993, p. 1346). They discussed four types of social capital: value introjections, bounded solidarity, reciprocity and enforceable trust. The use of examples from the immigration literature is not surprising since "immigrant communities represent one of the clearest examples of the bearing contextual factors can have on individual economic action" (Portes and Sensenbrenner 1993, p. 1322). In most immigration studies, specific measures of social capital and embeddedness are lacking (Portes 1995b, p. 262). “As a number of recent authors have pointed out, there is a considerable amount of ambiguity regarding the precise definition of social capital” (see Mouw 2006, p. 1). The Iranian study defines measures of social capital in terms of the extent to which immigrants give and/or receive economic and non-economic assistance, and distinguishes whether immigrants have access to economic/noneconomic network ties and whether or not they actually utilize these resources. By assuming immigrant networks include family members and close friends, the network theory does not discuss the nature of immigrant ties. In contrast, Granovetter's (1974, 1995) "strength of tie" hypothesis stresses the structural advantage of weak over strong ties. Weak ties have access to a wide range of choices that strong ties do not have access to (Granovetter 1974, 1995). Immigrants initially rely on strong ties (e.g. family ties), but later as they settle, their pool of weak ties increases and gives them access to different types of network ties. Immigrants who rely on weak ties might access better paying and more prestigious jobs. In this study, the strength of tie hypothesis is examined by considering the tie effect during the initial phase as well as the settled stages. IRANIAN IMMIGRATION TO THE U.S. Prior to 1950, the INS official data did not identify Iranians since their numbers were negligible (Bozorgmehr and Sabagh 1988; Lorentz and Wertime 1980). The period between 1950 and before the Iranian Revolution (1950-

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1977) marks the first immigration wave. According to INS data, in the 1960’s new Iranian immigrants arrived on average 369 per year increasing to approximately 1,253 per a year and reaching 2,351 in 1977. For non-immigrants, which included students and visitors, there was a sharp increase between 1950’s and 1970’s (Bozorgmehr and Sabagh 1988). During this phase, Migration to the U.S. was triggered by Iran’s gradually recovering economy and U.S. involvement in Iran. During WWII the Iranian economy had deteriorated due to foreign occupation and internal political instability. Iran’s economic condition further suffered in the course of the oil nationalization movement of 1951-1953. The West retaliated by boycotting Iranian oil, thereby drastically reducing the oil revenues and, as a result, the supply of foreign exchange. After the Shah was restored to power in 1953, American aid and the resumption of oil revenues rescued the economy and insured the Shah’s survival in those critical years. Thus began a period of direct U.S. influence in Iran. … The Iranian government embarked, as early as the 1960’s, on an industrialization program with an emphasis on the use of modern technology. Yet, Iran lacked the higher educational institutions to train enough skilled workers. An inevitable consequence of this policy was that it served as a major incentive for many Iranian students to study abroad, preferably in advanced industrial countries such as the United States (Bozorgmehr and Sabagh 1988, p.8-9). During the 1970’s while Iran was enjoying economic boom and explosion of oil revenues, there was not much incentive for Iranians to leave Iran. On the other hand, the U.S. economy in the 1970’s was struggling.- not much of an incentive for Iranian to migrate to the U.S. During this initial phase Iranian immigration to the U.S. was temporary where most Iranians who came to the U.S. were students

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pursuing higher education with the aspirations to return home for business opportunities. The U.S. was the preferred destination, “partly because of its close ties to Iran (at the time) but also because of the availability of educational and job opportunities in American society” (Bozorgmehr 1992, p. 127). As a result of the earlier interventionist role of Britain in Iran, and subsequently the increasing presence of Americans, the English language had become the standard foreign language taught in Iranian high schools. Familiarity with English directed high school graduates towards English speaking countries in pursuit of higher education. The choice of destination was narrowed to advanced industrial countries because Iranian students were mostly interested in technical education such as engineering to prepare for the rapidly industrializing Iranian economy (Bozorgmehr 1992, p. 127-28). The Iranian Revolution (1978-79), which marks the second phase of the Iranian immigration to the U.S., completely changed the migration pattern and the socioeconomic profile of Iranian immigration to the U.S. The political climate drastically changed during this period especially during the U.S. Embassy take over and the Iran Hostage Crisis in 1979. The departure of the Shah of Iran, Mohammad Rezah Pahlavi and the establishment of the Islamic regime solidified the permanency of the new era that took place so rapidly. These massive changes created political instability and caused much fear and uncertainty for many Iranians living in Iran and abroad. The political turmoil continued with the onset of the Iran-Iraq War in 1980 killing countless numbers of soldiers and civilians in the eight-year war. This continued political unrest forced many religious minorities to immigrate to the U.S. as refugees and asylees (Bozorgmehr 2000). Before the Revolution, Iranian students emigrated for educational reasons. After the Revolution many of the earlier students stayed in the U.S. and many new

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immigrants arrived as exiles or political refugees. “These exiles were disproportionately members of religious minorities who experienced, or feared, persecution in the Islamic Republic of Iran” (Bozorgmehr 2000, p.715). Since the mid 1990’s the Iranian immigration to the U.S. has slowed down. After 9/11 the acquisition of permanent residency has become harder and much more tedious for all Middle Easterners perhaps discouraging many to migrate to the U.S. Although traveling between Iran and the U.S. is easier today, the political climate between the two countries is very tenuous and speculative. CHARACTERISTICS OF IRANIAN IMMIGRANTS The 1990 U.S. Census enumerated about 285,000 foreignborn Iranians in the U.S., the largest number of Iranians outside of Turkey and Iran (Bozorgmehr 1995). The Census indicated that Los Angeles had the largest concentration of Iranian immigrants in the U.S. In 1990, approximately 35% (100,000) of the total U.S. Iranian population resided in the Los Angeles region, which includes the five counties (LA, San Bernandino, Ventura, Riverside and Orange). The Iranian population (including new immigrants and non-immigrants) in the Los Angeles region increased substantially between 1970 and 1990. During the 1970-80 period, the Iranian population grew six-fold and during the 1980-90 period it doubled (Bozorgmehr, Der-Martirosian and Sabagh 1996). The comparable number enumerated by the 2000 U.S. Census using the 5% PUMS data was about 338,000 Iran born and those of Iranian ancestry born elsewhere. This figure indicates less than a 5% increase since 1990. According to the 2000 U.S. Census, over half of Iranian immigrants (55%) lived in California, with Los Angeles still having the largest concentration of Iranian immigrants in California and the U.S. (see Bozorgmehr 2007). Iranian immigrants are one of the most distinctive groups in the U.S. They "constitute one of the most numerous new immigrant groups from the Middle East, and one of the highest status foreign-born groups in the United

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States" (Bozorgmehr and Sabagh 1988, p. 5). Many Iranian immigrants are highly educated and hold professional specialty and managerial positions (Bozorgmehr, DerMartirosian and Sabagh 1996; Bozorgmehr and Sabagh 1988). In 1980, the educational achievement of Iranians in the U.S. "(was) not only substantially higher than the educational level in urban Iran in 1976, but (was) also higher than that of natives and recently arrived immigrants in the United States" (Bozorgmehr and Sabagh 1988, p. 34). According to the 1990 U.S. Census, compared to other immigrant groups, "among males, Iranians (had) a much higher level of education than any group (65% with four or more years of college)" (Bozorgmehr, Der-Martirosian and Sabagh 1996 p.357) The Census also indicated that more than half of Iranian males held managerial and professional specialty positions (Bozorgmehr, Der-Martirosian and Sabagh 1996). In addition to their high socioeconomic status, Iranian immigrants are highly entrepreneurial (Light, Sabagh, Bozorgmehr and Der-Martirosian 1993; Light, Sabagh, Bozorgmehr and Der-Martirosian 1994), an important indicator of economic self-reliance and a common means for upward mobility. Iranians are not unique since other immigrant groups like Taiwanese, Asian Indians, and Israelis, have high status occupations (see Cheng and Yang 1996). Also, other immigrant groups, like Cubans, Koreans and Chinese, have high self-employment rates (Light and Bonacich 1988; Portes and Bach 1985; Cheng and Yang 1996; Min 1993). But what is distinctive about Iranians is the overlap between these two modes of economic incorporation: Iranian immigrants are professionals and high skilled entrepreneurs (see Bozorgmehr, DerMartirosian and Sabagh 1996). According to Portes and Rumbaut (1990) professional immigrants, compared to non-professionals, rely less on their networks and more on their skills and qualifications. Since Iranian immigrants are high skilled professionals and entrepreneurs, it is even more important to investigate the extent to which Iranian immigrants rely on co-ethnic networks ties for economic advancement.

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Iranian Immigrants in Los Angeles

Another distinctive characteristic of Iranian immigrants is the disproportionate over representation of ethnoreligious minorities, such as Bahais, Armenians, and Jews within this population (Bozorgmehr, Sabagh and DerMartirosian 1993; Bozorgmehr 1992). The Iranian ethnic economy is organized along these subgroups. "The Iranians in Los Angeles operated four distinctive ethnic economies, not one" (Light, Sabagh, Bozorgmehr and Der-Martirosian 1993). Light, Sabagh, Bozorgmehr and Der-Martirosian (1993) called this "internal ethnicity" in the ethnic economy, in which "each ethno-religious subgroup had its own ethnic economy, and these separate economies were only weakly tied to an encompassing Iranian ethnic economy" (Light, Sabagh, Bozorgmehr and DerMartirosian 1993, p. 581). Before the Islamic Revolution, approximately 2% of Iran's population constituted various minority groups (Bozorgmehr 1992), whereas in Los Angeles Iranian ethnoreligious groups comprise a much larger proportion of the total Iranian population (Light, Sabagh, Bozorgmehr and Der-Martirosian 1993). Muslims, Armenians, Jews and Bahais are the four largest Iranian subgroups in Los Angeles (Light, Sabagh, Bozorgmehr and Der-Martirosian 1993; Bozorgmehr 1992). In Iran only Jews and Armenians are officially recognized as minority groups (Bozorgmehr 1992). In addition to their minority status, these two groups have a long entrepreneurial tradition and have occupied middleman minority positions in Iran (Bozorgmehr 1992). The high self-employment rate among Iranians in Los Angeles is partly due to the presence of these minority groups. In addition to their entrepreneurial skills, these groups have had a long history of established communities in Iran. Once they arrived in the U.S., they developed an economic advantage over the Muslim majority since they could tap into an established community ties and networks. Furthermore, Armenian and Jewish Iranians in the U.S. are considered twice migrants or minorities, whereas Muslim Iranians are first-time migrants in the U.S. (see Espiritu 1989, and Bhachu 1993, for detailed discussions on twice migrants).

Economic Capital, Human Capital, or Social Capital

15

Survey studies of Iranian immigrants in the U.S. are rare. The 1987-88 Iranian survey is one of the few that provides detailed pre- and post- migration data. In addition to Muslim Iranians, data on economic and social characteristics were collected from three other Iranian ethno-religious groups: Jews, Bahais and Armenians. The next chapter describes in detail the study design and the sample characteristics of the 1987-88 Iranian survey.

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CHAPTER 2

The Iranian Survey

STUDY DESIGN This chapter describes the study design for the 1987-88 probability sample survey of Iranian immigrant heads of households, and discusses the study variables and measures used in subsequent chapters. This chapter also compares the study sample characteristics with the 1990 PUMS (Public Use Micro data Sample) U.S. Census to illustrate the representativeness of the survey sample. Since the U.S. Census does not collect data on religion, Jewish, Bahai and Muslim Iranian subgroups could not be identified. However, using the 1990 U.S. Census ancestry data, this chapter presents census data on Armenian Iranians and compares the socioeconomic characteristics between the two data sources. According to the 1990 PUMS data, Armenian Iranians – a Christian minority group - comprised 25% of the total Iranian immigrant population in the U.S. This is a large percentage since all ethno-religious minority groups constituted only 2% of the Iranian population in Iran before the Islamic Revolution (Bozorgmehr 1992). To ensure that a minimum number of respondents from each of the three ethno-religious minority subgroups were included in the study, and address the problem of unlisted telephone numbers, Jewish, Bahai and Armenian Iranian community 17

18

Iranian Immigrants in Los Angeles

lists were used to supplement the main sampling frame of eight LA County white-page telephone directories. From each telephone directory, 200 pages were randomly selected after a computer-generated list of random numbers had scrambled the numbers of the telephone directory pages. Persian-sounding names were identified in each selected page for all eight telephone directories. The vast majority of Persian surnames are distinctive and given the recency of their arrival in 1987, there were few surname changes in this population (for detailed description of the study design, see Bozorgmehr 1992). Only two telephone directories (Northwestern and Western) – which encompassed the highest concentration of Armenian Iranians and the lowest concentration of other Armenians - were used to identify Armenian sounding names. Since Armenian Iranian surnames are indistinguishable from other Armenian names (such as Lebanese Armenians and Armenians from Armenia) all Armenians names were extracted first and later screened by telephone. This method yielded 1,714 Persian names from the eight telephone directories and 1,059 Armenian names from the two directories. To exclude non-Iranian names, a separate list of uncertain names was created and this list was checked against a 1979 Immigration and Naturalization Service master list of 43,271 Iranian surnames. Given the low numbers of uncertain names, the list was later omitted from the final sampling frame. From the Armenian and Jewish community lists 500 names were selected (250 each) and 150 from the Bahai list. Prior to selecting the names, duplicates were dropped from the community lists. A total of 390 non-Armenian Iranians and 126 Armenian Iranians were interviewed from telephone lists. From the community lists, 69 Armenians, 45 Bahais and 39 Jewish Iranians were interviewed. The total sample of 671 Iranian householders includes 2 additional Muslim Iranians from the community lists. A total of 671 Iranian householders - 556 males and 115 females - were interviewed from August 1987 through March 1988 with a 30% refusal rate. Approximately 77%

The Iranian Survey

19

of the respondents' names were derived from the telephone directories and the remaining from Armenian, Jewish and Bahai community lists. Persian and Armenian speaking interviewers conducted in-person interviews, with 201 Muslim, 195 Armenian, 188 Jewish and 87 Bahai householders (Bozorgmehr and Sabagh 1989). SURVEY QUESTIONNAIRE Containing 172 items, the survey questionnaire was first developed in English and later translated into Persian and Armenian. Most Muslim, Jewish and Bahai respondents preferred to be interviewed in Persian rather than English. Among Armenian Iranians, the majority preferred Armenian rather than Persian or English. On average, the in-person interviews took one and a half hours. During these in-depth, structured interviews, Iranian heads of households answered questions on pre-migration characteristics, migration experience, and post-migration social and economic activities. In addition to answering questions regarding their own experiences, respondents answered questions regarding the household structure, expenses and income. Respondents were specifically instructed to answer more detailed questions regarding their own experiences and if married their spouses' employment status in Iran and the U.S., ethnicity of spouse, ethnicity of spouse's close friends, current residence of spouse's close and distant relatives, and spouse's social origins (i.e., parents' educational and occupational background). Overall, the survey generated over 940 variables and not all variables were used for this manuscript. The Iranian data set contains abundant information that includes both individual and household variables. Appendices A through G list brief description of the variables used in this study. Each Appendix is organized according to a major topic (e.g., Household Roster or Migration Experience). Appendix H lists the questions that were asked in the survey. The variable numbers listed in Appendices A thru G can be matched to the question

20

Iranian Immigrants in Los Angeles

numbers listed in Appendix H for the exact wording of the survey questions. Appendix A lists the household roster that provides an overview of the demographic and socioeconomic profiles of all household members. The household head reported the employment status, educational background, and demographic characteristics of all household members. Appendix B lists migration variables, such as year of arrival, whether they arrived with relatives and/or friends, whether relatives and/or friends were living in Los Angeles at the time of arrival, sources of information before arriving to the U.S., and type of help received from relatives and friends at the time of arrival. In addition to these variables, respondents were also asked whether they received any organizational help when migrating to the U.S. They were also asked to indicate the visa status under which they entered the U.S., and whether or not they were permanent residents or citizens. Regarding employment status, respondents were asked to indicate whether their first job in the U.S. differed from their current job. If so, they reported the characteristics of the first job, such as type of work, industry and class of worker. During the interviews, respondents were asked to specify whether they had received any help to locate their first job (see Appendix C). For current employment, detailed questions were asked regarding the year they started working, type of work, industry and whether selfemployed or wage/salary (see Appendix D). If selfemployed, separate sets of questions were asked about the reasons for going into self-employment, whether incorporated or unincorporated, ethnicity of partners, employees and clients, and number of paid employees. For wage and salary workers, respondents were asked to report the ethnicity of supervisors and co-workers. For current employment, the network data are more detailed and extensive. Respondents were asked to indicate whether they gave and/or received help on a list of economic and non-economic items. They were also asked to rank the order of who helped them or whom they helped on these lists of items (see Appendix E).

The Iranian Survey

21

In addition to first and current employment experiences in the U.S., respondents were also asked to indicate whether they were employed full-time before migrating to the U.S. If so, they indicated the type of industry, occupation, and class of worker before leaving Iran. The survey asked respondents to indicate whether their first full-time job in Iran was the same as their last job before leaving Iran (see Appendix F). For the household head, the survey questionnaire asked employment information for four points in time: first job in Iran, last job in Iran, first and current jobs in the U.S. For the spouse, the survey included employment information for two points in time: last job in Iran and current job in the U.S. The survey also gathered a limited amount of economic network data about the spouse (see Appendix G). SURVEY VS. CENSUS To assess the representativeness of the Iranian survey sample, the socioeconomic characteristics of the Iranian sample was compared to the 1990 U.S. Census PUMS 5% sample. Since the Iranian survey was conducted in 198788, the 1990 census data was the obvious choice for comparison. To create a comparable match, Iran-born householders and spouses who had migrated before 198788 were selected from the 1990 census data. For Iranian men and women, there were only minor differences between the two samples when compared on level of education, employment status, and occupational and industrial distributions (see Tables 2.1 and 2.2). The two data sources differed in terms of self-employment rate. The self-employment rate for Iranian men was 59%; a figure, which is much higher than the 38%, reported in the 1990 Census data (Table 2.1). The self-employment rate for women was 26%, which is also higher than the 17% rate reported in the Census (Table 2.2). Nevertheless, the overall comparison indicated that the survey sample was representative of Iran-born householders and spouses living in Los Angeles who migrated before 1987.

22

Iranian Immigrants in Los Angeles

Self-Employment Rates Design effect may be one possible reason for the discrepancy between the two self-employment rates. The 5% Census sample is based on a larger geographical base, and therefore, relies less on ethnically clustered areas. In contrast, the Iranian survey design targeted a specific immigrant group and included community lists, which may have inflated the number of Iranians living in ethnically clustered areas. Since immigrant entrepreneurship tends to occur near ethnically clustered neighborhoods, the Iranian survey sample might be over-represented in terms of selfemployment. Another possible explanation might be the oversampling of the minority groups especially the Jews since they have the highest rate of self-employment. According to the U.S. Census, 25% of all Iranians were of Armenian descent. Since Bahais, Jews and Muslims cannot be identified in the Census, there are no official data verifying their numbers. Hence, the determination of the sample size was based on this 25% census figure and the goal was to complete 200 interviews for each subgroup except for Bahais since they were the smallest Iranian subgroup in Los Angeles. These explanations are both plausible, but it is still puzzling that almost every other characteristic yields similar results in both the survey and the census samples. This brings us to the next possible explanation. The study questionnaire was constructed to include detailed questions regarding entrepreneurship, which might have prompted respondents to explain their employment situation in much greater detail. In fact, the questionnaire contained separate sets of questions for self-employed and wage/salary workers. If respondents indicated that they were currently self-employed, they answered thirteen additional questions regarding their business. If they indicated that they were wage/salary workers, they answered five other questions regarding their employment experience. Similarly, respondents answered different sets of questions depending upon whether they were self-employed in Iran.

