Immigration and Integration in Israel and Beyond 9783839466759

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Oshrat Hochman (ed.) Immigration and Integration in Israel and Beyond

Culture and Social Practice

A festschrift for Rebeca Raijman

Oshrat Hochman (PhD) is the leader of the team Social Surveys at the GESIS – Leibniz-Institute for the Social Sciences in Mannheim, Germany. She gained her PhD in Sociology at the Universität Mannheim in 2010, after which she worked in Israel studying and teaching immigration and integration at the Ruppin Academic Centre and at Tel Aviv University. Her research focuses on immigration and integration, and she is also interested in survey methods and social inequality.

Oshrat Hochman (ed.)

Immigration and Integration in Israel and Beyond

Bibliographic information published by the Deutsche Nationalbibliothek The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data are available in the Internet at http://dnb.d-n b.de

© 2023 transcript Verlag, Bielefeld All rights reserved. No part of this book may be reprinted or reproduced or utilized in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publisher. Cover layout: Maria Arndt, Bielefeld Printed by: Majuskel Medienproduktion GmbH, Wetzlar https://doi.org/10.14361/9783839466759 Print-ISBN 978-3-8376-6675-5 PDF-ISBN 978-3-8394-6675-9 ISSN of series: 2703-0024 eISSN of series: 2703-0032 Printed on permanent acid-free text paper.

Contents

Acknowledgments ........................................................................7 Introduction Oshrat Hochman .......................................................................... 9 Socio-economic Determinants of Ethnic Migration to Israel, 1991–2019 Sergio DellaPergola ....................................................................... 21 The theory of ethnic economics and ethnic economy Nonna Kushnirovich ...................................................................... 53 Employment, self-employment and precarity Routes to migrant integration? Sibylle Heilbrunn, Natalia Vershinina and Peter Rodgers ................................... 79 Inequality among older immigrants in Israel Does marital status matter? Haya Stier .............................................................................. 105 Motivation type and generation in higher education Differences between privileged, minority and immigrant students Hanna Ayalon ............................................................................129 Urban citizenship revisited Incorporating migrants and asylum seekers in Tel Aviv in times of restrictive policing Adriana Kemp ........................................................................... 153 Forced migrants in the city of Tel Aviv Possible avenues for their integration Lilach Lev Ari and Arnon Medzini .........................................................173

Threats, prejudice and opposition to immigration Anastasia Gorodzeisky, Moshe Semyonov, Eldad Davidov and Peter Schmidt .............. 193 Authors’ Biographies....................................................................219

Acknowledgments

The editor wishes to thank GESIS – Leibniz Institute for the Social Sciences for their support.

The authors thank the following institutions: Ruppin Academic Center URPP Social Networks, University of Zurich Oranim College of Education The Hebrew University of Jerusalem Kinneret College on the Sea of Galilee Tel Aviv University

Introduction Oshrat Hochman1

This volume offers a collection of studies that exemplify various aspects of migration studies from a multitude of social science perspectives. They address various types of immigrants, ranging from economic or labor immigrants, through forced immigrants, to ethnic immigrants. Using theoretical and empirical approaches and qualitative and quantitative data and analyses, the various studies complement each other to provide insights into three of the main questions dominating the field: 1) Why do individuals decide to immigrate? 2) How do their decisions affect their own lives and those of their offspring? 3) How do immigrants affect receiving societies structurally and socially at the urban, national, and cross-national levels?

Most of the studies included in this book are based on the case of Israel, an immigrant-receiving society that can be regarded as very successful in integrating immigrants. Israel shares many similarities with other immigration states, but also differs from them in many ways. It thus offers an interesting combination of generalizable and unique immigration-related phenomena. According to the World Migration Report 2022 (McAuliffe & Triandafyllidou, 2021), “the current United Nations global estimate is that there were around 281 million international migrants in the world in 2020, which equates to 3.6 percent of the global population” (p. xii). Overall, the number of international migrants has been continually increasing over the past five decades—from an estimated 84 million in 1970, through 153 million in 1990, to 281 million in 2020 (ibid.: 23). Immigration is a highly complex phenomenon that is intertwined with geographical, political, societal, and economic changes and challenges. It can be motivated by any and all of these factors, but can also lead to their change. Because of the relevance of immigration to many different social developments both in immigrants’ countries of origin and their countries of destination, it has been in the focus of the social sciences for many years.

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The unprecedented interdisciplinary interest in immigration is best exemplified by the fact that migration studies is now a separate and established field of research where one finds a richness of theories and practices, all of which are aimed at providing a better understanding of structural and individual factors that explain why people decide to migrate, where they migrate to, and what structural and individual consequences their decisions have for their countries of origin and destination. Massey et al. (1993) noted that there was currently “no single, coherent theory of international migration” (p. 432). Rather, a variety of theories and perspectives seeking to explain why international migration happens had developed over time, each pointing to causes on different levels of aggregation. These theories should not be taken as mutually exclusive but rather as an indication of the complexities associated with immigration and the processes driving it (ibid.). Without going into too much detail, causes of immigration can be traced back to micro-level considerations on the part of individuals (Borjas, 1990) or households (e.g., Stark, 1991), and to macro-level considerations associated with national and supranational regulations and considerations (e.g., Todaro, 1976; Morawska, 1990; Sassen, 1991; Jasso, 2021). Other theories of immigration point out that initial immigration serves as a selfsustaining mechanism that perpetuates international immigration (e.g., Massey, 1990). Emerging immigrant networks, communities, and institutions in receiving countries thus serve as additional motivational factors for immigration. The differences between these theories and perspectives go beyond the aggregation level and extend to issues associated with the question of whether immigrants are ‘pushed’ out of their countries of origin or ‘pulled’ toward other countries. The theories and perspectives also differ in their assumptions regarding the role of state policies and policy makers in regulating international immigration. Immigration policies can be differentiated along many different dimensions, for example, entry regulations, citizenship and naturalization, and integration efforts. In the early 2000s, scholars seem to have reached an understanding according to which immigration policies are converging. Joppke (2007), for example, argued that Western states’ immigration policies had become more universal over the course of the 20th century—that is to say, countries receiving immigrants had become less selective in terms of the immigrants that they allowed in. He pointed out that at the same time the role of citizenship as a means of excluding “aliens” had become stronger. Brubaker (2001) identified a trend toward the “return of assimilation” in terms of integration policies in the developed northern hemisphere, and more specifically in the United States, Germany, and France. Zolberg (1999) suggested understanding these developments in immigration policies as reflecting the tension arising from the need to impose immigration controls without undermining liberal values. This tension forces immigrant-receiving states to tolerate some level of unregulated migration. Zolberg (ibid.) suggested understanding state migration policies as the result of two types of interests—motivated by economic considerations

Introduction

on the one hand, and by considerations related to what she calls the identity or cultural dimension on the other. The complex relations between economic and cultural interests, as well as the need to exercise control while safeguarding liberal values, makes the Israeli context, within which most of the studies in this volume are located, particularly interesting. The reconciliation of these contradictory interests represents a particularly large challenge in Israel. Immigration policy in Israel is based on two separate models—a reception-encouragement model for Jewish immigrants and an exclusionary model for non-Jewish immigrants (e.g., Raijman, 2010). These logics developed from the establishment of Israel as a Jewish and democratic state designed to serve as the national home and as a ‘safe haven’ for all Jews. To fulfil this mission, Jewish immigration to Israel was defined by the Law of Return (1950; amended in 1970), which grants citizenship to Jews, children and grandchildren of Jews, and their nuclear families, regardless of whether they themselves are Jewish, immediately upon immigration to Israel (DellaPergola, 1998). As a Jewish state, Israel is not only committed to receiving all and any Jews who wish to make Israel their home, but also to sustaining the Jewish character of the state—among other things also by demographic means (Lustick, 1999). Between 1948, the year of its establishment, and 2019, some 3.3 million Jewish immigrants (or relatives of Jewish immigrants) arrived in Israel under the Law of Return (Israel Central Bureau of Statistics [ICBS], 2020). Cohen (2002) distinguished five periods of Jewish immigration to Israel between 1948 and 2000, the most recent of which (1989–2000) saw the arrival of around one million immigrants, most of whom came from the former Soviet Union (FSU). Immigrants from the FSU accounted for about 85% of all Jewish immigrants arriving during that period. Jewish immigration to Israel in the 1990s must be understood in the context of an important amendment to the Law of Return passed in 1970, which extended the rights conferred under that law to the non-Jewish relatives of Jews. Raijman (2009) noted that among the immigrants arriving from the FSU under the Law of Return, the share of non-Jewish immigrants increased from about one percent in 1990 to almost 50 percent in 2000. The large number of immigrants from the FSU who arrived between 1989 and 2000 (over 830,000) has changed Israeli society dramatically in more than one dimension. Demographically, they were the single largest group of immigrants of the ‘same’ origin (Leshem & Lissak, 2000).2 As Al-Haj (2002) suggested, one consequence of this was that the integration of the immigrants from the FSU was expected to be different from that of previous immigration periods. These immigrants had the numbers and the motivation to become politically relevant, to create 2

Naturally, it is hard to maintain today that the states of the former Soviet Union are one and the same, but in the early 1990s, this was still considered to be the case.

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their own economic enclave, and to form alternatives to the cultures already in place (Al-Haj, 2002, 2004). The immigrants from the FSU also created diverse challenges in the labor market. In demographic terms, the proportion of individuals aged 65 or older in this group was 15 percent, compared with ten percent in the Israeli population as a whole, and eleven percent in the Jewish population (Leshem & Sicron, 2004). The educational and professional qualifications of the immigrants from the FSU posed another challenge for their integration into the labor market, as levels of formal education in this group were considerably higher than among the (veteran) Israeli workforce (e.g., Mesch, 2002). This led to significant rates of downward occupational mobility upon integration into the labor market among immigrants from the FSU who arrived in Israel in the 1990s (Raijman & Semyonov, 1998). Alongside this most recent large wave of Jewish immigration, a new type of immigration emerged during the same period, namely, non-Jewish immigration. The recruitment of foreign labor migrants to Israel started during the uprisings in the West Bank (Intifada) in the late 1980s and early 1990s that brought to a halt the commuting of Palestinian workers who had been employed mostly in agriculture, construction, and services (Kushnirovich & Raijman, 2014). Compared with Jewish immigrants, these migrants were faced with a very different reception policy. Indeed, the organization of temporary labor migration was outsourced by the state to private companies, who were responsible for the recruitment of the workers, their documentation, and their living conditions. At the peak, some 250,000 individuals arrived in Israel as documented or undocumented labor migrants (Raijman, 2012). The Jewish-Israeli majority reacted to their arrival with exclusionary views associated, as could be expected, with perceptions of both symbolic or national as well as economic threats (Raijman & Semyonov, 2004; Raijman et al., 2008; Raijman & Hochman, 2011; Gorodzeisky, 2013). In the late 1990s and early 2000s, Israel invested significant efforts and resources to reduce the number of labor migrants (e.g., Kemp et al., 2020). The deportation of undocumented labor migrants destroyed established communities and tore families apart. Like many other countries, Israel too is additionally facing the challenge of forced (non-Jewish) migration. Specifically, it has become home to asylum seekers hailing for the most part from Sudan and Eritrea. With some exceptions, Israeli state authorities treat asylum seekers similarly to undocumented labor migrants. As in many other countries, policy makers suggested that these individuals needed a job rather than protection (e.g., Hochman, 2015). Israeli policy and the attitudes of the Jewish majority toward this migrant group have aligned to form a rather exclusionary disposition, which contrasts sharply with the inclusionist disposition toward Jewish immigrants (Raijman et al., 2022). As mentioned above, immigration policies throughout the developed world are aimed at controlling national borders and navigating between economic and cul-

Introduction

tural needs. Many of the studies in the present volume provide revealing examples of just how this is done in Israel. While the Israeli context has unique properties, it is also comparable to other large-scale immigration countries (DellaPergola, 1998). Germany and other countries in Europe and elsewhere also have different policies for different categories of migrants. A very recent example is the differential treatment of refugees from Ukraine and Belarus in Poland compared with other migrants, including refugees, arriving there. Other contributions in this volume discuss the incorporation of immigrants and their offspring in Israel and elsewhere, flagging the consequences of the policies implemented, and their intersection with these individuals’ own motives. *** A Festschrift is not only a book but also a celebration, and in our case a celebration of Rebeca Raijman’s retirement. Rebeca Raijman obtained a PhD from the University of Chicago in 1996. Her research interests then were focused on the Mexicans in Chicago, and on the ethnic entrepreneurship and informal self-employment of immigrants in the United States more generally. On finishing her PhD, she received a research grant as a principal investigator from the Golda Meir Institute for Labor Studies to investigate undocumented Latin-American immigrants in Israel. Ethnic and national minorities in Israel continued to play a central role in Rebeca’s scientific career. Most notably, she was among the first to identify the emergence of new minority communities in Israel at a time when most considered these people to be temporary guest workers. In a series of projects funded by research grants from the National Academy of Science, the German-Israeli Foundation for Scientific Research and Development (GIF), the international Metropolis BMU, and many others, Rebeca provided a comprehensive overview of the development of these new ethnic communities from many different perspectives. One important contribution that Rebeca made related to the investigation of the cultural, religious, and political organization of these new ethnic communities, and to the way in which state mechanisms of policing, control, enforcement, and deportation were involved in their making and unmaking. Another related to the development of Israel’s labor migration policy, focusing on the role of state and of non-state actors in the institutionalization of labor migration. In this context, Rebeca uncovered the mechanisms contributing to the creation of a precarious and captive labor force of non-citizens in the Israeli labor market. Further, her research provided insights into the emergence of the so-called ‘migration industry’ that developed in Israel, focusing on the operative dimension of labor migrants’ recruitment and management, and on how the actors involved in these operations made their profits. Placing the development of labor migrant communities in Israel within the context of the Palestinian Intifada and

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its consequences for the Israeli labor market, Rebeca studied the role that labor migration played in the relations between Israel and the Palestinians. She also investigated other factors contributing to the social construction of this immigrant group in Israeli society. Throughout her scientific work, Rebeca has used a wide range of research methods and perspectives that have enabled her to shed light on different aspects of immigration and to provide sound empirical evidence to support her profound theoretical reasoning. The multifaceted nature of Rebeca’s work is visible also through her research on the integration of Jewish immigrants in Israel, and specifically on South-African Jewish immigrants and immigrants from the FSU. Additionally, she became one of the leading scholars in the field of anti-immigrant sentiment in Israel and from a comparative perspective. Rebeca Raijman’s corpus of research thus provides a broad and coherent picture of Israel’s immigration policy, structure, composition, and consequences that accurately reflects what she calls the “double standard of immigration”—that is, an exclusionary model for non-Jews and an acceptanceencouragement model for Jews. Through her advisory positions, among others in the Israeli parliament (the Knesset), her impact reaches beyond the ivory towers of academia. The current volume provides new contributions related to many of Rebeca Raijman’s research domains. The selected studies thus all build to some extent on Rebeca’s work and add new insights of their own. *** In the first contribution in this volume, Sergio DellaPergola discusses the sociodemographic determinants of ethnic migration to Israel between 1991 and 2019. In his essay, he evaluates the extent to which sociodemographic and political push factors in the origin countries provide an independent explanation of observed trends in immigration to Israel, over and above the ideological pull factor often perceived to be central to Jewish immigration. DellaPergola’s analyses show that notwithstanding the possibility that Jewish immigration is ideologically motivated, other more common motives for immigration—such as unemployment levels in the countries of origin and in Israel —also play a role in this particular type of immigration. Thus, his contribution brings to the fore the question of just how unique Jewish immigration to Israel (aliya) really is, at least when considering more recent Jewish migration. The next contribution, by Nonna Kushnirovich, provides a typology of ethnic economics and an evaluation of its role for immigrant and ethnic minorities. It gives an overview of the terminology used in the literature to refer to different dimensions of ethnic economies and ethnic economics, and develops a framework for understanding the activities of members of ethnic and immigrant minorities in ethnic niches, enclaves, and neighborhoods. Kushnirovich then goes on to describe the Is-

Introduction

raeli case, focusing on the emergence of immigrant ethnic communities and niches, as well as ethnic business enclaves. In the third contribution, Sibylle Heilbrunn and colleagues shed light on the acculturation of immigrants from the Former Soviet Union (FSU), the largest group of immigrants arriving in Israel since 1990. Unlike other immigrant groups in Israel, this group has challenged the assimilationist ethos of Israel’s ‘absorption policy’ toward Jewish immigrants (olim) by preserving their Russian cultural identity. The authors analyze in-depth interviews that they conducted with 20 employed and self-employed immigrants from the FSU. In their investigation, they aim to understand how exposure to precarious working conditions affects the integration of immigrants into the receiving society. Their findings suggest that by running their own businesses and gearing them toward the FSU community, some FSU immigrants in Israel experience acculturation difficulties that lead to economic disadvantage, thus demonstrating the complex consequences of economic enclaves. In the fourth contribution in this volume, Haya Stier compares Jewish immigrants arriving in Israel from the FSU in the 1990s with the veteran Jewish population in terms of their economic wellbeing in old age. Stier explains differences in the economic wellbeing of elderly Israelis by looking at the interplay between immigration, gender, and marital status. Her focus on the elderly immigrant population reflects current trends in many immigrant societies, where long-term immigrants are aging, and late-age migration is rising. This study shows that in general the economic wellbeing of elderly immigrants (men and women) is less affected by their gender or marital status compared with the veteran Jewish population. At the same time, this group is more vulnerable due to disadvantages associated with their interrupted careers, their smaller pension funds, and difficulties associated with finding and securing good jobs on arrival in Israel. The next contribution, by Hanna Ayalon, discusses differences in types of motivation to engage in higher-education among privileged, minority, and immigrant students in Israel. Ayalon broadens the perspective on Israeli society to include not only the immigrant and veteran Jewish population but also the Arab population in Israel, and explores motivation as one explanation for the different choices that Arab students and Jewish students (with and without an immigrant background) make in terms of their higher-education trajectories. She demonstrates that independent of their ethnic or national group membership, most students in Israel are extrinsically motivated to engage in higher education. FSU immigrants seem to be driven neither by intrinsic nor by extrinsic motivation. Only the most privileged group—namely, continuing-generation Ashkenazi Jews—are driven by intrinsic motivation. In line with previous studies, Ayalon’s study, too, points to the central role of social status in shaping the motivation to acquire higher education. In the sixth contribution, Adriana Kemp turns the focus away from Jewish immigration to discuss the impact of Israel’s restrictive migration policies on the in-

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corporation of labor migrants and asylum seekers in Tel Aviv. Specifically, she investigates how the ‘restrictive turn’ in migration has shaped urban integration policies and notions of urban citizenship in that city. She discusses the paradoxes emerging from the exclusionary and punitive control policies of the state, leading to the re-negotiation of responsibilities between state and city, and to the reshaping of local logics of integration. Kemp suggests that urban actors in Israel have been able to increase the involvement of state actors in the incorporation of immigrants, while at the same time transforming their own role to a more reactive one. She concludes that in light of the neoliberal and global context in which immigration takes place, the relations between city and state, and the negotiation and renegotiation of their respective responsibilities are crucial for understanding actual forms of immigrant incorporation. The next contribution, by Lilach Lev Ari and Arnon Medzini, deals with the arrival of forced immigrants to Israel, and the process of their resettlement in Tel Aviv. Lev Ari and Medzini discuss in this study the circumstances under which these individuals have arrived in Israel, and the policies Israel has adopted in order to cope with their unexpected arrival. They go on to describe how Tel Aviv became an arrival city for African immigrants, discussing the negative consequences of this development both for the local native residents of the city and for the African immigrants. They then discuss the potential benefits that the city and its local native and new residents would enjoy if Tel Aviv became a sanctuary, or a resettlement city, for these people and their offspring. The authors also provide several policy recommendations that could facilitate this transition. In the final contribution in this volume, Anastasia Gorodzeisky and colleagues examine attitudes toward immigration from a comparative perspective. They thus complement the previous contributions by adding an essential aspect of immigrants’ integration—namely, the way in which members of the dominant majority in the receiving society perceive them. Gorodzeisky et al. use data from Round seven of the European Social Survey (ESS) to examine whether the threat of competition for social and economic resources, the threat to the cultural homogeneity of society, and racial prejudice each exert an independent net influence on opposition to immigration by members of host societies. This question is examined in six European countries, each with a different composition of immigrants and a different context of reception. Unlike the previous contributions, this one does not focus on Israel but rather on six other countries in Europe. The reason for this exception is that internationally comparative surveys, such as the ESS used in this study, are unsuitable for looking at special contexts like Israel, where terms like ‘immigrants’ or ‘people from other countries who come to live here’ can easily be interpreted in different ways by different respondents, thereby leading to biased answers. Indeed, studies conducted in Israel show that the attitudes of the Jewish-Israeli public differ depending on the specific immigrant group they are asked about (Raijman

Introduction

et al., 2022). Importantly, although Israel is not included in the analysis, previous studies conducted there on opposition to non-Jewish immigration indicate that these attitudes do not differ much from those in other European societies, and that in Israel, too, this type of opposition is shaped by perceived socioeconomic and symbolic threats (Raijman & Semyonov, 2004), as well as by prejudice (Raijman, 2012). *** As the editor of this book, I would like to thank all the authors for their insightful contributions and their highly appreciated advice along the way. I would also like to join them in celebrating the achievements of our colleague Rebeca Raijman and her extraordinary scientific contributions. Thanks to her critical, solution-oriented, pragmatic, and energetic nature, working with Rebeca has always been so much more than a scientific collaboration. Rebeca has never been only a student, a colleague, or a supervisor, she is also our friend, and one whom we all cherish very deeply.

References Al-Haj, M. (2002). Identity patterns among immigrants from the former Soviet Union in Israel: Assimilation vs. ethnic formation. International Migration, 40(2), 49–70. Al-Haj, M. (2004). The political culture of the 1990s immigrants from the former Soviet Union in Israel and their views toward the indigenous Arab minority: A case of ethnocratic multiculturalism. Journal of Ethnic and Migration Studies, 30(4), 681–696. Borjas, G. (1990). Friends or Strangers: The Impact of Immigration on the U.S. Economy. New York: Basic Books. Brubaker, R. (2001). The return of assimilation? Changing perspectives on immigration and its sequels in France, Germany, and the United States. Ethnic and Racial Studies, 24(4), 531–548. Cohen, Y. (2002). From haven to heaven: Changes in immigration patterns to Israel. Israeli Sociology, 4(1), 39–48 (in Hebrew). DellaPergola, S. (1998). The global context of migration to Israel. In E. Leshem & J. Shoval (eds.), Immigration to Israel – Sociological Perspectives (Studies of Israeli Society, Vol. 8). New Brunswick: Transaction Publishers, pp. 51–92. Gorodzeisky, A. (2013). Does the type of rights matter? Comparison of attitudes toward the allocation of political versus social rights to labour migrants in Israel. European Sociological Review, 29(3), 630–641.

