Intergenerational educational mobility and life course earnings in Israel

Social Science Research 83 (2019) 102302

Contents lists available at ScienceDirect

Social Science Research journal homepage: www.elsevier.com/locate/ssresearch

Intergenerational educational mobility and life course earnings in Israel

T

Limor Gabay-Egozia,1, Meir Yaishb,∗,1 a b

Bar-Ilan University, Israel University of Haifa, Israel

ARTICLE INFO

ABSTRACT

Keywords: Intergenerational educational mobility Life course earnings Upward and downward mobility Long-term economic consequences Israel

This study brings to the fore the importance of absolute intergenerational educational mobility rates and patterns and its consequences for long-term earnings trajectories. Building on the cumulative advantage mechanism, we develop a theoretical formulation for testing the consequences of educational mobility for long-term earnings trajectories. Using data linking the 1983 and 1995 censuses in Israel with annual registered earnings data from 1995 to 2013, we find striking differences in intergenerational educational mobility rates and patterns between Israel's sub-populations. We then show that the intersection of own and parental education (i.e., intergenerational educational mobility) is associated with growing (dis)advantages over the life course. These results are in sharp contrast to a snapshot perspective, where we find that parental education does not bear influence on their offspring's earnings. Finally, we find gender but not ethno-religious differences in the long-term earnings consequences of educational mobility in Israel. The implications of these findings are discussed.

1. Introduction Students of inequality – and more recently also politicians and policy makers – view social and economic mobility as one of the most important features of society and its inequality regime. For many, mobility and equality (of opportunity) are synonymous. It is not surprising, therefore, to find a large body of research on social mobility, its determinants and consequences (Sorokin, 1959; Blau and Duncan, 1967; Erikson and Goldthorpe, 1992; Breen, 2004, Yaish and Andersen, 2012, among others). The consensus in this literature is that education plays a major role in facilitating social mobility and is often referred to as the “great equalizer” in society (Bernardi and Ballarino, 2016). The centrality of parental education in the education attainment process suggests that intergenerational educational mobility is a major force shaping overall equality of opportunity in society, as “education is the main vehicle of social reproduction” (Ganzeboom et al., 1991: 284). Indeed, students of educational stratification have long been occupied with studying intergenerational educational mobility (e.g., Shavit and Blossfeld, 1993; Pfeffer, 2008; Hertz et al., 2008; VanDoorn et al., 2011, among others). These scholars, however, did not follow standard practice in mobility research to differentiate between absolute mobility (i.e., movements between origin and destination categories) and relative mobility (i.e., the net association between origins and destinations). Instead, most studies of intergenerational educational mobility have focused on the net intergenerational educational association (cf. Shavit and Blossfeld, Corresponding author. Department of Sociology, University of Haifa, Israel. E-mail address: [email protected] (M. Yaish). 1 The research presented in this paper is part of collaborative research project by the two authors. The order of authorship is systematically rotated from one paper to the next. The authors have made equal contributions. ∗

https://doi.org/10.1016/j.ssresearch.2019.04.015 Received 19 February 2018; Received in revised form 8 April 2019; Accepted 18 April 2019 Available online 19 April 2019 0049-089X/ © 2019 Elsevier Inc. All rights reserved.