The Iranian Survey

23

Unlike the survey questionnaire, the census identifies self-employed using limited amount of information - class of worker and self-employment income variables (Light and Rosenstein 1995). The census possibly underestimates the actual self-employment rate since it is not designed to gather detailed self-employment data. Another explanation for the census underestimation of self-employment is offered by Min (Forthcoming) where he explains, immigrants are more likely than natives to hold two jobs and “when a person has two jobs, one through employment and the other through self-employment, he/she may not report the self-employed job” in the census (Min Forthcoming, Ch. 4). The first two explanations suggest that the Iranian survey may have overestimated the self-employment rate, whereas the second explanation suggests that the census may have underestimated the Iranian self-employment rate. A combination of these explanations probably explains the discrepancy between the survey and census selfemployment rates. Armenian Iranians in the U.S. Census Since only one subgroup - Armenian Iranians - could be identified from the U.S. Census, Tables 2.3 and 2.4 compare survey and census data on Armenian-Iranian men and women. Once again, the percent self-employed was higher for the survey sample when compared to the census (45% versus 36%, see Table 2.3). This discrepancy was smaller for Armenian Iranians when compared to the overall sample (59% versus 38%, see Table 2.1). In addition to the over-representation of the self-employed, Armenian-Iranian male professionals were also overrepresented in the survey, while those in technical or sales occupations are under-represented (Table 2.3). For Armenian-Iranian women, the percents for each of the characteristics (presented in Table 2.4) were almost identical, except for the occupational distribution. The Armenian-Iranian female sample was slightly overrepresented in managerial/executive occupations and

24

Iranian Immigrants in Los Angeles

under-represented in service occupations. In addition to having a representative Iranian sample, these data illustrated that at the subgroup level, the Armenian-Iranian sub-sample was also representative of the ArmenianIranian population in Los Angeles County. MEN OR WOMEN Given the budgetary restrictions, interviewing both heads and spouses from each household would have cut the survey sample size in half. Instead, the Iranian survey interviewed household heads where the respondent, just as in the U.S. Census, was the informant. If respondents answered the same number of questions for themselves as well as for their spouses, the questionnaire would have been twice as long. With 172 questions, the survey questionnaire was already long. Expanding it any further would have discouraged some respondents from participating in the study. Instead, if married, respondents were asked to answer a limited number of questions regarding the social and economic activities of their spouses. Among the 556 male householders, over three-fourths (n=462) were married at the time of the interview. And among the 115 female householders less than half (n=50) were married. Accordingly, the survey sample included 606 males (556 male heads of households and 50 male spouses), and 577 females (462 female spouses and 115 female heads of households). On the surface, given the equal number of males and females, it would seem reasonable to conduct comparable sets of analyses for both genders. Unfortunately, since only limited amount of spouse information was collected during the Iranian study, comparable analyses cannot be conducted for both household heads and spouses. Moreover, since the number of employed Iranian women heads of household was rather small (n=66), separate multivariate statistical analysis for females would be meaningless. Therefore in subsequent chapters, the data analyses are based on 556 Iranian male heads of households.

The Iranian Survey

25

STUDY VARIABLES AND MEASURES Dependent Variables In this study, various statistical methods are used to examine the effects of network ties on the economic integration of Iranian immigrant men living in Los Angeles. Each chapter has a different dependent variable For the purposes of this study, four different measures of economic integration are used. In chapter 3, the dependent variable is length of time spent before starting the first job. In most studies, this variable is measured in number of days, weeks or months spent looking for employment. In the case of the Iranian study, such data are lacking. Survey respondents were asked to indicate in what year they started working. The time elapsed is constructed by subtracting year started working from year of arrival to the U.S. In chapter 4, the dependent variable is shift in occupational status. For the Iranian survey, occupational and industrial data was coded using the 1980 U.S. Census occupational and industrial three-digit codes. The most serious problem in using the census-coding scheme was that self-employed and managers of self-owned businesses received a code of 243 or 019, respectively. "Unfortunately, the (1980 and 1990) U.S. Census occupational coding procedure does not discriminate between the different types of self-employment. For example, a grocery store owner and a major importer and exporter both receive the same code of 243” (Bozorgmehr 1992, p. 66). As such, when examining occupational status shifts between Iran and the U.S., the self-employed were excluded from the analysis since including the selfemployed would have inflated the "no-status-change" category. The dependent variable for chapter 5 is income. When developing the Iranian questionnaire, the study researchers were aware of the cultural norms with regards to selfdisclosure of income/earnings. In the 1987-88 Iranian survey, the individual earnings question was left out

26

Iranian Immigrants in Los Angeles

fearing most respondents would refuse to answer this question. Instead respondents were asked to report their household income. In addition to this question, they were also asked to indicate the source of their household income. For the purposes of this study, the individual income variable was constructed by selecting male respondents who had marked head of household's income as primary source of household income. The dependent variable for chapter 6 is class of worker - self-employed versus wage/salary worker. This dependent variable was re-coded using the closed-ended question: "Are you: (1) working for a salary, wage, or commission for a private company, or (2) working for the federal, state, or local government, or (3) working in own business or profession which is: proprietorship not incorporated, or (4) partnership not incorporated or (5) corporation. Response categories 3, 4 and 5 were collapsed into selfemployed, and responses 1 and 2 were collapsed into wage/salary workers. Independent Variables The independent variables vary between chapters. Since chapters 3 and 4 focus on the initial settlement phase, the independent variables capture the effects of pre-migration characteristics. In these chapters, education is the highestlevel completed in Iran, knowledge of English is upon arrival, and work experience is in Iran. In chapters 5 and 6, the independent variables capture the effects of postmigration experiences after a few years of U.S. residence. The corresponding independent variables for chapters 5 and 6 are: highest level of education completed (anywhere), knowledge of English at the time of the interview, and years of work experience in the U.S. Except for age, all other control variables: marital status, period of immigration, and subgroup (Armenian, Bahai, Jewish and Muslim), remain the same in all four chapters. In chapters 3 and 4, age at immigration is used in the analysis, whereas

The Iranian Survey

27

in chapters 5 and 6, age at the time of the interview was entered in the analysis. The network variables vary between the two sets of chapters. In chapters 3 and 4, two network variables are included in the analyses. The first variable is the "general" economic help variable. Here respondents were asked to indicate the types of help that they received upon arrival economic and non-economic types of help. Economic items included: loan of money, finding a job, and identifying a business opportunity. Non-economic items included: free housing and meals, paid housing and meals, locating a place to live, and transportation. In addition to these variables, a "specific" network variable was also constructed where respondents answered whether they received any help finding the first job in the U.S., and specified who helped them to get this job. For chapters 5 and 6, two different network variables were constructed based on a list of economic and noneconomic items. Iranian respondents were asked to indicate whether they gave or received help on a list of items. Noneconomic items included: child or health care, housework, match-making, personal counseling, club or organizational referrals, moving a residence, transportation, meals, shopping, food, medical or dental referrals, consumer advice, and providing free housing. Economic items included: finding a job or business, referrals and references, professional information and advice, transportation to work, loans, discounts, legal, tax, or business advice, translation, employee or customer relations, free labor, goods or equipment. Respondents were also asked to indicate their relationship to people who helped them and whom they helped on these lists of economic and noneconomic items. The next chapter starts the data analysis for the first economic measure – timing of first job in the U.S.

Table 2.1 - Socioeconomic Characteristics of Iranian Males, LA County 1987-88 Survey

1990 PUMS

BA/BS degree or higher Employed Self-employed

66% 89% 59%

59% 84% 38%

Occupational Distribution: Managerial & executives Professional specialty Technical, sales & admin support Service Precision/crafts Operatives

18% 28% 40% 2% 8% 3%

22% 24% 32% 2% 13% 7%

Industrial Distribution: Construction Manufacturing Transportation/Communications Wholesale & retail trade Finance, insurance & real estate Business repair Professional services Agriculture, mining & public administration N

11% 17% 5% 27% 7% 13% 17% 3%

10% 15% 5% 29% 9% 10% 18% 3%

607

19,079

Data Sources: The 1987-88 Iranian Survey in Los Angeles, and U.S. Census Public Use Micro Data 5% Sample (PUMS), 1990

28

Table 2.2 - Socioeconomic Characteristics of Iranian Females, LA County 1987-88 Survey

1990 PUMS

BA/BS degree or higher Employed Self-employed

32% 39% 26%

32% 47% 17%

Occupational Distribution: Managerial & executives Professional specialty Technical, sales & admin support Service Crafts/crafts Operatives

12% 20% 49% 10% 5% 4%

17% 19% 44% 13% 4% 3%

Industrial Distribution: Manufacturing Transportation/Communications Wholesale & retail trade Finance, insurance & real estate Business repair Professional services Agriculture, mining & public administration N

8% 3% 25% 19% 17% 26% 2%

7% 2% 26% 18% 15% 27% 5%

577

14,301

Data Sources: see Table 2.1

29

Table 2.3 - Socioeconomic Characteristics of Armenian Iranian Males, LA County 1987-88 Survey

1990 PUMS

BA/BS degree or higher Employed Self-employed

35% 74% 45%

36% 77% 36%

Occupational Distribution: Managerial & executives Professional specialty Technical, sales & admin support Service Crafts/crafts Operatives

25% 21% 21% 3% 22% 8%

23% 13% 27% 3% 25% 9%

Industrial Distribution: Construction Manufacturing Transportation/Communications Wholesale & retail trade Finance, insurance & real estate Business repair Professional services Agriculture, mining & public administration N

13% 19% 5% 19% 8% 17% 13% 6%

17% 15% 5% 23% 9% 12% 15% 4%

165

3,851

Data Sources: see Table 2.1

30

Table 2.4 - Socioeconomic Characteristics of Armenian Iranian Females, LA County 1987-88 Survey

1990 PUMS

BA/BS degree or higher Employed Self-employed

20% 41% 14%

21% 42% 15%

Occupational Distribution: Managerial & executives Professional specialty Technical, sales & admin support Service Crafts/crafts Operatives

19% 11% 50% 8% 4% 8%

9% 15% 54% 14% 5% 3%

Industrial Distribution: Manufacturing Transportation/Communications Wholesale & retail trade Finance, insurance & real estate Business repair Professional services Agriculture, mining & public administration N

11% 2% 19% 32% 18% 15% 3%

6% 2% 20% 26% 14% 25% 7%

182

3,641

Data Sources: see Table 2.1

31

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CHAPTER 3

Timing of First Job

HUMAN CAPITAL To assess the economic attainment levels of immigrants, neoclassical economists often use cross-sectional U.S. Census data, mostly comparing the economic achievement of immigrants with that of the native-born for different immigration cohorts (see Borjas 1987, 1989; Chiswick 1978). They argue that socio-demographic variables such as education, labor market experience, age, marital status, and years since migration, are important predictors of how well immigrants do economically (Chiswick 1982, 1978; Borjas 1987). The returns on human capital characteristics for the native-born, as well as for the foreign-born, indicate that even though immigrants initially earn less than the native-born, after several years most immigrants' earnings equal, and in some cases, even exceed that of the nativeborn (Chiswick 1978). This perspective explains immigrants' labor market experience in terms of individual human capital. Since the 1970’s, sociological and historical studies have challenged this individualistic approach (for review see Portes and Bach 1985; also see Portes 1995). Factors beyond individual skills influence achievements of immigrant group members (Morawska 1990). These factors vary from structural determinants such as time, location, economic, and political environment to collectivist determinants which are socially embedded as opposed to individualistic strategies (Morawska 1990). 33

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Iranian Immigrants in Los Angeles

Recent writings in economic sociology and immigration studies emphasize that immigrant networks significantly shape immigrants' economic experience (Mouw 2006; Giorgas 2000; Portes 1998; Portes 1995; Boyd 1989; Portes and Sensennbrenner 1993; Massey and Espinosa 1996). In explaining how immigrants integrate into the U.S. labor market, one needs to consider individual as well as social determinants. In this chapter, in addition to human capital explanatory variables such as education, premigration labor market experience and English proficiency, network variables will be included measuring the extent to which Iranian immigrants mobilized immigrant network ties and received economic help. INITIAL SETTLEMENT Most quantitative studies assessing U.S. immigrants' economic behavior use census data. Even with human capital variables, such as education, the census contains limited information. It is almost impossible to measure the effects of pre-migration characteristics, even though in 1978, Chiswick indirectly estimated pre- and postmigration years of schooling and illustrated the significant effect of each variable on immigrants' earnings. Nevertheless, the census data can only be stretched so far. In addition to these shortcomings, the census does not allow one to examine immigrants' initial economic experience. One can only analyze this experience by collecting data on immigrants' first employment immediately after arriving to the U.S. Such data are not available in the census. Finally, using the census, one cannot ascertain how quickly immigrants find employment. When using the census data, one extracts the most commonly measured dependent variables - income and employment status. Time spent finding the first job is an economic outcome measure not often used but an important one to consider. Immigrants' initial economic experience in the new labor market can be better understood if one examines how quickly immigrants find employment. Immigrants' labor

Timing of First Job

35

market experiences are more favorable when they encounter shorter unemployment. By using the Iranian data set, and by examining whether Iranian immigrants found employment within first year of arrival, this chapter evaluates Iranian immigrants' labor market experience upon their arrival to the U.S. IMMIGRANT NETWORKS Every aspect of the migration process, including the decision to migrate or to stay, determination of the place of destination, and the adjustment process, are all influenced by kinship and co-ethnic networks in which immigrants participate (Light, Bhachu and Karageorgis 1993; Massey 1988; Morawska 1990; Hugo 1981; Boyd 1989). The role of immigrant networks as a determining factor promoting international migration has been studied extensively (see Massey, Alarcon, Durand and Gonzalez 1987; Mines 1984; Reichert and Massey 1979; White 1970). Terms such as "auspices of migration" - introduced by Tilly and Brown (1967) - and "migration chains" - introduced by McDonald and McDonald (1964) - all refer to the network ties established between sending and receiving communities facilitating the migration process. For Tilly and Brown (1967) auspices of migration were the "social structures, which establish relationships between the migrant and the receiving country before he moves," (1967, p. 142). Auspices of migration can range from kinship to such types as work related or organizationally arranged. McDonald and McDonald (1964) discussed two extreme possibilities: chain migration and impersonally organized migration. Chain migration was defined as "that movement in which prospective migrants learn of opportunities, are provided with transportation, and have initial accommodation and employment arranged by means of primary social relationships with previous migrants" (McDonald and McDonald 1964, p. 82). Impersonally organized migration was "conceived as movement based on impersonal recruitment and assistance" (McDonald and McDonald

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Iranian Immigrants in Los Angeles

1964, p. 82). Although recent writings no longer use terms such as "auspices of migration" and "chain migration", most immigration studies still place great emphasis on family, kinship, and friendship-based network ties (Portes 1998; Boyd 1989; Light, Bhachu and Karageorgis 1993; Portes 1995; Massey and Espinosa 1996). Migration networks are "sets of interpersonal ties that link migrants, former migrants, non-migrants in origin and destination areas through the bonds of kinship, friendship, and shared community origin" (Massey 1988, p. 396). During the initial phase, "international migration may originate in structural changes within sending and receiving societies, however, ... once begun, this migration eventually develops a social infrastructure that enables movement on a mass basis" (Massey 1988, p. 396). In 1990, Massey labeled this process "the cumulative causation of migration." Once set in motion, migrations create networks, which produce a self-sustaining process of promoting more migration. Borrowing from even more recent theoretical developments, Massey and Espinosa (1996) wrote, Social capital theory, posits a direct connection between networks and the costs and benefits of migration, and it emphasizes the non-recursive nature of relationship between international movement and network formation. Non-migrants are hypothesized to draw on the social capital embedded in ties to migrants to lower their costs and risks of movement and raise their benefits of U.S. employment (Massey and Espinosa 1996, p. 14). Studies of immigrants' integration have also documented the significant role networks play during the adjustment period. "Settlement and integration processes are influenced by kin and friendship ties" (Boyd 1989, p. 651). Personal ties provide wide range of assistance, making it easier for immigrants to find housing, employment, protection and companionship (Light, Bhachu and Karageorgis 1993; Lin and Dumin 1986; Massey, Alarcon, Durand and Gonzalez 1987; Boyd 1989;

Timing of First Job

37

Cornelius 1982). Immigrant networks facilitate the process of adjusting to a new way of life, and as they grow, these networks increase their efficiency, "thus maximally facilitating the introduction of new immigrant newcomers into them" (Light, Bhachu and Karageorgis 1993, p. 28). In reviewing this network literature, one should note that even when immigrants arrive with family members and/or friends, there are no guarantees that they will tap into these kinship or co-ethnic ties and receive economic help. Immigrants who arrive under the auspices of kinship have access to immigrant networks, but this does not mean that they will mobilize these resources. One way to conceptualize this is to consider the auspices of kinship as social structures that help generate social capital embedded in personal ties. Whether immigrants actually mobilize these social resources is a separate but an interrelated concept. Following this argument, the first hypothesis states: Network Hypothesis: Immigrants who mobilize economic network ties upon arrival find employment faster. In order to test this hypothesis, it is important to distinguish between whether immigrants arrive alone or with relatives and/or friends, and whether immigrants actually receive help from family members and/or coethnic friends. The first item refers to auspices of migration and the second refers to immigrant's ability to mobilize network ties and tap into these resources. To test this hypothesis, it is also important to further distinguish the types of help received from family and friends. Immigrants can obtain all types of help, ranging from finding employment to receiving free housing. The hypothesis specifically states economic network ties, which refers to the extent to which Iranian immigrants received specific economic assistance, ranging from locating a job to obtaining a business loan or advice. In the 1987-88 Iranian survey study, respondents were asked to indicate the types of help that they received when they arrived to the U.S. The types include: free housing and

38

Iranian Immigrants in Los Angeles

meals, paid housing and meals, locating a place to live, transportation, loan of money, finding a job, and identifying a business opportunity. The last three items are components of the economic help variable and the rest of the items are included under the non-economic help heading. In fact, among the male sample, 12% indicated receiving economic help whereas half of the sample (51%) indicated receiving non-economic help. Thus, it is important to distinguish between these two concepts since non-economic network ties might not have a direct effect on economic outcomes. FIRST JOB IN THE U.S. Economic integration is best captured by longitudinal data. When lacking longitudinal data, researchers can determine economic integration through personal accounts of immigrants reporting their pre- and post- migration experiences. In the Iranian study, respondents were asked to report their job histories for four points in time: first job and last job in Iran, first and current job in the U.S. This chapter examines the determinants of time elapsed before finding the first job in the U.S. The next chapter examines the employment transition between Iran and U.S. by considering the shifts in occupational prestige scores between last occupation in Iran and first occupation in the U.S. Earnings, the most commonly used measure of economic success, capture only one aspect of immigrants' economic integration. By merely measuring immigrants' earnings at one point in time, one cannot possibly understand immigrants' economic experience. Instead, in this chapter, a proxy measuring the amount of time Iranian immigrants spent finding the first job is used as the dependent variable to better understand Iranian immigrants' initial labor market experience immediately after arriving in the U.S. Time elapsed before finding the first job is a measure not often used, but it is an important outcome measure to consider since the less time immigrants spend

Timing of First Job

39

looking for employment, the better economic situation they establish. In most studies, this variable is measured in terms of the number of days, weeks, or months that immigrants spend looking for their first job. In this study, Iranian respondents were asked to indicate the year they started working and the year they began to continuously live in Los Angeles. Since respondents were not asked to report the actual length of time they spent looking for employment, the year of arrival was subtracted from the year they started their first job in the U.S. This is used as a proxy measure of whether Iranian immigrants found employment during the first year of arrival or after one year. Over half (54%) of male householders found jobs within the first year of their arrival. The dependent variable was dichotomized (1=within first year, 2=after one year). DETERMINANTS OF FINDING THE FIRST JOB Education Within the human capital explanation (see Chiswick 1978; Borjas 1983, 1987), education has been singled out as the most important determinant of economic success. Higher levels of education are related to higher labor force participation, higher earnings and higher occupational status (Chiswick 1978; Borjas 1987; Blau and Duncan 1967; Featherman and Hauser 1978). For the purposes of this chapter, highest level of education completed in Iran is included in the analysis since human capital resources play a critical role in explaining immigrants' economic integration. Among the male sample, 22% did not complete high school, 48% completed high school, and 30% received bachelor's degree or higher in Iran (Table 3.1). In addition to this variable, respondents were also asked to indicate whether they received any additional schooling outside of Iran. Almost two thirds of the male sample received additional schooling outside of Iran (Table 3.1). Due to the availability of data, both the highest level of education (in Iran) and additional years of schooling (outside of Iran) are

40

Iranian Immigrants in Los Angeles

included in the analysis. This method captures the effects of pre- and post- migration human capital characteristics. It should be noted that the hypothesized positive relationship between education and employment opportunities during the initial settlement period does not apply to highly specialized professional immigrants (M.A. degree or higher) who may have needed to take recertification programs to qualify to practice their specialization. The re-certification process might prolong the length of time to find the first job in the U.S. English Proficiency Another important human capital characteristic is English proficiency. "Language proficiency has long been viewed as one of the most important resources immigrants need in the new society" (Raijman and Semyonov 1995, p. 377; see Borjas 1983). As many researchers have noted, English proficiency allows immigrants to transfer their labor market skills into the U.S. economy. The ability to speak the native language increases employment opportunities and enhances the likelihood of upward mobility. This chapter includes knowledge of English at the time of arrival as an explanatory variable. Male Iranian householders were asked to report how well they spoke English when they arrived in the U.S. Four response categories were included: "very well," "well," "not well," and “not at all.” Due to the small cell sizes, the response categories were collapsed into “very well/well” and “not well/not at all” – dichotomizing the English proficiency variable. Among male Iranians, almost 40% reported "very well/well" and 60% reported "not well/not at all” knowledge of English at the time of arrival (Table 3.1). Job Experience Labor market experience in country of origin is another important pre-migration determinant. Prior labor market experience increases the likelihood of employment in the U.S., and accelerates the process of finding employment. During the interviews, respondents were asked to indicate