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Hochman, O. (2015). Infiltrators or asylum seekers? Framing and attitudes toward asylum seekers in Israel. Journal of Immigrant & Refugee Studies, 13(4), 358–378. Israeli Central Bureau of Statistics (ICBS) (2020). Selected Data on the Occasion of International Migrant Day. Media Release 402/2020. Jerusalem: CBS (in Hebrew). Jasso, G. (2021). Analyzing migration restriction regimes. Frontiers in Sociology, 6:610432. Joppke, C. (2007). Beyond national models: Civic integration policies for immigrants in Western Europe. West European Politics, 30(1), 1–22. Kemp, A., Raijman, R., & Geffen, R. (2020). Who drives migration discourse and in what direction? Claims-making and political mobilization analyses of labor migration in Israel. Journal of Ethnic and Migration Studies, 48(13), 3323–3343. Kushnirovich, N., & Raijman, R. (2014). Recruitment practices of labour migrants in Israel: The case of the domestic care sector. In M. Pajnik & F. Anthias (eds.), Work and the Challenges of Belonging: Migrants in Globalizing Economies. Newcastle upon Tyne: Cambridge Scholars Publishing, pp. 178–194. Leshem, E., & Lissak, M. (2000). The formation of the “Russian” community in Israel. Yehodee Brit Hamoatzut Bimaa’var, 4(19), 47–66 (in Hebrew). Leshem, E., & Sicron, M. (2004). The Soviet immigrant community in Israel. In U. Rebhun & C. I. Waxman (eds.), Jews in Israel: Contemporary Social and Cultural Patterns. Hanover: University Press of New England/Brandeis University Press, pp. 81–117. Lustick, I. (1999). Israel as a non-Arab state: The political implication of mass immigration of non-Jews. Middle East Journal, 53(3), 417–433. Massey, D. S. (1990). The social and economic origins of immigration. Annals of the American Academy of Political and Social Science, 510(1), 60–72. Massey, D. S., Arango, J., Hugo, G., Kouaouci, A., Pellegrino, A., & Taylor, J. E. (1993). Theories of international migration: A review and appraisal. Population and Development Review, 19(3), 431–466. McAuliffe, M. & Triandafyllidou, A. (eds.) (2021). World Migration Report 2022. Geneva: International Organization for Migration (IOM). Mesch, G. S. (2002). Between spatial and social segregation among immigrants: The case of immigrants from the FSU in Israel. International Migration Review, 36(3), 912–934. Morawska, E. (1990). The sociology and historiography of immigration. In V. YansMcLaughlin (ed.), Immigration Reconsidered: History, Sociology, and Politics. New York: Oxford University Press, pp. 187–240. Raijman R. (2009). Immigration in Israel: A map of trends and empirical research: 1990–2007. Israeli Sociology, 10(2), 339–379 (in Hebrew).

Introduction

Raijman, R. (2010). Citizenship status, ethno-national origin and entitlement to rights: Majority attitudes towards minorities and immigrants in Israel. Journal of Ethnic and Migration Studies, 36(1) 87–106. Raijman, R. (2012). Foreigners and outsiders: Exclusionist attitudes towards labour migrants in Israel. International Migration, 51(1), 136–151. Raijman, R., & Hochman, O. (2011). National attachments, economic competition, and social exclusion of non-ethnic migrants in Israel: A mixed-methods approach. Quality & Quantity, 45(6), 1151–1174. Raijman, R., & Semyonov, M. (1998). Best of times, worst of times, and occupational mobility: The case of Soviet immigrants in Israel. International Migration, 36(3), 291–312. Raijman, R., & Semyonov, M. (2004). Perceived threat and exclusionary attitudes towards foreign workers in Israel. Ethnic and Migration Studies, 27(5), 780–799. Raijman, R., Davidov, E., Schmidt, P., & Hochman, O. (2008). What does a nation owe non-citizens? National attachments, perception of threat and attitudes towards granting citizenship rights in a comparative perspective. International Journal of Comparative Sociology, 49(2–3), 195–220. Raijman, R., Hochman, O., & Davidov, E. (2022). Ethnic majority attitudes toward Jewish and non-Jewish migrants in Israel: The role of perceptions of threat, collective vulnerability, and human values. Journal of Immigrant & Refugee Studies, 20(1), 17–32. Sassen, S. (1991). The Global City: New York, London, Tokyo. Princeton, NJ: Princeton University Press. Sicron, M. (1998). Demography of the wave of immigration. In M. Sicron & E. Leshem (eds.), Profile of an Immigration Wave: The Absorption Process of Immigrants from the Former Soviet Union, 1990–1995. Jerusalem: The Magnes Press, pp. 13–40 (in Hebrew). Stark, O. (1991). The Migration of Labor. Cambridge, MA: Basil Blackwell. Todaro, M.P. (1976). Internal Migration in Developing Countries: A Review of Theory, Evidence, Methodology and Research Priorities. Geneva: International Labour Organization. Zolberg, A. R. (1999). Matters of state: Theorizing immigration policy. In J. DeWind, P. Kasinitz, & C. Hirschman (eds.), The Handbook of International Migration: The American Experience. New York: Russell Sage Foundation.

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Socio-economic Determinants of Ethnic Migration to Israel, 1991–2019 Sergio DellaPergola1

Ethnic migration and the Israeli case This chapter deals with ethnic immigration to Israel over the period 1991–2019, between the break-up of the Soviet Union to the last year fully documented at the time of writing. (For a more extensive treatment of the topic of this chapter see DellaPergola, 2020a). Ethnic migration is a particular case of international migration where people mostly identified with a given ethnic, national, religious, linguistic, cultural or otherwise socially or symbolically defined group move to another place where recognizable communities of the same origin exist or are reinforced (Joppke & Rosenhek, 2002; Zhou, 2013). Ethnic migrations fulfilled a significant role in enhancing cultural and functional relations within peer origin groups locally and easing networking between similar communities located elsewhere. Sometimes this occurred through a direct relationship with a perceived homeland or core state, sometimes not. Correlates of such migrations were the promotion and maintenance of cultural and linguistic communication, the creation and development of ethnically specialized economic activities, the promotion of ethnic local political interests and public representation, and the advocacy of ethnic or religious beliefs and ideals (Portes & Rumbaut, 1990; Waldinger et al., 1990). Often the main gist of ethnic migration consisted in a persisting sense of mutual identification. Such symbolic perceptions sometimes generated by one or more representative institutions, isolated or networking with others and inspired by shared or similar beliefs and allegiances. More broadly, ethnic migrations acted as a growingly visible connector between, and enhancer of transnational ethnic communities in the context of contemporary societies (Schiller Glick et al., 1992; Castles, 2000; Vertovec, 2003). Return ethnic migration is the particular case when migration of identified ethnics occurs from one or more countries of residence to the country perceived as the

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natural country of origin of that population group. The shared context of these different situations is the existence of a duality between a concrete or imagined homeland – a center or core country – and an existential presence elsewhere – a periphery or diaspora – of people with an actual or putative affinity to that homeland. This case seems to apply particularly to Jewish migration to the state of Israel. Israel is the ethno-national Jewish State which – along with an Arab State and a Jerusalem Corpus Separatum – was approved by the UN General Assembly resolution 181 of November 29, 1947 (United Nations, 1947). On May 14, 1948, independence of the state of Israel was proclaimed stressing that the Jewish State voted by the UN would be called Israel. Israel’s Declaration of Independence proclaimed: “We appeal to the Jewish people throughout the Diaspora to rally round the Jews of Eretz-Israel in the task of immigration and development and to stand by them in the great struggle for the fulfillment of the age-old dream – the redemption of Israel” (Israel Provisional Government, 1948). Since the beginning, the aspiration to represent the core country of the Jewish nation – understood as a coalescence of ethnicity and religion – and the expectation to absorb large scale Jewish immigration constituted a central tenet of Israeli society. This outlook was founded on the assumption of continuing solidarity between Jewish communities worldwide and on the postulates of the ingathering of the exiles and fusion of the diasporas in the sovereign political center created in the historical homeland. Large scale immigration actually followed Israel’s independence (ICBS Statistical Abstract, 2019).

Determinants and consequences of ethnic migration Migration theory focused on both microsocial and macrosocial determinants of migration. At the individual level, among prominent factors considered were fear for personal security and the need to protect one’s own family, frustration for the impossibility to achieve economic and other instrumental goals in the country of residence, lack of identification with the surrounding population’s religious, cultural and political values, and the aspiration to better, ideal, and sometimes utopian standards and forms of life (e.g., Eisenstadt, 1954). At the macrosocial level, migration was interpreted as one of the transformative elements in an integrated and hierarchical global system comprising dominant core and dependent peripheral countries (e.g., Wallerstein, 2004). Important migration stimulating roles were played by inter-country – but also intra-country – inequalities in socioeconomic development, wages, income, and legal systems (e.g., Massey et al., 1998). Migration policies in the countries of origin and destination played important roles in actually regulating the size and composition of migration flows (e.g., Easterlin et al., 1982; Kivisto & Faist, 2010). Clearly the main emphasis in migration analyses was on economic determinants at the local, national, and global level. A more comprehensive framework

Socio-economic Determinants of Ethnic Migration to Israel, 1991–2019

would also consider several qualitative factors, involving single individuals pertaining to a given ethnic group, and the respective ethnic communities as a whole. In spite of the avowed ideational motives behind Jewish ethnic migration to Israel, the question of the prevailing determinants of those migration patterns calls for elucidation. Was it mainly ideological, stimulated by the emotional pull exerted by Israel as a symbolic place of reference for Jews, as well as a social and cultural environment congenial to many of them? Was it mainly due to negative factors in the countries of origin, such as perceptions of antisemitism? In many surveys of immigrants to Israel, asked about the motives of their move, respondents indeed stressed ideological reasons like the desire to live in a Jewish state, to preserve Jewish culture, or to enhance the future Jewish identity of their children (e.g., DellaPergola & Tagliacozzo, 1978; Antonowski & Katz, 1979; Ben-Rafael & Ben-Rafael, 2013; Raijman, 2015; Mittelberg & Bankier-Karp, 2020). These observations, however, did not explain why immigration to Israel was more frequent in certain years and from certain countries, and less frequent from other places and in different periods. Such lack of synchronization suggested that migration might have been motivated by changing perceptions of economic or other instrumental factors, namely by the different advantages and disadvantages that could prevail at different points in time in each country of origin or in Israel (DellaPergola, 1984, 1986, 1988, 1998, 2009). In other words, it could be hypothesized that migration to Israel reflected socioeconomic rather than – or at least, not only – ideological stimuli, not unlike most other migrations worldwide. This became increasingly plausible since over time Israel became quite a developed – hence attractive – country ranking 22th out of 190 on the Human Development Index HDI (United Nations Development Programme, 2019). HDI expresses a country’s general standard of living based on levels of education, health and income.

Data and method To test these propositions, annual data on immigration to Israel – total and for each of 16 countries studied in this chapter – were obtained from Israel’s Central Bureau of Statistics (ICBS, Annual). Immigrants include new arrivals with an immigrant or potential immigrant status as well as tourists who changed their status to immigrant or potential immigrant. Cases of temporary labor immigrants and family reunion, as well as refugees and asylum seekers were not included in this study. For each country, annual Jewish population size estimates were computed from the database of the Division of Jewish Demography and Statistics at the A. Harman Institute of Contemporary Jewry of the Hebrew University of Jerusalem (Schmelz & DellaPergola, 1992–1996; DellaPergola, 1997–2020). Yearly country immigration rates were thus computed for each of the 29 years 1991–2019. Annual data on em-

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ployment were obtained from the World Bank database2 . For each country’s 29-year immigration rate series, regression equations were computed for rates of unemployment abroad, rates of unemployment in Israel, and country-Israel unemployment rate differences. Immigration rates were also compared with country Indexes of Human Development HDI (United Nations Development Programme, 2019).

Immigration to Israel, 1991–2019 Time variation The total number of new immigrants under Israel statehood between May 15, 1948 and the end of 2019 was 3,290,012 (Gil, 1950; Sicron, 1957; Bachi, 1977; ICBS, annual). This figure does not include Israeli citizens returning after a prolonged stay abroad (Nathan, 2019; ICBS, annual). Since 1948 the vast majority of all new immigrants were Jewish. However, since 1995 a growing percent of immigrants were not Jewish, under the enlarged Jewish population definition of the Law of Return (Gavison, 2010). In addition, immigration to Israel included persons with no connection with Judaism and the Jewish people under various rubrics: with labor contracts, in the framework of family reunions, under a status of long-term tourists and sojourners, or as refugees and asylum seekers (e.g., Raijman et al., 2003; Raijman, 2009, 2020; Elias & Kemp, 2010; Raijman & Pinsky, 2011). Some of these entered the country legally, some did not; and among those who had entered legally some became illegals when the time terms of their sojourn elapsed and they did not renew their sojourn visa (Israel Population and Migration Authority, 2019). These non-Jewish, non-Law-of-Return immigrants eventually came to constitute a substantial proportion and at times the absolute majority of all migrants to and from Israel (Raijman, 2009, 2020; DellaPergola, 2012). Since this chapter focuses on ethnic Jewish migration, these latter components of the whole immigration pool are not discussed here. During the years 1991–2019, 1,255,984 people immigrated to Israel out of a total population of 4,821,700 at the beginning and 8,967,600 at the end – an impact of 18 percent of the average population size during the whole period. In the process, Israel grew significantly to the point of hosting over 45 percent of the total world Jewish population in 2020, versus 29 percent in 1991 (and 5% in 1948) (DellaPergola, 2020b). Immigration figures, as noted, include non-Jewish immigrants as specified by the Law of Return. The total number of Jewish new immigrants between 1991 and 2019 was 1,019,800, or 81.2 percent of the total. 2

See: https://www.macrotrends.net/countries/. The information for each country can be accessed separately. For example, for France: https://www.macrotrends.net/countries/FRA/fra nce/unemployment-rate

Socio-economic Determinants of Ethnic Migration to Israel, 1991–2019

Before looking at the variation and possible explanations of migration frequencies to Israel from different countries, it is worthwhile to look at the total pace of immigrants’ arrival in the light of some of the main political, military and economic events that occurred in the country. Figure 1 represents annual immigration rates per 1000 total population in Israel. Given the wide quantitative gaps between high and low numbers of annual immigrants, ranging between a maximum of 176,100 in 1991 and a minimum of 13,681 in 2008, the figures are shown in logarithm scale. The 1991 initial point coincides with the 1990–1991 wave of immigration from the FSU which peaked in 1990. Gradual decline followed, down to 2008 – one of the weakest years ever in Israeli immigration history. With the exception of 1999, this was an unprecedented sequence of 18 years of consecutive decline in immigration, and it seemed to signal a turning away from the historical wavelike pattern that had prevailed since the end of World War I. However, starting with 2009 Israel featured 11 years of nearly uninterrupted immigration increase, which again contradicted the historically well-established wavelike immigration model.

Figure 1: Immigration rate per 1000 population in Israel with main intervening events, 1991–2019, logarithm scale

Sources: Israel population and immigration : ICBS.

In the light of periodical security problems, marked by violence outbursts on the Gaza and Lebanon fronts, acts of terrorism, and Israeli military interventions, one

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might attribute declining immigration to a feeling of lack of security among potential immigrants. But this is not consistent with the post-2008 immigration increase which occurred in spite of continuing security tensions, especially in the south of the country. The terminal date of the period considered catches Israeli society at an unprecedented point of political crisis and uncertainty, with five elections held between 2019 and 2022 which mostly produced precarious transition governments.

Country variation Migration to Israel is better evaluated by studying several cases of countries located in different geopolitical areas. The general hypothesis here is that country-of-origin effects on migration are no less important than country-of-destination effects (DellaPergola, 2011), hence it should be expected that those effects operate in somewhat different modes in each country. Here I examine 16 countries: eight in Europe, of which Russia and Ukraine in the former Soviet Union (FSU), France, Italy, Germany, Hungary and the UK in the European Union (the latter when still a member country), and Turkey (keeping in mind that the majority of Turkish Jews live on the European side of the straits – Tuval, 2004); four extra-European English speaking countries: the USA and Canada in North America, Australia and South Africa in the southern hemisphere; and four countries in Latin America: Mexico, Venezuela, Brazil and Argentina. In 2020 these countries comprised the 13 largest Jewish populations out of Israel, all above 40,000 Jews. In addition, Italy, Turkey and Venezuela were examined in view of special circumstances of interest in the study of Jewish emigration. The aggregate Jewish population of these 16 countries was 8,259,400 in 1991 and 7,739,300 in 2019, respectively 94.9 and 96.2 percent of the total Jewish population living out of Israel. Ethiopia was not included in this study although it constituted one of the main countries of immigration to Israel during the period 1991–2019. The reason for this omission is that the volume and timing of migration from Ethiopia was nearly entirely in the hands of Israel’s government. Very limited options for free decision making were actually available to the potential migrant themselves. Therefore, the analysis of size and timing of migration from Ethiopia deserves a separate treatment. For the 16 countries analyzed here, Table 1 reports the total, the highest and the lowest annual level of aliya (in absolute numbers and as a rate per 1000 Jews in the country of origin), the average for the whole period 1991–2019, and the ratio between highest and lowest annual aliya rate.

Socio-economic Determinants of Ethnic Migration to Israel, 1991–2019

Table 1: Immigration to Israel from 16 countries, 1991–2019 Immigration 1991–2019

Immigration p. 1000 Jews

Country Low

Average

Ratio H/L

Total

High

Low

High

Russia

336,717

44,774

2,600

107.26

12.38

41.39

8.7

Ukraine

305,921

39,769

1,310

182.92

17.70

84.46

10.3

France

64,958

6,627

966

14.18

1.82

4.61

7.8

Italy

2,631

353

21

12.79

0.75

3.18

17.1

UK

14,308

708

277

2.43

0.94

1.66

2.6

Germany

3,080

170

55

2.87

0.53

1.21

5.4

Hungary

3,380

272

36

4.97

0.73

2.25

6.8

Turkey

3,310

369

50

24.12

2.65

6.60

9.1

USA

59,454

2,568

1,236

0.45

0.22

0.36

2.0

Canada

6,628

338

129

0.88

0.35

0.63

2.5

Mexico

2,399

169

32

4.63

0.84

2.16

5.5

Venezuela

2,030

134

16

17.43

0.80

6.33

21.8

Brazil

7,871

618

151

6.61

1.56

2.83

4.2

Argentina

21,610

5,931

188

30.57

1.03

3.82

29.7

Australiaa

3,165

164

42

1.71

0.40

1.00

4.3

S. Africa Israel, Total

6,393

595

83

6.54

1.12

2.90

5.8

1,255,984

176,100

13,681

36.52

1.89

9.11

19.3

Sources: ICBS; a Including New Zealand.

Comparison of these raw data points to very significant inter-country but also intra-country variation. A huge gap exists between the maximum of 44,774 immigrants that arrived from Russia in 1991 and the minimum of 16 who arrived from Venezuela, by coincidence in the same year. When calculated as rates per 1000 Jews in the country of origin, gaps between immigration frequencies remain significant between a maximum of 182.9 immigrants per 1000 for Ukraine in 1999, and a minimum of 0.2 per 1000 for the USA in 2000 and in 2001. Yearly variation was remarkable within countries, too. The ratio between the highest and lowest rate of immigrants from Argentina between 1991 and 2019 was nearly 30 times, followed by Venezuela (21.8 times), while the high/low ratio for the USA was only twice. Canada and the UK had max/min aliya rate ratios of 2.5 and 2.6, respectively. Otherwise stated, there were huge differentials in the amount of stability or instability within countries and in the intensity of sudden migration stimuli experienced by the Jewish

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communities of those countries. A notable feature is that the top annual aliya rate never surpassed 0.45 per 1000 for the USA (in 2017), followed by Canada (never more than 0.88 per 1000, in 2015), Australia (max. 1.71 per 1000, in 1994), and the UK (max. 2.43 per 1000, in 2009). On the other hand, the weakest annual aliya rate remained below 1 per 1000 Jews in nine out of 16 countries of origin. Other than in the FSU republics, the highest among the weakest aliya years was Turkey’s (never less than 2.65 per 1000, in 1999), followed by France (min. 1.82 per 1000, in 1991) and Brazil (min. 1.56 per 1000, in 2002). Figures 2a and 2b (part A) report immigration rates per 1000 Jewish population in each of the 16 countries of origin. For better readability, the countries were subdivided into two groups – the eight with lower and the eight with higher levels of human development (HDI). There was a huge difference between the relative frequency of aliya from Russia and Ukraine, and all other countries. An important caveat in the calculation of immigration rates is that the number of immigrants refers to the extended population defined by the Law of Return, while the denominator includes only the so-called core Jewish population, excluding nonJewish household members. The proportion of non-Jewish immigrants steadily increased in recent years, surpassing 50 percent of all immigrants from Russia and other FSU republics (Fisher, 2019). Even keeping this in mind, immigration rates from Russia and Ukraine remained substantially higher than those from all other countries. In view of the significant similarity in Jewish identification patterns among Jews and the respective non-Jewish family members (Haskin, 2016), the basic variation over time in the motives and volume of immigration from the FSU could not be much affected by the formally recognized Jewish classification of immigrants. The same data in logarithm scale is presented in figures 2c and 2d (Part B) and allows for easier reading of the trends. The total immigration rate for Israel, computed on the basis of the total receiving population, resulted intermediate between the highest and lowest modalities just outlined. The shape of the Israeli curve was quite strongly influenced by that of Russia and Ukraine whose absolute input was highly dominant versus the other 14 countries.

Socio-economic Determinants of Ethnic Migration to Israel, 1991–2019

Figure 2a: Migration to Israel: rates per 1000 Jewish population in countries of origin, 16 countries, 1991–2019 (Israel: rates per 1000 Israel population)

Sources: Jewish populations: Schmelz & DellaPergola 1992–1996, DellaPergola 1997–2020; Israel population and immigration: Israel CBS.

Figure 2b: Migration to Israel: rates per 1000 Jewish population in countries of origin, 16 countries, 1991–2019 (Israel: rates per 1000 Israel population)

Sources: Jewish populations: Schmelz & DellaPergola 1992–1996, DellaPergola 1997–2020; Israel population and immigration: Israel CBS.

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Figure 2c: Migration to Israel: rates per 1000 Jewish population in countries of origin, 16 countries, 1991–2019 (Israel: rates per 1000 Israel population)

Sources: Jewish populations: Schmelz & DellaPergola 1992–1996, DellaPergola 1997–2020; Israel population and immigration: Israel CBS.

Figure 2d: Migration to Israel: rates per 1000 Jewish population in countries of origin, 16 countries, 1991–2019 (Israel: rates per 1000 Israel population)

Sources: Jewish populations: Schmelz & DellaPergola 1992–1996, DellaPergola 1997–2020; Israel population and immigration: Israel CBS.