Social Science Research 83 (2019) 102302

L. Gabay-Egozi and M. Yaish

1993), ignoring for the most part absolute mobility (but see Breen and Ermisch, 2017), and consequently neglecting to address the determinants and consequences of upward and downward mobility (cf. Reeves and Howard, 2013; McKnight, 2015). Building on insights from social mobility research, we focus here on absolute intergenerational educational mobility, its determinants and consequences.2 Thus, in conjunction with an analysis on intergenerational educational mobility patterns and rates, we examine the socio-economic and demographic determinants of such movements and also the degree to which the intersection of parental and own education bears on earnings. Contrary to most of the school-to-work literature, however, we offer an examination of the long-term economic consequences of intergenerational educational mobility and immobility. This is another unique and novel aspect of this paper as economic returns to education and intergenerational educational mobility were traditionally studied at a single point in time along the life course (i.e., a snapshot perspective) rather than across multiple points of time over the life course (cf. Song and Cheng, 2016). A major weakness of the snapshot approach is that it cannot detect changes in return on education over the life course (Cheng et al., 2017), even when recent scholarship, both in Israel and the US, has recognized increasing returns on education over the life course (cf. Cheng et al., 2018; Yaish and Gabay-Egozi, 2019). The social context for the study is Israeli society—an ethnically stratified society—mainly because of data availability, and is motivated by the following research questions: 1. What are the rates and patterns of the Israeli intergenerational educational mobility, and to what extent do Israel's sub-populations differ in their patterns and rates of intergenerational educational mobility? 2. What are the long-term economic returns to intergenerational educational mobility in Israel, and do Israel's sub-populations differ in this respect? The remainder of the paper is structured as follows. In the next section we discuss the literature that motivates the analysis on absolute mobility rates and patterns. That section is then followed by a discussion on the economic consequences of educational mobility. The section, moreover, discusses and develops a theoretical formulation derived from the cumulative advantage mechanism about enduring life cycle effects of educational mobility on earnings. In the social context section, we then present some of Israel's unique characteristics which guided us in the subsequent analyses. A section on the data and the methods is then followed by the results. In the final section we summarize and discuss the implications of the main findings for intergenerational educational mobility and its consequences for life course earnings. 1.1. Absolute intergenerational mobility rates and patterns The premise that education is the driver of one's socio-economic standing is by now an established fact shared by most scholars within the social sciences. Also well-established is the primacy of parental education from most other background characteristics in shaping their children's educational attainment. A plethora of explanations exists for the association between social background and educational attainment, the most rigorous of which is provided by rational choice theory. The concept of rational educational choices dates back to Boudon’s (1974) seminal work, but only with the work of Breen and Goldthorpe (1997) did it gain formal propositions. This theory argues that parents and their children make rational educational decisions based on the costs, utility, and success probability of educational alternatives. The crucial point is that social differential in educational choices arises from the assumed motivation of parents to ensure status maintenance for their children. Although this status maintenance mechanism refers to class, many have operationalized it also in terms of education (Need and deJong, 2001; Davies et al., 2002; Pfeffer, 2008). That is, parents are assumed to show a preference for their children to attain at least the same educational level as they did. The status maintenance mechanism is therefore an intergenerational educational reproduction mechanism. Motivation for intergenerational educational reproduction should be stronger at higher educational levels because children of highly educated parents can only experience downward mobility. To circumvent downward mobility, highly educated parents will recruit all their resources to guarantee status maintenance to avoid downward mobility. This “glass floor effect” (cf. Reeves and Howard, 2013; McKnight, 2015) reflects parental own experiences with the educational system (Becker, 2003), their ability to assist their children in their learning process, for example, through homework assistance (Teachman, 1987; McKnight, 2015), and their ability to successfully navigate their educational careers (Lareau, 1989). The tendency of those at the top of the stratification hierarchy to deny access to their rank (Sorokin, 1959: 159-60) is another way through which educated parents may promote immobility. This leads to the following hypothesis: H1. Highly educated parents' knowledge of the educational system and attainment process contribute to maintaining a relatively high level of intergenerational educational immobility at the top of the educational hierarchy. In sharp contrast, industrialization and modernization processes are expected to result in increasing meritocratic selection criteria (Treiman, 1970), leading to the following alternative hypothesis: H2. Intergenerational reproduction at the top (and bottom) of the educational hierarchy is relatively weak. Focusing mainly on relative mobility and the intergenerational educational association, previous studies overlook important and relevant findings on upward and downward intergenerational educational mobility (cf. Reeves and Howard, 2013; McKnight, 2015).3 2 That is, we are chiefly interested in upward and downward (im)mobility experiences, and not in the effect of social mobility per se (cf. Sobel, 1981).