Timing of First Job

41

whether they had been employed before leaving Iran. If employed, they also indicated the type of occupation, industry and class of worker. In Iran, 27% of Iranian men were not in the labor force, while the rest were employed full-time (Table 3.1). The majority of Iranians not in the labor force were students who had not started working fulltime. Immigrant Cohort Effect In addition to the human capital variables discussed above, immigrant cohort effect is another important explanatory variable that needs to be considered (Chiswick 1978; Borjas 1989). Borjas explained the inference by most human capital theorists that most immigrant groups do quite well, and are positively selected is completely erroneous (Borjas 1989, p. 474). "The bias is due to the well known problem that a single cross-section regression cannot differentiate between aging and cohort effects" (Borjas 1989, p. 474). Cohort effects capture productivity (or ability) differences across different immigrant cohorts. In other words, the positive correlation between earnings and years-since-migration documented by cross-section regressions may arise either because immigrants do experience higher earning growth than comparable natives, or because more recent immigrant cohorts have lower productivities (or are more likely to be negatively selected) than immigrants from earlier waves (Borjas 1989, p. 474). In the case of Iranians immigrants, three distinct immigration cohorts can be identified: 1947-77, 1978-79, and 1980-87. Among the male sample, almost 30% arrived before 1978, 35% between 1978-79 and the rest (35%) after 1980 before 1987 (Table 3.1). The earliest cohort was mostly comprised of students who left Iran for higher education. The market experience of U.S.-educated Iranians might differ from later cohorts, most of whom

42

Iranian Immigrants in Los Angeles

were political asylees/refugees escaping the political unrest caused by the Iranian Revolution. In some ways, later cohort’s (1980 to 1987) characteristics are similar to the 1978-79 wave. But in other ways, the two recent cohorts differ to the extent that the 1978-79 wave might comprise more economically secure immigrants who could afford to leave immediately after the Revolution began. On one hand, following the human capital argument, one would expect the most recent cohort, which is comprised of more refugees/asylees, to experience more economic hardship than previous groups. On the other hand, even though migrants may be disadvantaged economically, as Boyd explained, they also "enter an area with many more relatives, friends and contacts than did earlier migrants" (Boyd 1983, p. 652). The immigrant cohort effect is included in the analysis. Visa Status Upon Arrival In addition to human capital skills and immigrant cohort effects, Chiswick (1979) argued: It is useful to know whether the criteria for admitting immigrants into the United States are relevant for understanding their economic progress and impact. Immigration visas may be rationed on the basis of the person’s likely productivity in the country, whether the person has relatives in the country, or for humanitarian reasons, such as refugee relief (Chiswick 1979, p. 362). The U.S. Census data, "the principal source of information on the foreign born in the United States, do not provide any information on visa status at entry or at the time of the Census" (Jasso and Rosenzweig 1995, pp. 8687). However, in the Iranian study respondents were asked to indicate the type of visa category under which they came to the U.S. Almost half of the male Iranian sample (43%) arrived as temporary visitors, and 27% as students (Table 3.1). The rest of the sample arrived as refugees/asylees (15%), under the family reunification classification or as

Timing of First Job

43

returning permanent residents (15%) (Table 3.1). Given their disadvantaged situation, refugees/asylees are expected to be worse off economically, whereas "returning permanent residents" and those who arrived under the family reunification classification are expected to do well economically. Other Iranian immigrants, students and visitors who arrived with temporary visas, are expected to fall somewhere in between these two extremes, although, those with student visas might excel in the long run. Demographic control variables such as age at immigration and marital status are also included in the multivariate analysis. Age at immigration is likely to affect labor market integration. "The location of point transitions that occur within an individual's career trajectory has different consequences, depending on where they occurred during the individual's life course and career stage" (Raijman and Semyonov 1995, p. 377; see Elder 1990). For Iranian male householders, the mean age at immigration was 37, and the vast majority of the male sample was married at the time of the interview (Table 3.1). RESULTS Approximately three-fourths (70%) of Iranian male immigrants who received general economic help during the first year of arrival found employment faster (Table 3.2). In order to fully test the network hypothesis, non-economic network variable is included in the analysis. At the bivariate level, there is no association between receiving non-economic assistance and length of time spent finding the first job (Table 3.2). These two network measures are based on a general question in which respondents were asked to indicate the types of help they had received during their first year in the U.S. In addition to this question, respondents were also asked to indicate whether they had received any help from family and/or friends to locate their first job. This indicator is more specific. It differentiates between respondents who actually received help in locating the first job or business opportunity and those who did not. A cross-tabulation between this variable with the

44

Iranian Immigrants in Los Angeles

dependent variable shows that Iranian immigrants who received specific economic help to locate their first jobs found employment faster (Table 3.2). Once again, there is a positive network effect. To confirm these findings, Tables 3.3, 3.4 and 3.5 illustrate the multivariate results using logistic regression since the dependent variable - length of time before finding first job in the U.S. - is dichotomous. The first model (Table 3.3) includes the general economic network variable and the second model (Table 3.4) includes the general noneconomic variable. The multivariate results indicate a positive economic network effect. The odds of finding a job within the first year of arrival is 2.5 (exp (0.912)) times (see Table 3.3) higher for Iranian male immigrants who received general economic help compared to Iranians who did not receive economic help from family and/or friends. Iranian immigrants who relied on co-ethnic network ties were more successful in finding jobs faster than those who did not receive help. The results in the second model (Table 3.4) indicate that non-economic network ties do not have a direct effect on how long it takes for immigrants to locate jobs. The third model (Table 3.5) includes the specific economic network variable. The most important finding in the third model is the significant parameter estimate of the specific economic network effect. In this case, as explained earlier, respondents indicated whether they had received help finding their first job or starting their first business. The positive network effect in this model confirms the first model's finding and supports the network hypothesis. Upon arrival, Iranian immigrants who mobilized economic network ties were more likely to find employment faster. In addition to the economic network effect, other significant predictors include: employment experience in Iran, English proficiency, type of visa upon entry, and cohort effect (see Tables 3.3, 3.4, and 3.5). Results indicate that Iranian immigrants who found employment within the first year of arrival were younger, more proficient in English, and had labor market experience in Iran.

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45

For type of visa upon entry, the results indicate no significant difference between temporary visitors and refugees/asylees in terms of how long it took Iranian immigrants to find employment. But, returning residents and Iranians who entered under the family reunification classification found employment more quickly than refugees/asylees. This result shows the disadvantaged positions of Iranian refugees/asylees. Iranian students took even longer than refugees/asylees to find employment. This is not surprising since attending school usually prolongs entrance into the labor market (see Tables 3.3. 3.4 and 3.5). Surprisingly, both pre- and post- migration education variables do not have significant effects on whether Iranian immigrants found jobs within the first year after arrival. One might anticipate that higher levels of educational achievement in Iran would have accelerated the job search process. Even though the parameter estimates are not significant (in all three models), at least the direction of these estimates is in the predicted order. The two dummy coded education variables (high school diploma and college degree) both have positive estimates. This result indicates that Iranian high school and college graduates had a better chance of finding employment faster than Iranians who were high school dropouts. For the M.A. or higher educational levels, the parameter is not significant, but the parameter estimate is negative, which indicates that Iranian immigrants with high educational levels (M.A. or higher) took longer to find employment. This result captures the recertification process that professionals need to undergo before working in their specialized fields. The findings also indicate that more recent Iranian immigrants (1980-87) found employment faster than those who arrived before 1977. One possible explanation is that the earliest cohort included students who did not find employment immediately after arrival since they were attending school. But including the visa type upon entry into the regression equation has controlled this factor. Another explanation is that the economic opportunities in the 1980’s offered more possibilities compared to the 1970’s. This explanation is plausible since during the

46

Iranian Immigrants in Los Angeles

1980’s Los Angeles did experience economic growth. Another explanation might be that availability of jobs and/or access to job information increased for later, more recent immigrant cohorts. A tabulation of period of immigration by the general economic network variable indicates that there is a slight percent increase. The most recent immigrant cohort (1980-87) was more likely to receive economic help (14%) compared to the earliest cohort (10%), even though the difference was modest (Table 3.6). The tabulation by the general non-economic network variable also confirmed the finding that more recent immigrants were more likely to receive help (57% vs. 46%) (Table 3.6). In the study, respondents were asked to indicate whether they were informed about economic opportunities in the U.S. The percent informed increased for each subsequent immigration wave. This figure is 54% for the first wave (1947-77) and 74% for the most recent wave (1980-87). This difference in percentages is rather large which indicates that more recent immigrants arrived to more established immigrant networks (Table 3.6). SUMMARY Thus far, the above findings confirm the importance of both individual and social determinants of whether Iranian immigrants found employment within the first year of arrival to the U.S. Four out of five significant predictors (pre-migration labor market experience, English proficiency upon arrival, visa type upon entry and network ties) are not available in the census data sets yet they all showed significant effects. Iranian immigrants who found employment faster tended to be younger, more proficient in English, had work experience in Iran, entered U.S. as returning residents (or under the family reunification classification), and were more embedded in economic coethnic ties. More recent immigrants also found employment more quickly than the earliest cohort. This is possibly another indirect measure of embeddedness. More recent immigrants, compared to earlier cohorts, have the economic advantage of entering into more established

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47

immigrant network structures, thereby obtaining more access to job information and employment opportunities. Additional schooling outside of Iran was not a significant predictor of how quickly Iranians found employment in the LA labor market. Perhaps, it is too early to assess the impact of additional schooling since this chapter concentrates on the initial settlement phase. This variable is included in subsequent chapters to assess its impact during the more settled phase. As far as migration characteristics are concerned, the majority of Iranians arrived with temporary visitor or student visas, while the rest arrived as refugee/asylees or under the family reunification classification, or returning residents. Results indicated that Iranian refugees/asylees were the most disadvantaged group especially during the initial settlement phase. The most interesting result in this chapter is the significant network effect. For Iranian immigrants, networks sped up the initial job search process, which indicates a positive network effect during the early settlement phase. Immigrant networks, however, can constrain, as well as enhance, individual goals. The next chapter examines how immigrant networks affect occupational status shifts between Iran and the U.S. Immigrant networks might quicken the job search, but they might not lead to desirable or prestigious jobs, especially during the initial phase.

Table 3.1 - Sample Characteristics, Iranian Males, LA County 1987-88

Found employment w/in first year after arrival Highest level of education completed in Iran: Less than high school High school diploma BA/BS MA & + Employed in Iran Knowledge of English upon arrival: Not well/not at all Very well/well Additional schooling outside of Iran Period of Immigration: 1947-77 1978-79 1980-87 Immigration status/visa type: Refugee/Asylee Family/Return Temporary Visitor Student Received general economic help Received general non-economic help Received specific economic help Informed about job opportunities in U.S. Group: Muslim Armenian Bahai Jewish Currently married Age at immigration (mean) Age at time of interview (mean) N

Data Source: The 1987-88 Iranian Survey in Los Angeles

48

55% 22% 48% 17% 13% 73% 60% 40% 60% 30% 35% 35% 15% 15% 43% 27% 12% 23% 51% 61% 31% 25% 14% 30% 83% (37) (45) 557

Table 3.2 – Networks Measures by Timing of First Job, Iranian Males, LA County 1987-88 Found employment first year after arrival

Received Help:

Yes

No

General economic

70%

30%

61

General non-economic

53%

47%

123

Specific economic

60%

40%

273

Data Source: see Table 3.1

49

N

Table 3.3 – Logistic Regression, Finding First Job in the U.S., Iranian Males, LA County 1987-88 Explanatory Variables: Highest level of education completed in Iran: (Ref: Less than high school) High school diploma BA/BS MA & + Employed in Iran Proficient in English upon arrival Additional schooling outside of Iran Period of Immigration: (Ref: 1947-77) 1978-79 1980-87 Immigration status/visa type: (Ref: Refugee/Asylee) Family/Return Temporary Visitor Student Received general economic help Group: (Ref: Jewish) Muslim Armenian Bahai Currently married Age at immigration Model chi-square (df=17) Sample size Data Source: See Table 3.1

Parameter Estimate (Log odds) 0.542 0.415 -0.662 1.331*** 0.847** -0.075

0.551* 1.216*** 1.072* 0.415 -1.191* 0.912* -0.105 0.144 -0.650 -0.102 -0.030 155*** N=465

* p < .05, ** p<.01, *** p <.001

50

Table 3.4 – Logistic Regression, Finding First Job in the U.S., Iranian Males, LA County 1987-88 Explanatory Variables: Highest level of education completed in Iran: (Ref: Less than high school) High school diploma BA/BS MA & + Employed in Iran Proficient in English upon arrival Additional schooling outside of Iran Period of Immigration: (Ref: 1947-77) 1978-79 1980-87 Immigration status/visa type: (Ref: Refugee/Asylee) Family/Return Temporary Visitor Student Received general non-economic help Group: (Ref: Jewish) Muslim Armenian Bahai Currently married Age at immigration (mean) Model chi-square (df=17) Sample size Data Source: See Table 3.1 * p < .05, ** p<.01, *** p <.001

51

Parameter Estimate (log odds)

0.572 0.404 -0.652 1.323*** 0.740** -0.111 0.590* 1.300*** 1.194* 0.500 -1.056* -0.233 -0.073 0.169 -0.613 -0.122 -0.029 149*** N=465

Table 3.5 – Logistic Regression, Finding First Job in the U.S., Iranian Males, LA County 1987-88 Explanatory Variables: Highest level of education completed in Iran: (Ref: Less than high school) High school diploma BA/BS MA & + Employed in Iran Proficient in English upon arrival Additional schooling outside of Iran Period of Immigration: (Ref: 1947-77) 1978-79 1980-87 Immigration status/visa type: (Ref: Refugee/Asylee) Family/Return Temporary Visitor Student

Parameter Estimate (log odds) 0.636 0.512 -0.547 1.351*** 0.757** -0.069 0.590* 1.312*** 1.179* 0.436 -1.079*

Received specific economic help

0.513*

Group: (Ref: Jewish) Muslim Armenian Bahai Currently married Age at immigration (mean) Model chi-square (df=17) Sample size

-0.124 0.128 -0.724 -0.073 -0.029 152*** N=465

Data Source: See Table 3.1 * p < .05, ** p<.01, *** p <.001

52

Table 3.6 – Period of Immigration by Networks Measures Iranian Males, LA County 1987-88 Received General Help Economic

NonEconomic

N

Informed about job opportunities in the U.S.

1947-77

10%

46%

165

54%

1978-79

12%

48%

194

65%

1980-87

14%

57%

198

74%

Data Source: see Table 3.1

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CHAPTER 4

Shift in Occupational Status

STRONG VS. WEAK NETWORK TIES The most interesting result presented thus far has been the significant positive effect of economic networks on Iranian immigrants finding employment within the first year of arrival to the U.S. Iranian male immigrants who mobilized economic network ties immediately after their arrival were more likely to find employment faster. This chapter still concentrates on the initial economic experience of Iranian male immigrants and focuses on the determinants of occupational status after migration. Immigrant networks might facilitate and accelerate the job search process during the initial phase, but not much is known on how these network ties affect the quality of jobs that immigrants find. By examining shifts in occupational prestige scores between the last job in Iran and the first job in the U.S., this chapter examines the effect of Iranian immigrant network ties on occupational status. Assuming that family members and co-ethnics are the main sources of support, migration network theorists rarely discuss the nature of immigrant network ties. Granovetter (1974, 1995), on the other hand, argued that strong ties help immigrants find employment opportunities, especially during the initial settlement period. In a study of professional, managerial and technical non-immigrant workers living in a Boston suburb, Granovetter (1974) found that weak ties rather than strong ties were 55

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Iranian Immigrants in Los Angeles

structurally instrumental in the job search process. Granovetter in his 1974 study proposed that: Weak ties were strong in connecting people to information beyond what they typically had access to through their strong ties, since our acquaintances are less likely than our close friends to know one another, and more likely to move in circles different from and beyond our own. Although close friends and relatives might be more motivated to help us with job information, I argued that 'weak' ties were structurally located in such a way as to be more likely to offer help (Granovetter 1995, p.7). Lin (1990) expanded Granovetter's argument with a "strength-of-ties" hypothesis: "The use of weaker ties is positively related to access to and use of social resources. For status attainment, it states that there is a positive relationship between the use of weaker ties and the likelihood of contacting a source of better resources" (Lin 1990, p. 251). In other words, according to Lin and Dumin (1986), "weaker ties provide better access to white-collar or more prestigious occupations" (Lin and Dumin 1986, p. 365). However, newly arrived immigrants rarely have access to any network tie besides their own immediate family members and close friends. Thus, during the initial settlement phase, immigrants who rely on strong ties might have limited access to more prestigious jobs. When immigrants settle, they start establishing other types of networks by moving into work-related and/or other social circles increasing their pool of weak ties. This chapter examines the extent to which Iranian male immigrants relied on strong versus weak ties, and assesses which type led to more prestigious jobs.

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STRENGTH OF TIE Granovetter (1974, 1995) showed that personal contacts play an important role in the job search process. In fact, the premise of Getting a Job is that "finding jobs via information supplied through a social network (is) widespread and important" (1995, p. 139). After more than twenty years of research in sociology and economics, many researchers have documented how, why, and when contacts provide information about employment opportunities (Lin, Ensel and Vaughn 1981a, 1981b; Mortensen 1986; Devine and Kiefer 1991; Burdett and Wright 1994). Granovetter (1973, 1974) argued that compared to strong/close ties, weak ties are even stronger in "connecting people to information beyond what they typically had access to through their strong ties" (Granovetter 1974, p. 52). “Family and close friends are often more motivated than acquaintances to help us to find jobs, but acquaintances and indirect ties give access to other circles” (Granovetter 1974, pp. 52-54). In his study, Granovetter measured tie strength by asking respondents how often they saw the person (the contact) who passed on the job information. In a random sample of professional, technical, and managerial job changers who lived in a Boston suburb and found their new jobs through contacts, 17% indicated that they saw their contact "often – twice a week”, 56% "occasionally – more than once a year", and 28% "rarely – once a year" (Granovetter 1973, p. 1371). Granovetter found weak ties to be more instrumental than strong ties when looking for employment. Since 1974, many other researchers have conducted their own studies testing the strength of ties hypothesis (Lin, Ensel and Vaughn 1981a, 1981b; Lin 1990; Bridges and Vilimez 1986; Marsden and Hurlbert 1988; Marsden and Campbell 1984; De Graaf and Flap 1988). Lin and colleagues (1981a) argued: "when seeking a job weak ties rather than strong ties will provide the seeker with a more extensive reach and hence with a greater likelihood of contacting people who possess job-related information and influence" (Lin, Ensel and Vaughn 1981a, p. 396).

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In a 1975 random sample of 20-64 years old males living in the tri-city area of Albany-Schenectady-Troy, New York, 57% used personal contacts to locate their new jobs. Among these respondents, the occupational status and the educational levels of the contact person were used as two distinct measures of social resources. Lin and colleagues (1981a) argued, "when seeking a job an individual will gain more by contacting someone upward in the hierarchical structure, who has, in other words, greater social resources" (1981a, p. 395). In addition to measuring social resources, they also measured strength of ties. Weak ties included acquaintances, or indirect ties. Strong ties included relatives, friends and neighbors (1981a, p. 397). Using structural equation models, Lin and colleagues (1981a) conclude: "The evidence suggests a concatenation in which the use of weak ties facilitates the reaching of higher status contacts, which in turn directly affects the attained occupational status" (1981a, p. 398). In other words, "weak ties lead to better social resources" which ultimately leads to higher status jobs. This finding modifies Granovetter's argument by illustrating how weak ties indirectly lead to better jobs. Unlike Lin et al.'s study, which concentrated on occupational status attainment, in a 1981 Chicago survey, Bridges and Villemez (1986) examined the effects of tie strength on income. They found that employees earned higher income when they obtained their jobs through weak ties. However, this bivariate relationship disappeared when control variables such as gender, race, educational background and job experience were included in the analysis. Using a 1970 Detroit Area Study, Marsden and Hurlbert (1988) replicated Lin et al's, and Bridges and Villemez's studies. Marsden and Hurlbert (1988) examined the effects of social resources on occupational status and income. Consistent with Bridges and Villemez's findings, they found "no net effects of social resources" on wages. When reviewing these studies, Granovetter (1995) raised the issue that the control variables used in these studies might somehow proxy social capital, thus masking the effect of weak ties.