Socio-economic Determinants of Ethnic Migration to Israel, 1991–2019

Migration rates tended to be higher in countries with lower development levels. Moreover, each country’s time dynamics was different from the others, with some partial country overlaps from time to time. Nearly all countries had one or more single years with significantly higher migration rates than in preceding and following years. This discontinuity unveils that some special, short-lived local events occurred at those points in time. The main exception to this pattern of temporary discontinuity was the USA where changes over time were quite modest and smooth. As to the dispersion over time of those special events, it is nearly impossible to find years in which immigration rates varied simultaneously for all 16 countries, whether upwardly or downwardly. Only in 1998 all 16 went down, in 2001 15 out of 16, and in 2011 14 out of 16. In 2009 14 out of 16 went up, in 2015 13 out of 16, and in 1993 12 out of 16. Specific timing and level similarities appeared for France and Italy, and to some extent for the USA and Canada, as well as for Mexico and South Africa. Most countries experienced some migration upturn during the mid-1990s. After the 2008 USA-driven global financial crisis, some general up-turn was felt everywhere. Israel naturally was only one of many possible countries of destination for Jewish migrants. Alternative countries of destinations were primarily the USA, Canada, Australia, and certain countries in the European Union such as Germany and to some extent the UK – at least before the Brexit. Past evidence, including observation of mass emigration from North Africa during the 1950s and 1960 and large scale leaving of the Soviet Union during the 1970s and 1980s indicated only partial co-variation over time of migration flows to Israel and to other countries (DellaPergola, 1998). These time gaps and lags were significantly related to the sociodemographic composition of Jewish migrants. Western countries like the USA, Canada and France tended to absorb more of the middle-upper classes, while more of the lower social classes went to Israel. This entailed higher proportions of children, higher dependency ratios, more frequent downward socioeconomic mobility, and at least temporary poverty in Israel (DellaPergola, 1998, 2007). In more recent years, internal socioeconomic gaps within Jewish communities may have diminished somewhat, and emigration from one country to several alternative destinations may have become relatively more synchronous. It can be fairly assumed that during the years considered here a majority of Jewish emigrant families moved to Israel (DellaPergola, 2020a), with some exceptions like South African Jews moving mostly to Australia (Graham, 2020), and Mexican and Venezuelan Jews (but also the few Jewish emigrants from Canada) moving mostly to the USA (Bokser Liwerant, 2013; Brym & Erdei, 2022).

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Unemployment in countries of origin and in Israel Turning now to an examination of the possible effect of socioeconomic determinants on migration to Israel, unemployment levels were tested as a possible factor. Unemployment rates were compared for the 16 countries studied here and for Israel. Table 2 reports the highest, lowest and average percent country rates of unemployment between 1991 and 2019, as well as the ratio between highest and lowest, and the difference between each country’s and Israel’s average unemployment rates. The country Human Development Index (HDI) for 2018 is also reported (United Nations Development Programme, 2019).

Table 2: Unemployment rates in 16 countries and in Israel, 1991–2019 Unemployment % Country

Average Diff. Country Israel

HDI 2018

High

Low

Average

Ratio H/L

Russia

13.26

4.74

7.42

2.80

-0.15

0.824

Ukraine

11.86

1.90

7.75

6.24

0.18

0.750

France

12.59

7.06

9.79

1.78

2.22

0.891

Italy

12.68

6.08

9.95

2.09

2.38

0.883

UK

10.35

3.85

6.46

2.69

-1.11

0.920

Germany

11.17

3.04

7.27

3.67

-0.30

0.939

Hungary

12.10

3.40

7.96

3.56

0.39

0.845

Turkey

12.55

6.50

9.29

1.93

1.72

0.806

USA

9.90

3.50

5.80

2.83

-1.77

0.920

Canada

11.38

5.56

7.79

2.05

0.22

0.922

Mexico

6.89

2.49

3.96

2.77

-3.69

0.767

Venezuela

16.78

6.10

9.62

2.75

2.05

0.726

Brazil

12.82

6.03

8.61

2.13

1.04

0.761

Argentina

19.59

5.44

10.97

3.60

3.40

0.830

Australiaa

10.87

4.23

6.57

2.57

-1.00

0.938

S. Africa

33.47

22.4

27.92

1.49

20.35

0.705

Israel, Total

11.20

3.86

7.57

2.90

0.00

0.906

Sources: ICBS; World Bank, https://www.macrotrends.net/countries/; UNDP; a Including New Zealand.

Socio-economic Determinants of Ethnic Migration to Israel, 1991–2019

All countries surpassed at least once a threshold of 10% unemployed with the exception of the USA (9.90% in 2009) and Mexico (6.89% in 1995). South Africa featured the top figure with 33.47% unemployed (in 2002). The highest among the bottom yearly unemployment rates was France (never below 7.06% unemployed, in 2008) followed by Turkey in 2000 (never below 6.50%, in 2000), Venezuela (never below 6.10%, in 2009), and Italy (never below 6.08%, in 2007). Over the period 1991–2019 only six of the 16 countries featured average unemployment rates lower than Israel’s: Russia, the UK, Germany, the USA, Mexico, and Australia. The low Mexican unemployment data actually raise serious doubts about the homogeneity of measurement criteria as against other countries, also considering that Mexico featured the lowest productivity among OECD (Organization for Economic Co-operation and Development) countries (Fleck & Sorrentino, 1994; OECD, 2015). Unemployment data for Russia and Ukraine, too, seem to be affected (or perhaps plagued) by un-homogeneous computation criteria, at least for certain years. Unemployment rates refer to a country’s total population and do not necessarily represent the situation among the Jewish labor force. Because of the significant differences in educational attainment, economic branch specialization, occupational stratification and residential concentration that prevail between Jewish communities versus the majority of population of each country, unemployment could touch the Jewish labor force selectively, occasionally resulting in lower or higher levels than average. I am not implying that those directly affected by unemployment would be involved in emigration. Of interest here is rather the perception of the general socioeconomic conjuncture prevailing in the local national economy and society. In Figures 3a and 3b annual percent rates of unemployment were presented separately for the eight less developed and for the eight more developed countries, according to the 2018 HDI, also including Israel for comparison (among the more developed). Nearly all countries experienced at some point sudden and sharp unemployment increases, as demonstrated by Argentina in 1995 and 2002 (close to 20%), Mexico in 1995 (close to 7%, with all due reservation), and as noted by nearly all countries on the occasion of the 2008–2009 global financial crisis. Other cases of significantly high unemployment rates involved Russia (over 13% in 1998), Ukraine (over 10% in 1998–2002), Brazil (over 10% in 1999 and again in 2016–2019), France and Italy in the second half of the 1990s (both over 12%) and again all along the 2010s (over 10% and 12% respectively), the UK, Canada and Australia (over 10% in the early 1990s), Germany (over 10% in 2004–2006), Hungary (over 10% in 1993–1996, and again in 2003–2009), Venezuela (over 10% between 1995 and 2005, and again between 2015 and 2019), and Turkey (over 10% in 2002–2005, over 12% in 2009, and again over 10% in 2015–2018). In 2011 unemployment diminished in 15 out of 16 countries as well as in Israel, and in 2005 and 2006 it diminished in 14 out of 16 countries. In 2009, following the financial crisis, unemployment increased in 15 out of 16 countries.

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Figure 3a: Unemployment rates (%) in 16 countries and in Israel, 1991–2019

Sources: Israel: ICBS; other countries: World Bank, https://www.macrotrends .net/countries/

Figure 3b: Unemployment rates (%) in 16 countries and in Israel, 1991–2019

Sources: Israel: ICBS; other countries: World Bank, https://www.macrotrends .net/countries/

Socio-economic Determinants of Ethnic Migration to Israel, 1991–2019

In Israel unemployment rates were generally intermediate among the countries examined here. After reaching a high at the beginning of the 1990s (11.2% in 1992), evidently under the impact of massive immigration from the FSU but also Ethiopia, unemployment again surpassed 10% as an echo effect of the rise in the USA in the early 2000s. Subsequently, however, Israel’s unemployment steadily declined reaching historical lows of 3–4% at the end of the 2010s.

Explanatory relationships Development and migration The assumption that migration frequencies to Israel are inversely related to country development was tested by exponentially regressing the four indicators of migration frequency reported in Table 1 on the country average HDI over the years 1990–2018. The HDI-migration relationship indeed resulted generally negative. The respective regression R2 coefficients were: 0.286 for the average migration rate in 1991–2019; 0.316 for the highest migration rate during the same period; 0.229 for the lowest migration rate; and 0.209 for the ratio of highest to lowest migration rate. In Table 3 as well as in subsequent tables, in order to allow for easier descriptive reading of the data, the continuous distribution of the 16 countries studied here was somewhat arbitrarily classified according to three levels of intensity of migration to Israel and three HDI levels. The negative relationship between country development and migration intensity is clearly portrayed.

Table 3: Average immigration rate per 1000 Jewish population, 16 countries, 1991–2019, by country HDI, 2018 Immigration rate per 1000 Jewish population

HDI 0.700-0.800

0.800-0.900

High > 6

Ukraine, Venezuela

Russia, Turkey

Medium 2 – 6

Mexico, Brazil, South Africa

France, Italy, Hungary, Argentina

Low < 2 Source: Tables 1 and 2.

>0.900

UK, Germany, Canada, USA, Australia

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None of the less developed countries with HDIs lower than 0.8 in 2018 appears in the group of countries with low average migration frequencies of less than 2 per 1000 Jews in the countries of origin. Likewise, none of the more developed countries with HDIs higher than 0.9 appears in the group with average migration frequencies higher than 2 per 1000 Jews in the country of origin. Russia and Turkey with HDIs of between 0.8 and 0.9 are included among the countries with relatively higher migration rates above 6 per 1000, while Mexico, Brazil and South Africa with HDIs between 0.7 and 0.8 featured intermediate migration rates to Israel of between 2 and 6 per 1000. As already noted, significant Jewish emigration moved from Mexico and South Africa to other countries, respectively the USA and Australia.

Unemployment and migration Table 4 displays the effects of unemployment rates on immigration rates to Israel lagged one year for the 16 countries examined here. Unemployment was measured, respectively in the country of origin, in Israel, and as the difference between the country of origin and Israel. The assumption behind lagging annual migration data by one year versus unemployment data was that a certain amount of causality exists between the two phenomena. Indeed, many (though clearly not all) migrants need some time between the moment of perceiving a certain migration propensity, making a firm decision about it, and actually implementing the migration project. Lagged data actually resulted more efficient than unlagged data in testing this hypothesis. Regression coefficients in Table 4 reflect the best fitting equation, whose type – exponential or polynomial – is indicated in the table. The further assumption here is that migration does not simply reflect a mechanical relationship with its determinants, but responds more or less powerfully if and when certain thresholds of sensitivity are attained. Besides the simplistic and deterministic underlying logic of a linear fit, in no instance did such assumed pattern produce the highest coefficient of determination. The influence of unemployment in the country of origin as an explanatory determinant of aliya was strongest for Argentina, Russia, Hungary, Brazil, and Australia, followed by Mexico. In most other countries the relationship was expectedly positive though weaker. In two cases – Venezuela and South Africa – the relationship was negative, meaning more unemployment at home was related to less migration to Israel, and in Germany it was also negative but close to nil. In these cases aliya followed different decisional paths. In Venezuela and South Africa local Jewish communities operated under immediate severe stress and local security concerns evidently predominated upon evaluations of relative economic benefit. In Germany most of the Jewish population consisted of former immigrants from the FSU. It can be hypothesized that emigration to Israel from Germany was more affected by enlarged family

Socio-economic Determinants of Ethnic Migration to Israel, 1991–2019

links with previous direct migrants to Israel, or by post-immigration difficulties in Germany, than by immediate socioeconomic evaluations.

Table 4: Quotients of determination (R2) of regression of immigration rates to Israel, lagged one year, on unemployment rates in country of origin and in Israel, 16 countries, 1991–2019

Country

Unemployment in country of origin

Type of Equation

Unemployment in Israel

Type of equation

Expected sign (-)

Expected sign (+)

Unemployment difference country – Israel

Type of Equation

Expected sign (+)

Russia

0.605

Polynom.

0.022 (+)

Polynom.

0.432

Polynom.

Ukraine

0.290

Polynom.

0.096

Polynom.

0.341

Polynom.

France

0.177

Polynom.

0.256

Exponent.

0.108

Polynom.

Italy

0.291

Polynom.

0.331

Exponent.

0.336

Polynom.

UK

0.130

Polynom.

0.154

Exponent.

0.335

Exponent.

Germany

0.032 (-)

Polynom.

0.179 (+)

Polynom.

0.367 (-)

Polynom.

Hungary

0.610

Exponent.

0.118 (+)

Polynom.

0.190

Exponent.

Turkey

0.119

Polynom.

0.457

Polynom.

0.514

Polynom.

USA

0.148

Exponent.

0.428

Polynom.

0.569

Polynom.

Canada

0.175

Polynom.

0.044

Polynom.

0.192

Exponent.

0.321

Polynom.

0.128

Exponent.

0.215

Exponent.

0.079 (-)

Polynom.

0.447

Polynom.

0.148

Polynom.

Mexico Venezuela Brazil

0.543

Polynom.

0.726

Polynom.

0.734

Polynom.

Argentina

0.696

Exponent.

0.123 (+)

Exponent.

0.485

Exponent.

Australia NZ

0.598

Polynom.

0.032

Exponent.

0.438

Exponent.

S. Africa

0.211 (-)

Polynom.

0.366

Polynom.

0.063

Polynom.

In bold the stronger of the three effects for each country. In parenthesis effects contrary to expectation.

As to unemployment in Israel, its influence was strongest for Brazil, Turkey, Venezuela, the USA, followed by South Africa and Italy. The relationship was expectedly negative for all countries with three exceptions – Argentina, Germany,

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and Hungary. Extremely weak negative relations obtained also for Ukraine, Canada and Australia while in Russia the relation was close to nil. It appears that for those countries with the strongest effect of unemployment at home, the situation in Israel was quite negligible regarding aliya decision-making. Immediate pressures at home overwhelmed any other considerations. Of the other countries, in Ukraine, Canada, and Mexico unemployment at home provided more powerful an explanation than unemployment in Israel, while the contrary was true for the UK. For all these countries the relationship of migration to unemployment in the country of destination was negative as expected. All in all, of the 16 countries examined here, the relationship with unemployment in the country of residence was stronger in seven cases and unemployment in Israel was stronger in nine cases. When also considering the difference between country and Israel unemployment, the country effect was strongest in five countries, the Israel effect in three countries, and the difference in eight countries. Figure 4 provides a synthesis of the strongest effect of unemployment on migration for each country as reported in Table 4. The R2 coefficients appear in black if the dominant effect came from the difference between unemployment in the country of origin and in Israel, in light gray if it was unemployment abroad, and in dark gray if it was unemployment in Israel.

Figure 4: Highest quotients of determination (R2) on immigration rates to Israel, lagged one year, by main type of unemployment effect: in country of origin, in Israel, difference country of origin – Israel, 16 countries, 1991–2019

Source: Table 4. Unemployment measure: Light Gray: at origin, Dark Gray: in Israel, Black: difference

Socio-economic Determinants of Ethnic Migration to Israel, 1991–2019

Most effects were actually strong or extremely strong, suggesting high sensitivity of migration to Israel to socioeconomic circumstances of general import, and particularly to unemployment levels as hypothesized. Two Latin American countries, Brazil and Argentina, featured the strongest – actually massive – effects of unemployment on migration (R2 = 0.7 or more). Other countries with R2 s of or above 0.5 included Russia, Hungary, and Turkey, all countries that experienced substantial instability under totalitarian or semi-totalitarian regimes, as well as the USA and Australia, where on the contrary, in a context of stable democracy, emigration rates were among the lowest. The direction of the relationships was extraordinarily consistent in the expected direction, with the only exception of Germany where the explanatory relationships consistently run in the opposite direction. Germany is indeed a special case where the Jewish population rapidly grew since 1991 under the impact of significant immigration from the FSU. Germany also was one of the preferred targets of recent Israeli emigration (Rebhun et al., 2016). As such it may have been particularly sensitive to possible re-migration by recent immigrants. Some of these different patterns may have been affected by the location of family networks that were split at the stage of primary migration and strove to reunite at a later stage.

Table 5: Best fit of quotients of determination (R2) of immigration rates to Israel, lagged one year, on unemployment rates in country of origin and in Israel, by type of measurement, 16 countries, 1991–2019 Strength of effect

Unemployment in country of origin

R2 < 0.3 R2 > 0.3

Russia, Hungary, Mexico, Argentina, Australia

Unemployment in Israel

Unemployment difference country of origin – Israel

France

Canada

Venezuela, South Africa

Ukraine, Italy, UK, Germany, Turkey, USA, Brazil

Source: Table 4.

Table 5 summarizes the effects on migration reported in Table 4 and Figure 4. Countries were regrouped according to the strength of the determination coefficient somewhat arbitrarily chosen to distinguish between weaker and stronger effects of unemployment on migration. The threshold was set at R2 = 0.3, the equivalent of a simple correlation above 0.5, actually closer to 0.55. Each country is represented by the higher coefficient among the three possible fitting equations – unemployment in the country of residence, unemployment in Israel, and country of origin-Israel unemployment difference. Only two countries fell in the weaker explanation category

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with a R2 lower than 0.3: France and Canada. The respective contexts were quite different. Canada featured relatively high levels of cultural pluralism, peaceful integration of Jewish communities, and relatively low and stable levels of unemployment. Aliya was better predicted by the difference between unemployment in Canada and in Israel. France instead experienced periods of high unemployment and repeated acts of terrorism aimed at Jews, but aliya was better predicted by unemployment in Israel. Of the 14 countries where unemployment in one way or another provided a determination coefficient for migration higher than 0.3, in seven the better predictor was the difference between unemployment rates (Ukraine, Italy, UK, Germany, Turkey, USA, Brazil), in five it was unemployment in the country of origin (Russia, Hungary, Mexico, Argentina, Australia), and in two unemployment in Israel (Venezuela and South Africa). The latter two countries, as noted, were among those where a majority of Jewish emigrants preferred a country of destination other than Israel. Table 6 summarizes the combined strength of the effects of the hypothesized relations between migration and, respectively, unemployment in the countries of origin and in Israel in the 16 countries (disregarding the country-Israel difference) – again with a threshold set at R2 = 0.3.

Table 6: Typology of effects of unemployment rates in country of origin and in Israel on migration rates from 16 countries, 1991–2019 Country of origin effect

Israel effect R2 < 0.3

R2 > 0.3

R2 < 0.3

Canada, France, UK, Germany, Ukraine

Italy, Turkey, USA, Venezuela, South Africa

R2 > 0.3

Mexico, Argentina, Australia, Russia, Hungary

Brazil

Source: Table 4.

Five countries displayed relatively weaker effects at both ends. Four of these – Canada, France, the UK and Germany – exemplify developed democracies. France’s diminishing Jewish population in a context of higher unemployment and serious terrorism actions contrasted with the other three countries where Jewish populations were stable or growing, also reflecting some incoming immigration. The fifth case – Ukraine – must be interpreted in the light of local disruptive factors far more powerful than unemployment in shaping the size and timing of a very large migration inflow. In five countries a strong country of origin effect went together with

Socio-economic Determinants of Ethnic Migration to Israel, 1991–2019

a weaker Israeli effect. Four of these – Mexico, Argentina, Russia, and Hungary – experienced years of significant economic recession if not disruption as well as political instability. The exception was Australia where in spite of a generally stable and friendly environment Jewish emigration appeared to be strongly influenced by home unemployment. Part of the explanation possibly reflected the mechanism of remigration from a country that absorbed significant Jewish immigration relative to the size of the community. A similar hypothesis could even better apply to Germany whose recent rapid Jewish population growth depended exclusively on largescale immigration from the FSU. In other countries, the effects of Israeli unemployment were more visible. In five countries, Israeli effects were strong along with weaker home effects. Three of these – Venezuela, South Africa and Turkey – experienced political upheavals, while a fourth – Italy – was struck by prolonged high unemployment. The exception here was the USA, which at times had represented the prime country for Jewish immigration. In this case, the strong sensitivity to Israel market circumstances was remarkable keeping in mind the very low absolute numbers and relative frequencies of immigration from the USA. An explanation may be that USA aliya is managed through Nefesh b’Nefesh, an organization plausibly attentive to Israel market’s opportunities. Finally, Jewish migration from Brazil – with prolonged periods of political and economic instability – was strongly influenced by factors at both ends. In Table 7, the three types of unemployment effects were cross-checked against average levels of migration rates between 1991 and 2019.

Table 7: Best fit of quotients of determination (R2) of immigration rates to Israel lagged one year on unemployment rates in country of origin and in Israel, by type of measurement and level of average immigration rate, 16 countries, 1991–2019 Immigration rate per 1000 Jewish population

Unemployment in country of origin

Unemployment in Israel

Unemployment difference country of origin – Israel

High > 6

Russia

Venezuela

Turkey, Ukraine

Medium 2 – 6

Hungary, Mexico, Argentina

France, South Africa

Italy, Brazil

Low < 2

Australia

Source: Tables 2 and 4.

USA, Canada, UK, Germany

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Like in Table 3, to help readability three categories of migration intensity were set at thresholds of less than 2 per 1000 Jewish population, 2–6 per 1000, and more than 6 per 1000. Unemployment in the country of origin more often fitted countries with high-medium levels of aliya, whereas difference between unemployment in country of origin and in Israel worked better with countries with low levels of aliya. Table 8 cross-checked the different types of unemployment effects on migration against the average level of the Index of Human Development (HDI) in 2018. Again, like in Table 3, the country development continuum was classified into three levels, respectively set at HDI thresholds of less than 0.800, between 0.800 and 0.900, and above 0.900. Unemployment in the country of origin was a better predictor in countries with lower-medium HDI levels, whereas difference between unemployment in country of origin and in Israel worked better in countries with higher HDI levels.

Table 8: Best fit of quotients of determination (R2) of immigration rates to Israel lagged one year on unemployment rates in country of origin and in Israel, by type of measurement and country HDI, 16 countries, 1991–2019 HDI

Unemployment in country of origin

Unemployment in Israel

Unemployment difference country of origin – Israel

0.7000.800

Mexico

Venezuela, South Africa

Ukraine, Brazil

0.8000.900

Russia, Hungary, Argentina,

France

Italy, Turkey

>0.900

Australia

USA, Canada, UK, Germany

Source: Table 2 and 4.

A final test of the unemployment/aliya relation is presented in Figure 5. The explanatory variable was represented here by the country’s average unemployment rate between 1991 and 2019. Clearly an average figure was derived from a data series which could feature significant internal variation. The dependent variable chosen here was the ratio between the highest and the lowest rate of migration to Israel per 1000 Jewish population in the countries of origin. South Africa whose unemployment levels were absolutely anomalous in comparison with the other countries was excluded, reducing the test to 15 countries.

Socio-economic Determinants of Ethnic Migration to Israel, 1991–2019

Figure 5: Average country unemployment rate and ratio between highest and lowest annual rate of migration to Israel per 1000 Jewish population, 15 countries , 1991–2019

Source: Tables 1 and 2; Not including South Africa.

Using the High/Low aliya ratio – a relatively unexplored measure – assumed that a country’s inherent economic uncertainty not only affected the average level of emigration propensities, but even more significantly determined periodical brusque fluctuations in migration frequencies. Such fluctuations added to the precariousness of the community environment out of which emigration stemmed. The regression in Figure 5 provided a high coefficient of determination of R2 = 0.742 for polynomial regression (or R2 = 0.495 for exponential regression). Further experimentation with the unemployment-migration relationship provided similarly high or higher determination coefficients (Table 9). One should be cautious at drawing conclusions from high determination coefficients based on a very small number of cases (here 15 countries). However, it should be stressed that each reported observation did not represent a single individual but a complex synthesis of thousands if not hundreds of thousands of individual cases. Statistical relations of such unusual strength seem to provide quite conclusive evidence.

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Table 9: Coefficients of determination R2 for various combinations of unemployment and migration rates, 15 countries , 1991–2019 Regression type

Explanatory variable: Unemployment rate

Dependent variable: Migration rate

Exponential

Polynomial

Country highest

High/Low ratio

0.564

0.832

Country average

High/Low ratio

0.495

0.742

Diff. country-Israel

High/Low ratio

0.490

0.741

High/Low ratio

Country average

0.238

0.772

High/Low ratio

Country highest

0.177

0.700

Source: Tables 1 and 2; Not including South Africa.