2

Social Science Research 83 (2019) 102302

L. Gabay-Egozi and M. Yaish

Focusing on absolute mobility, by analyzing intergenerational educational mobility tables, we can address questions about how much and what kind of intergenerational educational mobility there is, as well as identifying the upwardly and downwardly educationally mobile (cf. Goldthorpe, 2016). To anticipate, we distinguish four educationally mobile groups by cross classifying parental and children's education (see Table 2 below), as follows: immobile college degree holders (immobile high); immobile below college degree (immobile low); upwardly mobile (mobile up); and downwardly mobile (mobile down). In the next section, then, we present and discuss arguments pertaining to the economic consequences associated with each mobility group. 1.2. Intergenerational educational mobility and economic success When economic returns to education are the focus of attention, human capital theory comes to the fore (Becker, 1964). Accordingly, education enhances skills that have a direct influence on workers' productivity (Mincer, 1974). Over individuals' life courses, the human capital model suggests a positive relationship between education level and earnings, with parabolic-shape earnings curves fanning-out whereby the higher the educational level the steeper the curve (Weisberg, 1995: 146). As such, parental education is irrelevant to workers' productivity and should not determine individuals’ earnings. This leads to the following standard hypothesis: H3. College degree holders, compared to those without a degree, will have both the highest average earnings level and the highest and steepest parabolic earnings curve. Net of this, parental education does not affect their children's earnings trajectories. While human capital theory dismisses origin effects on one's labor market returns, cultural reproduction theory highlights the importance of social background on such returns (Bourdieu, 1984; Bourdieu and Passeron 1990; Bowles and Gintis, 1976; Halsey et al., 1980). Thus, for example, when education functions as a legitimate means for social inclusion and exclusion practices (for an extensive review, see Van de Werfhorst, 2011), a person's credentials indicate whether he/she comes from the ‘right’ social circles. The same goes for parental education and other forms of distinction. Parental education is a resource in the transition from school to work also because educated parents can guide their children on what (fields) and where (institutes) to study. Finally, educated parents are likely to have more social capital than less educated parents, which may facilitate and maximize their offspring's transition from school to work. In sum, cultural and social capital theories postulate that earnings might be affected not only by one's own education but also by parental education. That is, differentiations in returns to education, like many other dimensions of social inequality, might be path-dependent. This concept is often used interchangeably with the notion of cumulative advantage (Bernardi, 2014). The underlying idea behind it is that an initial advantage in access to a particular resource tends to grow over time (DiPrete and Eirich, 2006). If success follows success, parents with more resources at their disposal may help their offspring launch a career with higher earnings, thereby leading to a longterm advantage of their offspring over their less fortunate counterparts. This basic cumulative advantage mechanism then leads to the following hypothesis: H4. College degree holder to parents with college degree (immobile high), will have both the highest average earnings levels and the highest and steepest parabolic earnings growth curve. By contrast, a cumulative disadvantage mechanism (cf. DiPrete and Eirich, 2006) suggests that initial disadvantage in access to a particular resource tends to grow over time, leading to the following hypothesis: H5. Those without a college degree to parents without a degree (immobile low), will have the lowest average earnings and also the lowest and least steep parabolic earnings growth curve. Extending the notion of cumulative advantage, Bernardi (2014) introduces a compensatory advantage mechanism, akin to the ‘glass floor’ mechanism discussed earlier in the context of downward mobility (cf. Reeves and Howard, 2013; McKnight, 2015). Accordingly, the “life course trajectories of individuals from privileged backgrounds are less dependent on prior negative outcomes” (Bernardi, 2014: 75). Thus, the average earnings and the earnings growth curve of the intergenerational downwardly mobile will be positively affected by the college degree of their parents. Accordingly, we hypothesize that: H6. The earnings trajectory of those without college degree to parents with college degree (mobile down), will be below that of the immobile high group but above that of the immobile low group. Finally, we propose here to extend the compensatory advantage mechanism to those of disadvantaged background who then experience upward mobility early on in their life course. In our context, this scenario fits first generation academics, and we label it the offsetting advantage mechanism, suggesting that the low parental education level will work to offset their educated offspring's earnings growth curve. Accordingly, we hypothesize that: H7. The earnings trajectory of those with college degree whose parents do not have college degree (mobile up) will be above that of the downwardly mobile group. Table 1 summarizes the above discussed mechanisms and hypotheses about the effects of parental and own education on earnings trajectories. 3 Even in the extreme scenario of complete independence between parental and respondent's education – when null intergenerational educational association exists – some will experience downward mobility while others experience upward mobility. Thus, questions about rates and patterns of such movement, as well as about the determinants and consequences of such movement, are worth asking and important to answer.

3

Social Science Research 83 (2019) 102302

L. Gabay-Egozi and M. Yaish

Table 1 Expected effects of parental and own education on earnings trajectories. Theoretical Mechanism

Human Capital Cumulative Advantage Offsetting Advantage Compensatory Advantage Cumulative Disadvantage

Parental Education

Irrelevant Irrelevant Degree No Degree Degree No Degree

Respondent's Education

Degree No Degree Degree Degree No Degree No Degree

Mobility Group

Immobile High Mobile Up Mobile Down Immobile Low

Earnings Trajectories Entry Level

Growth Rate

High Low Highest High Moderate Lowest

Highest Lowest Highest High Moderate Lowest

Note: Degree refers to college degree.