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59

More importantly, Granovetter (1995) pointed out that these "blanket assumptions about the payoff to use of contacts - (are) too individualistic." Individuals come to the labor market as part of well-defined groups. "Thus, in particular groups, finding jobs through contacts may be one's best option, yet the jobs found may still be of poor quality by general standards if this is all the group can provide" (Granovetter 1995, pp. 150-151). Granovetter wrote: "Mostacci-Calzavar (1982, p. 153), in her Toronto sample, found that working-class ethnic ties often led to jobs, but that the lower the average income in one's ethnic group, the less advantage in income from using one's samegroup contacts, compared to other methods of finding jobs" (Granovetter 1995, p. 151). An immigrant group's level of resources dictates the quality of jobs that immigrant networks have access. Strength of Tie Hypothesis: During the initial settlement phase, immigrants are more likely to rely on "strong" ties as opposed to "weak" ties when looking for employment. "Weak" ties, however, are more likely to lead to more prestigious jobs. In his revised edition of Getting a Job, Granovetter (1995) classified two types of contacts: work-related and family-social (p. 41). Even though he did not identify these contacts as weak or strong, the underlying assumption is that family-social contacts are stronger compared to workrelated contacts. According to Granovetter (1995), workcontacts are more likely to lead to better jobs (1995, p. 44), and "jobs in the lowest income category are most likely, and those in the highest least likely to have been found through family-social contacts" (1995, pp. 44-45). In this chapter, the specific economic help indicator discussed in chapter 3 is used. In the1987-88 Iranian study, respondents were asked to indicate the persons who helped them find their first job or start their first business in the U.S. The response categories to this question are divided into family and non-family ties with the assumption that family ties are stronger compared to non-family ties.

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SEI OR PRESTIGE SCORE In most stratification and occupational mobility studies either Duncan's Socioeconomic Index (SEI) or prestige scales (such as Treiman's Standard International Occupational Prestige Scale) are used to assess inter- or intra- generational shifts in status (Duncan 1961; Blau and Duncan 1967; Featherman and Hauser 1978). Even though these two scales are conceptually distinct, they are still highly intercorrelated. "While the Standard (Prestige) Scale measures the relative prestige of occupations as popularly evaluated, the Duncan scale measures their socioeconomic status, that is, a combination of the education and income levels of incumbents" (Treiman 1977, p. 208). There are advantages and disadvantages when using either type of scale. The SEI scores were originally calculated for all 1950 Census occupational categories by using the NorthHatt prestige study in 1947 (NORC 1947). Blau and Duncan (1994) explained: In the derivation of the socioeconomic index of occupational status, prestige ratings obtained from a sizable sample of the U.S. population in 1947 were taken as the criterion. These were available for 45 occupations whose titles closely matched those in the census detailed list. Data in the 1950 Census of Population were converted to two summary measures: percent of male workers with four years of high school or higher level of educational attainment, and percent with incomes of $3,500 or more in 1949 (both variables being agestandardized). The multiple regression of percent "excellent" or "good" prestige ratings on the education and income measures was calculated. ... Using the regression weights obtained in this calculation, all census occupations were assigned scores on the basis of their education and income distributions (Blau and Duncan 1994, pp. 205-207).

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The SEI scale varies from 0 to 96 and can be interpreted as the occupational socioeconomic or occupational status (Blau and Duncan 1994). By using similar regression algorithms, the General Social Surveys (GSS, 1972-1993) updated SEI scores for the 1970 and the 1980 Censuses. On the other hand, in order to maximize cross-national comparability, Treiman (1977), using the International Standard Classification of Occupations (ISCO) coding scheme, constructed a universal standard occupation prestige scale called the Standard International Occupational Prestige Scale (also known as the Standard Scale). Treiman (1994) explained: To derive generic prestige scores for each of these occupations, I converted all the data to a standard metric and then simply averaged scores across all countries in which a given title appeared. Scores for higher levels of aggregation (unit groups, minor groups, and major groups) were derived by various averaging procedures (Treiman 1994, p. 209). For the purposes of this study, Treiman's prestige scores were assigned to each occupational category (Treiman 1977, Appendix A). Since Treiman's Standard Scale scores are not available for the 1980 Census occupational categories, Treiman's prestige scores were checked against the 1989 Hodge-Siegel-Rossi scale (Siegel 1971), which was updated for the 1980 Census occupational categories (GSS 1993, Appendix F). For the Iranian male sample, the correlation between these two prestige scales is high (r=.81) and significant at p < 0.0001. Since the 1989 Hodge-Siegel-Rossi scale is more updated, the analysis in this study is based on the Hodge-SiegelRossi prestige scores. OCCUPATIONAL STATUS IN IRAN VS. U.S. In order to measure shift in occupational status, two prestige scores were assigned for each respondent - one for Iran and one for the U.S. Respondents who were not

62

Iranian Immigrants in Los Angeles

employed in Iran, and were less than 30 years old before leaving Iran, received a prestige score of 41, indicating that they were not in the Iranian labor force (see Treiman 1977). Occupational status change is defined the difference between the two prestige scores: (first occupation in the U.S. - last occupation in Iran). Negative values indicate status loss, and positive or zero values indicate gain or no loss, respectively. As discussed in chapter 3, there is a positive association between education and occupational status. Education is the most important determinant of occupational status (Blau and Duncan 1967; Duncan and Hodge 1963; Duncan, Featherman and Duncan 1972; Featherman and Hauser 1978). Having additional schooling outside of Iran should also have a positive effect on occupational status. Similarly, English proficiency upon arrival should increase the likelihood of experiencing a positive shift in occupational status in the U.S. In addition to human capital variables, as discussed in chapter 3, visa status at entry to the U.S. is included in the analysis. Due to their disadvantaged position, refugees and asylees are expected to experience a downward occupational shift while those who arrived under the family reunification classification might experience an upward shift during the initial settlement phase. The experience of temporary visitors falls in between these two extremes. Just as in the previous chapter, age at immigration, marital status, period of immigration and group are used as control variables. As for the network effect, following the strength of tie hypothesis, access to network ties will lead to negative shift in occupational status since during their initial settlement phase immigrants often rely more on their family ties which are stronger compared to non-family ties. RESULTS Table 4.1 reports cross tabulations between last occupation in Iran and first occupation in the U.S. immediately after arrival for male Iranian heads of households. In Table 4.1, column percentages are presented in the first panel and row

Shift in Occupational Status

63

percentages in the second panel. The first panel percentages can be interpreted as inflow percents and the second panel percentages can be interpreted as outflow percentages (see Hout 1983, p. 12). The inflow percents refer to the image of labor flowing into a given destination. For example, for the manager/executive category, the 59% (Table 4.1, first panel) indicates that almost two-thirds of Iranian male managers/executives held similar jobs in Iran. The outflow percentage, on the other hand, describes the distribution of destination for each category of origin. In this case, the corresponding figure for the manager/ executive category (41%, Table 4.1, second panel) indicates that less than half of Iranian male managers/ executives continued to work in similar managerial positions in the U.S. The diagonal percents in both panels (see Table 4.1 italics), except for the sales/technical/administrative support category, exceeded all other off-diagonal percents. This indicates that there was a carry-over effect from Iran. There was a tendency for Iranian immigrants to work in the same occupational category as they did in Iran. In professional specialty, sales supervisor and the "other" categories, the carry-over effect from Iran was strong. Almost three-fourths of sales supervisors continued to work as sales supervisors after migrating to the U.S. The majority of these "sales supervisors" were self-employed, indicating that the majority of self-employed Iranians continued their self-employment status in the U.S. Nevertheless, in contrast to these three occupational categories, in the managerial and sales/technical/ administrative support categories almost two-thirds and three-fourths have moved into other types of jobs respectively (Table 4.1). Although informative, these results offer limited information about the migration process and shifts in occupational status. The most important shortcoming is the exaggerated carry-over effect for the sales supervisor category, which is partly due to the limitations of the 1980 U.S. detailed occupational coding scheme and lack of data from the Iranian survey regarding the status of self-

64

Iranian Immigrants in Los Angeles

employed business owners in Iran. As such, the "no change" category for the occupational shift variable between Iran and U.S. becomes inflated because it includes the self-employed. Given this shortcoming, the rest of the analysis focuses on wage and salary earners – dropping the self-employed. Results in Table 4.2, which are based on the HodgeSiegel-Rossi prestige scale, indicate that 38% of the male Iranian wage and salary earners experienced a negative shift in their occupational status. Overall, however, Iranians' experience was positive - 41% of the male sample experienced a positive status shift and 21% no shift. In addition to using Siegel's prestige scores, Treiman's International Prestige scores were also used. The results are identical - 37% negative shift, 41% positive and 20% no shift (data not shown). For the multivariate analysis, the dependent variable is shift in occupational status - zero and positive values were combined into one category creating a dichotomous variable (1=zero/positive and 2=negative). Table 4.3 presents logistic regression results for change in occupational status for wage and salary Iranian workers. The logistic regression is predicting “positive” change in occupational status between Iran and the U.S. Male Iranians who possess good command of English were more likely to experience positive change in occupational status compared to those having poor English language skills, which can be interpreted as lowering occupational cost. On the other hand, age has a negative effect, which indicates older immigrants were less likely to experience positive change in occupational status. In other words, occupational cost increases with age. This is consistent with earlier findings that older immigrants might have a more difficult time adjusting to the new labor market (see Raijman and Semyonov 1995, p. 378; also see Elder 1990). In addition to age, marital status is also a significant control variable. Being married increased the odds of experiencing a positive status change. Ethno-religious group, another control variable, also has a significant effect. The only significant group difference was between Jewish Iranians and Bahais. Compared to Jewish Iranians,

Shift in Occupational Status

65

Bahais were less likely to experience a positive status change during their initial settlement phase. This finding, however, should be cautiously interpreted since the Bahai Iranian sample was small. The most important finding in Table 4.3 is the significant effect of network ties. Receiving specific help while looking for first job increased occupational cost. In other words, Iranian immigrants who received help while looking for their first job were less likely to find prestigious jobs compared to those who did not receive any help. These findings prove Granovetter's conceptualization of strong vs. weak network ties. During the initial settlement phase 69% of Iranian male immigrants relied on family ties (and 31% relied on non-family ties) when looking for first employment in the U.S. These results confirm the strength of tie hypothesis – Iranian male immigrants were more likely to rely on family ties to find their first job in the U.S., however, the use of these strong (family) ties led to less prestigious jobs in the U.S. during their initial settlement phase. SUMMARY The nature of Iranian immigrant ties during the initial settlement phase helps explain the negative network effect. Results indicate that network ties tend to be strong during the initial phase. But since weak ties are more likely to lead to more prestigious jobs, the negative network effect found in the logistic regression makes sense. In other words, the network tie during the initial phase captures the effect of strong ties, which seem to reduce occupational status shift during the initial settlement period. The positive network effect illustrated in the previous chapter does not contradict the negative network effect found in this chapter. Finding a job is one issue; whether or not the job is desirable or prestigious is another. During the initial phase, most Iranians immigrants who rely on network ties depend on family members. Family ties often accelerate the job search but do not broaden access to better paying or more prestigious jobs.

66

Iranian Immigrants in Los Angeles

As in the previous chapter, this chapter captures the initial economic experience of Iranian immigrants. As Iranian immigrants establish other types of ties and increase their pool of weak ties, they can access a broader range of jobs. The next two chapters focus on the settled economic integration phase focusing on their "current" job (i.e., employment information in 1987-88). The next two chapters assess Iranian immigrants’ reliance on weak ties and the effect of these ties on income and self-employment during the more settled period.

Table 4.1 – Last Occupation in Iran by First Occupation in the U.S., Iranian Males, LA County 1987-88

Last Occupation in Iran Inflow % Executives/ Managers Professional Specialty Technical/ Admin Support Sales Supervisor All Other* N Outflow % Executives/ Managers Professional Specialty Technical/ Admin Support Sales Supervisor All Other* N

First Occupation in the U.S. Exec/ Manag

Prof Speclty

Tech/ Admi n

Sales Superv

All Other

59%

13%

33%

14%

9%

9%

66%

18%

14%

13%

11%

13%

27%

6%

25%

9% 11% 54

4% 4% 76

13% 9% 55

60% 6% 95

11% 42% 71

41%

13%

23%

17%

8%

6%

58%

12%

15%

10%

11%

18%

27%

11%

33%

6% 12% 79

4% 6% 87

9% 10% 55

71% 12% 80

10% 60% 50

Data Source: The 1987-88 Iranian Survey in Los Angeles * All Other includes: service/farm, precision production, craft and repair, operators and laborers

67

Table 4.2 – Change in Occupational Status* Between Last Job in Iran and First Job in the U.S., Iranian Male Wage & Salary Workers, LA County 1987-88 Negative Change Positive Change No Change

38% 41% 21%

Total N

277

Data Source: See Table 4.1 * Based on the 1989 Hodge-Siegel-Rossi Prestige Scores

68

Table 4.3 – Logistic Regression Predicting Positive Change in Occupational Status, Iranian Male Wage & Salary Workers, LA County 1987-88 Explanatory Variables:

Parameter Estimate

Highest level of education completed in Iran: (Ref: Less than high school) High school diploma BA/BS MA & +

(log odds)

-0.128 -0.265 1.059

Proficient in English upon arrival Additional schooling outside of Iran Period of immigration: (Ref: 1947-77) 1978-79 1980-87 Immigration status/visa type: (Ref: Refugee/Asylee) Family/Return Temporary Visitor Student

1.008** 0.401

Received specific economic help Group: (Ref: Jewish) Muslim Armenian Bahai

-0.748*

Currently married Age at immigration

0.829* -0.034*

Model chi-square (df=16) Sample size

56*** N=277

Data Source: See Table 4.1

-0.182 -0.566 -1.161 -0.367 0.366

-0.460 -0.033 -1.370**

* p < .05, ** p<.01, *** p <.001

69

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CHAPTER 5

Determinants of Income

SOCIAL VS. ECONOMIC EMBEDDEDNESS The results thus far illustrate the significant effect of network ties – whether positive or negative – on economic integration during the initial settlement phase for Iranian immigrants in Los Angeles. This chapter continues to examine the effect of network ties but shifts its focus to the settled phase. Following the embeddedness argument explained, this chapter discusses various measures of social capital and applies them to Iranian immigrants using the 1987-88 Iranian Survey focusing on their current employment. Coleman (1988) conceptualized the family's social capital as "the relations between children and parents" and other members if they live with the family (Coleman 1988, p. S110). Coleman (1988) measured social capital by using three indicators:1) Number of parents present, 2) Additional children, and 3) Mother's educational expectation for child (Coleman 1988, p. S112). Coleman argued, "if the human capital possessed by parents is not complemented by social capital embodied in family relations, it is irrelevant to the child's educational growth that the parent has a great deal, or a small amount, of human capital" (Coleman 1988, p. S110). Massey and Espinosa (1996) classified social capital theory as a possible explanation for international migration. In his study, Massey measured social capital in four ways: 71

72

Iranian Immigrants in Los Angeles

1) Whether or not one of the respondent's parents was in the U.S. before or at the time the respondent migrated to the U.S., 2) Number of siblings living in the U.S., 3) An estimate of the proportion of persons aged 15 or more who were in the U.S. during that time, and 4) Whether or not any of the household members had been legalized under the 1986 Immigration Reform and Control Act. Since the above indicators can apply to any individual, regardless of whether or not he had any plans to migrate, Massey, in addition to what he termed "general social capital" indicators included three other measures of "migrationspecific social capital." These measures were: 1) Whether or not the wife was a migrant, 2) Number of children who had migrated, and 3) Whether or not any children were U.S. citizens. Massey's operationalization of the concept resembles Coleman's since both scholars used the presence of family members, whether at home or in the U.S., as an indicator measuring the "actual and potential" social resources. For Coleman, these resources are created through parents interacting with their children, and for Massey, they are created through prospective immigrants' interaction with family members who already live in the U.S. In contrast, Portes (1995b), when examining the immigrant children's educational achievement, conceptualized two types of social capital - one for the immigrant parents and another for their children. He wrote: For immigrant parents, social capital consists of the ability to call on co-ethnics to reinforce normative expectations vis-à-vis their offspring and to supervise their behavior. For the children, social capital consists of the ability to command resources controlled by the ethnic community ... Quite clearly, the amount and quality of these resources depend on the physical, financial, and human assets possessed by fellow co-ethnics, but the mobilization of these resources and their availability to secondgeneration children depends on social capital (Portes 1995b, pp. 257-258),

Determinants of Income

73

Portes, in his analyses did not directly measure these concepts. Instead, he used a dummy coded variable for the four immigrant nationality groups: Cuban, Mexican, Haitian, and Vietnamese, to measure the extent to which immigrants' children have access to social resources. Through ethnographic accounts, he described each immigrant community and the extent to which each group has tightly knit networks. In other words, he used the dummy coded group variable to indirectly measure the immigrant group's social capital. Portes explained: I regressed each indicator of academic performance on a vector of independent variables which comprise all individual/family characteristics that can plausibly bear on these outcomes plus dummy variables for each immigrant nationality ... After controlling for this vector of predictors, three of the four nationality variables retain a strong independent effect on GPA ... I interpret these findings as providing an indirect evidence of the role of community social resources in facilitating second-generation upward mobility and fending off the threat of downward assimilation (Portes 1995b, pp. 271-272). The assumption that immigrant networks differ from one group to next has validity. "Immigrant networks are qualitatively different from one another" (Light, Bhachu and Karageorgis 1993, p. 42). "Networks are cultural. Therefore, they are unique" (Light, Bhachu and Karageorgis 1993, p. 39). However, a problem arises when group is used to indirectly measure an immigrant group's social resources. The significant group differences reported by Portes merely indicate that there are important group differences as far as immigrant children's educational achievements are concerned. After controlling for the various individual characteristics such as knowledge of English, father's occupation, mother's education, and length of residence in the U.S., Portes' study showed that group differences persist. These differences can be due to many factors other than the group's social resources, including

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Iranian Immigrants in Los Angeles

group's legal status, class resources, and discrimination experienced by the group. Portes emphasized that "the resources themselves are not social capital; the concept refers instead to the individual's ability to mobilize them on demand" (Portes 1995a, p. 12). Therefore, a better way to measure the availability of social resources, and whether or not immigrants actually utilize them, is to collect data on immigrant networks and the extent to which immigrants rely on family members and co-ethnics. As mentioned earlier, Coleman (1988) discussed the concept of social capital in terms of the parents' relationship to the children. In other words, he specifically measured a family's social capital as it related to its offspring’s academic achievement. Accordingly, in this chapter, immigrants' social capital is measured by the extent to which Iranian immigrants have access to and are embedded in economic networks. To illustrate this point, both economic and non-economic network scales are included in the analyses. Embeddedness Hypothesis: For immigrants, economic embeddedness is associated with higher income during the settled phase. Strength of Tie Hypothesis: Immigrants will rely more on non-family (weak) ties during the settled period. Compared to family ties, non-family (weak) ties are more likely to lead to jobs with higher income. In the 1987-88 survey, Iranian respondents were asked to indicate whether they had received or given any help on a list of items during the settled period. Two lists were presented to each respondent. The first list asked about non-economic items, such as childcare, housework, personal counseling, transportation, consumer advice and free housing. There were a total of 13 items for this list. The second list included 11 economic items, such as finding a job, referrals and references, professional information, loans, legal, tax and business advice.