Concluding remarks This chapter examined migration to Israel as a particular case of international ethnic migration. Among the broader array of countries across the Jewish diaspora, 16 were singled out including the 13 largest Jewish populations outside Israel and three more countries of special interest in the study of Jewish international migration. The period studied covered, 1991–2019, comprised a rich array of political events and economic crises in a general context of enhanced perceptions of antisemitism on the Jewish side. The single most important event was the collapse of the Soviet Union as a unified country and global power. This chapter focused on the effects of socioeconomic factors, namely unemployment levels in the countries of origin and in Israel as a determinant of the size and timing of migration to Israel. Over the last one hundred years, nearly 3.8 million Jewish immigrants provided the demographic foundation to the establishment and growth of the state of Israel. Of these, over 1.2 million arrived between 1991 and 2019. Immigration was mostly characterized by waves, significantly different in timing and intensity according to the individual countries of origin. Through its legal system, Israel facilitated immigration of Jews through a set of incentives, such as offering immediate citizenship and civil rights to those eligible, plus a package of fiscal, financial and other benefits aimed at easing the absorption of new immigrants in the country. These features provided Israel an advantage as against other possible countries of destination which competed with Israel in attracting Jewish migrants (Bensimon-Donath, 1970, 1971; Bensimon & DellaPergola, 1984; Dashefsky et al., 1992; Gold, 1994; Remennik, 2007; Cohen et al., 2011; Ben-Rafael et al., 2006; Bokser Liwerant et al., 2015; Gitelman, 2016).

Socio-economic Determinants of Ethnic Migration to Israel, 1991–2019

In our test of the possible relationship between socioeconomic variables – namely unemployment rates – and migration frequencies, several different country patterns emerged. The relationship unemployment-migration to Israel was not entirely homogeneous or straightforward but was mediated by the varying circumstances of the individual countries. One or more typologies of the various countries can be derived from single country experiences. Summing up, immigration to Israel predominantly came from those countries (here exemplified by Russia, Ukraine and Argentina, but also Mexico, Venezuela, Brazil, Hungary, Turkey, and South Africa) where local economic circumstances were negative. Such circumstances periodically were so powerful that they overcame the possible propensity to rationally evaluate the advantages or disadvantages offered by Israel’s employment conditions at that point in time or close to it. Other countries of this type, not studied here, with substantial immigration to Israel included Belarus, Moldova, the Caucasian and Central Asian FSU republics, some other Latin American cases, and Ethiopia. In all these instances, substantial numbers of migrants decided to move disregarding a possibly bad or worsening conjuncture for Israel employment rates. In other words, the country of origin’s socioeconomic push factors manifestly represented a dominant and crucial determinant of migration to Israel – whether or not directly compared against the situation in Israel. In other cases where the local situation was less stringent due to higher levels of development and generally lower unemployment, attractive conditions of the Israeli economy and of the labor market in particular occasionally provided an incentive to migration. All in all, one or another measure of unemployment in the country of origin, was capable of explaining over 70 percent of the variance in yearly immigration rates across 16 countries studied over 29 years. Factoring in unemployment in Israel significantly improved the explanatory power of the relationship. A most efficient dependent variable was the ratio between the highest and the lowest annual rate of migration to Israel. The important aspect, therefore, was not only the general frequency of aliya but especially its more or less irregular spread over the years. This reflected sudden and relatively short-lived waves of migration which expressed extreme stress suffered by the Jewish population in a given country at a given point in time. This is perhaps the most crucial lesson to be learned about the mechanisms of recent migration to Israel. As a case of return ethnic migration to the putative homeland, migration to Israel was supposed, and might indeed be expected to respond mainly to the ideological pull exerted by the core country – Israel in the case of Jews. Another significant factor enhancing such migration could be the socio-cultural push determined by negative attitudes on the side of total population in the current countries of residence against members of the ethnic group in question – antisemitism in the case of Jews. The latter was tested (results not reported here, see DellaPergola, 2020a), and proved much less strongly associated with migration when compared to the so-

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cioeconomic determinants examined in this chapter. Without forgetting other possible causes, unemployment alone was sufficient to provide a plausible explanation of trends and variation in migration levels to Israel. Population gaps and differentials within the ethnic group considered are likely to determine mechanisms of self-selection among migrants (Lee, 1966). It appears that the symbolically perceived ethos of ethnic solidarity and repatriation, and the explicit call by agents in the homeland/core country to the respective diaspora to return home may be necessary but not sufficient to generate a consistently large flow of (returning) ethnic migrants. A trickle of the more idealistically motivated can be expected to occur all the time. More importantly, family ties created in the course of time by previous migrants to the core country may operate as a pole of attraction to the extended family still living abroad. But under the present circumstances, migration decisions tend to reflect primarily a rational evaluation of socioeconomic needs and opportunities. If the voices of ethnic identity calling to live in or to transfer to the core ethnic state can be compatible with such evaluations, all the better. If not, migration decisions will be taken anyhow disregarding those voices.

Figures Figure 1: Figure 2a:

Figure 2b:

Figure 2c:

Figure 2d:

Figure 3a: Figure 3b: Figure 4:

Immigration rate per 1000 population in Israel with main intervening events, 1991–2019, logarithm scale | page 25 Migration to Israel: rates per 1000 Jewish population in countries of origin, 16 countries, 1991–2019 (Israel: rates per 1000 Israel population) | page 29 Migration to Israel: rates per 1000 Jewish population in countries of origin, 16 countries, 1991–2019 (Israel: rates per 1000 Israel population) | page 29 Migration to Israel: rates per 1000 Jewish population in countries of origin, 16 countries, 1991–2019 (Israel: rates per 1000 Israel population) | page 30 Migration to Israel: rates per 1000 Jewish population in countries of origin, 16 countries, 1991–2019 (Israel: rates per 1000 Israel population) | page 30 Unemployment rates (%) in 16 countries and in Israel, 1991–2019 | page 34 Unemployment rates (%) in 16 countries and in Israel, 1991–2019 | page 34 Highest quotients of determination (R2) on immigration rates to Israel, lagged one year, by main type of unemployment effect: in country of origin, in Israel, difference country of origin – Israel, 16 countries, 1991–2019 | page 38

Socio-economic Determinants of Ethnic Migration to Israel, 1991–2019

Figure 5:

Average country unemployment rate and ratio between highest and lowest annual rate of migration to Israel per 1000 Jewish population, 15 countries, 1991–2019 | page 43

Tables Table 1: Immigration to Israel from 16 countries, 1991–2019 | page 27 Table 2: Unemployment rates in 16 countries and in Israel, 1991–2019| page 32 Table 3: Average immigration rate per 1000 Jewish population, 16 countries, 1991–2019, by country HDI, 2018 | page 35 Table 4: Quotients of determination (R2) of regression of immigration rates to Israel, lagged one year, on unemployment rates in country of origin and in Israel, 16 countries, 1991–2019 | page 37 Table 5: Best fit of quotients of determination (R2) of immigration rates to Israel, lagged one year, on unemployment rates in country of origin and in Israel, by type of measurement, 16 countries, 1991–2019 | page 39 Table 6: Typology of effects of unemployment rates in country of origin and in Israel on migration rates from 16 countries, 1991–2019 | page 40 Table 7: Best fit of quotients of determination (R2) of immigration rates to Israel lagged one year on unemployment rates in country of origin and in Israel, by type of measurement and level of average immigration rate, 16 countries, 1991–2019 | page 41 Table 8: Best fit of quotients of determination (R2) of immigration rates to Israel lagged one year on unemployment rates in country of origin and in Israel, by type of measurement and country HDI, 16 countries, 1991–2019 | page 42 Table 9: Coefficients of determination R2 for various combinations of unemployment and migration rates, 15 countries , 1991–2019 | page 44

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The theory of ethnic economics and ethnic economy Nonna Kushnirovich1

Introduction Ethnic economics and ethnic economy have become a subject of growing interest in the literature. While the concepts of economy and economics have different meanings in economic theory, other studies often use them interchangeably (see, for example, Hum, 1997). The term economy refers to a system of production, distribution, consumption and trade of goods and services in a country, region, or geographic area. It is “a social domain that emphasizes the practices, discourses, and material expressions associated with the production, use, and management of resources” (James et al., 2015, p. 53). The economy is embedded in social networks. Rational economic action is supported and restrained by ties of friendship, shared experiences, professional associations, business connections, and the interlocking ownership of corporations (Mitchell, 2008). How economic agents (individuals, businesses, organizations, or governments) behave and interact in an economy is an issue of economics (Goodwin et al., 2015). Generally saying, economics is the study of how people use resources and respond to incentives, or the study of decision-making (American Economic Association, 2021). The concepts of ethnic economy and ethnic economics relate to the economic activity of ethnic minorities and focus on the intertwine of ethnicity and such activity. Ethnicity refers to shared cultural and religious characteristics (Roosens, 1989), and may be applied to self-defined or identified-by-others membership in an ethnic group (Cruickshank, 2013). Ethnicity may have an impact on a group member’s behavior and thinking (Marger, 2005) including economic behavior. Ethnic economy is usually defined on the basis of ethnic concentration in economic activity. Extensive literature is available on ethnic concentration and ethnic economy. While most studies pertaining to specific dimensions of ethnic economy regard labor market processes as separate from business activity with property rights, a few of them try to aggregate these dimensions. Bonacich and Modell (1980) described ethnic economy as consisting of co-ethnic self-employed and employers, 1

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their co-ethnic employees, and unpaid family workers. However, this definition may be better suited to ethnic entrepreneurship, which is an important but not the only part of ethnic economy. According to Logan and colleagues (1994), ethnic economy is an aggregation of ethnic-controlled industries characterized by an overrepresentation of ethnic workers and employers. A similar definition was given by Light and Gold (2000). According to them, ethnic economy includes so-called ‘ethnic ownership economy’ and ‘ethnic-controlled economy’. The first relates to ethnic business ownership. The second occurs where there is ethnic control in the workplaces, but it does not require business ownership. However, ethnic economy is a part of the general economy in a country, region, or geographic area, therefore, its definition should also include geographic or regional aspects. The crucial factor of ethnic economy is not territory, but a space socially produced through the manufacture and flow of distinctive goods (Nestorowicz, 2011; Werbner, 2001). Portes and Bach (1985) claimed that a geographical location characterized by ethnic concentration, a self-supporting economy, and a variety of inputs and outputs generated therein, may be regarded as an ethnic economy. Inconsistencies in existing definitions of ethnic economy create the need to conceptualize it, define and map its sub-dimensions, formulate criteria for them, and depict their characteristics. Most studies of ethnic economy and ethnic economics usually include immigrants (Elo et al., 2019; Masurel & Nijkamp, 2004; Volery, 2007), since immigrants of a given origin usually constitute a separate ethnic group (Heilbrunn & Kushnirovich, 2008). They often prefer to deal with their own community (Menzies et al., 2003; Toussaint-Comeau, 2005) and preserve their culture of origin. Immigrants create diasporas, which are well-defined groups of immigrants and their offspring with a shared cultural identity and ongoing identification with the country or culture of origin (Constant & Zimmermann, 2016). Ethnic economy is widespread and comprises an essential part of national economies of both developed and developing countries (Constant & Zimmermann, 2016). In Chicago, the businesses of about 90 percent of Koreans and 60 percent of Mexican entrepreneurs might be defined as ethnic since they deal with co-ethnic professionals sharing a common language, cultural understanding, and trust (Raijman & Tienda, 2003). In Israel, more than half of the businesses owned by immigrants are ethnic (Heilbrunn & Kushnirovich, 2008). Despite the extensive literature on the role of ethnicity in economic activity, the concepts of ethnic economics have still not been well developed. In recent decades, a new discipline of ethnic economics has emerged and has rapidly developed in Chinese schools of economics and anthropology (Min et al., 2016). The subject studied in this discipline pertains mostly to the economic problems of ethnic minority groups in China, and has a very specific regional emphasis, focusing on the development of

The theory of ethnic economics and ethnic economy

the economy of ethnic minority regions, mostly in Western China2 . The rare studies by Western scholars that use the term ‘ethnic economics’ discuss different aspects of the economic behavior of ethnic groups in an ethnic economy, but do not provide a clear definition of this concept. Some of them focus on ethnic entrepreneurship (Chaudhary, 2019; Galbraith, 2003), others on the labor market incorporation of ethnic groups of immigrants of the same origin (Hum, 1997). However, the concept of ethnic economics may not be reduced merely to describing ethnic concentrations in certain spheres (industries, occupations, or entrepreneurship). According to the definitions of economics of the American Economic Association (2021) mentioned above, it should consider a broad network of interrelations of ethnic group members who operate in the economy. There is a need to conceptualize ethnic economics as a broad concept that can explain the behavior of economic ethnic agents in the economy, their interaction, use of resources, and response to opportunities for economic activities, in which ethnicity and ethnic concentration play a role. The purpose of this chapter was to formulate the concepts of ethnic economics and ethnic economy and clarify their interrelations, to develop their typology and a framework for understanding the economic activities of members of ethnic minorities, and to describe the advantages and disadvantages of ethnic economics for ethnic minorities. This chapter defines the dimensions of ethnic economy and formulates criteria and characteristics of these dimensions. It also maps resources and opportunity structures that shape ethnic strategies, resulting in the creation of an ethnic economy.

Ethnic economics Ethnic economics refers to a set of interrelations of ethnic group members who operate in the economy. This is a broad concept, which includes the behavior of ethnic agents in the economy, and their interaction in economic activities in which ethnicity and ethnic concentration play a role. Any economic activity depends on available resources expressing the supply side of economic processes, and market opportunities expressing the demand side (Kloosterman, 2010). Resources may be individual or group based; the latter are extremely important when discussing the economic activities of ethnic minorities (Waldinger et al., 1990). Bourdieu (1997) suggested a basic definition of personal resources in terms of different forms of capital: (1) economic capital is a material embodiment of work, which is directly convertible into money, and may sometimes be institutionalized in the form of property rights; (2) social capital is defined in terms of possession 2

For a comprehensive literature review on the development of the research field of ethnic economics in China’s research school see Min, Ying and Guang (2016)

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of a durable network, and mutual acquaintances and recognition (i.e., membership in a group, which provides its members with the backing of the collectively owned capital); and (3) cultural capital. Cultural capital may be institutionalized – in the form of credentials, academic qualifications, and certificates, objectified – as material objects and media, such as writings, paintings, monuments, and other art works, and embodied – representing a person’s knowledge, abilities, values and cultural tastes. Embodied capital integrated within the person forms a habitus – a set of behaviors (politeness, speech, manners) and tastes (in clothing, friends, food, hobbies). These personal resources may explain the co-activity of minorities in the labor market, but this typology should be adapted for describing resources for entrepreneurship and business activity. There are different classifications of resources for setting up a business. According to the definition of attributes of resources suggested by Barney (1991), firm resources are “all assets, capabilities, organizational processes, firm attributes, information, knowledge, etc. controlled by a firm that enable the firm to conceive of and implement strategies that improve its efficiency and effectiveness” (Barney, 1991, p. 101). The model of new venture performance developed by Cooper and colleagues (1994) delineated four types of capital: human capital (education, gender and race of entrepreneur), management know-how, industry-specific know-how, and financial capital. The resource theory of entrepreneurship posits that either class or ethnic resources shape the establishment of ethnic and immigrant businesses (Light & Bonacich, 1988; Light & Rosenstein, 1995). Ethnic entrepreneurs differ from local entrepreneurs because they are endowed with ethnic resources (Heilbrunn & Kushnirovich, 2008). Lerner and Khavul (2003) developed the typology of resources needed by entrepreneurial immigrant business start-ups. They include social capital embodied in social relationships, specifically those enabling members of ethnic/immigrant groups to cooperate with one another to realize goals; institutional capital in terms of governmental and public support; and human capital (education, age, experience, length of residence in the host country, length of experience in the host country, professional and occupational status before and after immigration). It is important to differentiate between imported human capital – education on arrival, knowledge of the host country’s language on arrival, and occupation before immigration, and human capital acquired after immigration to the host country – education, language skills, experience, and vocational training in the host country (Cohen-Goldner & Eckstein, 2008). The forms of capital are intertwined. For example, institutional capital influences the scope of economic capital for setting up a business owned by ethnic entrepreneurs, while social capital helps to mobilize institutional capital (Kushnirovich, 2009). Based on the literature review, the kinds of capital (resources) for ethnic entrepreneurship may be stated as follows:

The theory of ethnic economics and ethnic economy

• • • • •

Human capital (education, experience, professional training, language skills); Economic capital (money and properties); Social capital (social networks), both ethnic and non-ethnic; Cultural capital (understanding and sharing cultural particularities of ethnic clients/workers/suppliers/partners, production of ethnic products, etc.); Institutional capital (government and public support).

Resource structure describes the supply side of economic activity, and the demand side is usually described by the market opportunity structure. The opportunity structure was defined by Waldinger and colleagues (1990) in their model of ethnic entrepreneurship, which considered interrelations between ethnic group characteristics and opportunity structure. Opportunity structure included market conditions for co-ethnic and non-ethnic oriented business activities, and the ease of access to business ownership, a factor which highly depends on ethnic competition and institutional frameworks. Group characteristics included pre-migration circumstances, reaction of the ethnic group to conditions, and resource mobilization based on utilizing ethnicity and ethnic communities. As ethnic groups adapt to the environment, ethnic strategies emerge from the interaction of opportunity structure and group characteristics. The theory of mixed embeddedness offered a new perspective on two structures: the resource structure for ethnic entrepreneurship, the opportunity structure, and the connection between them (Kloosterman & Rath, 2001). This theory considered a three-level strategy (at national, urban/regional, and neighborhood levels) for analyzing the opportunity structure, and stressed the importance of policy (institutional capital, in the typology of resources for ethnic entrepreneurship) for the development of opportunities for successful entrepreneurship. It emphasized two dimensions of the opportunity structure: accessibility to start a business, and the growth potential of the markets in which the business operates. For market growth, the size of the ethnic group matters (Aliaga-Isla & Rialp, 2013). The larger the ethnic community, the greater the probability of employing ethnic group members, and for ethnic entrepreneurs, the greater the chances to access ethnic clients and partners, and recruit workers: namely, the greater the chance for business growth in the ethnic enclave (Damm, 2009; Volery, 2007). Volery’s enhanced model captured additional factors that impact ethnic entrepreneurs. The ethnic factors are the cultural differences between host and home country, and discrimination. The greater the cultural differences between the ethnic group and the majority population, the greater the need for ethnic goods and the larger the potential ethnic market (Volery, 2007). Thus, ethnic economics includes resource structure (human capital, economic capital, ethnic and non-ethnic social capital, cultural capital, and institutional capital) and opportunity structure (market and labor market conditions, size of the ethnic group, cultural differences between ethnic minority and majority, and discrimina-

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tion/acceptance by the majority population). Interaction of these structures shapes ethnic strategies resulting in the creation of an ethnic economy (niches, business enclaves, and neighborhoods).

Ethnic economy Ethnic economy is usually described in terms of ethnic concentration and overrepresentation of ethnic or immigrant minorities in various spheres of the economy. Approaches to measuring overrepresentation differ. Some scholars measure it by odds ratio, claiming that ethnic niches or enclaves may be defined as such if the concentration of ethnic groups in an industry/sector/occupation/neighborhood is 1.5 times the general population representation, i.e., when overrepresentation is at least 50 percent (Carter & Leslie, 2015; Logan et al., 2000; Model, 1993). Others use a threshold of 1.4 times the concentration of the group in the population/labor force (Alba et al., 1997), or a 20 percent level of overrepresentation (Hudson, 2002; Wright & Ellis, 2000). Some scholars define ethnic overrepresentation in the labor market merely as a statistically significantly higher concentration of ethnic minorities within certain sectors of the economy compared with the concentration of these ethnic or immigrant groups in the overall population (Kushnirovich, 2010). Ethnic overrepresentation may also be measured by using location quotients (the percentage of an ethnic group in a sector divided by the percentage of all workers, ethnic and non-ethnic, in this sector), or representation indexes (the share of an ethnic group in a particular sector divided by the share of all other workers, who do not belong to this group, in this sector). The literature did not find significant differences when using any of these measures for defining ethnic representation (Wang & Pandit, 2007). Based on the literature review, ethnic overrepresentation in the labor market may be measured by two alternative ways. It occurs when: (1) the percentage of an ethnic group within a particular industry/occupation/business sector is significantly higher than the percentage of that group in the entire population; or (2) the percentage of persons in a particular industry/occupation/business sector within an ethnic group is significantly higher than within the entire population. The literature describes different forms of ethnic concentration: ethnic niches, enclaves, and neighborhoods. These concepts are not well distinguished in the literature and often overlap. The term ‘ethnic enclave’ is sometimes used to describe the concentration of an ethnic group in some residential neighborhoods (Logan et al., 2002; Zhou, 2013), and sometimes for the concentration of ethnic or immigrant employees in a labor market sector or entrepreneurs in a business sector (Carter & Leslie, 2015; Wilson & Portes, 1980; Xie & Gough, 2011). The same can be said for the term ‘ethnic niche.’ Some scholars integrate these concepts, and talk about a concentration of ethnic businesses in a certain territory (Waldinger, 2010), or ethnic

The theory of ethnic economics and ethnic economy

employees in businesses (Razin & Light,1998; Zhou, 2013). All these create the need to conceptualize and map the ethnic economy and its dimensions, and to formulate criteria for each of them. Commonly, ethnic niche is a concept that describes ethnic division in the labor market, where ethnic and immigrant minorities are commonly clustered in certain industries/sectors (Waldinger, 1994; Wilson, 2003). Lieberson (1980) defined ‘special niches’ as the concentration of ethnic and racial groups in certain jobs. Some scholars distinguish between ethnic industrial and occupational niches, where industry refers to the economic sector, and occupation refers to a worker’s specific function (Wang & Pandit, 2007). Being in an ethnic niche in the labor market may be described in terms of shared co-ethnic colleagues, shared co-ethnic employers, and speaking a language at work different from that of the host country (Kushnirovich, 2010). Some studies refer to ethnic origin not only among employees but also among business owners. Zhou (2013) posited that ethnic niches occur where certain types of businesses are disproportionately owned and/or staffed by ethnic minorities. The concept of ethnic business enclave relates mostly to ethnic entrepreneurship (Carter & Leslie, 2015; Logan et al., 2000). Waldinger and colleagues (1990) defined ethnic entrepreneurship as “a set of connections and regular patterns of interaction among people sharing a common national background or migration experience” (p. 3). Clients, suppliers, workers, and partners from inside an ethnic community are the indicators of co-ethnic business dealings (Heilbrunn & Kushnirovich, 2008, 2015; Light & Bonacich, 1988; Legros et al., 2013; Lerner & Khavul, 2003; Nestorowicz, 2011; Volery, 2007; Zhou, 1992). Ethnic businesses, operated by members of ethnic communities, offer ethnic products and services like ethnic food and drinks (HackPolay, 2019), clubs for co-nationals, and places for practicing religion (Damm, 2009). In this study, businesses are regarded as ethnic if they are ethnically owned and meet the following criteria: co-ethnic overrepresentation among business owners and (1) workers, (2) clients (customers), (3) suppliers, (4) business partners, (5) business advisors – auditors, tax advisors, marketers, lawyers, etc., or (6) if they produce ethnic products. If at least three of these six criteria are met, a business may be defined as ethnic and as operating in an ethnic business enclave. Since ethnic enclaves usually include employment of ethnic workers (ethnic niche), they may be the driving factors behind niche formation (Carter & Leslie, 2015). Commonly, ethnic businesses are owned by members of an ethnic or immigrant minority. However, if a representative of the majority population is inclined to deal with workers, clients, suppliers, partners and advisors of her/his ethnic background, such a business may also be regarded as ethnic for the majority population. The characteristics of ethnic businesses often differ from those of the majority population. Ethnic businesses comprising an ethnic enclave are characterized by:

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•

• • •

•

Specific industry structure: they are underrepresented in production, professional and financial services, and overrepresented in low-income retail and personal services (Heilbrunn & Kushnirovich, 2015; Razin & Light,1998). One possible explanation is that operating businesses in low-profit retail and service industries does not require investment in growth and long-term expansion (Wingfield & Taylor, 2016). Smaller size of business in terms of workers, clients, or turnover (Ensign & Robinson, 2011; Heilbrunn & Kushnirovich, 2008; Neville et al., 2014). Low financial investment for setting up the business (Heilbrunn & Kushnirovich, 2008; Huck et al., 1999; Mata & Pendakur, 1998). Mobilization of start-up economic capital from non-formal financial sources (peer-to-peer, relatives and friends) (Bates, 1997; Collins, 2000; Macchiavello, 2015; Raijman, 2001a; Smallbone et al., 2003), and credit rotating associations (Heilbrunn & Kushnirovich, 2008). Lower financial viability in terms of profit (Heilbrunn & Kushnirovich, 2008) and higher failure rates (Bates, 1996).