To apply and test these hypotheses on data pertaining to Israeli society, in the next section we present Israel's unique characteristics so as to place them within this social context. 1.3. The social context The social context for this study is Israeli society, but mainly because of data availability. That being the case, in the analyses that follow we take into account Israel's unique characteristics. In particular, Israel is a socially heterogeneous society comprising a majority of just below 80% Jews and a minority of about 20% Arabs. The Jewish population consists of two main ethnic groups: Ashkenazi-Jews, who originated in Europe and America, and Mizrahi-Jews, who originated in the Middle East and North Africa. The Arab minority consists of a large Muslim majority (about 80%) and Christian and Druze minority groups, each comprising about 10% of the Arab population. Research in Israel indicates that Ashkenazi-Jews are the most educationally and economically advantaged ethno-religious group, while the Muslims are the least advantaged (cf. Yaish, 2004). In a review of the lively debate on educational stratification in Israel, Bar-Haim, Yaish, and Shavit (2008) conclude that ethnic inequality in educational attainment in Israel is persistent and is best described as following a constant fluctuation trend over time. They also point out to the rarity of studies that focus on intergenerational educational reproduction processes, which this study aims to remedy. Students of educational stratification in Israel, by contrast, take on the educational and status attainment approach to study educational inequality in Israel. These studies, moreover, focus primarily on ethnic, national, and gender differences in educational stratification, and find enduring ethno-religious educational inequality (Bolotin-Chachashvili et al., 2002; Ayalon and Yogev 2005; Friedlander et al., 2002; Okun and Friedlander, 2005; Dahan et al., 2003; Shavit, 2017) extending over multiple generations (Cohen et al., 2007). Studies on the school-to-work transition in Israel mainly focus on the economic value of education in the labor market (cf. BarHaim et al., 2013; Rotman et al., 2016), and on returns to education, net of parental economic standing (cf. Shavit et al., 2007; Yaish, 2004; Bar-Haim et al., 2013). Common to these studies are findings that own education, and not parental's, is the main driver of economic success. Furthermore, a large part of the ethnic inequality in the Israeli labor market is explained away by ethnic inequality in the educational attainment process (cf. Haberfeld and Cohen, 2007; Plaut and Plaut 2015). These studies, however, have adopted a snapshot rather than a life course perspective to study returns on education. We add to this literature by studying intergenerational educational mobility rates and patterns of Israel's sub-populations, and also studying how these mobility patterns might condition life course earnings trajectories of these sub-populations. In line with previous findings on ethnic inequality in Israel, reviewed above, we expect the intergenerational educational mobility patterns across Israeli sub-populations to mirror the hierarchical position of each ethno-religious group within Israel's stratification structure. However, we do not expect ethno-religious differences in earnings trajectories across mobility groups. This is because, as previous research has shown, earnings inequality between the groups is mostly associated with education inequality. Finally, the literature on gender gaps in education and earnings led us to expect marked gender differences both in educational attainment processes, and consequently mobility, and in the earnings trajectories of such mobility. 2. Data and methodology 2.1. Data and sample On the basis of unique nationally assigned identification (ID) numbers, given at birth or on immigrating to Israel, the Central Bureau of Statistics (CBS) has merged for us parents and their offspring across the 1983 and 1995 censuses, and then linked to each offspring his/her registered annual gross earnings data from employment (not business) from 1995 to 2013. Our origin (parental) information comes from the 1983 census, wherefrom we selected all households with children aged 13–20 (born in 1963-70). The destination (child) information comes from the 1995 census, wherein we traced the children from the 1983 census data, now aged 25–32. Since each census is a random sample of the population, consisting of only 20 percent of the Israeli population, the merged file includes only 4% of the population in 1983. 4

Social Science Research 83 (2019) 102302

L. Gabay-Egozi and M. Yaish

Table 2 The four mobility groups in the 2X2 intergenerational educational mobility table.