Determinants of Income

75

Before constructing the network scale, an exploratory factor analysis was conducted by entering all 24 items. The 11 economic items loaded on the first factor, and the remaining 13 non-economic items loaded on the second factor. To test the embeddedness hypothesis, two scales were developed. The 11 items make-up the economic network scale and the 13 items make-up the non-economic network scale. Both are quantitative measures where a zero value indicates no economic or non-economic ties and higher values indicate higher level of social capital or embeddedness. In addition to whether or not they had received or had given help, respondents were also asked to rank order the persons whom they helped and/or who helped them. To test the strength of tie hypothesis, two response categories were created: family ties vs. non-family ties. DETERMINANTS OF INCOME The ultimate concern of many researchers studying immigrants’ economic integration is how well immigrants fare in the U.S. labor market. In this chapter, income is the dependent variable. There are many determinants of income. Individualistic explanations of income focus on human capital (Boxman, De Graaf and Flap 1991). In this study, this individualistic level encompasses highest educational achievement and additional schooling outside of Iran. English proficiency is another important indicator of how well immigrants fare in the U.S. labor market. Likewise, labor market characteristics such as job experience, class of worker, and type of job or occupation are also individual attributes. The only demographic variable used in this analysis is marital status since age is already included in the job experience variable. All of these individual traits help explain income levels. High levels of education, additional schooling outside of Iran, and English proficiency are all associated with higher income levels. Compared to other immigrant groups, Iranian men are highly educated (Bozorgmehr, Der-Martirosian and Sabagh

76

Iranian Immigrants in Los Angeles

1996). In the study, 36% of the Iranian male sample had a M.A. degree or higher, and 28% had a B.A./B.S. degree. Almost two thirds (60%) of the sample had attended schools outside of Iran, and most (79%) of the male sample was proficient in English at the time of interview. Job experience, another individual attribute, is positively associated with higher income. On average, the Iranian male sample had 27 years of job experience. There is ample evidence that documents the higher earnings of selfemployed compared to wage and salary workers. About two-thirds of the male sample was currently self-employed at the time of the interview (Table 5.1). The type of occupation is also an important determining factor of income. The occupational distribution of Iranian males was: 18% managers, 27% professionals, 43% technical/sales, and 12% other occupations (Table 5.1). In addition to this individualistic approach, one must consider he immigration process. Each immigrant group has a different migration experience. In the case of Iranians, there are differences between the ethno-religious subgroups. For example, during the Iranian Revolution and the Iran-Iraq War, Muslim Iranian immigrants did not qualify for asylee/refugee visas since they did not have minority status in their country of origin. The situation was different for Armenians, Jews and Bahais. Since these subgroups were minorities in Iran, they qualified for asylee/refugee status. Many Muslim Iranians arrived with student visas, whereas many immigrants in the other three subgroups arrived as temporary visitors or asylees. In this chapter, unlike previous chapters, the visa status upon entry is not used in the analysis. Since over 90% of the Iranian sample had permanent residency at the time of the interview, how respondents attained their permanent residency status is used as an explanatory variable. In the survey, 21% of the Iranian male sample received their permanent residency status as asylees, 27% received their permanent residency status on the basis of family reunification, and 32% were classified as "highly" or as “needed" skilled workers (Table 5.1).

Determinants of Income

77

Given their disadvantaged position, non-residents and those classified as asylees are expected to be worse off economically compared to the others. "The presumption has always been that immigrants selected by job matching criteria are more likely to be successful in the U.S. labor market than family immigrants" (Jasso and Rosenzweig 1995, p. 86). Accordingly, Iranian immigrants who became residents on the basis of employment qualifications are expected to have an economic advantage over those who became residents on the basis of family reunification criteria. There is a general practice of using the time of migration as an individual trait measuring length of stay in the U.S. This measure is used as a proxy for assimilation, and in some cases for aging. Time since migration measures both. Therefore, it is difficult to interpret this variable. The most obvious interpretation is that the longer immigrants stay in the U.S., the better they fare economically. This statement makes a blanket statement that over a period of time; immigrants' economic situation improves as migrants acquire more labor market skills and language skills (i.e., increasing their human capital). Timing of migration can also measure the extent to which immigrants are embedded in different types of networks. As immigrants continue to settle in a new environment, they also become more embedded into different types of networks, thereby increasing not only their human capital but also their social capital. Over time, immigrants tap into non-immigrant networks. At the same time, they keep their immigrant ties. Consequently, they maximally benefit from both networks. RESULTS In order to tease out network effect, the economic network scale is included in the analysis as an independent variable. To show the breakdown of how many network items were selected by the Iranian study respondents, the distribution of both economic and not-economic network scales are presented in Table 5.1. For economic support, 16% of the

78

Iranian Immigrants in Los Angeles

Iranian male sample indicated receiving “no help” from family members, friends or acquaintances. On one end of the spectrum, 19% of the respondents received help on 1-3 items (lower level of economic embeddedness). On the other end, 22% received help for 9 and more items, indicative of high levels of economic embeddedness. Comparable data for the non-economic network scale are: 5% for no help, 12% for 1-3 items, and 40% for 9 & + items (Table 5.1). Table 5.2 illustrates the mean annual income by level of embeddedness: none, low, moderate, high, and very high. There is a positive linear relationship between economic embeddedness and income – the higher the level of embeddedness the higher the income level. On average, male Iranian immigrants reporting no economic embeddedness had the lowest income ($37,183 in 1987 dollars), whereas Iranian immigrants with the highest level of embeddedness had the highest mean income ($55,856 in 1987 dollars, see Table 5.2). These results are only at the bivariate level. Next, the effect of embeddedness is examined after controlling for other relevant variables. The determinants of income are explored using Ordinary Least Squares Multiple Regression. The quantitative embeddedness measure (ranging between 0 and 24) is used in the multivariate analysis. The results are presented in Table 5.3. Both models include individual variables such as educational background, English proficiency, labor market experience, job characteristics, and marital status. These two regression models also include three group dummy variables and migration variables, such as period of immigration, and type of classification when applying for permanent residency. In addition to these variables, the first model includes the economic network scale, which has a significant coefficient – indicating a positive linear relationship between the economic network scale and income. The predicted income values change 1.6% for each additional increment of the economic network scale (Table 5.3). In other words, a higher level of economic embeddedness is associated with a higher income levels.

Determinants of Income

79

In order to illustrate the sensitivity of the economic embeddedness measure, the non-economic network scale was included in the second model instead of the economic network variable (Table 5.3). Unlike the economic network scale, the non-economic variable had no significant effect, indicating no association between non-economic ties and income. These findings support the embeddedness hypothesis. Economic embeddedness is associated with higher income during the settled phase among Iranian immigrants in Los Angeles. Other significant predictors of income include: education, type of occupation, self-employment, length of residence in the U.S., and type of residency classification. The results revealed that Iranian males with higher levels of education earned higher incomes. Compared to wage and salary earners, the self-employed had higher incomes as well. Professionals had the highest income levels, followed by managers and then by technical/sales. Period of immigration was an important indicator of how well Iranian immigrants fare in the labor market. Iranian male immigrants, who arrived before 1977, had higher income compared to those who arrived after the Iranian Revolution. Once again, this proves that length of residence is highly correlated with income levels. An interesting finding is the significant effect of the classification criteria for permanent residency. Among the residents, on average, employmentbased Iranians (Iranian immigrants who received their permanent residency as “needed” unskilled and professional workers) had the highest income, and Iranians who were classified as asylees had the lowest income. The income levels for Iranians immigrants who received their residency status through the family reunification criteria fell between the employment and asylee classifications (Table 5.3). As for the strength of tie argument, the results are mixed. Among respondents who used personal contacts to find their current employment, 56% relied on "non-family" tie – a significant increase from the 31% figure reported during the initial settlement phase. This finding partially supports the strength of tie hypothesis – as immigrants

80

Iranian Immigrants in Los Angeles

settle in the U.S., their access to different types of network ties expands. But in terms of whether or not these nonfamily ties lead to higher paying jobs, the findings are not conclusive. The most striking difference is between the "no-help" category and those who received assistance, regardless of the type of tie strength. The bivariate association between strength of tie and income indicates that Iranian male immigrants who did not receive any help had the lowest mean annual income ($37,222 in 1987 dollars) compared to Iranian immigrants who received help (family or nonfamily) (see Table 5.4). Similarly, the bivariate association between strength of tie and job satisfaction, indicates that both non-family and family ties led to more job satisfaction when compared to the “no help” category, even though the differences are not statistically significant (Table 5.5). The most salient finding is the difference between Iranian immigrants who were economically embedded vs. those who were not. The overall pattern suggests that for Iranian immigrants economic embeddedness increased the likelihood of finding employment with higher earnings and more desirable jobs. SUMMARY In this chapter, economic embeddedness had a significant positive effect on Iranian immigrants’ earnings during the settled phase. Even though there was an increase in the pool of non-family network ties among Iranian immigrants during the settled phase (as compared to the initial phase), the results for the effect of the type (weak vs. strong) of network ties on immigrant earnings was not conclusive. The most salient distinction in terms of immigrant earnings was whether or not Iranian immigrants received help when finding their current job. Iranian immigrants who had no help (i.e., no economic embeddedness) had the lowest mean annual income – worse off when compared to Iranian immigrants who received help from family or non-family ties.

Determinants of Income

81

In terms of job satisfaction, Iranian immigrants who reported receiving no help were least likely to be satisfied with their current job. Those who reported, "not satisfied" with their current job had the lowest mean economic network score, while those who reported "very satisfied" had the highest mean score. Regardless, these differences in the mean scores were not statistically significant, however, the direction of the relationship between economic embeddedness and job satisfaction confirms earlier findings that immigrants find more desirable jobs through ties. As first and second generation Iranians spend more time in the U.S., they will establish more non-ethnic ties and expand their access to networks outside of the immigrant community. This expansion will undoubtedly increase their social resources. It will also provide more employment opportunities as future Iranian generations take advantage of both ethnic and non-ethnic ties.

Table 5.1 –Sample Characteristics, Iranian Males, LA County 1987-88 (N=557) Highest Level of Education High School or less BA/BS MA & + Additional Schooling Outside of Iran English Proficiency (at time of interview) Very well Well Not well Not at all Job Experience (mean value in years) Currently Self-employed Current Occupation: Managers Professional Specialty Technical/Sales Other Permanent Residency Status: Non-resident Refugee/Asylee Family Skilled Worker Other Economic Network Scale: No help 1-3 items 4-5 items 6-8 items 9&+items Non-economic Network Scale: No help 1-3 items 4-5 items 6-8 items 9&+items Mean Income (in 1987 dollars) Data Source: The 1987-88 Iranian Survey in Los Angeles

82

36% 28% 36% 60% 26% 53% 18% 3% (27) 61% 18% 27% 43% 12% 10% 21% 27% 32% 10% 16% 19% 19% 24% 22% 5% 12% 17% 25% 40% $49,262

Table 5.2 – Mean Annual Income* by Economic Embeddedness, Iranian Males, LA County 1987-88 Economic Embeddedness None (No help) Low (1-3 items) Moderate (4-5 items) High (6-8 items) Very High (9 & + items) Total Data Source: See Table 5.1 * In 1987 dollars

83

Mean Income

N

$37,183 $42,737 $53,000 $52,177 $55,856

87 107 108 133 122

$49,262

557

Table 5.3 –Multiple Regression Predicting Log(income),Iranian Males, LA County 1987-88 Highest Level of Education (Ref: High School or less ) BA/BS MA & + Additional Schooling Outside of Iran English Proficiency (Ref: Not well/none) Very Well Well Job Experience (in years) Currently Self-employed Current Occupation: (Ref: Other) Managers Professional Specialty Technical/Sales Permanent Residency Status: (Ref: Non-resident) Refugee/Asylee Family Skilled Worker Other Period of Immigration: (Ref: 1980-87) 1947-77 1978-79 Economic Network Scale Non-economic Network Scale Married Group: (Ref: Jewish) Muslim Armenian Bahai Constant Adjusted R-square Data Source: See Table 5.1 NI = not included * p < .05, ** p<.01, *** p <.001

84

Model 1

Model 2

0.313*** 0.144* 0.015

0.312*** 0.141* 0.019

0.158 0.093 0.004 0.336***

0.147 0.097 0.003 0.353***

0.227* 0.311** 0.152

0.235* 0.316** 0.171

0.017 0.194 0.253* 0.326**

-0.007 0.173 0.302 0.232**

0.197*** -0.008 0.016* NI 0.047

0.222** 0.007 NI 0.001 0.070

-0.109 0.040 0.002 0.354* 27.11***

-0.104 0.043 0.012 0.436* 26.04***

Table 5.4 – Mean Annual Income* by Strength of Tie, Iranian Males, LA County 1987-88 Strength of Tie

Mean Income

N

No help Family Non-Family

$37,222 $50,417 $52,381

88 262 207

Total

$49,262

557

Data Source: See Table 5.1 * In 1987 dollars

85

Table 5.5 – Job Satisfaction by Strength of Tie, Iranian Males, LA County 1987-88

Strength of Tie

Very satisfie d

Satisfie d

Not satisfie d

N

No help Non-Family Family

30% 24% 25%

30% 50% 45%

40% 26% 30%

44 236 190

Total

25%

46%

29%

470

Data Source: See Table 5.1

86

CHAPTER 6

Self-Employment

INTRODUCTION Compared to other groups, Iranian immigrants have a much higher self-employment rate. In 1990, the self-employment rate for Iranians was 21% in the U.S. The self-employment rate in Los Angeles was even higher, 33%. This figure was much higher than the self-employment rates for nonHispanic whites (24%), Asians (19%) and Hispanics (8%), in Los Angeles (in 1990). In 2000, the Iranian selfemployment rate was still as high as 22% (see Bozorgmehr 2007). Given the high rate of self-employment among Iranian immigrants in Los Angeles, this chapter focuses on the role of economic networks as a determinant of entrepreneurship among Iranians in Los Angeles. The findings thus far indicate a direct, positive association between economic embeddedness and income. Is there a positive network effect on self-employment among Iranian immigrants? A great deal of literature stresses that co-ethnic networks play an important role in promoting and supporting immigrant entrepreneurship. Min and Bozorgmehr (2000) wrote: Traditionally researchers emphasized ethnic resources as the major determining factor for immigrant and ethnic entrepreneurship. However, contemporary immigrants are socioeconomically heterogeneous, with a large proportion of Asian, 87

88

Iranian Immigrants in Los Angeles African and Middle Eastern immigrants originating from middle-class backgrounds. Immigrant entrepreneurs with enough class resources do not have to depend on ethnic resources as much as earlier small business owners (Min and Bozorgmehr 2000, p. 731).

Light and Gold (2000) argued that there are no empirical studies that illustrate immigrant entrepreneurship relies only one resource versus the other. The convergence of the two resources, help explain immigrant entrepreneurship. Iranian immigrants are professionals, with high status jobs who have access to class resources. On the other hand, the presence of Iranian minority groups emphasizes the access to co-ethnic ties. The two resources together help explain Iranian immigrant entrepreneurship. ECONOMIC THEORIES The various economic theories of immigrant entrepreneurship focus on the individual's decision to turn to self-employment. As Farlie and Meyer (1993) explained, after combining the various economic models of selfemployment, "the decision to become an entrepreneur is based on comparing the expected utility from selfemployment to that from wage/salary work" (1993, p. 10). Moreover, an "individual's entrepreneurial ability, initial assets, and possibly non-wage attributes of selfemployment affect the decision to become an entrepreneur" (1993, p. 11). This approach to ethnic-owned businesses focuses entirely on individual characteristics and neglects other factors, such as demand conditions. Neoclassical economists expanded this approach by explaining "ethnic entrepreneurship in terms of prior demand conditions and human and financial capital" (for review, see Light and Karageorgis 1994). By focusing on the demand side, economists ultimately concentrated on "the money rewards of entrepreneurship" (1994, p. 655). "This approach leads one to inquire about the conditions

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under which entrepreneurs earn more and less" (1994, p. 655). Light and Karageorgis (1994) synthesized the demand-led explanations into four headings: 1) Special consumer demands (Aldrich and Waldinger 1990; Light 1972; Light and Karageorgis 1994), 2) Local industrial mix (Bonacich 1987), 3) Vacancy chains (Waldinger 1992), and 4) Political encouragement (Light and Sanchez 1987; Bernery and Owens 1985). SOCIOLOGICAL THEORIES To explain entrepreneurship, sociologists usually stress the supply side, whereas economists typically concentrate on the demand side. Waldinger, Ward and Aldrich (1985) combined the two arguments and recommended an "interactive approach" where one would look at the "congruence between the demands of the economic environment and the informal resources of the ethnic population" (1985, p. 589). This approach claims that ethnic entrepreneurship depends upon the fit between ethnic group's resources (i.e., pre-disposing factors) and the environmental demands (i.e., opportunity structures) (Waldinger, Ward and Aldrich 1985). For some sociologists the supply side of ethnic entrepreneurship (Light 1984; Waldinger 1989; Bonacich 1973; Waldinger 1992), includes: 1) Sojourning orientation, 2) Disadvantages in the labor market, and 3) Class and ethnic resources. Bonacich (1973) claimed that sojourners are more likely than permanent settlers to choose self-employment in order to quickly accumulate capital and return to their homeland. According to Piore (1979), permanent settlers are more likely to enter into selfemployment since they "have a reputation for being more aggressive and more successful than temporary migrants" (Piore 1979, p. 56). This approach contradicts the sojourning hypothesis. Waldinger (1986) explained: Setting up a business is far riskier endeavor than working for someone else. If we assume that even the most entrepreneurial of sojourning immigrants

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Iranian Immigrants in Los Angeles begin as employees, it is likely that they will accumulate a nest egg that can either be safely banked for returning home, or it can be invested in a small business whose chances for success is always open to doubt. ... Another condition of immigrant business activity is settlement pattern. Permanent immigrants usually either come with family or import immediate relatives shortly after settling; temporary immigrants leave family members at home. The consequence for temporary immigrants is that they must continue to funnel remittances that are needed to support relatives still living in the home country rather than use those monies to start up a business (Waldinger 1986, pp. 43-44).

In the Iranian study, respondents were asked to indicate whether they intended to stay or return to Iran at the time of their arrival. This chapter includes this "intent" variable in the analysis to measure Waldinger’s (1986) concept of “circumstances of migration” and tests the sojourning hypothesis. Closely tied to the circumstances of migration factor, but not included under this concept, are immigrants' visa status upon entry and their permanent residency status. In terms of starting own businesses, non-residents and refugees will have the hardest time. Non-residents do not have the legal documents to start their own businesses, and refugees/asylees are less likely to have access to start-up capital. As for other immigrants, there might be differences between family and employment immigrants in terms of self-employment. As such, the data analysis in this chapter also includes permanent residency status as a predictor. In addition to immigrant sojourning intention and their legal status, the immigration literature explains immigrant self-employment in terms of poverty, discrimination and victimization. According to this perspective, disadvantage in the labor market encourages immigrants to enter into self-employment (see Light and Rosenstein 1995). As Light and Karageorgis (1994) pointed out, this approach has several shortcomings. First, the most disadvantaged lack the resource to start-up a business. Second, "groups

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subjected to discrimination are unequally successful in entrepreneurship" (Light and Karageorgis 1994, p. 657). And third, most small businesses depend on owner's startup money or money borrowed from relatives and friends instead of bank loans. Therefore, "disadvantage alone is insufficient to create entrepreneurship" (Light and Rosenstein 1995, p. 152). Instead, Light and Rosenstein (1995) introduced the resource-constraint variant version of disadvantage theory. "The resource-constraint formula for entrepreneurship is disadvantage + resources" (Light and Rosenstein 1995, p. 152). Class versus Ethnic Resources The next supply-side explanation of self-employment explores how class and ethnic resources support immigrant entrepreneurship. Class resources are both material and cultural endowment of bourgeoisie (Light and Rosenstein 1995, p. 23). "On the material side, class resources include private property in the means of production and distribution, human capital, and money to invest" (Light and Rosenstein 1995, p. 23). On the cultural side, it includes "occupationally relevant and supportive values, attitudes, knowledge, and skills transmitted in the course of socialization" (Light and Rosenstein 1995, p. 23). For the purpose of this chapter, class/human capital indicators include: highest level of education, job experience (in years), and English proficiency. Unlike class resources, ethnic resources are "sociocultural and demographic features of the whole group" (Light and Karageorgis 1994, p. 659). Examples of ethnic resources include: entrepreneurial heritage, entrepreneurial values and attitudes, low transaction costs, reactive solidarities, multiplex social networks, and social capital (Light and Rosenstein 1995, p. 22). Social capital is a typical ethnic resource. "Ethnicity extends social trust, a key form of social capital. All businesses require mutual trust. Multiplex social networks permit ethnics to trust one another in business. Enhanced trust makes possible many

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advantageous business arrangements" (Light and Karageorgis 1994, p.662). According to social capital theory: Economic Embeddedness Hypothesis: Economic embeddedness increases the likelihood of immigrant entrepreneurship. Economic embeddedness can depend on weak as well as strong ties. Werbner (1987) found both types of ties (weak and strong) played important, yet different, function in promoting entrepreneurship among Pakistani immigrants. These immigrants mostly used strong ties to obtain personal loans and valued services. At the same time, they utilized weak ties to participate in rotating credit associations. Accordingly, the second hypothesis states: Strength of Tie Hypothesis: For the selfemployed, the type of economic embeddedness varies according to the strength of the tie. In the 1987-88 Iranian study, strength of tie is measured by using family versus non-family ties with the assumption that family ties are stronger than non-family ties, even though the response categories did not distinguish between close friends and acquaintances. The next section explores the relationship between social capital and selfemployment by concentrating on the various measures of social capital as it relates to immigrant self-employment. SELF-EMPLOYMENT AND SOCIAL CAPITAL The immigration literature has discussed the relationship between self-employment and social capital in terms of the paternalistic relationship between employers and employees (Portes and Bach 1985; Portes and Manning 1986), and in terms of financing through the use of social ties (e.g., rotating credit associations) (Light 1972). According to Portes and Manning (1986), the relation

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between owners and employees within an ethnic enclave transcends a "contractual wage bond." The close social bond and the common ethnic community of owners and employees reinforce the norm of reciprocity. In return for low wages paid to enclave co-ethnic workers, owners are "expected to respond to emergency needs of their workers and to promote their advancement through such means as on-the-job training, advancement to supervisory positions, and aid when they move into self-employment" (Portes and Manning 1986, p. 62). Many researchers have documented how social networks promote ethnic entrepreneurship (see Light 1972, 1984; Waldinger 1986: Werbner 1987; Portes and Bach 1985; Boyd 1989). "Migrant networks are most significant in the setup and development of migrant ethnic enterprises and enclaves" (Light, Bhachu and Karageorgis 1993, p. 28). By channeling business related information, migrant networks enable ethnic entrepreneurs to have an edge over non-ethnic competitors (see Light and Karageorgis 1994; Bailey and Waldinger 1991). "If immigrants can rely on networks of kin and friends for information and for assistance in finding jobs and homes", they can also mobilize these networks "to raise capital or to obtain trustworthy workers willing to work long hours at lower wages" (Waldinger 1989, p. 56). Many studies have demonstrated that ethnic entrepreneurs raise capital to start their own businesses through the use of social ties (see Waldinger 1989; Zimmer and Aldrich 1987). Rotating credit associations exemplify such mutual assistance (Light 1972) which have become "de rigueur as an illustration of the significance of embeddedness, social capital, and group solidarity" (Portes and Sensenbrenner 1993, p. 1333). Portes and his colleagues have extensively written about bounded solidarity and enforceable trust as two sources of social capital. According to Portes and Sensenbrenner (1993), these are two network-based mechanisms promoting ethnic entrepreneurship. These sources operate at the group or the community level. On the other hand, Sanders and Nee (1996), discussed another dimension: the social capital of families. They argued:

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Iranian Immigrants in Los Angeles In contrast to studies that concentrate on common ethnicity as a source of economically productive social capital, we emphasize that the family is an institution that embodies an important form of social capital that immigrants draw on in their pursuit of economic advancement. As a social organization of production, the family's chief advantages are not simply tangible products, such as unpaid labor, but also involve the mutual obligation and trust characteristic of solidaristic small groups. It is this latter aspect of family that we identify with social capital (Sanders and Nee 1996, p. 233).