Low investment and reliance on informal ethnic resources may be explained by the fact that ethnic entrepreneurs more frequently encounter problems when recruiting capital (Raijman, 2001a; Waldinger et al., 1990), especially bank loans, than their majority counterparts (Constant, 2004; Heilbrunn & Kushnirovich, 2008). The classification of ethnic financial funding for setting up businesses developed by Kushnirovich and Heilbrunn (2015) includes the following ethnic sources: personal savings brought from the home country, loans and gifts from family and ethnic friends, ethnic banks, trade credit from ethnic suppliers, and rotating credit associations. Ethnic banks (mostly banks from the immigrants’ country of origin) give minorities an opportunity to raise capital (Hack-Polay, 2019), sometimes taking as security properties in their countries of origin. Rotating credit associations are based on the principle that each member contributes a fixed sum of money, and each one of them receives the entire sum in turn (Bates, 1997; Collins, 2000). Such associations may be regarded as informal savings banks (Light et al., 1990). The term ‘ethnic enclave’ is sometimes used to describe urban neighborhoods in which ethnic groups are residentially concentrated (Zhou, 2013). Logan and colleagues (2002) called distinctive settlement areas inhabited by immigrants ‘immigrant enclaves.’ In this sense, ‘enclave’ indicates segregation within a territorial configuration (Waldinger, 2010). To distinguish between territory and business enclaves, this chapter uses the term ethnic neighborhood for territories with an overrepresentation of ethnic residents. The literature distinguishes between three types of ethnic neighborhoods: ‘ethnic living enclave’, ‘ethnic living community’ and ‘minority ghetto.’ Ethnic living enclaves concentrate ethnic groups who have poor socio-economic resources and arise in response to economic and language

The theory of ethnic economics and ethnic economy

constraints. Such neighborhoods meet minorities’ needs for affordable housing, maintaining family ties, and understanding neighbors with the same culture, and help in finding work (Logan et al., 2002; Thomas & Znaniecki, 1974). In contrast, ethnic living communities emerge based on the taste preferences of successful and resourceful representatives of ethnic groups with high socio-economic status rather than on necessity, and their desire to maintain ethnic identity (Bonacich, 1973; Clark, 1991; Logan et al., 2002). The third type, ‘minority ghettos’ emerge irrespective of their residents’ resources (high or low) or preferences. They are often the result of discrimination, reluctance of the majority to accept and live with minorities, and are sometimes defined as residential apartheid (Massey & Denton, 1993). Some scholars tried to include the geographical aspect by using the term ‘ethnic enclave economy’ to define an economy clustered around a territorial core. According to Portes and Jensen (1992), enclave economy is “a concentration of ethnic firms in a physical space – generally a metropolitan area – that employ a significant proportion of workers from the same minority” (Portes & Jensen, 1992, p. 418). In this chapter, ethnic economy is defined as a system based on an ethnic concentration in the labor market, business sector, and neighborhoods for the production, distribution and consumption of goods and services. An ethnic economy provides ethnic minorities and immigrants with economic independence based on mutual help and mutual defense. It is a part of the general economy in a country, region, or geographic area. Based on the literature review, three main forms of ethnic economy may be designated: ethnic niche, ethnic neighborhood, and ethnic business enclave. The concept of ethnic niche is mainly attributed to salaried workers, ethnic enclave refers to ethnic entrepreneurship, and ethnic neighborhood refers to areas in which a particular ethnic group is overrepresented among neighborhood residents as compared to the share of this group in the country’s population. In an era of developed communications and internet, ethnic economy has significantly expanded to include not only co-ethnic clients, suppliers, workers, advisors and partners in the country where one lives, but also abroad, in the ancestors’ countries (Volery, 2007). This contributes to the creation of a so-called ’diaspora economy,’ which is expressed in the purchase of ethnic goods abroad, the purchase of general non-ethnic goods from ethnic suppliers abroad, remittances, and business and financial investments in ancestors’ countries (Drori et al., 2010). Ethnic economy, which includes the immigrant and diaspora economy, is usually transnational. Immigrants are inclined to engage in transnational activities, a phenomenon observed among first generation (Brzozowski et al., 2017; de Haas & Fokkema, 2011; Stark & Dorn, 2013) and second-generation members (Fokkema et al., 2013). Immigrants who are less integrated and poorly adjusted to the host country, including those in ethnic niches and enclaves, may maintain more ties with their countries of origin and be more engaged in transnational activities (de Haas & Fokkema, 2011). Ethnic non-immigrant minorities also may be involved in transnational activities. For

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example, African Americans are emotionally attached to their ancestors’ countries in Africa. Consequently, they tend to travel to these countries and develop transnational connections. Thus, ethnic economics is not only locally oriented within a minority but also transnationally. In this way, it has an impact on the economy of both host countries and the countries of ethnic groups’ origins (Miera, 2008).

Interaction of ethnic economics and ethnic economy The conceptual model of interaction between ethnic economics and ethnic economy is presented in Figure 1. The first part of the model pertaining to ethnic economics is based on the interactive model of ethnic entrepreneurship developed by Waldinger, Aldrich and Ward (1990). Their model considered interrelations between ethnic group characteristics and opportunity structure. Ethnic group characteristics are usually regarded as resources; therefore they were replaced in the model by the classification of resources, which was suggested on the basis of the literature review. Opportunities and resources shape ethnic strategies of economic activity; thus, ethnic division of the economy may ensue from the non-equal allocation of resources and opportunities between ethnic groups. Ethnic economy includes three sub-dimensions: ethnic niches, ethnic business enclaves, and ethnic neighborhoods. There are a few different theoretical approaches to the formation of an ethnic economy: human capital theory, segmentation and succession theories, and disadvantage theory. According to the first, both workers and employers seek to maximize their utility, where workers belonging to an ethnic/immigrant group maximize their wages, and employers maximize their production and profits. This may be achieved when the human capital of the worker fits the labor market demand in the host country (Wang, 2004). In this case, niches are created based on the skills and abilities of an ethnic/immigrant group. Segmentation theory (or dual labor market theory) stresses division of the labor market into primary segments (with better work conditions, higher wages and lower precarity) and secondary segments (with poor work conditions, low wages and high precarity) (Sanders & Nee, 1987; Wang, 2004; Wilson & Portes, 1980). According to this approach, niching emerges when ethnic and immigrant minorities utilize their social ethnic ties for finding a job. Since most of their relatives and friends work in the secondary segment of the labor market, they have more knowledge about it and more opportunities to find jobs there. As a result, ethnic minorities are concentrated in the secondary segment. Ethnic social capital gives minorities opportunities, but also prevents them from moving to the primary segment. Therefore, ethnic niches in the secondary segment reinforce themselves, and workers remain there for a long time. However, some mobility exists, and may be explained by the succession theory. It maintains that workers from

The theory of ethnic economics and ethnic economy

the dominant ethnic groups (the majority population) leave lower-wage and less-desirable jobs vacant for ethnic minorities and immigrant workers. With time, more successful representatives of ethnic and immigrant minorities move from the secondary to the primary segment. This process is described as the so-called ‘vacancy chain’ (Kloosterman & Rath, 2001; Wang, 2004).

Figure 1: The conceptual model of ethnic economy and ethnic economics

The disadvantage theory claims that minorities are pushed to the ethnic economy due to lack of other opportunities and disadvantage (Light, 2010). It distin-

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guishes between labor market disadvantage and resource disadvantage. The first occurs when minorities cannot obtain wages equivalent to prevailing market remuneration for their productivity. Resource disadvantage occurs when group members have fewer resources than others. A combination of labor market disadvantage and resource disadvantage impels them into the ethnic economy. In this case, dealing within ethnic economy is a strategy that minorities adopt to cope with a lack of information about the host country market (Carter & Leslie, 2015), unemployment (Alici, 2005), poor resources (Kushnirovich, 2007), cultural and language barriers (Lindemann, 2013; Volery, 2007), and discrimination (Kushnirovich, 2018; Volery, 2007). Discrimination vs. acceptance by the majority population is a salient factor for creating ethnic niches, enclaves and neighborhoods. Nestorowicz (2011) distinguished between employer discrimination in the labor market, discrimination in the capital market when trying to mobilize business capital, and discrimination from the consumers’ side. When an ethnic minority experiences exclusion, prejudice and discrimination, it can stimulate reactive ethnicity; namely, a reactive and defiant assertion of ethnic difference (Rumbaut, 2008; Kushnirovich & Youngmann, 2017). If the majority population accepts ethnic minorities and immigrants, they may be pulled to ethnic economy by opportunities, cultural understanding and trust (Raijman & Tienda, 2003), their desire to save their culture, and for immigrants, to proceed at their own pace in the process of adaptation (Kushnirovich, 2010).

Advantages vs. disadvantages The literature presents contradictory approaches to the consequences of dealing within an ethnic economy. The possible outcomes of dealing within an ethnic economy, according to different sources, range from benefitting ethnic minorities, hindering them, or neither. Dealing within an ethnic economy may be regarded as either an advantage or disadvantage for minorities. Minorities often have poorer resources than the majority population (Aliaga-Isla & Rialp, 2013; Dabić et al., 2020; Gorodzeisky & Semyonov, 2012; Kushnirovich, 2016). According to the disadvantage theory (Light, 1979), a co-ethnic community helps ethnic minorities and newcomers (immigrants) to mobilize resources, such as information (Cruickshank, 2013; Damm, 2009; Waldinger, 1994), and job opportunities and job training (Bates, 1997; Carter & Leslie, 2015; Cruickshank, 2013; Raijman & Tienda, 2001b, 2003; Volery, 2007; Waldinger, 1994). Finding work in an ethnic niche is easier for minorities and particularly for immigrants, since it does not necessarily need host country language skills (Damm, 2009; Zhou, 1992), and may be achieved immediately after migration without a long adaptation period. Employment in an ethnic niche also provides

The theory of ethnic economics and ethnic economy

minority workers with benefits such as upward mobility, investment in human capital, etc. (Wilson & Portes, 1980). For entrepreneurs, ethnic networks help to mobilize start-up capital, access clients, and locate partners and a committed labor force (Hack-Polay, 2019; Light & Bonacich, 1988; Legros et al., 2013; Nestorowicz, 2011; Volery, 2007; Zhou, 1992), and, in this way, increase their income (Kushnirovich, 2015). Entrepreneurs’ reliance on co-ethnic networks is based upon mutual trust, a term of great importance in entrepreneurship studies (Bates, 1996; Kushnirovich, 2016). Understanding cultural norms and dispositions, as well as the desires of clients from co-ethnic communities, helps to satisfy their needs better. From this point of view, co-ethnic dealings provide competitive benefits to immigrant entrepreneurs (Kloosterman & Rath, 2001; Waldinger et al., 1990). In ethnic neighborhoods, members of ethnic groups may find housing faster (Volery, 2007). Since minorities utilize advantages provided by ethnic communities and ethnic protected markets, they may have better outcomes than those who deal in the open economy, as in the case of Asian-owned businesses in the USA (Robb & Fairlie, 2009). Workers learn from ethnic networks about job opportunities and characteristics (Hack-Polay, 2019), and employers learn about workers’ abilities and characteristics. There are mutual benefits for both of them (Waldinger, 2010). Ethnic communities may provide solidarity and protection against discrimination (Knies et al., 2016), and give moral support (Cruickshank, 2013). Feelings of solidarity and mutual support may have a positive effect on the subjective wellbeing of minorities (Dana & Morris, 2007; Knies et al., 2016; Kushnirovich & Youngmann, 2017). Co-ethnic market orientation can also inhibit immigrant businesses. Residence in ethnic neighborhoods diminishes contacts with the majority population and hinders majority language acquisition, which limits earning opportunities (Chiswick & Miller, 1995; Damm, 2009). The costs of living in ethnic enclaves are usually lower than those in other localities, and ethnic goods are more accessible. Therefore, members of ethnic minorities living in such neighborhoods may accept lower wages to work closer to home (Chiswick & Miller, 1995). That may explain why working in an ethnic niche is often characterized by lower wages (Light & Gold, 2000; Zeltzer-Zubida, 2004). Some studies posit that the ethnic economy benefits ethnic entrepreneurs but hinders ethnic employees (Xie & Gough, 2011). The exploitation of migrants by ethnic employers may be through low wages (Cruickshank, 2013; Sanders & Nee, 1987), longer working hours (Cruickshank, 2013), poor working conditions, and lack of training (Hack-Polay, 2019; Mussida & Parisi, 2018). However, other scholars claim that co-ethnic dealing does not necessarily provide ethnic entrepreneurs with higher benefits; sometimes they are even penalized economically (Aguilera, 2009; Bates, 1996; Ndofor & Priem, 2011). Since the income of minority persons is

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usually lower than that of the native population (Hack-Polay, 2019), they tend to buy cheaper commodities that can reduce the revenue of immigrant businesses serving co-ethnic clients. The growth of businesses dealing within the ethnic enclave is limited by the size of the ethnic economy; thus, reliance on co-ethnic markets can constrain market penetration and business growth (Ibrahim & Galt, 2011). Other scholars claim that dealing within the ethnic economy provides neither benefits nor detriments for minorities. Cubans who worked for Cuban employers in the USA had returns on their human capital similar to workers within the general labor market, where they could not take advantage of their culture-specific human capital (Wilson & Portes, 1980). Adopting a co-ethnic strategy of business performance does not necessarily mean lower income (Kushnirovich, 2015). Zhou and Logan (1989) also did not find any advantages in terms of workers’ earnings or entrepreneurs’ income between individuals who dealt within and outside the ethnic economy. It seems that ethnic orientation may simultaneously restrict and broaden immigrants’ success, so that it does not necessarily lead to lower or higher economic achievements. Thus, dealing within ethnic niches and enclaves compensates for minorities’ lack of resources, but does not endow them with a competitive advantage. Minorities rely on the benefits granted by ethnic solidarity, but this diminishes their opportunities of mobility to general markets (Raijman & Tienda, 2001a; Waldinger, 2010). Inclusion of one ethnic group may turn out to be exclusion of other groups where finding workers is based on the “friends bring friends” principle. Minorities who deal in an ethnic economy have no need to apply strategies of social integration (Kushnirovich, 2015). Thus, an ethnic economy provides benefits for minorities through isolation from the open competitive market (Raijman, 2001b; Wilson & Portes, 1980). This isolation, lack of social information about minorities, and poor social connection with them may reinforce prejudices and discrimination against minorities (Damm, 2009). Ethnic economy reinforces and perpetuates itself by motivating people to remain within their economic ethnic clusters (Aliaga-Isla & Rialp, 2013). Such clusters provide members with support and opportunities for development. Members of ethnic economic clusters are forced to compete in the same market (Cruickshank, 2013). All these prevent their further growth and development beyond the cluster, essentially trapping them in an ethnic economy and, in some sense, bonding them to it.

The Israeli case The Israeli society is stratified based on ethnicity and origin of its citizens (Al-Haj, 2004; Heilbrunn & Kushnirovich, 2015). About 21 percent of the total Israeli population comprise Israeli Palestinians, and another 16 percent are immigrants (Israel

The theory of ethnic economics and ethnic economy

Central Bureau of Statistics, 2020). Although immigration to Israel is selectively open to all Jews, cultural and ethnic differences between groups are evident by country of origin (Al-Haj, 2004). Based on their origin, immigrants to Israel are traditionally divided into sub-ethnic groups: Western immigrants from Europe and the Americas are called Ashkenazim, and immigrants from Asia-Africa are called Sephardim (Semyonov et al., 2016). Most Sephardim emigrated from Moslem countries of Asia and North Africa, and Ashkenazim arrived mostly from Christian countries in Europe and the Americas. Immigrants from the Former Soviet Union (FSU) and Ethiopia are usually considered separate groups of immigrants, because of their cultural particularities. Ethnic and immigrant minorities in Israel tend to create strong immigrant ethnic communities utilizing ethnic networks (Horowitz, 2005). Immigrants who arrived in Israel since 1990 are disadvantaged in terms of income (Youngmann & Kushnirovich, 2020; Semyonov et al., 2016). They live in disadvantaged neighborhoods, and engage in ethnic economic activities (Heilbrunn & Kushnirovich, 2015). The occupational distribution of ethnic groups in Israel compared to the majority population in 2019 is presented in Table 1. Since half of Israeli- born residents are the second generation of immigrants, the majority population was divided into sub-groups according to the father’s country of birth. Ethnic and immigrant minorities are overrepresented among skilled workers. Among immigrants from Asia and Africa skilled workers comprise 15.8%, among immigrants from Europe-America 13.1%, and among Israeli Palestinians 32.2%, compared to 8.5% among the native-born Jewish majority. Among the second generation of immigrants from Asia-Africa, skilled workers comprised 13.5%, which is larger than for the Israeli born persons whose father was born in Europe, America or Israel. Thus, the ‘skilled workers’ occupation may be regarded as an ethnic niche for Israeli Palestinians and immigrants from Asia and Africa. Immigrants from Asia and Africa are also concentrated in the ‘service and sales workers’ category, making this occupation an ethnic niche for them. All minority groups are overrepresented in elementary occupations but underrepresented in managerial positions. Studies of immigrant and ethnic entrepreneurs in Israel revealed that minority-owned businesses are co-ethnic oriented. Heilbrunn and Kushnirovich (2015), in their study of ethnic and immigrant business minorities in Israel, revealed that more than half of the clients and suppliers in businesses owned by FSU immigrants, Ethiopian immigrants or Israeli Palestinian entrepreneurs were from the same immigrant or ethnic community (Table 2). Minority entrepreneurs employed workers of the same ethnic background almost exclusively; 95.1 percent of workers employed by FSU immigrants, 99.7 percent of workers employed by Israeli Palestinians, and all workers (100%) employed by immigrants from Ethiopia were of the same origin as the businesses’ owners. All partners of immigrants from Ethiopia and Israeli Palestinians were co-ethnic. Among FSU immigrants, 81.4% reported having co-ethnic

67

3.9 19.6 6.0 4.5 19.2 1.3 32.2 13.1

Managers

Professionals

Practical engineers, technicians, agents, and associate professionals

Clerical support workers

Service and sales workers

Skilled agricultural, forestry and fishery workers

Skilled workers in manufacturing and construction, and other skilled workers

Elementary occupations

Source: Israel Central Bureau of Statistics (2020).

100.0

Israeli Palestinians

Percentage

Occupation

5.0

13.1

0.3

19.9

6.4

15.3

34.3

5.7

100.0

Immigrants from Europe-America

10.8

15.8

0.8

28.5

7.9

15.5

14.7

6.0

100.0

Immigrants from Asia-Africa

Ethnic and immigrant minorities

Table 1: Employed persons, by occupation and continent of birth, 2019

3.5

8.5

0.8

17.7

8.3

17.6

33.3

10.3

100.0

Total

2.3

6.2

1.1

13.0

7.5

18.4

39.9

11.6

100.0

EuropeAmerica

3.2

13.5

0.8

19.1

9.1

18.5

25.2

10.5

100.0

AsiaAfrica

Father born in

4.1

6.2

0.7

18.7

8.1

16.7

35.8

9.7

100.0

Israel

Majority population – Born in Israel

68 Nonna Kushnirovich

The theory of ethnic economics and ethnic economy

partners. Since FSU immigrants and Israeli Palestinians constituted only 13% and 20% of the Israeli population, respectively, and immigrants from Ethiopia constituted only about one percent of the population, co-ethnic clients, suppliers, workers and partners are overrepresented in the minorities’ businesses. Most Ethiopian entrepreneurs (82.52%) produce ethnic products, whereas among FSU immigrants and Israeli Palestinians this phenomenon is rarer. Based on the criteria for defining business as ethnic, 83.45% of businesses owned by FSU immigrants, 82.1% of businesses owned by immigrants from Ethiopia, and 64.2% of the businesses owned by the Israeli Palestinians, were ethnic.

Table 2: Businesses owned by minority entrepreneurs in Israel FSU immigrant entrepreneurs

Immigrant entrepreneurs from Ethiopia

Israeli Palestinian entrepreneurs

Ethnic business, %

83.4

82.1

64.2

Share of co-ethnic clients, %

72.2

97.0

59.0

Share of co-ethnic workers, %

95.1

100.0

99.7

Share of co-ethnic suppliers, %

53.0

78.0

67.2

Share of co-ethnic partners, %

81.4

100.0

100.0

Ethnic products, %

35.3

82.5

21.6

Source: Heilbrunn & Kushnirovich (2015) and own elaboration

Thus, the Israeli labor market is strongly divided into ethnic niches, and minority entrepreneurs are inclined to deal within ethnic business enclaves. The literature review suggests that ethnic minorities in Israel are concentrated in specific neighborhoods and are disadvantaged in terms of income. Thus, engaging in an ethnic economy does not provide them with a competitive advantage in the Israeli market.

Conclusions This chapter provides new insights into the literature on ethnic economics and expands the literature on ethnic embeddedness and its consequences for minorities. It formulated the concepts of ethnic economics and ethnic economy, explaining differences between them, and providing their typology. Ethnic economics was formulated as a broad concept, which includes the behavior of ethnic agents in an ethnic economy, and their interactions during economic activities, in which ethnicity and ethnic con-

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centration play a role. Based on the fusion of theories of forms of capital (Bourdieu, 1997) and attributes of resources developed by Barney (1991), a typology of resources for ethnic entrepreneurship was developed. Ethnic economy was conceptualized as a part of the economy characterized by ethnic overrepresentation, and includes three sub-dimensions: ethnic niches, enclaves, and neighborhoods. The chapter developed the criteria for these sub-dimensions and described their characteristics. It explained how the interactions between personal and business ethnic resources and opportunities shape ethnic strategies, resulting in the creation of an ethnic economy. Dealing within the ethnic economy can be considered as either an advantage or a disadvantage. On the one hand, utilizing ethnic networks provides competitive benefits to immigrant and ethnic minorities, helping them to find jobs, access information, gain promotion to managerial positions, mobilize capital, acquire clients and suppliers, and facilitate sales. However, co-ethnic dealing can also inhibit the actors of ethnic economics. The income from ethnic jobs and co-ethnic business activity is usually lower than the remuneration of the majority population. Reliance on co-ethnic markets can constrain market penetration and company growth.

Figures Figure 1: The conceptual model of ethnic economy and ethnic economics | pages 63

Tables Table 1: Employed persons, by occupation and continent of birth, 2019 | pages 68 Table 2: Businesses owned by minority entrepreneurs in Israel | pages 69

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Employment, self-employment and precarity Routes to migrant integration? Sibylle Heilbrunn1 , Natalia Vershinina2 and Peter Rodgers3

Introduction Migration constitutes one of the most complex and politically polarizing challenges (Laczko & Appave, 2013) of the twenty-first century. At the same time, labor markets are undergoing structural changes, becoming increasingly competitive with emerging new and more flexible patterns of employment (Meardi et al., 2012), including precarious work practices (Foster & Scott, 2015; Glavin et al., 2019; Kalleberg, 2011). In particular, the change from the traditional ways of protecting workers’ rights involving the ‘employee-employer’ model, to accommodation of individual rights for workers through flexible contracts (Pollert & Charlwood, 2009) increased precarity in work environments (Prosser, 2016). For self-employed individuals, the situation is equally precarious. Whilst mainstream labor markets with secure, formal employment opportunities were developed to protect domestic workforces, migrant workers are often structurally disadvantaged. Several scholars (Kloosterman & Rath, 2001; Kone et al., 2020; Vershinina, et al., 2011) suggest that structural, cultural and political barriers and obstacles for different groups of migrants often hinder integration into the labor market in host societies. Therefore, for migrants, self-employment is increasingly seen as a viable option for integration and assimilation into host country labor markets for migrants (Vershinina, et al., 2018) additionally, providing clear economic benefits for host societies and the individuals concerned (Phillimore et al., 2018). This view often ignores the fact, that self-employment of migrants is accompanied by high levels of precarity (Sumption & Somerville, 2010; Mustchin, 2014), a central tenet characterizing the migrants’ nature of work. Informed by Berry (1990)’s theory of acculturation, we pose the following research question: 1 2 3

Faculty of Social Sciences and Humanities at the Kinneret Academic College on the Sea of Galilee Entrepreneurship, Business & Society Department, Audencia Business School Southampton Business School, University of Southampton

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How do the experiences of precarious work conditions impact the integration of employed and self-employed migrants within a specific institutional setting? Our empirical site of investigation is Israel, a country which since the 1970s, has received migrants from predominantly the former Soviet Union (FSU), a group that comprises over one million migrants, representing 16 percent of the Israeli population (Raijman, 2020) and 80 percent of Israel’s migrant population. Extant research into this migrant group has revealed that whilst this group had a strong motivation to leave the Soviet Union for political reasons, they also were keen to preserve Russian cultural identity (Dose et al., 2018; Heilbrunn et al., 2016). In other words, this migrant group in Israel is characterized by a desire to hold onto the country of origin cultural values. Considering the discourse on integration, focusing on this migrant group has the capacity to develop novel contributions to existing theorizations relating to work, migration and integration into host societies. In addition to this contribution, our study aims to gain a better understanding of how contextual factors of the host culture environment impact on the micro level behavior of migrants in relation to work and acculturation. Moreover, our study aims to present evidence of how institutional structures of a specific host society or lack thereof may produce undesired outcomes in terms of assimilation of migrants. The remaining parts of the paper are structured as follows. The following section reviews the literature on precarious work conditions underpinning modern work arrangements whether through employment or self-employment for migrants. The next section presents the methodology used in this research study, which is followed by the findings of the empirical study. The final section of the paper discusses the findings, drawing conclusions reflecting on the contributions and implications for theory and practice and identifying directions for future research.

Theoretical background Within the literature on industrial relations, the notion of ‘precarity’ has been examined by specifically drawing attention to the nature of insecure employment and labor which represent forms of non-traditional employment practices whereby the individual worker’s rights are unprotected and as such the workers are exposed to low levels of income, irregularity of employment and often limited or sporadic statutory regulatory entitlements (Kalleberg, 2009; Vosko, 2010). Following Jonna and Foster (2016), scholars define worker precariousness by focusing on what workers lack: access to work; protection from arbitrary firing; possibility for advancement; longterm employment; adequate safety; development of new skills; adequate income; and union representation (see Standing, 2011, p. 10). Following Gill and Pratt (2008, p. 3) precarious work arrangements are ‘atypical’ or ‘non-standard work’ including temporary, illegalized work, home working, piece working and freelancing (Gill &

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Pratt, 2008, p. 3; Schmiz, 2013) and bogus or false self-employment (Vershinina et al., 2018). For the purposes of this article, we adopt Vosko (2010)’s conceptualisation of precarious employment as a multidimensional construct that is formed through a combination of ‘employment status, form of employment, extent of labor market insecurity, social context and social location’ (Vosko, 2010). Empirical studies have focused attention on the bottom-of-the-pyramid perspective, including informal work at the margins of society (Pulignano et al., 2016). In such contexts, workers are often exploited and exposed to a lack of protection and forced to work in poor and unsafe working conditions (Standing, 2011). The conceptual tool of vulnerability explains the exploitation of marginalized workers with migrant workers forming part of this ‘precariat’ (Choudry & Thomas, 2013; Sohani, 2015). For example, in Vershinina et al. (2018)’s study of illegal Ukrainian migrant workers in London’s construction sector, the authors outline the key tenets of workers’ vulnerability including issues such as wage theft, wage withholding, lower wages than the formal national minimal wage, accidents and illness and deskilling. In a similar fashion, in North America employer-employee relationships have weakened and there has been a shift in power away from employees to employers as a result of the increased mobility of cheap migrant labor within increasingly globalized, interconnected labor markets (Kalleberg, 2011). Adjacent to this trend is the prevalence for moving away from full-time, protected contracts towards short-term, temporary assignments. For instance, in Canada, temporary work and self-employment account for a third of the total workforce (Evans & Gibb, 2009) causing a reduction in pay and protection for workers, leading to increased precarity (Nienhüser & Matiaske, 2006).

Precarity and self-employment Discourses on precarious work are based upon the assumption that transitioning to flexible labour markets within the remits of economic and political deregulation implies higher risks for the majority of participants in the labour market (Schmiz, 2013). More so, the self-employed are seen to benefit from such flexibility (Frade et al., 2004). Consequently, self-employment can be seen as precarious associated with poor quality work resulting in low income, flexible (long) schedules and lack of security for the worker. Table 1, outlined below, summarises the dimensions associated with precarity and self-employment.

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Table 1: Precarity and its dimensions Precarity Factors

Current View

Low income

Statistics indicate that income from self-employed is lower in comparison to income from ‘traditional’ labor relations (OECD, 2015).

Flexible schedules

Flexible schedules allow self-employed to feel more unrestricted and autonomous, an increasingly desired trait (Demerouti et.al., 2014). However, an unfixed schedule is not synonymous with less working time; in fact, one’s daily work can invade other milieus and expand to fill the whole day.

Work Insecurity

Having and retaining a job, as well as benefits such as protection in case of illness, disability, maternity, misfortune, or old age constitute work security (Anker et al., 2003, p.169). Work insecurity involves the lack of the above.

Precarity, migrant workers and integration The literature on migrants engaging in work identifies self-employment as a survival strategy in response to blocked mobility into mainstream labor markets (Riva & Lucchini, 2015). While many studies on migrant self-employment view it as a significant economic activity (Levent et al., 2003) and arguably a viable path for migrants’ social integration (Hiebert, 2003), critics of such a view have argued that selfemployed migrants become trapped in ethnic enclaves and in low skilled employment (Villares, et al., 2018) as is often also the case for employed migrants. Despite some social benefits, ethnic enclaves often limit the potential income of self-employed migrants (Aguilera, 2009; Kloosterman & Rath, 2001; Ndofor & Priem, 2011) due to the migrants reliance on suppliers and customers from within the narrow ethnic enclave. Economic participation interplays with socio-cultural aspects in the processes of integration thus Chiswick & Repetto (2000) for example suggest that the knowledge of the local language can facilitate cultural integration into the host country setting and offer such individuals higher earnings compared to those who have a language barrier (Constant & Zimmermann, 2006). Furthermore, migrants with poor language skills have fewer social or business links with the native community that restricts their opportunities to seek and find work (Stone & Stubbs, 2007). Additionally, research shows that time spent in the country of residence constitutes an important aspect of socio-cultural integration (Chiswick & Repetto, 2000). The level of socio-cultural integration, moreover, depends upon preferred social contacts and ethnic interaction as well as the self-identification of migrants, defined as a feeling of belonging to the country of origin and to the host society, feelings of connectedness and finally an evaluation of their decision to migrate (Ersanilli & Koopmans,

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2010). Furthermore, Beckers and Blumberg (2013) suggest that the level of integration determines the extent to which migrant business owners are able to identify and seize business opportunities in mainstream economies within the host country setting. Therefore, there is an expectation that better integration into a host society improves the economic prospects for self-employed migrants. For many migrants, self-employment represents the only viable option in terms of work in the new host societies, especially where labor markets hold limited opportunities even for the indigenous population. Furthermore, Mustchin (2014) suggests that the vulnerabilities that migrants are exposed to in the host country through wage underpayment, theft and withholding, together with lack of protection for illness and accident represent the key features of the precarious, insecure nature of migrant work, findings which resonate with a recent ILO (International Labor Organization) report (Buckley et al., 2016). Despite their attempts to escape their precarious role as a ‘disposable underclass’ in the new host society, for most migrants the prospects for integration into host societies are very limited (Sumption & Somerville, 2010, p.10).

Theory of acculturation Acculturation process occurs when “groups of individuals sharing different cultures come into continuous first-hand contact” (Redfield, Linton & Herskovitz, 1936, p. 149). It represents how people learn and try to adapt themselves to the norms and values of a host culture, which may be different from their cultural background (Cleveland & Laroche, 2007). As such, acculturation involves a set of behaviors. Four acculturation strategies emerge in the meeting of two cultures: integration, separation, marginalization, and assimilation (Berry, 1997). The former two types imply retention of attachment to one’s ‘country of origin’ culture that is or is not accompanied by increasing attachment to the respective ‘host country’ culture (integration or separation respectively). The latter two types imply a loss of attachment to one’s ‘country of origin’ culture that is or is not accompanied by lack of attachment to the respective ‘host country’ culture (marginalization or assimilation respectively). These strategies define a person’s own cultural identity and have an influence on the person’s behaviors and attitudes. Whilst migrants can chose acculturation strategies, their choice is impacted by a number of mechanisms. Some authors suggest contextual influences, particularly related to the acculturation climate in the host society which is revolving around the immigration policy and the historical developments, as well as attitudes of the host society citizens toward migrants (Berry, 1997; Schwartz et al., 2010). In a study of Turkish migrants in Europe, Dimitrova et al. (2014) showcased inconsistent acculturation levels across countries with migrants from the same country experiencing

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adjustments to the new host society and its cultural environment differently, dependent on the country’s acculturation climate (Yagmur & van de Vijver, 2012). The acculturation model has the capacity to offer a nuanced understanding of how migrants learn to integrate into new, host societies through engaging in work and self-employment. By adding the lens of ‘precarity’ to the above model, we can showcase how the different levels of precarious work conditions, whether imposed through self-employment or work in the mainstream labor market, impact on the ability of migrants to integrate. In the next section, we outline the rationale for selecting our specific empirical site of investigation and the methodology adopted within this research project.

FSU immigrants in context Israel continues to be an important destination for primarily Jewish migration. Israel exemplifies an ethnonational regime, where granting citizenship is based on the principle of jus sanguinis (Raijman & Kemp, 2010). Whereas the migration regime is inclusionary for Jewish immigrants, migration policy towards non-Jewish migrants is exclusionary. The explicit position of the State is that Israel is not an ‘immigration’ country but rather an ‘aliya’ (Jewish immigration) country (Raijman & Semyonov, 2004). There have been a number of initiatives developed in Israel to support newly arrived migrants (Ministry of Immigrant Absorption, 2014). As a result of historically open assimilation policy, the society of Israel is by no means homogeneous. As Kimmerling (2004, p. 295) puts it succinctly, Israel is “an aggregate of cultures and counter-cultures with varying degrees of autonomy and separate institutional development within a common framework.” Underpinned by the notion of safeguarding the cultural traditions of minorities, Kretzmer (1990) presents Israel as an “antiassimilationist” nation. Immigration represents a core strategy for Israel’s national development (Stephan et al., 1998) and consequently statistics show that in 2019 more than a quarter of its population were migrants (ICBS, 2019a). Since 1948 and till 2018, 3.3 million immigrants entered Israel, with 67.7 percent of them from the Former Soviet Union (ICBS, 2019b). Thus, Russian-speaking migrants from the Former Soviet Union (FSU) represent the most populous migrant group in Israel. This group holds high educational attainment levels in terms of academic credentials and professional qualifications but nevertheless experiences difficulties to capitalize on their human resources in the Israeli labor market (Raijman & Geffen, 2018). For many their socioeconomic status severely deteriorated upon or after immigration. Integration patterns of FSU immigrants into the Israeli labor market are diverse, with relative high rates of employment but parallel lower income level and relative high rate of poverty especially among the elderly (Kushnirovich, 2015). Additionally,

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FSU immigrants are increasingly seen as economically threatening by Israeli born Jews (Raijman, 2010).

Methodology The lead author conducted semi-structured interviews with twenty, self-employed and employed migrants from the FSU in 2015. These interviews took place in the city of Alpha (anonymized), Israel and the participants were purposefully selected based on their country of origin (FSU) and the time of migration (arrived in Israel between 1989–2005). The sampling strategy included approaching participants using a referral-chain method (Heckathorn, 1997; Vershinina et al., 2018), which is particularly beneficial to obtain access to hidden populations. Initially, the networks of NGOs and other FSU related organizations were approached, who offered access to their contacts. These contacts correspondingly provided access into their social networks from where we drew participants for this research study. The sample represents a criterion sample (Patton, 2001, p. 238), involving the need for each individual to comply with a set of conditions, namely that they have migrated to Israel between 1989–2005 and were either self-employed (ten individuals) or were employed (ten individuals). Given their age and the fact that all participants had migrated to Israel at least ten years prior to fieldwork, the participants all had sufficient Hebrew language skills and were active in the labor market either as employed or self-employed persons. The study aimed to and achieved a matched sample (Osili, 2007) method. Selfemployed respondents were chosen as the base, and were then paired with employed migrants who matched them approximately in terms age, gender and educational attainments. We reached the employed migrants via the self-employed respondents and were therefore able to achieve a rather high homogeneity between the respondents, except for their occupational status. Efforts were additionally made to maintain a balanced gender and age division among the participating pairs. The interviews were conducted in the business location or homes of the participants. These interviews lasted between 60–90 minutes. The lead author conducted twelve interviews alone with participants who had very good Hebrew language proficiency. A Russian speaking assistant accompanied the lead author and conducted the remaining eight interviews in the Russian language. In developing the interview instruments we were informed by studies by Constant and colleagues (2009) and Ersanilli and Koopmans (2010) in order to establish questions on the process of socio-cultural integration. Accordingly, the following core themes were addressed during the interviews: language proficiency (language spoken most frequently with friends, partners and children; reading Hebrew books, self-evaluation of Hebrew writing, reading and speaking skills); cultural consump-

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tion in terms of preferred media (newspaper, TV channel, radio stations and websites); social contact/ethnic interaction (socialization with friends and relatives); national identification (feeling of belonging to country of origin/country of residence, feeling of connectedness to country of origin/country of residence); and finally an evaluation of their decision to migrate to Israel. To understand precarity of working conditions for migrants, we utilized Vosko’s (2010) conceptualization of precarious employment as formed through a mixture of ‘employment status, form of employment, extent of labor market insecurity, social context and social location’ (Vosko, 2010) and therefore focused our interview instrument questions on control over work schedule and content, working hours, social insurance and retirement coverage, and adequacy of income and economic hardship. In Table 2, we present the demographic characteristics of our participants. Their names were anonymized. Instead, we assigned individuals in matched pairs the same name with number 1 for the self-employed and number 2 for the employed participant. We audio recorded the interviews which were then transcribed verbatim. For the data analysis, we engaged in an iterative, non-linear process of compiling, disassembling and reassembling the data (Yin, 2003). Furthermore, we developed a coding manual, including definitions for each category and illustrative quotations (DeCuir-Gunby et al., 2011). This was an iterative process and as we developed the coding manual, we continued to update it whilst analyzing the text to ensure coding consistency (Zhang & Wildemuth, 2009). The word-processed files were analyzed using the coding process, proposed by Braun and Clarke (2006). During the process of data analysis, we revealed certain group patterns and overarching themes.

Age

30

32

35

32

30

32

Name

Igor1 (Self-employed)

Igor2 (employed)

Marissa1 (Self-employed)

Marissa2 (Employed)

Lea1 (Self-employed)

Lea2 (Employed)

Female

Female

Female

Female

Male

Male

Gender

Married + 2

Married + 1

Married + 2

Married + 3

Married + 2

Married + 1

Family status

Table 2: Summarizing Demographic Data of Participants

9 (1994)

Engineering diploma (electronic engineering) Engineering diploma (mechanical engineering)

MA in educational psychology

MA in clinical psychology

BA in biology

10 (1993)

4 (1989)

10 (1993)

10 (1990)

10 (1995)

Education

BA in physical education

Age at Immigration (Year of immigration)

IT company

Pilates studio (3 years)

High-school Clinic for psychological treatment (1 year)

Municipality

Army service, academic studies, high-school teacher for 5 years, starting the business Army service, academic studies, high-school teacher, long- term contract Army service, academic studies, 5 years of employment at clinic, starting the business Army service, academic studies, employed as psychologist, long-term contract

Computer shop (2 years)

Army service, academic studies, 3 years of employment, starting the business Army service, academic studies, employment at same company, long-term contract

Company/Type of Business

Employment History

Employment, self-employment and precarity 87

Age

35

37

40

36

46

42

Name

Karin1 (Self-employed)

Karin2 (Employed)

Sasha1 (Self-employed)

Sasha2 (Employed)

Nili1 (Self-employed)

Nili2 (Employed)

Contiunation of Table 2

Female

Female

Male

Male

Female

Female

Gender

Married + 3

Married + 2

Single

Married + 1

Married + 1

Single

Family status

12 (1991)

20 (1989)

17 (1990)

Law studies + attorney diploma

High school exam and apprenticeship in hairdressing

High school exam and cosmetics studies

15 (1990)

14 (1992)

BSC in infrastructure engineering Law studies + attorney diploma

10 (1991)

BSC in interior design

Education

Age at Immigration (Year of immigration) Company/Type of Business

Interior designer (5 years)

Furniture company

Lawyer's office (10 years)

Lawyer's office

Hairdressing studio (5 years)

Beauty Salon

Employment History

Army service, academic studies, 5 years employed at a furniture company, then starting the business Army service, academic studies, long-time employment contract at furniture company Army service, academic studies, 5 years employed, starting the business Army service, academic studies, 10 years employed at 3 different law firms, no longterm contract Army service, apprenticeship, 3 years unemployed, 9 years employment at different hairdressers, starting the business Army service, unskilled jobs for 5 years, 1.5 years of cosmetic studies, employed in 3 beauty salons for the last 10 years

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Age

60

55

57

60

61

Name

Anja1 (Self-employed)

Anja2 (Employed)

Herbert1 (Self-employed)

Herbert2 (employed)

Rosetta1 (selfemployed)

Contiunation of Table 2

Female

Male

Male

Female

Female

Gender

Divorced

Married + 1

Divorced + 2

Married + 3

Divorced+ 2

Family status

BA in Education in COO

MA in Physics in COO

Material Engineering in COO

Technician diploma in COO

BA in chemistry in COO

Education

50 (2004)

40 (1995)

41 (1999)

35 (1995)

43 (1998)

Age at Immigration (Year of immigration) Company/Type of Business

Shop (joint ownership with a friend) (13 years)

Manufacturing company

Consulting company (11 years)

Contractor company for security services

Home-made food restaurant (4 years)

Employment History

13 years of employment in COO, since immigration 4 years of employment in cleaning company, starting the business 15 years of employment in COO, since immigration factory worker, sub- contractor contract 8 years of employment in COO, since immigration 5 years of employment as security guard, starting business 12 years of employment in COO, since immigration employed as security guard, sub- contractor contract 35 years of employment in COO, 3 years employed in cleaning company, 3 years unemployed, starting the business

Employment, self-employment and precarity 89

Age

58

55

58

Name

Rosetta2 (Employed)

Alex1 (Self-employed)

Alex2 (employed)

Contiunation of Table 2

Male

Male

Female

Gender

Divorced + 1

Married + 2

Divorced + 1

Family status

48 (2005)

42 (2002)

46 (2003)

Technician diploma in COO

PhD in Mathematics in COO

PhD in Statistics in COO

Education

Age at Immigration (Year of immigration)

Company/Type of Business

IT manufactory company

Homework Center, Afternoon school (8 years)

IT company

Employment History

28 years of employment in COO, 9 years of employment in 3 manufactory companies, contractor contract Academic career in COO, 5 years of employment as teacher in High school, then starting the business Academic career in COO, employed in start-up for 4 years, since then employed at a IT company, sub-contractor contract

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Findings The analysis of the interview data revealed a set of patterns centered around levels of precariousness and corresponding levels of acculturation. Acculturation theory (Berry, 1990) presents four distinct modes of acculturation, including assimilation, integration, separation and marginalization. None of our participants could be classified within the assimilation or the marginalization mode. Such a finding is perhaps not surprising and aligns to findings of previous studies, which noted the continuation of dual identity – Russian-Israeli or Israeli Russian, emphasizing the importance of holding on to the culture of country of origin (Kushnirovich, 2015; Remennick, 2004; Heilbrunn et.al., 2016) within this specific demographic group in Israel. Table 3 summarizes key criteria used to create clusters (A, B, C, and D) and key narratives and illustrative quotes are followed in the section. Cluster A participants were all employed in Israel at the time of the interviews and they firmly fit into the integration category as narratives of our participants revealed their engagement in contact and involvement with the new host society alongside their simultaneous continued participation within the FSU ethnic community, mainly via family ties. This cluster also showed the lowest level of precariousness. These individuals were young, well-educated and were in stable, professional employment. As outlined by Marissa2, a teacher,“My work is very challenging. Israeli kids generally do not show enough respect to teachers. That said, I am very happy with the working conditions and social benefits I receive”. Similarly, Igor2, a father of two children explained the importance of job security. As he stated,“When I discussed job conditions with my employer I made it clear that I wanted pension and social security payments. I was prepared to work long hours but I needed the protection”. Participants in Cluster A were fluent in Hebrew and consumed local/national as well as Russian speaking media. They also had the most contact with members of the host society, enacted through their employment. As Igor2 continues, “At work I am in professional contact with many Israeli born colleagues and also some immigrants from France and the USA. We socialise during and after work.” Such a narrative was persistent in the stories told by participants in cluster A. Their integration was achieved through interactions primarily in the realms of the workplace, which then spilled over into friendship networks developed beyond the formal remits of the workplace. The story for their matched pairs (cluster B), who were entrepreneurs demonstrated clear differences (see Table 3 for summary). These were self-employed individuals and their conditions of employment rated high for levels of precariousness. Their stories revealed how these participants endured challenging work-lives involving long working hours, stress and little time for social and family life. Karin1 was a participant who worked from home and although her business was already well

91

Employed in professional work (5)

Younger 32–37 y.o

Low

High level

8–9

+

Sufficient income, no economic hardship

Integration

Hebrew and Russian

Russian and Hebrew

Russian friends, and Israeli work colleagues

With both Israel and FSU

Emigrated as children

Work Status

Work Age Group

Level of Precariousness

Control over work schedule

Working hours a day

Social insurance and retirement coverage

Adequacy of income and perceived economic hardship

Acculturation

Language use and proficiency

Preferred media

Social contact/ethnic interaction

Identification

Evaluation of Migration decision

Cluster A

Emigrated as children

With both Israel and FSU

Russian friends, and Russian clients

Russian and Hebrew

Hebrew and Russian

Integration

Insufficient, experiencing economic hardship

–

10–12

Low level

High

Younger 30–35 y.o.