Sanders and Nee (1996) used three family characteristic variables: 1) Presence of cohabiting marital partners, 2) Number of other related adults, and 3) Number of teenagers, to measure family-based social capital. Using the 1980 PUMS data set, Boyd (1990) conducted a similar analysis predicting Asian and black self-employment. Boyd's family composition variables differed from Sanders and Nee's measures. Instead of using presence of spouse and mean number of adult and teenage relatives, Boyd used family type (married w/ children versus married w/ no children), and presence of extended family as measures of family ties. He hypothesized that Asians and blacks in husband-wife families with children were more likely to turn to self-employment compared to other family types. He also hypothesized that "the presence of a extended family will increase the odds of self-employment" (Boyd 1990, p. 262). Like Boyd (1990), Sanders and Nee (1996) reported a positive, significant relationship between family composition characteristics and self-employment, after controlling for human capital and other variables (see Sanders and Nee 1996; Boyd 1990). Sanders and Nee concluded: "the pervasiveness of immigrant-owned businesses in the United States can best be understood in terms of the combined effects of human capital/class

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resources and social capital embodied in family relations" (Sanders and Nee 1996, p. 247). Social capital embodied in non-family co-ethnic ties are also important means of getting business-related support such as loans, business advice and information. Boyd, Sanders and Nee documented the importance of family ties, but they neglected the effect of non-family co-ethnic ties. There is evidence that documents immigrant group’s use of non-family co-ethnic ties to establish ethnic-owned businesses (Light 1972; Werbner 1987). Moreover, the family composition variables available in the census are only proxies of family ties. To replicate the same analysis conducted by Sanders and Nee (1996), two human/class resource variables (education, and English proficiency), three family composition variables (living with spouse, number of adult relatives and teenage relatives), and five control variables (age, age squared, professional occupation, period of immigration, and U.S. citizenship status) were included in the 1990 census analysis. Simultaneously, using the 198788 Iranian household questionnaire, Sander’s and Nee’s three family composition family variables were constructed: 1) Married and living with spouse; 2) Number of adult relatives; and 3) Number of teenage relatives. The economic network scale (as discussed in chapter 5) was also included in the analysis in addition to the three family variables to examine the determinants of self-employment among Iranian immigrants in Los Angeles. RESULTS Like most immigrant-owned businesses, approximately two-thirds of Iranian businesses were unincorporated (Table 6.1). Over half (56%) of the Iranian male sample started their businesses within the first five years of residence in Los Angeles (Table 6.1). The largest industrial concentration of Iranian businesses was in the order of: retail trade, manufacturing, professional services, wholesale trade, construction, business repair, finance/real estate and other businesses (Table 6.1). Less than 21% of

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self-employed Iranians worked alone, while 47% worked with partners, 63% worked with paid employees and 11% worked with unpaid employees (Table 6.1). The majority of business partners and unpaid employees were either close relatives, other relatives or co-ethnics, whereas the vast majority of paid employees and customers of Iranianowned businesses were non-coethnics (data not shown). Using the 1990 PUMS data set, Sanders and Nee's analysis was replicated predicting self-employment among Iranian immigrants living in the Greater Los Angeles area (Table 6.2). For Iranian immigrants, being married increased the net odds of self-employment. Similarly, the number of teenage relatives in the family was positively associated with self-employment. However, having other related adults in the household, did not have an effect on self-employment. These findings were very similar to Sanders and Nee's results for Asian and Hispanic selfemployment. Nevertheless, when the same analysis was conducted using the 1987-88 Iranian sample, none of the three family variables had significant effects on selfemployment. Spouses, teenagers and/or adult relatives increased the pool of family workers and access to cheap or free labor for the self-employed. The survey data indicates that at the bivariate level compared to salaried Iranian householders, the self-employed had a higher marriage rate (84% versus 77%), a higher mean number of adult relatives (1.69 versus 1.34), and a higher mean number of teen relatives (0.36 versus 0.22). However, after including all other related explanatory and control variables, the effects of these family variables disappear. To study the determinants of self-employment among Iranian householders living in Los Angeles, Table 6.3 illustrates the same logistic regression analysis presented in Table 6.2, but uses the survey data. All three models include individual variables such as educational background, English proficiency, and labor market experience. Two circumstances of migration variables (intent to stay or return, and permanent residency status) are included, as well. Furthermore, the analysis includes

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pre-migration self-employment. This variable is theoretically discussed at length but typically not included in most quantitative studies. In this chapter, period of immigration, occupation type, additional schooling outside of Iran, and dummy coded group variables are included as control variables. In the first model (Table 6.3), the three family composition variables, as discussed by Sanders and Nee (1996), are included as predictors of self-employment. However, unlike the census findings (Table 6.2), the survey sample shows that none of the three family composition variables are statistically significant predictors of selfemployment among Iranian immigrants (Table 6.3, Model 1). On the other hand, instead of using the family composition variables, when the economic network scale is included in the analysis, the results indicate a significant network effect (Table 6.3, Model 2). In other words, economic embeddedness increased the net odds of selfemployment. To further illustrate the sensitivity of this scale, the non-economic scale is included in the analysis instead of the economic network scale (Table 6.3, Model 3). Unlike the economic scale, the non-economic scale does not have a significant parameter estimate. This finding confirms the previous chapter's results that it is important to distinguish between economic and non-economic (social) embeddedness, thus supporting the economic embeddedness hypothesis. In addition to the significant effect of economic ties, the results also indicate that pre-migration self-employment experience significantly affects Iranian self-employment in Los Angeles (Table 6.3). Interestingly, none of the conventional determinants of self-employment (such as education and English proficiency) are significant predictors of self-employment among Iranian immigrants in Los Angeles. In this case, the only variables that are statistically significant predictors are economic network ties and pre-migration self-employment, two uncommonly measured variables. Net of all explanatory and controlled variables, there are significant subgroup differences (Table 6.3, Model 2).

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Compared to Armenians, Bahais and Muslims, Jewish Iranians had the highest self-employment rate after controlling for other variables. Eighty four percent of the male Jewish householders were self-employed during the interview. For the other groups the self-employment rate varied between 49% and 55%. Almost half of the selfemployed Jewish Iranians were in wholesale and retail trade. Among the rest, 16% were concentrated in manufacturing, 12% had businesses in professional services, and the remaining were in finance/real estate, construction and other businesses. These group differences may suggest cultural explanations of self-employment. More research is needed to explore the extent to which inter-ethnic variations in cultural practices contribute to intergroup differences in self-employment rates. As for the strength of tie hypothesis, tabulations of types of support by tie strength (family versus non-family) indicate that each type of tie strength provides different types of economic support (Table 6.4). Among the selfemployed, strong (family) ties were primarily used for providing valued services (such as employee or customer relations, transportation to work, and major discounts). Weak ties (non-family) were primarily used for obtaining business loans and receiving legal, tax or business advice. As for the rest of the items (such as referrals, professional information, translation and free labor), there was a split between weak and strong ties. Both family and non-family tie types played important yet different roles. Both network types are important – Iranian immigrant entrepreneurship is embedded in both family and non-family ties. SUMMARY For Iranian immigrants, economic embeddedness increased the odds of being self-employed net of all other related variables. The data for current employment showed that economic network ties had a significant net effect on economic outcome variables, such as income and selfemployment, whereas the non-economic network scale had

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no effect. These findings call for a conceptual distinction between economic and social embeddedness. By distinguishing between weak and strong ties, the data also indicated that the type of economic support (i.e., loans, referrals, etc.) varied according to the type of tie (family versus non-family). Both family and non-family coethnic ties promoted Iranian entrepreneurship. Studies of immigrant entrepreneurship that only consider the effect of family characteristics, entirely neglect the importance of non-family networks. In the case of the 1987-88 Iranian survey sample, the census defined family composition variables did not explain Iranian self-employment. In contrast, the economic network tie construct, which is based on both family and non-family ties demonstrated statistically significant effect. In addition to economic embeddedness, prior selfemployment experience increased the odds of selfemployment in the U.S. It is not surprising to see a high correlation between pre- and post- migration selfemployment rates. What is interesting in this case is that pre-migration data are often not measured in immigrant entrepreneurship studies. When using the 1987-88 Iranian survey with detailed network as well as pre-migration data, none of the traditionally used determinants of selfemployment showed significance. Economic embeddedness and types of tie strength are significant determinants of economic integration for Iranian immigrants in Los Angeles. Furthermore, the significant subgroup effect alludes to the importance of cultural explanations of entrepreneurship where qualitative data are needed to capture the essence of why and how some Iranian minority groups are more likely to choose entrepreneurship while others are less likely to do so.

Table 6.1 – Sample Characteristics of SelfEmployed Iranian Males, LA County 1987-88 Started own business during the: 1st year 2nd year 3rd year 4th year 5th year 6th year & later Incorporated Unincorporated: Proprietorship Partnership Industry: Construction Manufacturing Wholesale Retail Finance/real estate Business repair Professional services Other Work (select all that apply): Alone With partners With unpaid employees With paid employees Data Source: The 1987-88 Iranian Survey in Los Angeles

100

20% 11% 10% 8% 8% 43% 34% 47% 19% 10% 17% 11% 26% 7% 9% 14% 6% 20% 47% 11% 63%

Table 6.2 – Logistic Regression Predicting Self-Employment, Iranian Males, LA County, 1990 Determinants

Paramete r Estimate (log odds)

Human capital variables: High School diploma Four or more years of college (Ref: High School or less)

-0.159 0.138

English Proficiency

0.315***

Family social capital variables: Married and living with spouse Number of adult relatives Number of teenage relatives

0.486** -0.049 0.354**

Control Variables Age Age squared U.S. citizen Immigrated 1985-1989 Professional Occupations

0.079* -0.001 -0.251 -0.875*** -0.683***

Constant

-3.330***

Model Chi-square (df=11) Somer’s D

165.43*** 0.403

Number of observations

1,579

Data Source: U.S. Census Public Use Micro Data 5% Sample, 1990 * p < .05, ** p<.01, *** p <.001

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Table 6.3 – Logistic Regression Predicting SelfEmployment, Iranian Males, LA County 1987-88 Model 1 Highest level of Education: (ref: HS) MA & + BA/BS English proficiency: (ref: not well) Very well Well Self-employed in Iran Intent to stay in the U.S. Permanent Residency:(ref: non-residents) Refugee Family Other Needed worker Economic Network Scale Non-economic Network Scale Living w/ spouse Number of adult relatives Number of teenage relatives Additional schooling Job experience Occupation: (ref: Other) Managers Professionals Technical/Sales Period of immigration: (ref:1980-87) 1947-77 1978-79 Groups: (ref: Jewish Iranians) Muslims Armenians Bahais Constant Model chi-square (df=23, 21, 21)

Model 2

Model 3

0.342 0.453

0.336 0.441

0.361 0.452

-0.075 -0.025 1.609*** -0.114

-0.078 -0.104 1.627*** -0.098

-0.054 -0.040 1.623*** -0.089

-0.026 -0.170 -0.135 0.335 NI NI -0.043 0.030 0.207 0.193 0.017

0.090 -0.128 -0.080 0.420 0.078** NI NI NI NI 0.195 0.023

0.035 -0.171 -0.107 0.383 NI 0.033 NI NI NI 0.181 0.021

-0.056 -0.707 0.804*

-0.069 -0.702 0.736*

-0.079 -0.727 0.791*

0.417 0.276

0.302 0.208

0.371 0.286

-1.132*** -0.907* -0.693 -0.365 130***

-1.171*** -0.949** -0.754* -0.809 137***

-1.132*** -0.918* -0.756* -0.644 131***

Data Source: See Table 6.1 NI=not included, * p < .05, ** p<.01, *** p <.001

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Table 6.4 – Economic Network Scale Items by Type of Tie, Iranian Males, LA County 1987-88 Type of Tie Economic Network Scale Items

Family

Nonfamily

N

Transportation to work Discounts Employee Relations

53% 55% 53%

47% 45% 47%

94 101 94

Loans Finding a job Help w/ legal or tax

46% 44% 46%

54% 56% 54%

89 174 122

Referrals Professional information Translation Free labor

50% 51%

50% 49%

167 193

49% 51%

51% 49%

109 71

Data Source: See Table 6.1

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CHAPTER 7

Ethno-Religious Groups

ECONOMIC EMBEDDEDNESS The 1987-88 Iranian questionnaire, with detailed pre- and post- migration data, allowed for in depth empirical analysis. The survey included detailed questions about respondents’ employment experience in Iran and in the U.S. The respondents were asked to report their, highest educational achievement, English proficiency, additional schooling outside of Iran, visa status upon entry, permanent residency status, and whether they had given or received help immediately after arriving to the U.S. and later when they were more settled in Los Angeles. For current employment as well as first job in the U.S., respondents were asked the same set of detailed questions, and for the self-employed the survey included an additional set of questions about their pre- and post- migration selfemployment experience. Using the theoretical concepts of embeddedness, which was introduced by Karl Polanyi in the1940’s, and social capital, introduced by James Coleman and Pierre Bourdieu in the 1980’s, Portes and his colleagues applied these concepts to the field of immigration in the mid 1990’s. They argued social capital is a resource that exists between immigrant co-ethnics and their ability to access these resources when needed. Granovetter (1974, 1995) contributed to these discussions by distinguishing between 105

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strong and weak ties. He argued strong ties are limited. Weak ties, on the other hand, are more likely to give access to a wide range of social circles that might lead to better employment opportunities. In this study, embeddedness was defined as the extent to which Iranian immigrants mobilized network ties. The nature of these ties was also explored by distinguishing between family and non-family ties, which exemplified strong versus weak ties, respectively. Using the 1987-88 Iranian survey with detailed network data, two types of social capital were defined: 1) Social capital embedded in economic network ties, and 2) Social capital embedded in non-economic network ties. The results showed, social capital embedded in economic network ties had a significant effect on economic integration for Iranian immigrants in Los Angeles during both the initial settlement and the more settled phases. When looking for first employment in the U.S., the network effect was positive. Similarly, during the settled phase, Iranian networks had a positive effect on earnings and self-employment. In the case of shift in occupational status between Iran and U.S., however, use of network ties led to less prestigious jobs. This was the only negative network effect found in this study. The study also indicated that during this initial phase most Iranian immigrants, who relied on network ties, depended on immediate or extended family members. Family ties accelerated the job search process but did not broaden access to prestigious jobs during this initial settlement phase in the U.S. Besides network effect, other theoretically important variables such as pre-migration and migration variables also had significant effects on the four economic outcome measures. During the initial phase, pre-migration labor market experience, English proficiency upon arrival and visa type upon entry had significant effects on whether Iranian immigrants found employment within the first year of arrival. During the settled phase, in addition to education, class of worker, length of residency, and permanent residency status (an indicator that is discussed in immigration studies but rarely measured), had significant

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effects on income. Non-residents had the lowest income, and among the residents, employment-based Iranians had the highest income. Iranians who were classified as asylees also had low income but were better off than non-residents. Iranian immigrants who received their residency status through the family reunification criteria fell between the employment and asylee classifications. Having selfemployment experience in the country of origin was one of the most important predictors of self-employment in the U.S. This is another variable that is discussed at length but often not measured in immigrant entrepreneurship studies. INTERNAL ETHNICITY The significant group effect for the self-employed brings the discussion to the next topic – internal ethnicity. There is ethnic and religious diversity among the Iranian immigrant population in Los Angeles. Bozorgmehr (1992) referred to this as internal ethnicity within an immigrant group. In the 1987-88 Iranian Survey, the study design intentionally included the four largest subgroups. Armenians and Jews – two ethno-religious groups in Iran; Bahais – a religious minority in Iran; and Muslims – the majority population in Iran (Bozorgmehr 1997; 1992). These four Iranian ethno-religious groups in Los Angeles have different socioeconomic and demographic profiles. Pre-migration characteristics Muslims and Bahais share similar pre-migration characteristics including education, English proficiency, employment rate and additional schooling outside of Iran. Before their migration, Muslim and Bahai male respondents had a higher level of education, lower employment rate, a better command of English and were more likely to receive high levels of education outside of Iran (Table 7.1). Armenian and Jewish Iranians share some premigration characteristics. In terms of being proficient in English (upon arrival) and receiving additional schooling abroad, Jewish and Armenian Iranians resemble each other

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(Table 7.1). Nevertheless, there are still some important differences. For example, Armenians had a lower level of education in Iran. Approximately 20% of the Armenian sub-sample had received a Bachelor's degree or higher in Iran. The same figure for Jewish Iranians was 30%. The employment rate also varied between the two groups. In fact, Jewish Iranians had the highest employment rate (83%) in Iran and Armenians the next highest rate (77%). Moreover, Jewish Iranians had the highest self-employment rate in Iran (70%) while Armenians had the lowest selfemployment rate (30%). Minority Groups The high rate of self-employment among Iranians can be attributed to the presence of “former commercial minorities such as Jews and Armenians and availability of capital” (Bozorgmehr 1996, p. 224). Jewish Iranians have a long history of settlement and entrepreneurship in Iran dating back to the sixth century B.C. “when the exiled Jews decided to remain in Zoroastrian Iran despite official permission to return to their homeland” (Bozorgmehr 1997, p. 392). Throughout their settlement in Iran, “Jews were merchants and shopkeepers and also engaged in a variety of artisan and handicraft jobs” (Bozorgmehr 1997, p. 392). Christian Armenian settlement in Iran dates back to the early 17th century “when Armenians were recruited from Central Armenia to improve agriculture, crafts and trade” (Bozorgmehr 1997, p. 393). Armenians also have a long history of entrepreneurial experience in Iran as many became merchants and were instrumental in the international silk trade connecting the non-Christian Persian empires to the international Christian trade market. Unlike Jews and Armenians, Bahai Iranians are a religious group – not an ethnic group. In the mid 19th century, a group of Muslims broke away from Shiism and brought new scripture challenging the Twelver Shiite Islam (see Bozorgmehr 1992). Since Bahais are not considered “a people of the book” by devout Shiite Muslims they were “subjected to severe persecutions in Iran in the 19th