Self-Employed (4)

Cluster B

Table 3: Clusters and Dimensions of Precariousness and Acculturation

Migrating to Israel is seen as positive

Mainly with FSU

Mainly with Russian friends and some Israelis

Russian media

Some Hebrew and high Russian

Separation

Insufficient pay, experiencing hardship

+

8–9

Low level

Medium

Older 55–60 y.o

Employed as subcontractors (4)

Cluster C

Migrating to Israel is seen as positive

With both Israel and FSU

Mainly with Russian friends and some Israeli clients

Russian media

Some Hebrew and high Russian

Separation

Insufficient takings form the business, they take on extra work to make ends meet

–

10–12

Very low level

High

Mid-Aged 40–57 y.o.

Self-employed (3)

Cluster D

Unhappy with migration decision

With FSU

Exclusively Russian friends and clients

Russian media

Russian language only

Separation

Not able to earn sufficient amount, clients do not stay with them

–

10–12

Very low level

High

Older 58–61 y.o.

Mixed (2SE +1E)

Cluster E

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established, she was concerned about the future and how she existed in a constant state of stress. As Karin1 stated, “I have two or three projects on the go at the same time. On top of this, I have to recruit new clients. It is very stressful and unpredictable. I am usually recommended by my clients, who are almost exclusively migrants from the FSU like me.” Also Lea1 complained about long working hours, “you know I have to accept clients beyond their working days, so sometimes I’m working until 22:00 at night.” Within cluster B there was an overarching concern highlighting the precarious nature of their businesses including the unpredictability of contracts of work, the lack of time available to develop contacts beyond the narrow remits of the FSU ethnic enclave and constant worries about being excluded from the host society pension scheme arrangements. Igor1 explains: “my computer shop is usually working rather well, but sometimes I wonder whether I should better look for a job. I am always worried about the future”. Furthermore, there existed few opportunities for them to engage with members of the host society. The participants in this cluster rated similar to cluster A in terms of indicators of level of acculturation, thus their strategy is also one of integration. But, in conjunction with integration strategy, cluster B reported general difficulties in their ability to adjust socially as well as economically to the new host society. As Igor1 reports, his business location in the ethnic enclave meant, “All my friends and acquaintances are Russian”. Similarly, Lea1 highlights the feelings of exclusion heard within cluster B, “Israelis don’t want to be treated by Russian psychologists. As such, 95 percent of my clients are Russians and they come because they want to speak Russian during the session. My kids have some non-Russian friends but I have no time to make contact with their parents.” Finally, Marissa1 who works long hours from her home depicts the difficulties of reaching out to the host population. As Marissa1 explains, “I am often not available to participate in school festivities and children’s parties where I could meet the non-Russian parents. My business keeps me within the FSU community, which is nice, but feels also like it is a mistake.” Cluster C represents employed individuals. Three of them (Herbert2, Rosetta2 and Anja2) are working for a subcontractor, so they have no direct employment contract with the place of work. Nili2 works as an employee in a beauty salon. In comparison to their employed counterparts (cluster A), this group does not enjoy the benefits and security of long-term permanent contracts. These individuals tended to be middle-aged and overall struggling with their occupational situation. Their narratives

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revealed the ephemeral and unpredictable nature of their employment and associated high levels of stress and anxiety regarding their ability to earn sufficient income to survive. Although these individuals were well-educated they unfortunately could not convert (Light, 1989; Vershinina et al., 2011) their qualifications predominantly from the FSU into relevant tools to enable them to earn higher levels of income and thereby further integrate into the mainstream host society. As one interviewee, Herbert2 stated, “I feel exploited. I am an academic but I work as a security guard because I could not find any adequate employment. I am working long hours and do not earn enough money. I can't help our son with his education financially. My wife has to work too, despite having health difficulties.” Cluster D includes the self-employed individuals, who like Cluster B own businesses located within the FSU ethnic enclave. They are a bit older than cluster B self-employed (who are all in their 30ies) and are less fluent in Hebrew. As Herbert1 (the oldest of cluster D persons) explains, “When I was a security guard, I dreamt about starting my own business, I was busy learning business but did not think that Hebrew was so important.” Sasha’s Hebrew is excellent since he served in the IDF and studied at an Israeli university, Nili1 reported that her Hebrew language skills are decent especially since she served in the army, she also mentioned that “when I learned to become a hairdresser and worked as an employee, it was with Russian speaking colleagues and clients only, so very soon I did not feel very comfortable with Hebrew any more.” Cluster D persons spent significant amounts of time working and communicating with fellow FSU migrants and had little contact with members of the host society. Sasha1 explains that “most of my clients at the law office are from the FSU, they don’t feel comfortable in Hebrew and don’t trust Israelis, that’s why they come to me.” He also complains that “there is so much competition since there are so many lawyers in Israel”, contemplating that he does generate the income he was expecting. The stories of Sasha1, Nili1 and Herbert1 revealed the realities of the absence of a meaningful social contact for migrants within Israeli society. In order to survive, these individuals worked tirelessly for little economic returns and even less cultural returns. As one interviewee, Nili1, stated “I am doing my best but usually I do not pay as much social security contributions as I should. I don't know how much money I will get once I want to retire and close the salon. Sometimes I am afraid that I will never be able to retire.” Such sentiments were echoed by other interviewees, demonstrating how these individuals seek to negotiate the everyday nature of vulnerabilities and precariousness within their working lives. Individuals in this cluster questioned their decision-

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making with respect to developing businesses so intrinsically embedded within the ethnic enclave. As Herbert1 explained, “I have run a consultancy business for 11 years now. I have 2 employees who are of Russian origin and 90 percent of my clients are Russian speakers. I work long hours and as such I don’t have much time for socializing beyond my close friends from the FSU.” Their separation strategy from the host society only acts as a detriment to their overall livelihood and wider social positioning. Finally, in contrast to the previous four clusters who despite obvious hardships were not considering returning to the FSU or migrating elsewhere, cluster E represents an opposing narrative. These individuals were not only struggling socially and economically, but also overall had begun to question their decision to migrate to Israel. These individuals showed the highest levels of precariousness and had the least contact with the host society. As Anja1 explained, “If I had known what was waiting for me” such individuals wouldn’t have migrated to Israel. Similarly, Alex2 stated, “If I had known before that my academic qualification is worth nothing here, I would never have come to Israel.” The stories of constant struggles were repeatedly narrated in relation to their work situation and their ability to generate income. Individuals referred to feeling that they were ‘self-enslaved’ through firstly their decision to migrate and subsequently through their self-employment. As Anja1 explained, “Every day is a struggle. I feel that the shop is taking me over.” Rosetta2 echoes these sentiments, “I am working all day long. Sometimes it is just too much.” These individuals have no alternative employment opportunities available to them as one of them stated succinctly, “It is either the shop or unemployment” (Anja1). The stories of Anja1 and Rosetta1 are particularly revealing of the experiences of migrants being pushed into self-employment from unemployment or very low paid jobs. As Rosetta1 succinctly put it, “I am alone, I have no children and my ex-husband did not migrate. So on the one hand the business is all I have, but at the same time, it prevents me from getting to know more people. I have no time and am always living on the edge of not making it financially.” It is evident that cluster E are the most marginalized, not in Berry’s sense (Berry 1990) but rather they are socially marginalized. The persons in Cluster E in our study are at the margins of Israeli society since they did not manage to integrate either professionally, nor language-wise or in terms of their feeling of belonging. The fact that they migrated in their mid 40ies and live alone (one single and two divorced) might explain the problematic situation in comparison to the others. They com-

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pose the only group unhappy with the migration decision. Thus, cluster E does not present a marginalization strategy case but a positioning at the margins of society.

Discussion Israel represents a unique context (Welter, 2011) insofar as during the last two decades, it has sought to transform itself into an enterprising ‘start-up’ nation (Almor & Heilbrunn, 2013), offering opportunities and institutional support for entrepreneurial individuals not only from within the host society population but critically inclusive of migrants. Importantly though, institutional support (Klooserman & Rath, 2001) has targeted predominantly businesses in high-technology sectors, producing high-growth enterprises which have fueled the wider neoliberal business and political policies of the Israeli state. Within this article, we have focused on one specific migrant group in Israel, namely migrants from the former Soviet Union (Dose et al., 2018; Heilbrunn et al., 2016). This group is the largest migrant group in Israel and importantly is geographically dispersed across all regions of the country. We interviewed ten employed and ten self-employed migrants, who were matched on key characteristics apart from their occupational status. As outlined in our findings, the FSU migrants, whom we interviewed, presented a converging narrative of the ‘promised land’, hoping for plentiful opportunities for work and support for small business development (Dose et al., 2018; Heilbrunn et al., 2016; Kimmerling, 2004; Kretzmer, 1990). In practice, the lived experiences of our respondents illuminate the precariousness of their work conditions (Foster & Scott, 2015; Glavin et al., 2019; Kalleberg, 2011) coupled with the limitations of acculturation (Berry, 1990; Dose et al., 2018) within the Israeli institutional space for self-employed individuals and some employed individuals. Whilst the lived experience for migrants setting up businesses in high-technology sectors may be much more positive and may produce better economic and acculturation outcomes, our results demonstrate the importance of taking into account sectoral and social class positions, specifically linked to resources individuals command. Indeed, our findings reveal the heterogeneous effects of precariousness within the work conditions of this migrant group and associated heterogeneity of experiences of acculturation represented by integration for the few and entrenched separation for the majority. Furthermore, language plays an important role in enabling migrants to obtain employment in mainstream labor markets (Wei et al., 2019). At the same time, mother tongue can entrap migrants within ‘ethnic enclaves’ (Villares et al., 2018), which limit their ability to acculturate within the host society setting. Our findings confirm that the reliance on the mother tongue, in this case Russian, may have negatively impacted upon the possibilities for individuals to inte-

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grate into mainstream Israeli society. The very fact that the FSU migrant population in Israel is so large and also geographically dispersed, decreases the motivation and thereby paralyses the ability for more mature FSU migrants to learn Hebrew. In simple terms, it is ‘too easy’ to continue just speaking Russian (Dose et al., 2018) and also seeing the Russian-speaking population as the main customer base for small businesses. As demonstrated in the findings, self-employed individuals have all positioned their businesses within the Russian-speaking enclaves, created businesses specifically for ‘ethnic’ demand (Ndofor & Priems, 2011). They did so also because they believed that the local Israelis would not want to go to Russian speaking business professionals. Thus, they perpetuated the ethnic markets thereby locking themselves within the ethnic enclave. Whilst self-employment and entrepreneurship have been held up as a beacon of opportunity in many countries around the world (Levent et al., 2003; Hiebert, 2003), this message conceals the true nature of how for socially marginalized populations, whether these are migrants or not, self-employment can plunge individuals into unseen levels of precariousness. The narratives in our study reveal the process of selfimposed enslavement, in which migrants have set up their businesses, which have no meaningful growth potential. As such, individuals are wholly dependent upon the business to maintain their financial livelihood whilst at the same time, they have themselves closed the doors for upward mobility within Israeli society and opportunities for acculturation. Self-employment does not only lead to entrapment within a low income, precarious work environment but more broadly hinders any potential to develop links with the host society (Villares et.al., 2018). In contrast, individuals, who have obtained permanent employment within established Israeli companies, seemingly have not only have better levels of financial remuneration and corresponding lower levels of precariousness, but also have developed integration into the host society through contacts developed at the workplace and through greater use of the Hebrew language. Whilst our sample is relatively small, our findings demonstrate clear shortcomings of the idealized notion that small firms have the agency to promote social inclusion (Levent et al., 2003; Hiebert, 2003; Phillimore et al., 2018). Instead our findings reveal the heterogeneous nature of outcomes in terms of acculturation and social inclusion, specifically for self-employed individuals trapped in ethnic enclaves (Blackburn & Ram, 2006) as ‘disposable underclass’ (Sumption & Sommerille, 2010; Vosko, 2010). Therefore, our findings illuminate the need for a more nuanced understanding of the relationship between ethnic enclaves, levels of precariousness and social inclusion within institutional spaces.

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Conclusions In this article we have examined how precariousness emerges from employment choices and how this impacts on social inclusion. By focusing on migrants from the FSU in Israel, our study revealed their continued attachment to the culture of their country of origin. We offer the following contributions. First, whilst migrant employment pathways depicted self-employment and formal employment in the mainstream Israeli labor market, the study revealed that engaging in self-employment and any precarious formal work does not lead individuals towards integration within the host society. As such, small business is not per se the panacea for social inclusion. Second, FSU migrants in Israel represent the largest ethnic minority group which is geographically dispersed. The size of this group and the ethnic enclaves which have sprouted up across Israel trap individuals within their ethnic enclave boundaries. Whilst speaking Russian and not learning Hebrew, may be on the face of it convenient, it has detrimental effects. Individuals that have set up their own businesses, wholly reliant on the ethnic market, become entrapped and have no possibility for upward mobility and social inclusion. Third, our findings reveal the heterogeneous nature of precariousness marked by different forms of work relationships particularly in self-employment and temporary contractual work, entrenching individuals into social exclusion. Fourth, whilst extant literature on precariousness in the main focuses on precarity emanating from the structural conditions within globalized societies, in this article, we highlight how precariousness can result from the agency of individuals. By making the choice of not speaking Hebrew and aligning the business wholly to the convenient ethnic market with the Russianspeaking enclave, individuals in our study have self-imposed precariousness within their working conditions and correspondingly, do not have any desires for meaningful acculturation. In our study, integration into the host society was only achieved by those individuals engaged in formal work. Finally, from a methodological perspective, we have benefitted from purposefully selecting a matched sample. We have showcased migrants with similar social class positioning and other characteristics, yet their employment outcomes are completely different but underpinned multiple levels of precariousness. Such a sampling method can be particularly useful for future studies seeking to reveal the richness and diversity of a phenomenon under investigation. This chapter’s findings have implications for policy-makers and practitioners alike. In light of the results outlined in this study, policy-makers should reconsider the assumption that self-employment necessarily leads to social inclusion and integration. Furthermore, policy-makers need to be mindful of viewing migrant groups as a homogenous entity. Finally, if integration is a serious objective then clearly additional financial resources need to be allocated towards language training for mi-

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grants. As indicated in our study, the paucity of Hebrew language knowledge acts as barrier for social inclusion.

Tables Table 1: Precarity and its dimensions | page 82 Table 2: Summarizing Demographic Data of Participants | page 87 Table 3: Clusters and Dimensions of Precariousness and Acculturation | page 92

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Inequality among older immigrants in Israel Does marital status matter?1 Haya Stier2

Introduction Immigrants constitute an economically vulnerable group. They often face inferior opportunities in the labor market when compared to the veteran population, and encounter discrimination at higher rates than other groups (Raijman & Semyonov, 1995, 1997). Some of the disadvantages faced by immigrants can be overcome with time, but the long-term consequences of immigration are more pronounced in old age (Hao & Kawano, 2001; Lewin & Stier, 2003; Matras, 1993). Even if they work in the host country, immigrants accumulate fewer resources and entitlements than others, what is especially the case for immigrants who moved to a new country at older ages. Economic wellbeing in old age is affected, in general, by resources accumulated along the life course by individuals and families. Particularly important are assets and entitlements to pension funds, together with state support. But economic wellbeing is also affected by living arrangements and marital status, which may change considerably in older ages. Although most of the population is married or living in households headed by couples, as people get older many experience periods of being non-married, mainly due to widowhood. Indeed, with the increase in life expectancy for both men and women, more people remain married for longer periods; hence, the proportion of widowed middle-aged adults in the population has declined over time (Kreider & Ellis, 2011). However, due to demographic changes in family behavior, the share of those divorced or never-married has increased considerably in recent years (Lin & Brown, 2012). This means that among older-age adults, the proportion of unmarried individuals is not negligible. Unmarried individuals are more economically vulnerable than are the married (Waite & Gallagher, 2000; Brown & Wright, 2017). Inequalities associated with marital status can accrue over the life course, especially for those who interrupt their 1 2

This study was supported by Israel Ministry of Science, Technology and Space, grant no. 3–14765. Tel Aviv University

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economic activity. Such interruptions can occur if workers lose their jobs, take time off to care for family, or move from one country to another (Dannefer, 2003; Ferraro & Shippee, 2009). Therefore, it has been argued, inequalities in wellbeing increase with age (Hughes & Waite, 2009; Johnson & Favreault, 2004) and among those who stay unmarried for longer periods. Economic disadvantages are also more severe among minority groups including women and immigrants. Numerous studies have documented the disadvantages encountered by immigrants in general, and the double disadvantages of women immigrants in particular (Raijman & Semyonov, 1998; Boyd, 1989). Because immigrants often have low-skilled jobs and interrupted employment histories (Raijman & Tienda, 1999; Stier & Tienda, 2001), they become more economically vulnerable as they age (Carr & Tienda, 2013; Danziger & Gottschalk, 1995; Matras, 1993; O’Rand & Henretta, 1999; Lewin & Stier, 2003). They are more likely than the native-born population to be poor and to have less access to pension funds. That said, inequalities within an immigrant group, on the basis of marital status and gender, may differ from inequalities observed within the native-born population. On the one hand, because immigrant men and women alike have lower income and fewer accumulated resources, inequalities within the group may be lower than those observed in the general population. In such a case, for example, immigrant widows and widowers may not have any pension funds, leaving both entirely dependent on state support. That is, they will be much poorer than widowed nativeborn individuals but in a similar economic position to one another. On the other hand, as women immigrants suffer from a double disadvantage, it is possible that gender inequalities, especially those between the unmarried and the married, will exacerbate in old age among immigrants also. For example, immigrant widows may not have pension funds of their own because they had jobs of more-inferior quality than do men, or because they had many job interruptions. They will be therefore more vulnerable than immigrant widowers or native-born widows. The study described here focuses on the interplay of immigration, gender, and marital status in determining the economic wellbeing of elderly immigrants. Conducted in Israel, the study compares inequalities between older immigrants arriving in Israel after 1990 and the veteran population in the same age group; within each group, it examines the joint effect of marital status and gender on economic standing. As noted above, because inequalities increase with age, gender differences are expected to be more pronounced at older age. Women, more than men, are likely to be unmarried at those ages – they are more likely to be widowed and to remain divorced. They are also more likely than men to have interrupted their employment along the life course and to have worked for low wages. These differences have implications for gender inequality in socioeconomic wellbeing late in life (Lin & Brown, 2012; Lurie & Stier, 2022). However, the level of gender inequality might differ between immigrants and veterans because the two groups differ in their marital and

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work histories, household living arrangements, and access to pensions at older ages (Lewin & Stier, 2003; Stier & Bleikh, 2014).

Immigration and the economic wellbeing of the adult population The foreign-born population is growing older in many countries due to the aging of long-term immigrants and the rise of late-age immigration (Batalova, 2012; Carr & Tienda, 2013; Leach, 2008; Wilmoth, 2012). Older immigrants, especially those who immigrated later in life, face economic hardship and often suffer more from emotional distress and language barriers than do those who immigrated when younger. Studies show that length of stay in the host country is positively associated with earnings and labor market position, language proficiency and various indicators of wellbeing (Espenshade & Fu, 1997; Raijman & Semyonov, 1995; Van Hook, 2003; Vigdor, 2009). Those immigrating at a later age have less time to accumulate resources or adjust their skills to those needed in the host country (Borjas, 2011; Treas & Mazumdar, 2002). Many suffer doubly as both immigrants and as older people; they thus have difficulties in finding employment or securing high income. Older immigrants who are inactive in the labor force and did not accumulate (sufficient) pension funds are entirely dependent on state support and therefore often experience poverty and economic hardship (Angel & Angel, 2006; Oneil & Tienda, 2014; Stier & Bliekh, 2014; Lewin & Stier, 2003). However, those who worked for a longer period in the host country and those who managed to transfer savings and pensions from their home countries are expected to assimilate economically even when older. Relatively few studies have examined the economic wellbeing of older immigrants (e.g., Boyd, 1989; Carr & Tienda, 2013; Hao & Kawano, 2001; Hu, 1997; Lewin & Stier, 2003; Matras, 1993; Stier & Bleikh, 2014). These studies did, however, highlight the economic hardships older immigrants face while shedding light on the unique circumstances of immigration, its timing, and experience of events along the life course that introduce variation into immigrants’ economic wellbeing. The study reported in this chapter builds on these findings while focusing on marital status as a stratifying category. It asks to what extent this factor plays a role in understanding the economic position of immigrant men and women and whether differences in economic wellbeing by marital status and gender also differ between immigrants and veterans.

Marital status, gender, and economic wellbeing With changes in family formation and dissolution, in addition to rising life expectancy, the older population has changed as well. Most Western countries have

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experienced similar changes in family behaviors, including postponement of marriage and a decline in fertility, increasing variation in the age entering parenthood and significant escalation in divorce (Hughes & Waite, 2007). These trends have affected the marital composition of the older population: While the majority may still be married, the rates of those experiencing divorce and of those remaining unmarried have grown substantially (Frey, 2010; Hughes & O’Rand, 2004; Lin & Brown, 2012). Moreover, important behavioral changes have occurred alongside these demographic changes, especially regarding the labor market. During the second half of the 20th century, women’s participation in the labor force expanded dramatically in all industrialized countries (Van der Lippe & van Dijk, 2002), with women’s work attachment along the life course also increasing (Stier et al., 2018). Furthermore, the retirement age was raised in many countries, allowing more older men and women to remain in the labor market for longer periods than in the past (Stier & Endeweld, 2015). These changes have profound implications for the economic risks associated with old age, and suggest that over time, gender differences will be mitigated. We should, however, recall that many older women had interrupted careers and worked for low wages; implying that many women are still dependent on their spouses’ resource accumulation and on their marital history (Glaser et al., 2009; Smock, 1993; Waite & Gallager, 2000). Indeed, the economic position of households headed by married couples is much stronger than that of other households. The economic consequences of divorce are well documented (e.g. Andreß et al., 2006; Manting & Bouman, 2006; Tach & Eads, 2015). Women usually suffer from income decline, economic hardship and poverty upon divorcing but also years later (Lewin & Stier, 2018). Widowhood, too, exhibits similar patterns (Ahn 2005; Angel et al., 2007). Some studies that have documented the economic disadvantages of being unmarried suggest that divorced adults have accumulated great economic resources and enjoy better health than do the widowed and the never-married. Widows appear to be the most disadvantaged because they have access to fewer economic resources and enjoy poorer health than do either divorced or never-married women. This occurs because the divorced, as these studies show, are more likely to have participated in paid employment along their life course and thus have higher household incomes. They are also less likely to rely on public assistance as compared to other unmarried groups (Brown et al., 2006). However, there is a substantial difference by gender, as divorced women often have custody of children; hence, their labor force participation is limited by the family burden. Similarly, widowhood is often associated with a decline in economic resources among women (Angel et al., 2007; Ahn, 2005), but this is most likely related to pension policy and may therefore vary across countries. In contrast, the never-married are the least advantaged among men (Lin & Brown, 2012). Despite having relatively high levels of education, never-married men are in poorer economic circumstances

Inequality among older immigrants in Israel

and are most likely to live alone, whereas divorced and widowed men are comparatively advantaged. Both marital status and the gender of the unmarried are therefore crucial to understanding the risks and vulnerabilities facing this growing group (Lin & Brown, 2012).

Gender, marital status and immigration Immigrant women are often seen as doubly disadvantaged in labor market outcomes, as women and as immigrants (Boyd, 1984). Their labor force participation rates, occupational prestige and wages are lower than that of native women and immigrant men (Raijman & Semyonov, 1997; Blau et al., 2011; Donato et al., 2014). However, some studies found that the gap between immigrant and native women in various labor market outcomes is smaller than those observed for men (Adsera & Chiswick, 2007; Gorodzeisky & Semyonov, 2011; Rebhun, 2010). These inconsistencies might be related to the socio-demographic characteristics of immigrants, especially their country of origin and own human capital resources. It is also possible that gender and nativity differences in the labor market are mediated by marital status. Donato, Fiya and Jacobs (2014) claim that marriage may exacerbate immigrant women’s disadvantage, especially for those immigrants with more traditional expectations regarding women’s labor market involvement. As argued above marriage is associated with higher economic resources and improved wellbeing. For women’s experience in the labor market marriage and more so parenthood entails high penalties while the opposite is true for men (Budig & England, 2001; Budig & Hodges, 2010; Killewald & Gough, 2013). However, it is not entirely clear whether immigrants experience a similar gendered effect of marriage and family obligations. Donato and colleagues (2014) found that unmarried immigrant women have similar work experience as unmarried native women, while this is not always the case for married women with immigrants often lag behind native married women. Men, however, benefit from marriage, but those benefits are larger for natives than for immigrants. These differences affect the gender and nativity gaps in earnings, but their effect on the economic wellbeing of households among the older immigrant population is not straightforward. On the one hand, as argued above, the fact that married immigrant women might have lower attachment to the labor market while immigrant men are more involved in paid employment means that in old age gender gaps exacerbate, especially when marriage ends. On the other hand, to the extent that unmarried immigrant women had better prospects in the labor market along their life course, while unmarried men fare less than unmarried native men, means that the gender gap between unmarried immigrants should be lower than among the natives.

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The current study examines the economic standing of immigrants aged 55 and over who arriving in Israel from 1990 onwards. Importantly, these immigrants moved to Israel at working age, some even late in life. While they probably did not accumulate much in the Israeli labor market, they came with high human capital and cultural norms which encourage women’s employment. How these characteristics affect their wellbeing when they get older is still an open question. Hence, the study poses three main questions: first, among the older population, how do immigrants fare compared to the veterans? Second, do gender inequalities within each group (veterans and immigrants) differ? And lastly, how is marital status related to the economic wellbeing of men versus women within each subgroup. I expected that immigrants would be worse off than veterans. However, it was not entirely clear whether variations by gender and marital status would be similar within each group. Alternatively, because immigrants, on average, are more vulnerable than veterans, I pondered whether inequalities within the former group would be lower, especially when taking into account their lower fertility rates and higher female labor force participation. It was nevertheless possible that women immigrants, especially those widowed (or divorced), might suffer greater economic hardships than would veteran women in the same situation. While the latter may enjoy the pension benefits of their late spouses, and perhaps their joint accumulated assets, women immigrants have very little to rely on. Similarly, divorced or single veteran women may have accumulated assets and pension benefits that put them in a better position than are divorced immigrants, who might have migrated after divorce and lack any support from former spouses.

The Israeli context The study focuses on older adult immigrants reaching Israel as of the early 1990s. During the last twenty years, Israel has witnessed massive immigration flows. The total number of immigrants amounted more than one million people, with the vast majority coming from the Former Soviet Union (Raijman & Kemp, 2010). Among the immigrants arriving in the early 1990s (who represent the majority of this wave), about 14 percent were aged 65 and above (Sicron, 1998). In consequence, the share of the veteran population as a segment of the older population in Israel declined during the last three decades (from 74% in 1997 to about 65% to date). With respect to disadvantages, those who immigrated to Israel at older ages since the 1990s had accumulated very few pension rights either in their country of origin or in Israel; the percentage almost totally dependent on state support has since increased (Stier & Bleikh, 2014). Moreover, among older immigrants, especially those arriving from the former USSR, women comprised the majority of im-

Inequality among older immigrants in Israel

migrants, with a relatively high proportion of single mothers among them (Sicron, 1998). As a result, the older immigrants arriving from 1990 onwards faced multiple risk factors that put them in a more-vulnerable position than that of the rest of Israel’s elderly. Other changes occurring over the last two decades that impacted on the older population’s economic vulnerability in one or another direction include an increase in life expectancy for both men and women; a rise in the percentage of young people obtaining higher education (Addi-Raccah & Mcdossi, 2009; Shavit & Bronstein, 2011), and a shift in the labor market from labor-intensive to knowledge-based industries. At the same time, social services and white-collar sectors increased as well. Among the older population, labor force participation has risen in recent years, reflecting the rise in education and life expectancy on the one hand, and the flexible employment arrangements which allow many to stay in the labor market even after retirement age on the other hand (Kimhi & Shraberman, 2013). Family behavior patterns also changed. While marriage is still fundamentally universal in Israel, the divorce rate has risen (from 7.2% in the mid 1990s to 8.8% to date) (Israel, 2023). Similarly, the percentage of never-married in the adult population has gradually grown, from less than 3 percent in the early 1990s to about 5 percent in 2015.

Data and method The study is based on the annual Israeli Income Surveys conducted by the Bureau of Statistics during 2008, 2009, 2010 and 2011. These four surveys, which include a representative sample of households in Israel, were pooled to obtain a sufficient number of immigrants. In the current study, the non-Arab population was selected. The final sample included those aged 55 and older, comprised of 28,350 veterans and 8,458 immigrants who had arrived in Israel from 1990 onward. The dependent variable was household income from all sources, measured in 2011 prices. For the descriptive analysis, I used household income as well as household income per capita; for the multivariate analysis – the natural log of household income. The main independent variable was marital status, measured as a set of dummy variables – whether the respondent was divorced, single or widowed, with married as the reference group. All models also controlled for age (in years); work status (working=1, otherwise 0); years of schooling; number of earners other than the respondent; whether the household had income from a pension (=1), and the number of persons in the household. Regression models were analyzed separately for immigrant and veteran men and women. Some models were restricted to include only those who reported themselves as heads of households (or spouse of the head in the case of a married couple).

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Findings The analysis was begun by reviewing the distribution of households according to family status of the immigrant and veteran respondents at the time of the survey. Table 1 presents this distribution – accordingly, among men aged 55 and older, the majority are married – 84 percent of the veterans and 80 percent of the immigrants.

Table 1: Respondents’ characteristics by immigration status and gender, population aged 55+ Immigrants (1990+)

Veterans

Men

Women

Men

Women

% Married

79.9

48.6

84.0

60.4

% Divorced

8.1

16.5

7.0

10.5

Marital Status

% Single

1.7

3.6

2.3

2.9

% Widowed

10.3

31.4

6.7

26.2

Average Age (SD)

67.9 (9.2)

68.6 (9.7)

66.8 (9.3)

67.4 (9.5)

Average years of Education (SD)

13.6 (4.6)

13.3 (4.1)

12.6 (4.5)

11.6 (4.8)

% Work

39.8

29.0

47.6

30.3

% with pension

10.6

9.5

37.3

34.8

Average Household size (SD)

2.6 (1.4)

2.4 (1.3

2.7 (1.3)

2.2 (1.2)

Average no. of earners (SD)

0.6 (0.8)

0.7 (0.8)

0.7 (0.8)

0.6 (0.8)

N

3500

4985

13200

15150

Other Variables

Source: Israel Income Surveys 2008–2011

This was not the case for women – among veteran women, the majority (60%) was married, but among immigrant women, the figure drops to less than 50 percent. This was partly due to the fact that more immigrants from the former USSR were already divorced upon arrival, a characteristic especially pronounced among women (Sicron, 1998). Indeed, while only 7 percent of men and 10.5 percent of women were divorced in the veteran population – the comparable figures were 8.1 percent for men and 16.5 percent for women among immigrants. The percentage of never-mar-

Inequality among older immigrants in Israel

ried (singles) was low among all groups, whereas more immigrants than veterans were widowed –about a third among women and 10 percent among men – compared to about a quarter of veteran women and less than 7 percent of veteran men. Overall, these figures partly reflect differences in family behaviors between the two groups and partly the age differences between them: the immigrants in the sample were, on the average, older than the veterans. Other of the differences between immigrants and veterans are only partly related to marital status. The immigrants in the sample are more educated than the veterans although fewer immigrant men (but not women) participate in the labor force (about 40%, compared to 45.3% of the veterans). The most striking difference is the rate of those having occupational pensions – only about ten percent of immigrant men and women, compared to a third of the veterans, have income from a pension fund. This again points to the more vulnerable position of the immigrants in general.

Table 2: Inequality in household income, by marital status and gender Immigrants (1990+)

Veterans

Men

Women

Men

Women

Household income

10708.7

9657.0

18822.1

15377.7

Household income per capita

4227.6

4060.2

7704.2

7241.8

Married

11187.2

10679.6

19983.4

19331.8

Divorced

9166.2

8391.7

13662.6

9650.0

Single

7951.4

6455.9

9312.8

10468.8

Widowed

8669.1

8391.7

12955.5

9128.0.72

Married

4173.9

4166.2

7568.0

7816.0

Divorced

4873.2

4098.1

8731.8

6574.2

Single

3796.4

3694.8

7318.2

7304.3

Widowed

4207.1

3917.7

8434.1

6180.8

Marital Status

Household income per capita

Source: Israel Income Surveys 2008–2011

To understand their economic standing relative to the veterans, Table 2 presents the household income of all groups, focusing on the relative standing of immigrant men and women, and within each group, the relative standing of the unmarried.

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Looking first at household income, the figures indicate the economic vulnerability of immigrants – their household income is on the average lower than that of veterans, whether the comparison in based on total income or on income per capita. Figure 1 presents immigrant household income as percentage of veteran household income for the total population by marital status, displayed for men and for women separately. On average, immigrant men’s household income is 57 percent of that of veteran men’s household income; for women, the rate is 63 percent. However, the average masks a large variation across marital statuses and gender. Similar to the observed rates in the total population, married immigrants (men and women alike), have household income that is only 55 percent of the household income of married veterans. Among the other groups, the income differences are lower but the variation between marital status and gender is more substantial.

Figure 1: Household income of immigrants as % of veterans’ income, by marital status and gender

Source: Israel Income Surveys 2008–2011

Divorced and widowed immigrant men have income that is 67 percent of their veteran counterparts but the income of single immigrant men is closer to that of single veterans (85%). For women, the picture is different – single immigrant women’s household income is only 62 percent of that of single veteran women, but divorced immigrant women have income that is 87 percent of their veteran counterparts, with no differences in their household income found between immigrant and veteran

Inequality among older immigrants in Israel

widowed women. These differences may perhaps be explained by the groups’ different living arrangements. When calculating the ratios for income per capita (Figure 2) within marital status groups, the household income of immigrants in all groups falls below that of veterans at about 50–60 percent, although single and widowed immigrant women are still in a better position when compared to all the other groups.

Figure 2: Household income per capita (HIPC) of immigrants as % of veterans’ HIPC by marital status and gender

Source: Israel Income Surveys 2008–2011

I next ask to what extent is inequality within each of the groups – immigrants and veterans –related to marital status and whether gender plays a role in understanding this inequality. Figure 3 presents the household income of each unmarried group as a percentage of the household income of the married group, shown separately for immigrant and veteran men and women. This figure reveals two important findings – first, among immigrants and veterans, men and women alike – being married is associated with having higher income when compared to all other groups. Second, the figure clearly shows that inequality between marital statuses is more severe among veteran men and women than among immigrants. This finding is probably explained by the lower income of immigrants when compared to veterans, with the difference especially high between those married, as shown earlier. The gaps found between unmarried and married immigrants is con-

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sequently smaller than the gaps of those groups among veterans. Comparing the differences by income per capita reveals that among men, immigrants and veterans alike, there are almost no differences between the married and the unmarried: in fact, divorced men of both groups and veteran widowed men have higher income per capita than do the married. Among immigrant women, the differences are also relatively small but among veteran women, divorced and even more so widowed, income per capita falls below that of married women (84% and 79%, respectively). That is, gender inequalities by marital status are more pronounced among the veteran population than among the immigrants although, on average, the latter have lower income than the former.

Figure 3: Household income of the unmarried as % of household income of married, by immigration status and gender

Source: Israel Income Surveys 2008–2011

How do women fare as compared to men within each marital group? Figure 4 presents the income of unmarried women as a percentage of that of unmarried men, by immigration status. Several important findings can readily be seen. First, when comparing total household income (the left panel), Figure 4 shows that while divorced and widowed women in the veteran population have lower income than their male counterparts, this is not the case for immigrants. Among the latter, widows live in households with somewhat higher income than do widowed men, while divorced women have slightly lower income than do divorced men (92%). Single immigrant women do worse than do single men. However, moving to income per capita among

Inequality among older immigrants in Israel

immigrants, the picture changes – divorced women have only 84 percent of the per capita income of divorced men, whereas widows have 93% of the per capita income of widowers. This is not the case among veterans, where very slight differences appear when comparing gender inequality by total household income and income per capita. This once more points to the living arrangements of divorced and widowed immigrants, who are more likely to live with extended families, and to the relatively small gaps among the different immigrant groups. Overall, the figure suggests that gender inequality is lower among immigrants compared to veterans for almost all marital groups (excluding singles).

Figure 4: Women’s household income (total income and per capita) as % of men’s household income among the unmarried

Source: Israel Income Surveys 2008–2011

Inequality between immigrants and veterans and between the genders in each group could result from the different financial resources available to them – most of which are related to market work along the life course or at the time of the survey. Because men and women have different work patterns, their accumulated benefits, especially their pension funds, differ. Men and women may also differ in their living arrangements, which could also affect household income. Among the widowed, women are also older than men; this, too, may affect their resources as well as their living arrangements. Indeed, in this survey it was found that living in extended family arrangements is much more common among immigrants, especially among unmarried immigrant women – for example, about half of immigrant widows compared with a quarter of veteran widows and a third of immigrant widowers lived in

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extended families. The difference is also pronounced among the divorced, where a third of immigrant women and about a fifth of immigrant divorced men shared living arrangements with other family members, a practice rarely adopted by veterans (15% of divorced veteran women and 10% of divorced veteran men lived in extended families). The next stage, then, is to examine differences within each group while controlling for their sociodemographic, household and work characteristics. I estimated a set of regression analyses separately for immigrant and veteran men and women. The aim of this analysis is first to understand how marital status affects the economic wellbeing of each group while taking their traits and household circumstances into account, and second, to compare these effects across groups. In these analyses, household income (natural log) is the dependent variable. The models (presented in Appendix A1) focus on the effect of marital status (the three unmarried groups compared to married as the reference category) while controlling for age, work status, education, household size, number of additional earners in the household, and whether the respondent receives an occupational pension. Figure 5a shows the marital status coefficients obtained with the regression equations. The figure shows that all unmarried groups live in households with significantly lower income than that of the married group. However, there are important differences across groups in the penalty of being unmarried. First, the figure highlights the vulnerable position of veteran unmarried women, who have the lowest income compared to married women. Divorced women’s income is 56 percent lower than that of married women while that for widows and single women is about 45 percent lower. Second, the penalties associated with marital status are higher for veterans than for immigrants. For example, a veteran divorced man lives on an income that is 27 percent lower than that of his married counterpart, while the difference between immigrant divorced and married men is only 13 percent. The same is true when comparing single veterans (40%) to single immigrants (less than 15%). But the most striking finding is that there are almost no gender differences in income among immigrants relative to married, divorce and widowed men and women, and single women, have incomes lower by 13 to 14 percent when compared to married couples, while single men have incomes similar to those of married couples.

Inequality among older immigrants in Israel

Figure 5a: Effect of marital status on household (ln) income by gender and immigration status

Source: Israel Income Surveys 2008–2011

Figure 5b: Effect of marital status on household (ln) income by gender and immigration status, household heads.

Source: Israel Income Surveys 2008–2011

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Overall, these figures show that while immigrants in general live in households with lower income, they do not suffer penalties due to their marital status; in fact, marriage is not as protective as has been assumed in the literature. It is plausible to suggest that the difference between immigrants and veterans in the effect of marital status results from the two groups’ different income distributions. Immigrants have, on average, lower incomes than do veterans; among those with low incomes, differences by marital status may be minimal, especially for those living on state allowances (which are universal in Israel). Testing the same models for the respondents with incomes above and below the median showed very similar patterns (not shown here). Another explanation may be that the two groups – immigrants and veterans – differ in their living arrangements, with immigrants more likely to live in extended families. This is especially the case of women, and more so for the widowed than for other groups. Figure 5b presents the marital status coefficients for those who are heads of households (or spouses of the head in the case of married couples). As the figure shows, while the penalties of being unmarried are more substantial for all groups, the basic pattern remains the same, with higher penalties for veteran women, followed by veteran men, and almost no gender differences among the immigrants.

Summary and conclusions This study posed three questions: The first focused on the effect of marital status on the wellbeing of older immigrants in Israel. The second asked whether the effect of marital status differs between immigrants and the veteran population, and the third, whether the effect differs between men and women. Studies examining differences in the economic conditions of older adults by their marital status have highlighted the vulnerability of the unmarried (e.g., Lin & Brown, 2012), especially of divorced and widowed women. These findings are largely supported for the Israeli population in general (Lurie & Stier, 2022) and, as the findings in this study show, also for the veteran Jewish population. These differences are expected as women earn less than do men, accumulate lower levels of pension benefits due to their interrupted employment along the life course but also the discrimination they face in the labor market. These vulnerabilities are especially pronounced for divorced women who cannot benefit, like widows, from the resources accumulated during marriage and from spouses’ pension benefits. Men are less likely to be affected as they earn more money and usually have continuous employment, two factors that further contribute to gender differences at old age. But this is not the case for immigrants. As the findings show, the economic wellbeing of immigrant men and women is less affected by their marital status. In fact, there are no particular economic benefits from marriage for this groups and there

Inequality among older immigrants in Israel

are no gender differences in the effect of marital status. Several explanations can be posited for these findings. First, immigrants, especially those arriving to a new country at an older age, encounter difficulties in finding employment and securing good jobs. Consequently, many of them, men and women alike, have interrupted work histories, low wages and smaller pension funds. Most of these immigrants rely on state support, which is universal in Israel. Immigrant women, especially the group surveyed in this study, arrived mainly from the former USSR. Despite the disadvantages they face in the labor market, they exhibit strong attachment to paid employment, which contributes to their economic standing. Among the veteran population, the work activity of men may explain the penalties faced by women because veteran men have higher rates of labor force participation and incomes that are, on average, higher than those of women. This is not necessarily the case among the immigrants as men did not gain substantial advantages in the labor market and in accumulation of pensions benefits compared with women. In addition, more unmarried immigrants than unmarried veterans tend to live in extended family arrangements, a strategy found to help alleviate the economic position of the family in general, and that of unmarried immigrants in particular (Stier & Bleikh, 2014; Tienda & Angel, 1982). However, living with other family members, which is more common among immigrant women than among immigrant men, only partly explains the lower gender gap revealed. To summarize, while immigrants are clearly at a more vulnerable position at old age when compared to the veteran population, inequalities within immigrant groups on the basis of marital status and gender are less pronounced. In other words, there is no ‘double disadvantage’ of immigrant women, at least in comparison with immigrant men. These findings highlight the importance of work attachment and probably human capital in providing economic security for women, especially when they get older or when they exit marriage. It is plausible to expect that with the increase in women’s education and the rise in their continuous attachment to the labor force, gender gaps will also be mitigated at older ages. Regarding the economic wellbeing of immigrants, the study challenges the argument that immigrants will close the gap with veterans as they stay for longer time in the host country. As the findings show, this is not the case especially for those immigrating at older ages. This finding points to the importance of social policies in ensuring the immigrants’ wellbeing especially for those who immigrated late in life. While some policies in Israel acknowledge the difficulties experienced by more vulnerable workers in general and older immigrants in particular (e.g., mandatory pension funds, extended allowances) special attention should be given to the latter group as the difficulties they encounter in the new country affect their wellbeing for a prolonged period.

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Figures Figure 1: Figure 2: Figure 3: Figure 4: Figure 5a: Figure 5b:

Household income of immigrants as % of veterans’ income, by marital status and gender | page 114 Household income per capita (HIPC) of immigrants as % of veterans’ HIPC by marital status and gender | page 115 Household income of the unmarried as % of household income of married, by immigration status and gender | page 116 Women’s household income (total income and per capita) as % of men’s household income among the unmarried | page 117 Effect of marital status on household (ln) income by gender and immigration status | page 119 Effect of marital status on household (ln) income by gender and immigration status, household heads | page 119

Tables Table 1:

Respondents’ characteristics by immigration status and gender, population aged 55+ | page 112 Table 2: Inequality in household income, by marital status and gender | page 113 Table A1: Effects on (ln) Household Income Entire Population by Migration Status and Gender | page 127 Table A2: Effects on (ln) Household Income – Heads of Households, by Migration Status and Gender | page 128

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Inequality among older immigrants in Israel

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Appendix Table A1: Effects on (ln) Household Income Entire Population by Migration Status and Gender Immigrants

Veterans

Men

Women

Men

Women

Divorced

-0.093* (0.028)

-0.130* (0.018)

-0.274* (0.020)

-0.559* (0.016)

Single

-0.011 (0.058)

-0.148* (0.034)

-0.408* (0.036)

-0.453* (0.028)

Widowed

-0.090* (0.027)

-0.145* (0.016)

-0.183* (0.021)

-0.462* (0.013)

Age

0.003* (0.001)

0.004* (0.001)

0.003* (0.001)

0.005* (0.001)

Education

0.022* (0.002)

0.019* (0.002)

0.051* (0.001)

0.047* (0.001)

Employed

0.565* (0.020)

0.408* (0.017)

0.677* (0.014)

0.437* (0.012)

No. of earners

0.399* (0.012)

0.480* (0.011)

0.319* (0.008)

0.411* (0.008)

Pension Income

0.344* (0.025)

0.279* (0.021)

0.455* (0.012)

0.410* (0.011)

No. of persons in household

0.122* (0.008)

0.147* (0.007)

0.014* (0.005)

0.046* (0.011)

Constant

7.630 (0.091)

6.923 (0.069)

7.975 (0.058)

7.998 (0.051)

Adjusted R2

0.630

0.666

0.503

0.558

N

3488

4975

13149

15098

Marital Status

Other Variables

Source: Israel Income Surveys 2008–2011; *p