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century” (Bozorgmehr 1992, p.84). Bahais emphasize assimilation, integration and education. “Bahais like Jews, on the average, were more educated than Muslims until the mid 20th century when the newly established public school system in Iran narrowed the gap” (Bozorgmehr 1992, p. 85). Bahai Iranians are Persian speaking whereas Jews and Armenians in addition to speaking Farsi (the Persian language) also have a distinct ethnic language – Hebrew and Armenian, respectively. The distinct ethnic and religious backgrounds of these three minority groups affect their migration and integration processes in the U.S. Occupational and Industrial Distributions In terms of occupational and industrial distributions, important variations exist between the four subgroups as reported by (Bozorgmehr 1992). The vast majority of each group were concentrated in the two top occupational categories of managerial and professional specialty, as well as technical, sales and administrative support. These data show a very high overall occupational background of Iranians in Los Angeles. Nevertheless, there are differences among the four groups, which are worth pointing out (Bozorgmehr 1992, p. 111). Approximately, 13% of the Armenian sub-sample specialized in precision production, craft and repair. This figure was twice as high as the other three groups. Sixty one percent of Jewish Iranians were concentrated in technical, sales and administrative support occupations. Sixty two percent of Muslims were concentrated in managerial and professional specialty occupations. The Bahai sub-sample was more evenly distributed compared to the other three groups (see Bozorgmehr 1992). Migration Patterns In terms of migration patterns, there were significant differences between the four subgroups as well. Nearly half

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of the Muslims arrived with student visas, whereas many of the minorities arrived with visitor visas (Table 7.1). Upon arrival, more than two-thirds of Muslims (66%) intended to return to Iran, whereas almost 50% of minorities preferred to stay in the U.S. permanently. Even though a small percentage of the Iranian sample received organizational help during their migration process (11%), more minorities received help compared to Muslims. This is not surprising as migrating from a Muslim country Iranian Muslims were not eligible to apply for refugee or asylee status. Compared to the three minority subgroups, Muslims were more likely to arrive alone (55%). Many Muslims came to the U.S. to further their education especially during the late 1960’s and 1970’s (Bozorgmehr 1992). In contrast, family migration characterizes the Armenian and Bahai experience. For Jewish Iranians, the finding was mixed: 43% immigrated alone and the rest arrived with relatives and/or friends. Most Bahai, Jewish and Armenian Iranians arrived after the Revolution. The 1978-79 Revolution was the major impetus for emigration for these minorities. In terms of demographic characteristics (see Table 7.1), the mean age at immigration varied among subgroups. Muslim heads of households were the youngest and Jewish Iranians the oldest. Armenian and Jewish heads of households, tended to be older and married. Network Ties In terms of the network measures, there were no significant differences in terms of the extent to which respondents received or gave economic or non-economic help. There also were no significant differences in the types of help they received or gave. One point needs to be stressed. Even though Iranian subgroups interact with one another, Iranian networks predominantly operate at the subgroup level. When Iranian respondents were asked to rank in order of those who helped them (or who they helped), their responses ranked from immediate family, other relatives, Iranian co-religionist, other Iranians, and all others. Over 90% of the responses included the first three categories.

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Just as in the case of the Iranian ethnic economy, which operates at the subgroup level, the Iranian economic and social networks operate at this subgroup level as well (Bozorgmehr, Sabagh and Der-Martirosian 1993; Light, Sabagh, Bozorgmehr and Der-Martirosian 1993; Bozorgmehr 1992). Bozorgmehr (1997) explained: “On the whole, informal and formal social ties in Los Angeles tend to reinforce internal ethnicity rather than an encompassing Iranian nationality. Armenians and Jews are more ethnic than Bahais and Muslims. The subgroup differences in the patterns of social ties among various Iranian subgroups are not simply a result of immigration; they also existed in Iran” (Bozorgmehr 1997, p. 403). Iranian’s high group resources explains the positive network effect. Given their high status, at the group level, Iranians have access to well paying and prestigious jobs. These group resources, however, vary according to each subgroup. Even though there is overlap between the four subgroups – Muslims, Jews, Bahais and Armenians. Iranian network ties operate at the subgroup level. IRANIAN WOMEN Unlike ethno-religious differences, gender differences were not examined in this manuscript. For reasons explained earlier, this study focused on Iranian men heads of households. According to the Iranian survey, almost 40% of Iranian women were employed in 1987-88. This figure is rather high given the traditional patriarchal family roles evident in the Iranian culture with the husband as the breadwinner and the wife the homemaker. Among employed women, 26% were self-employed, a rate three times higher than that of native-white men. Iranian women were actively involved in the Iranian ethnic economy in Los Angeles (Dallalfar 1996; 1994; 1989), either as entrepreneurs or paid/unpaid employees or partners of family-owned businesses. "Many operate

112

Iranian Immigrants in Los Angeles

catering, baking, or pastry-distribution businesses, often supplying Iranian food stores. Others provide services for women, such as hair removal, hair dressing, manicures, and facials" (Tohidi 1993, p. 187). Outside of the ethnic economy, Iranian women work in service and technical fields, including computer programming, nursing, medical technology, and engineering. In addition, they were well represented among financial, insurance, and real estate workers; in sales and clerical services; and in government offices, private companies and banks (Tohidi 1993). Networks operate differently for men and women. Based on his study on Israeli immigrants, Gold (1992) argued that immigrant women's social capital - unlike men's - centers on domestic and communal life. Dallalfar (1994) pointed out that for Iranian women, especially for those who run their businesses from home, the social visit and networking that occurs (at home) is essential to the economic transaction that follows. The public and private spheres merge in these women's work environments where information regarding on goings in the community, new friends as well as employment and purchasing possibilities occur simultaneously (Dallalfar 1994). In the Iranian survey, for the 115 female heads of household, the mean economic network score was 3.2. This figure is much lower than the mean score of 7.1 for social (non-economic) network items. For employed women, this same pattern persisted. This pattern is completely the opposite of what was found for men. Employed Iranian women seem to rely less on economic ties when compared to social ones. These preliminary results suggest that the network structure and types of network ties might differ between Iranian men and women – a topic for future research. ECONOMIC NETWORK EFFECT Whether studying the immigration experience of men or women, network data should be collected as a standard procedure. Knowledge about network ties is just as

Ethno-Religious Groups

113

important as socio-economic and demographic variables. Using the Iranian survey, economic and non-economic embeddedness was distinguished, as well as family versus non-family ties. The findings indicated that economic embeddedness directly impacted economic integration measured by income, change in occupational status, selfemployment and length of time spent finding first job in the U.S., while non-economic ties had no effect. In addition to these measures, family and non-family types of ties were used to define strong and weak ties with the assumption that family ties are stronger than non-family ties. Iranian immigrants relied on both network types. During the initial phase, immigrants used family ties more frequently than non-family ties. For this population, reliance on family ties is not surprising. Family is an important part of Iranian immigrants' daily life. During the settled phase, Iranian male immigrants began to also rely on non-family ties, thereby increasing their pool of weak ties. The findings throughout this study challenge the belief that high skilled professional immigrant groups are less likely to rely on network ties and more on their labor market skills. In terms of human capital resources, Iranian immigrants in Los Angeles are highly educated, proficient in English, and have entrepreneurial experience. Iranian immigrants exemplify high status immigrants in the U.S. In terms of economic capital, concrete data are not available but one might speculate that some proportion of the early Iranian migrants who left prior to the Iranian Revolution may have transferred funds oversees. For these high status immigrants, one would have expected minimal reliance on co-ethnic network ties. The data, however, showed the contrary. Regardless of socioeconomic status, over half of Iranian male immigrants reported receiving economic help. Over half were embedded in economic network ties during both initial and more settled phases. Overall, the most striking differences were found between those who received help versus those who did not receive help. Economic embeddedness, whether family or non-family, increased the odds of finding jobs faster upon arrival. Economic networks had a positive effect on

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Iranian Immigrants in Los Angeles

individual earnings, and increased the odds of being selfemployed. The distinctions between economic and non-economic embeddedness, as well as family versus non-family ties, should encourage immigration researchers to construct better network measures. More importantly, this study urges immigration researchers to collect network data and inquire about immigrants having access to network ties and the extent to which they use them. Measures of social capital and embeddedness in the field of immigration are relatively new. More research is necessary to better expound on these concepts and examine their effects on economic outcome measures. ECONOMIC PROSPERITY The ultimate question that most everyone is interested is, what are the prospects for immigrants and their children? Bozorgmehr, Sabagh and Der-Martirosian (1996) show an earnings gap when comparing adjusted earnings of foreignborn Iranians to native-born whites. Despite this finding, for Iranians living in Los Angeles the future is promising. Iranian immigrants are highly entrepreneurial with a sizable Iranian ethnic economy “occupying 61% of Iranian heads of households labor force”(Light, Sabagh, Bozorgmehr, and Der-Martirosian 1994, p.73). The Iranian ethnic economy operates at the subgroup level since according to the Iranian data set, the partners, employees, and unpaid workers of Iranian owned businesses are mostly family members and co-ethnics or co-religionists (Light, Sabagh, Bozorgmehr, and Der-Martirosian 1993). For ethnic business owners, self-employment is one avenue of upward mobility. For co-ethnic employees, the ethnic economy provides employment opportunities that otherwise might not have been available in the general economy. Hence, the Iranian ethnic economy, or the subgroup economies, can provide an economic advantage for their co-ethnics.

Ethno-Religious Groups

115

Occupational and Industrial Niches In addition to their entrepreneurial background, Iranian immigrants hold professional specialty jobs. Using the 1990 PUMS data set, Table 7.2 presents the top ten occupational and industrial niches for Iranian immigrants employed in the LA labor market (see Bozorgmehr, Sabagh and Der-Martirosian 1996). Four out of the ten niches were professional jobs – Accountants, Civil Engineers, Physicians and Other Engineers. The Supervisor Sales occupational category (code 243) was the top occupational niche, with 60% self-employment rate. The other three niches were also related to sales – Managers, Sales, and Real Estate (Table 7.2). In terms of self-employment rate, only three of the top ten occupational niches had a selfemployment rate less than 15%, the rest of the niches had self-employment rates over 30% (Table 7.2) once again showcasing the high rate of entrepreneurship among Iranian immigrants in Los Angeles. The top ten industrial niches confirm the patterns found for the occupational niches. The professional specialty industries included: Engineering, Physician Office, Accounting Services and Banking. For sales, the top niches were: Real Estate, Retail Apparel, Retail Department Store, and Retail Furniture. There were two niches with no selfemployment - Banking and Department Stores - the rest had self-employment rates above 20% and 30% (Table 7.2). As Waldinger points out, ethnic niches are products of networks. “Immigrants tend to cluster in activities in which others of their own kind are already established. Thus, the repeated action of immigrant social networks yields the ethnic niche – a set of economic activities in which immigrants are heavily concentrated” (Waldinger and DerMartirosian 2001, p.236-7). The final section discusses the quality of Iranian niches using the 1990 U.S. Census PUMS data and the DOT job ratings to further assess the economic integration of Iranians in Los Angeles. Quality of Iranian Niches This section characterizes the quality of jobs in overrepresented Iranian industrial and occupational niches by

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Iranian Immigrants in Los Angeles

applying Dictionary of Occupational Titles (DOT) to the 1990 U.S. Census PUMS data. The DOT, which contains information on the requirements, contents, and structures of more than 10,000 occupations based, in part, on extensive on-site observation of jobs as they were actually performed. Each of the occupations identified in the DOT is rated according to 3 worker functions (complexity of work in relation to data, people and things) and 41 worker traits, having to do with such aspects as training times, the aptitudes, temperaments, and interests best suited for satisfactory job performance; and the physical demands of the job. Because the worker function and worker trait indexes tend to be redundant, with considerable intercorrelations among the items, numerous researchers have sought to reduce them to a much smaller set of underlying dimensions (Waldinger and Der-Martirosian 2001, p.245). There are many studies that have attempted to reduce these trait indices into identifiable constructs. This section relies on Shu and her colleagues’ (1996) tabulations of the DOT indices where by using confirmatory factor analysis they identified seven latent constructs. This analysis focuses on four dimensions of quality of niches – physical demand, social skills, work conditions and substantive complexity (for detailed explanation see Waldinger and Der-Martirosian 2001). Substantive complexity and social skills serve as indicators of required cognitive skills. Job characterized by substantive complexity involve relatively high levels of “intelligence, verbal and numerical aptitudes, advance educational development, long vocational preparation, involvement in abstract and creative processes, and high complexity of function in relation to data” (Shu, Fan, Li and Marini 1996, p. 171). Although

Ethno-Religious Groups

117

the concept of social skills overlaps somewhat with substantive complexity, this dimension is more closely linked to those aspects of jobs that involve coordination with others. By contrast, physical demands and work conditions tap into the environmental aspects and requirements of the job. Jobs with high physical demands are those that involve stooping, climbing, loading-brawn rather than the finely developed manual proficiencies involved in traditional skilled crafts. Work conditions picks up not so much the tasks involved in the job as the environment which the jobs takes place, with negative score pointing to unfavorable conditions. Most desirable jobs are those that require high social and cognitive skills, have low physical demands and provide favorable work conditions” (Waldinger and Der-Martirosian 2001, p. 245-6). The industrial and occupational job ratings for Iranian immigrant niches are presented in Table 7.3. The intersection between industrial and occupational scores best describes the quality of the Iranian immigrant niches. The negative occupational and industrial scores for physical demands place this dimension in the negative/negative (3rd) quadrant if one were to graph these two scores. The positive/positive scores for the other three dimensions – social skills, substantive complexity and work conditions – place them in the 1st quadrant. According to these findings Iranian jobs require high social and cognitive skills, favorable work conditions with low physical demands. The DOT characterization of the Iranian immigrant niches using the 1990 U.S. Census, indicate that jobs in Iranian immigrant niches are very desirable. Even though there is an earnings gap between foreignborn Iranians and native-born Americans, the overrepresentation of Iranian immigrants in self-employment, professional specialty and high quality jobs (as illustrated by the Iranian niche data) all point to economic prosperity and success among Iranian immigrants in Los Angeles. As

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for future Iranian generations, they will most probably continue achieving high levels of education and excel in various professional fields. In terms of entrepreneurship, only time will tell whether future generation Iranians will follow their parent’s footsteps and continue the tradition of entrepreneurship. Moreover, for future American Iranian generations it might be hard to distinguish the effects of strong vs. weak ties since Iranians already have a high level of social capital due to their group’s high class and ethnic resources. As future American Iranian second and third generations enter the labor market, their access to both coethnic and non co-ethnic ties will undoubtedly increase giving them an economic advantage with even better employment and business opportunities.

Table 7.1 – Characteristics of Ethno-Religious Groups, Iranian Males, LA County 1987-88 Muslims

Armenians

Bahais

Jews

37% 64% 33%

19% 77% 30%

36% 68% 40%

30% 83% 70%

45%

38%

45%

33%

78%

50%

63%

49%

14% 12% 43% 31%

18% 19% 17% 46%

13% 14% 29% 43%

12% 17% 18% 53%

34% 55%

66% 34%

41% 36%

57% 43%

40% 29% 31%

28% 36% 36%

33% 32% 35%

19% 41% 40%

Informed about job opportunities in U.S.

58%

68%

63%

57%

Self-employed (U.S) Married

47% 75%

43% 92%

54% 76%

83% 86%

43

49

46

51

BA or + in Iran Employed in Iran Self-employed in Iran Proficient in English upon arrival Additional schooling outside Iran Visa Type: Family/Return Refugee/Asylee Student Temp Visitor Intended to stay in U.S. Migrated alone Period of Immigration: 1947-77 1978-79 1980-87

Mean age (at interview)

Data Source: The 1987-88 Iranian Survey in Los Angeles.

119

Table 7.2 – Top Occupational and Industrial Niches, Iranian Immigrants, LA County 1990 Top 10 Occupational Niches* Supervisors in Sale Accountants Sales (other) Sales Representatives Civil Engineers Managers (Food/Lodging) Physicians Real Estate Agents Hair Dressers Engineers (not specified) Top 10 Industrial Niches** Real Estate Engineering Banking Physician Office Retail Gas Station Retail Apparel Beauty Shop Accounting Services Retail Department Store Retail Furniture

% Self-Employed 60% 13% 30% 52% 13% 30% 52% 62% 32% 0% % Self-Employed 50% 22% 0% 47% 42% 27% 38% 36% 0% 50%

*Data Source: The 1990 U.S. Census PUMS – (See Bozorgmehr, Sabagh and Der-Martirosian 1996, Table 12.5). **Data Source: The 1990 U.S. Census PUMS.

120

Table 7.3 – Mean Scores for Quality of Jobs, Iranian Occupational and Industrial Niches, LA County 1990 Physical Social Substantive Work Demands Skills Complexity Conditions Occupationa l Niches

-47

149

141

179

Industrial Niches

-38

80

5

137

Data Source: The 1990 U.S. Census PUMS; DOT Scores: Tabulations by Shu et al. 1996 (see Waldinger and Der-Martirosian 2001).

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APPENDIX A Iranian Survey, LA County (Household Roster) Variable Description

Variable Number

Respondent’s age Gender Marital Status

v13/v20 v11/v18 v16/v23

Year of arrival to LA Highest degree completed in Iran Additional schooling

v75/v83 v76/v84 v77/v85

Full time employment last week Number of hours worked last week Occupation Industry Class of Worker

v146/v151 v147/v152 v148/v153 v149/v154 v150/v155

Father’s education Mother’s education

v218 v219

Father’s occupation Father’s class of worker

v220 v221

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APPENDIX B Iranian Survey, LA County (Migration Experience) Variable Description

Variable Number

Year of arrival to the U.S. Lived continuously in the U.S. Relatives at time of arrival Type of help received Plans to stay or return at arrival Sources of information before arrival Specific information Visa status upon arrival Current immigration status Permanent residency classification

v231 v232 v238 v239 v376 v382-90 v391-98 v399 v400 v401

Arrived alone or w/ others Organization assistance to migrate

v402 v408

English proficiency at time of arrival English proficiency at time of interview

v444 v443

Experienced discrimination

v520-25

124

APPENDIX C Iranian Survey, LA County (First Employment in the U.S.) Variable Description

Variable Number

Year started first job Same job as current job Industry Occupation Who helped to find first job Class of worker

v702 v703 v709 v710 v711-17 v718

125

APPENDIX D Iranian Survey, LA County (Current Employment) Variable Description

Variable Number

Wage/Salary Worker Year started current job Industry Occupation Number of hours worked Satisfied with current job Satisfied with income Job compatible with education Class of worker Household income for 1986 Sources of household income

v543 v546 v547 v548 v549 v550 v551 v552 v894 v873-90

Self-Employed Year started business Reasons for going into business Incorporated vs. not incorporated Ethnicity of top four officers Ethnicity of partners Work alone or w/ partners Number of paid employees Ethnicity of paid employees Ethnicity of customers/clients

v553 v554 v555 v557-62 v563-68 v574-77 v583 v584-89 v590-93

126

APPENDIX E Iranian Survey, LA County (Network Ties) Variable Description

Variable Number

List of non-economic items (e.g., childcare, housework, etc.)

v619-44

Rank who helped (or received help) (e.g., spouse, relatives, etc.) List of economic items (e.g., finding a job, loans, etc.) Rank who helped (or received help) (e.g., spouse, relatives, etc.)

127

v647-60 v661-82 v687-99

APPENDIX F Iranian Survey, LA County (Employment in Iran) Variable Description

Variable Number

Last job prior to migration Industry Occupation Class of worker First job same as last job in Iran Industry Occupation Class of worker

128

v775 v777 v778 v779 v781 v783 v784 v785

APPENDIX G Iranian Survey, LA County (Spouse’s Employment) Variable Description

Variable Number

Employment in the U.S. Currently employed Industry Occupation Number of hours worked Class of worker

v789 v793 v794 v795 v796

Employment in Iran Last job prior to migration Industry Occupation

v786 v787 v788

129

APPENDIX H Iranian Survey, LA County (Study Questionnaire) Household Roster (1 of 2) v13/v20 – In what month and year was he/she born? v11/v18 – Is this person male or female? v16/v23 – Is he/she: 1.Never married 2.Married 3.Divorced 4.Separated 5.Widowed v75/v83 – In what month and year did he/she come to live in Los Angeles? mo ____ yr _____ v76/v84 – What was the highest degree or level of school he/she completed in Iran? v77/v85 – How many additional years of school did he/she complete outside of Iran? v146/v151 – Did he/she work at any time last week? 1.Yes, full-time 2.Yes, part-time 3.No v147/v152 – How many hours did he/she work at all jobs combined last week?

130

APPENDIX H Iranian Survey, LA County (Study Questionnaire) Household Roster (2 of 2) v148/v153 – What kind of work was he/she doing last week? v149/v154 – What kind of business or industry was this? v150/v155 – Last week was he/she: 1.Private salary 2.Government Salary 3. Proprietorship 4. Partnership 5.Corporation v218/v219 – Please estimate the highest degree or level of schooling of your father and mother? v220 – When you were 16, what type of work did your father do? v221 – Was he? 1.Self-employed 2.Salaried, or 3. Both?

131

APPENDIX H Iranian Survey, LA County (Study Questionnaire) Migration Experience (1 of 4) v231 – In what year did you come to the U.S. for the first time? ______ year v232 – Have you lived continuously in the U.S. since you first arrived, excluding short trips abroad? 1.yes 2.no v238 – Did you have any relatives or friends living in LA? 1.yes 2.no v239 – What kind of help, if any did you receive from them? (circle all that apply) 1.Free housing and meals 2.Paid housing and meals 3.Locting a place to live 4.Transportation 5.Loan of money 6.Finding a job 7.Identifying a business opportunity 8.Other (specify) 9.No help at all v376 – When you first came to the U.S., what were your plans? 1.Stay in the U.S. permanently 2.Go back to Iran soon 3.Go back to Iran someday 4.Move to another country 5.Other (specify)

132

APPENDIX H Iranian Survey, LA County (Study Questionnaire) Migration Experience (2 of 4) v382-90 – Before coming to the U.S., what were your sources of information? 1.Iranians who had been to the U.S. 2.Relatives in the U.S. 3.Friends in the U.S. 4.Americans in Iran 5.Iranian magazines, newspapers, TV 6.English language media and books 7.American films and TV programs 8.Own previous residence in the U.S. 9.Other (specify) v391-98 – How informed were you about the following subjects? 1.Housing 2.Cost of living 3.Employment opportunities 4.Business opportunities 5.Salary & wage levels 6.Language requirements 7.Educational opportunities 8.Other (specify)

133

APPENDIX H Iranian Survey, LA County (Study Questionnaire) Migration Experience (3 of 4) v399 – What was the visa category under which you came to the U.S? 1.Brothers/Sisters of U.S. citizens 2.Married sons/daughters of U.S. citizens 3.Parents of U.S. citizens 4.Refugees and asylees 5.Returning permanent residents 6.Returning U.S. citizens 7.Skilled professionals 8.Spouses, unmarried sons/daughters of permanent residents. 9.Students 10.Temporary visitors 11.Unmarried sons/daughters of U.S. citizens 12.Workers in short supply 13.Other (specify) v400 – What is your current immigration status? 1.Non-resident 2.Permanent resident, or 3.Naturized citizen v401 – How did you become a permanent resident? 1.Married a U.S. citizen or permanent resident 2.Classified as Asylee 3.As a highly skilled professional worker 4.As a needed, skilled or unskilled worker 5.Under investor’s exemption 6.Other (specify)

134

APPENDIX H Iranian Survey, LA County (Study Questionnaire) Migration Experience (4 of 4) v402 – When you first came to the U.S., did you come alone or did you arrive with your spouse, or others? 1.Arrived alone 2.Arrived with spouse 3.Arrived with some relatives 4.Arrived with friends 5.Arrived with spouse, friends and relatives v408 – Did any organization (not friends or relatives) help you move to the U.S.? 1.yes 2.No v444 – How well did you speak English when you last came to the U.S. to live here continuously? 1.Very well 2.Well 3.Not well, or 4.Not al all v443 – How well do you speak English? Would you say: 1.Very well 2.Well 3.Not well, or 4.Not al all v520/v525 – Have you recently experienced discrimination in these areas: 1.Getting a job to fit your education 2.Being promoted on the job 3.Going into business for yourself 4.Getting a loan from a bank 5.Getting a good education for your children 6.Renting or buying a house or apartment

135

APPENDIX H Iranian Survey, LA County (Study Questionnaire) First Employment in the U.S. (1 of 1) v702 – In what year did you start your first full-time (or near full-time) job or business? v703 – Was this job exactly the same as you have now? 1. yes 2.no v709 – What kind of business or industry was this? v710 – What kind of work were you doing? v711/v717 – Please identify the persons who may have helped you to find your first job or start your first business? 1.Spouse, parents, children, siblings 2.Other relatives 3.Iranian co-religionists 4.Other Iranians 5.Non-Iranian co-religionists 6.Other v718 – In which of these categories was this first job or business? 1.Working for a salary, wage or commission for a private company 2. Working for federal, state or local government 3. Working for own business/ proprietorship not inc. 4. Working for own business/ partnership not inc. 5. Working for own corporation

136

APPENDIX H Iranian Survey, LA County (Study Questionnaire) Current Employment (1 of 4) v543 – In what year did you start working? v546 – What kind of business or industry was this? v547 – What kind of work were you doing last week? v548 – Approximately how many hours did you work for pay at all jobs last week? v549 – How satisfied are you with your job or business? 1.Very satisfied 2.Satisfied 3.Somewhat satisfied 4.Dissatisfied 5.Very dissatisfied v550 – How satisfied are you with your income? 1.Very satisfied 2.Satisfied 3.Somewhat satisfied 4.Dissatisfied 5.Very dissatisfied v551 – Would you say that your current job and income are compatible with your education and other qualifications? 1.Very compatible 2.Compatible 3.Somewhat compatible 4.Not compatible

137

APPENDIX H Iranian Survey, LA County (Study Questionnaire) Current Employment (2 of 4) v542 –Are you? 1.Working for a salary, wage or commission for a private company 2. Working for federal, state or local government 3. Working for own business/ proprietorship not inc. 4. Working for own business/ partnership not inc. 5. Working for own corporation v894 – Which category includes the entire gross income of your household from all sources in 1986: (13 categories in 10k increments). 1.less than $14,999 … 13.$125,000 & over v873-90 – Please rank the sources of your household income for 1986: 1.Own business 2.Head of household’s wage/salary 3.Spouse’s wage/salary 4.Child’s earnings 5.Earnings of relatives in household 6.Savings interest 7.Dividents and investment earnings 8.Rental income 9.Payments from Iran 10.Social Security 11.Retiriement benefits or pensions 12.Alimony 13.Child support 14.Inheritance 15.Welfare 16.Other 138

APPENDIX H Iranian Survey, LA County (Study Questionnaire) Current Employment (3 of 4) v553 - In what year did you start your own business or professional practice? v554 – What were your reasons for going into business for yourself? v555 – Is your current business or professional practice? 1. Proprietorship not incorporated 2. Partnership not incorporated 3. Corporation v557-62 – Excluding yourself, how many of the four top officers of your corporation are: 1.Spouse, parents, children, siblings 2.Other relatives 3.Iranian co-religionists 4.Other Iranians 5.Non-Iranian co-religionists 6.Other v563-68 – If you have any partners or co-owners are they: 1.Spouse, parents, children, siblings 2.Other relatives 3.Iranian co-religionists 4.Other Iranians 5.Non-Iranian co-religionists 6.Other v583 – How many paid workers and employees do you have? 139

APPENDIX H Iranian Survey, LA County (Study Questionnaire) Current Employment (4 of 4) v574/v577 - Do you work? (circle all that apply) 1.Alone 2.With partners or co-owners 3.With unpaid family members or 4.With paid employees v584/v589 – Are your paid workers and employees? 1.Spouse, parents, children, siblings 2.Other relatives 3.Iranian co-religionists 4.Other Iranians 5.Non-Iranian co-religionists 6.Other v590/v593 – Approximately how many percentage of your customers and clients are? 1.Iranian co-religionists 2.Other Iranians 3.Non-Iranian co-religionists 4.Other

140

APPENDIX H Iranian Survey, LA County (Study Questionnaire) Network Ties (1 of 2)

v619/v6944 – Since you came to Los Angeles, have you (or your spouse) often given any such local help? Have you often received help? Gave Received Help Help 1.Childcare 2.Housework 3.Match-making 4.Personal counseling 5.Club or organizational referrals 6.Interest free loans of money/goods 7.Moving a residence 8.Transportation 9.Meals, shopping, Food 10.Medical or dental referrals 11.Consumer advice 12.Providing free housing 13.Other (specify) v647/v660 - Rank the order of persons whom you helped socially and then who helped you? 1.Spouse/parents/children/sibling 2.Other relatives 3.Iranian co-religionists 4.Other Iranians 5.Non-Iranian co-religionists 6.Other (specify)

141

Gave Help

Received Help

APPENDIX H Iranian Survey, LA County (Study Questionnaire) Network Ties (2 of 2)

v661/v686 – Since you came to Los Angeles, have you (or your spouse) often given any help? Have you often received help? Gave Help

Received Help

Gave Help

Received Help

1.Finding a job or business 2.Referrals and references 3.Professional advice and information 4.Transporation to work 5.Loans 6.Discounts 7.Legal, tax or business advice 8.Translation 9.Employee or customer relations 10.Free labor, goods or equipment 11.Other (specify) v687/v700 - Rank the order of persons whom you helped economically and then who helped you? 1.Spouse/parents/children/sibling 2.Other relatives 3.Iranian co-religionists 4.Other Iranians 5.Non-Iranian co-religionists 6.Other (specify)

142

APPENDIX H Iranian Survey, LA County (Study Questionnaire) Employment in Iran (1 of 1) v775 – Going back to your adult life before you left Iran, did you have full-time job or business? 1. yes 2. no v777 – What kind of business or industry was this? v778 – What kind of work were you doing? v779 –Were you? 1.Working for a salary, wage or commission for a private company 2. Working for federal, state or local government 3. Working for own business/ proprietorship not inc. 4. Working for own business/ partnership not inc. 5. Working for own corporation v781 – Thinking about the job you held in Iran, was this first job or business you ever held exactly the same as your last job or business in Iran? 1. yes 2. no v783 – If not, what kind of business or industry was this? v784 – What kind of work were you doing? v785 –Were you? 1.Working for a salary, wage or commission for a private company 2. Working for federal, state or local government 3. Working for own business/ proprietorship not inc. 4. Working for own business/ partnership not inc. 5. Working for own corporation 143

APPENDIX H Iranian Survey, LA County (Study Questionnaire) Spouse’s Employment (1 of 1) v786 – Going back to the time before your spouse left Iran, did she/he have full-time job or business? 1. yes 2. no v787 – What kind of work was she/he doing? v788 – What kind of business or industry was this? v789 – What was she/he doing most of last week? 1.At work 2.Had a job but was not at work 3.Unemployed, seeking work 4.Unemployed, not seeking work 5.Unable to work 6.Attending school 7.Retired 8.Keeping house 9.Other (specify) v793 – What kind of business or industry was this? v794 – What kind of work was she/he doing? v795 – Approximately how many hours did she/he work for pay at all jobs last week v796 – Was she/he? 1.Working for a salary, wage or commission for a private company 2. Working for federal, state or local government 3. Working for own business/ proprietorship not inc. 4. Working for own business/ partnership not inc. 5. Working for own corporation 144

References

Aguilera, Michael, and Douglas Massey. 2003. “Social Capital and the Wages of Mexican Migrants: New Hypotheses and Tests.” Social Forces 82(2):671-701. Aldrich, Howard, and Roger Waldinger. 1990. "Ethnicity and Entrepreneurship." Annual Review of Sociology 16:111-35. Bailey, Thomas, and Roger Waldinger. 1991. "Primary, Secondary, and Enclave Labor Markets: A Training Systems Approach." American Sociological Review 56:432-45. Bernery, Robert, and Ed Owens. 1985. "A Theoretical Framework for Small Business Policy." Policy Studies 13:681-89. Bhachu, Parminder. 1993. “Twice and Direct Migrant Sikhs: Caste, Class, and Identity in Pre- and Post- 1984 Britain.” Pp. 163-84 in Immigration and Entrepreneurship: Cultural, Capital and Ethnic Networks, edited by Ivan Light and Parminder Bhachu. New Brunswick, NJ: Transaction. Blau, Peter M., and O. D. Duncan. 1994. "Measuring the Status of Occupations." Pp. 204-7, in Social Stratification in Sociological Perspectives, edited by David B. Grusky. Boulder, Colorado: Westview Press. 145

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Index

additional schooling outside of Iran, 39 47-52, 62, 69, 75, 82, 84, 97, 102, 105, 107, 119 age, 26, 27, 43, 48, 5052, 62, 69, 88, 110 Armenian Iranians, 14, 15, 26, 50-52, 69, 76, 84, 102, 107 characteristics, 30, 31, 48, 97, 107, 108, 109, 110, 119 history, 108-109 sampling, 18, 19 U.S. Census, 17, 22-24, 30, 31 asylees, see refugees auspices of migration 35, 37

characteristics, 48, 50-52, 76, 79, 107, 109-111, 119 history, 107-109 sampling, 15, 18, 19, 22 Bonacich, Edna, 13, 89 Borjas, George, 2, 3, 33, 39, 40, 41 Bourdieu, Pierre, 1, 5-8, 105 Bozorgmehr, Mehdi, 914, 18, 19, 25, 75, 87, 107, 111, 114, 115

California, 12 chain migration, 35 Chiswick, Barry, 2, 3, 33, 34, 39, 41, 42 cohort effect, 3, 4, immigrant cohort, 41, 42, 44 Coleman, James, 2, 6-8, 71, 72, 74, 105

Bahai Iranians, 14, 17,26, 64, 65, 69, 84, 102 159

160 cultural capital, 6

Dallalfar, Arlene,111, 112 Der-Martirosian, Claudia, 12-14, 23, 75, 111, 114-117 Duncan, Otis, 45, 60-62

earnings, see income economic capital, 1, 6, 7, 113 economic embeddedness, 2, 71, 74, 78, 80, 81, 83, 87 economic integration, 1, 25, 38, 66, 71, 75, 99, 106, 113, 115 economic mobility, 4 economic network scale, initial phase, see general economic help settled phase, 74, 75, 77, 78, 82, 84, 95, 97, 98, 102, 103 economic network ties, see network ties education, completed in Iran, 26, 39, 48, 50-52, 69, highest anywhere, 82, 84, 91, 102 English proficient, 3, 4,

Index at arrival, 26, 40, 48, 50-52 at time of interview, 82, 84, entrepreneurship, 22, 99, 115, 118, networks, 87 resources, 88, 91 social capital, 92, 93 ethnic economy, 110-112, 114 ethnic networks, 5, 14, 35, 87, see also network ties ethnic niche, see niche ethno-religious groups, 14, 15, 17, 43, 64, 76, 107, 111, see also Armenians, Bahais, Jews and Muslims exiles, see refugees

females, 18, 21, 24, 111-112 Armenian Iranian, 23, 31 sample, 18, 23, 29 first job in the U.S., 20, 27, 44, 50-52, 113 occupational status, 55 timing of first job, 38, 39, 65, 105,

Index general economic help, 43, 48, 50 general non-economic help, 48, 51 Gold, Steve, 88, 112 Granovetter, Mark, 2, 5, 6, 9, 55-59, 65, 105

heads of households, 17, 19, 24, 26, 62, 110-112, 114 high status, 13, 88, 111, 113 human capital, 1, 9, 3334, 39, 40, 42, 113 income, 75, 77 self-employment, 91, 94, 101

immigrant networks, 1, 2, 5, 9, 34-38, see also network ties immigration status, 48, 50-52, 69 income, 1, 4, 19, 25, 26, 71-86 industrial distribution, 21, 28, 29, 30, 31, 109 industrial niches, 114, 115, 120 initial settlement, 1, 26, 47, 55, 56, 59, 62, 65, 106

161 intent to stay, 96, 102 internal ethnicity, 14, 107-111, see also ethno-religious groups interpersonal network ties, 4, see also network ties interviews, 18-20, 22, 24, 26, 27, 40, 43 Iran-Iraq War, 11, 76 Iranian immigration, 1, 9-12 Iranian Revolution, 1, 10, 11, 12, 14, 17, 41, 42, 76, 79, 110, 113 Iranian survey, 15, 17-27

Jewish Iranians, 14, 15, 17, 26, 69, 84, characteristics, 22, 48, 50-52, 66, 76, 97, 98, 107, 109, 110, 119 history, 107-109 sampling, 18, 19 job experience, see work experience job satisfaction, 80, 81, 86 labor market, 2, 3, 13, 14, 38, 40, 43, 45, 46, 75, 77, 89, 90, 106, 113

162 last occupation in Iran, 38, 62, 67 Light, Ivan, 4,13, 14, 23, 35-37, 73, 88, 89-93, 111, 114 Lin, Nan, 36, 56, 57, 58

males, 28, 48-53, 67-69, 82-86, 100-103, 119 marital status, 26, 33, 43, 62, 64, 75, 78, 94 married, 19, 24, 43, 48, 50-52, 64, 69, 84, 95, 96 Massey, Douglas, 1, 4, 5, 34-36, 71, 72 migration experience, 19, 26, 38, 76, 112 Min, Pyong Gap, 13, 23 minority groups, see ethno-religious groups Muslim Iranians, 14, 15, 17, 22, 26, 69, 84, 102 characteristics, 48, 50-52, 76, 97, 107-111, 119 sampling, 18, 19

neoclassical economists, 3, 33, 87

Index network ties, 1, 2, 5, 9, 25, 35, 37, 38, 44, 46, 71, 79, 112-114 family vs. non-family, 80, 110 men vs. women, 112 strong vs. weak, 55, 62, 65 subgroup level, 111 niche, 114-117, 120 non-economic network scale, 11, 106 initial phase, 38, 43, 44, 46 settled phase, 74, 75, 78, 79, 82, 84, 98, 102

occupational distribution, 23, 28-31, 76 occupational niches, 114, 115, 120 occupational status, 1, 25, 39, 47, 55-69

period of immigration,26, 46, 48, 50-53, 62, 69, 78, 79, 84, 95, 97, 102, 119 physical capital, 7, see also economic capital Polanyi, Karl, 6, 105 political refugee, see refugee

Index Portes, Alejandro, 2, 4, 6, 8, 9, 13, 33-36, 72-74, 92-94 pre-migration, 1, 26, 34, 107, 108 professionals, 13, 14, 23, 27-31, 40, 45, 55, 57, 63, 67, 74, 76, 79, 82, 84, 88, 95, 98, 100-103, 109, 113-118 professional specialty, see professionals PUMS, yr 2000, 12 yr 1990, 12, 17, 28, 29, 30, 31, 96, 101, 120, 121 yr 1980, 94

quality of niches, 115 physical demands, 115-117, 120 social skills, 115-117, 120 substantive complexity, 115117, 120 work conditions, 115117, 120

Refugee, 3, 11, 12, 41, 42, 45, 47, 68, 5052, 62, 76, 82, 84, 90, 102, 110, 119

163 Sabagh, Georges, 10, 12, 14, 19, 75, 111, 114, 115 sampling, 18, 22 Sanders, Jimmy, 93-97 self-employment, 1, 13, 14, 20, 87-102 Iran, 22, 102, 119 subgroup, 119 U.S., 22, 23 social capital, 2, 5-7, 36, 74, 91, 92, 105, 118 bounded solidarity, 9 family, 71-74 immigration, 8, 37, 72, 73, 77, 114 measures, 9, 58, 71, 75, 92, 101, 106 reciprocity, 9 self-employment, 92-95 value introjections, 9 women, 110 social embeddedness, 2, 98 social networks, 5, 8, 57, 91, 93, 111, 115 social relations, 4, 5, 6, 35 social structure, 4, 7-9, 35, 37 sociologists, 4, 89 specific economic help, 32, 44, 48, 49, 52, 59, 69 spouse, 19, 21, 24, 94-96, 101, 102

164 strength of tie, 57-59, 62, 65, 79, 80 family vs. non-family, 75, 85, 92, 98 strong vs. weak, 55-59, 65, 73, 106, 113, 118 student (visa), 43, 47, 48, 76, 109 study design, 17-19 survey questionnaire, 19-21 Tilly, Charles, 4, 35 Tohidi, Nayereh, 112 Treiman, Donald, 60-64

Index U.S. Census, see PUMS

variables, 17, 19, 20, 25-27 Waldinger, Roger, 2, 5, 89, 90, 93, 115-117 work experience, 2, 4, 26, 40, 41, 46, 58, 75, 76, 82, 84, 91, 102