From this merged file we then removed respondents who migrated to Israel after age 14. This common practice in school-to-work transition studies in Israel is meant to ensure that our respondents were educated in the Israeli educational system. The result is an intergenerational file of parents and their offspring aged 25–32 in 1995, born around 1966, graduated high school around 1984, and entered college in the late 1980s. Removing individuals with missing values on any of the variables described below resulted in a working file consisting of 11,769 respondents. 2.2. Variables Our main variables are parental and respondents' highest educational qualifications, which were coded as dummies indicating 1 for college education. Parental education is based on the parent with the highest education. We coded respondents who were still in university in 1995 but did not graduate as having a college degree. This is a common practice in studies based on registered data provided by the National Insurance Institute (cf. Heller, 2017). Our intergenerational educational mobility tables are the result of cross-classifying parental education and respondent's education, presented in Table 2. Each of the four cells in these 2X2 mobility tables represents a distinctive category in the variable educational mobility group. The two shaded grey cells on the main diagonal represent the two educationally immobile groups: the immobile degree holders (immobile high), and the immobile non-educated (immobile low). The two cells off the main diagonal represent the two educationally mobile groups. Below the main diagonal is the cell representing the educationally upwardly mobile group (mobile up), composed of first generational degree holders. Above the main diagonal is the cell representing the educationally downwardly mobile group (mobile down). Average annual monthly earnings are based on registered annual gross earnings in New Israeli Shekels (NIS) and number of months employed per year from 1995 to 2013. We recoded all amounts to 2014 NIS. Individuals without information on earnings were coded missing values rather than zero, because they might have earnings from business.4 We also followed a standard practice in analyzing such data in Israel and assigned missing values to respondents who earned less than 1000 NIS a year, and to those who were employed fewer than two months in a year. Similarly, the very few respondents who earned more than 800,000 NIS a year were treated as if they earned 800,000 NIS.5 With this, for each respondent we calculated his/her average annual monthly earnings by dividing the annual earnings by the number of months employed. In the analyses we employed the natural log of average annual monthly earnings to correct for the positive skew of the earnings distribution. Finally, we calculated a snapshot earnings variable, corresponding to the average monthly (ln) earnings in the first six years in our data: 1995–2000. For most respondents these years represent entry to the labor market, as they were as young as 25–32 in 1995 and as old as 30–37 in 2000, and thus correspond with the standard measure of earnings in most school-to-work studies. To capture the differentiation in the intergenerational educational attainment distribution between the various sub-populations, we generated a dummy for female and three dummy variables for ethno-religious origin: Ashkenazi-Jews; Mizrahi-Jews; and Arabs. Jews and Arabs were already identified in the data. Among Jews, ethnicity was coded based on country of origins of respondents, their parents and grandfathers. Other control variables of interest are: respondents' educational history, differentiating between three groups: secondary education in the academic track, secondary education in the vocational track, and less than high school education;6 age, migration status (a dummy variable representing immigrants); household size (indicating the number of household members in 1983); and a dummy for father absent in 1983. Finally, we tap parental economic wellbeing with a measure of household SEI, indicating the maximum value of either parent's SEI in the 1983 census, based on Israel's 1972 three-digit occupational code (Tyree, 1981). Appendix A presents the means (standard deviations) and proportions of respondents' characteristics by the four intergenerational educational mobility groups. 4 We also ran the analysis with earnings zero for those without information on earnings and the results did not change our conclusions. These analyses can be obtained from the authors on request. 5 These restrictions and truncations of the data did not alter the results in any way that could lead to different conclusions. 6 Respondents for whom no information was available on track placement in secondary education were assigned to the 'less than high school' category, as the earnings attainment of these two groups was similar.

5

Social Science Research 83 (2019) 102302

L. Gabay-Egozi and M. Yaish

2.3. Methods and statistical models As noted, our analysis was motivated by two sets of questions. The first pertains to patterns of intergenerational educational mobility. The second pertains to the economic consequences of intergenerational educational mobility, particularly to the earnings trajectories of the educationally mobile and immobile populations. The statistical models we used to analyze the data reflect these different sets of questions. The intergenerational educational mobility pattern is studied on the basis of mobility tables, by cross classifying parental college education with that of their offspring, separated by gender and ethno-religion. On the basis of such 2X2 mobility tables we calculated mobility rates and patterns, and produced the four mobility groups to which we applied multinomial logit techniques to describe social selection into these groups. Focusing on the school-to-work transition, we first take a snapshot examination of returns to educational mobility using OLS regression on a six-year (1995–2000) average (ln) monthly earnings. Taking a life course perspective to study life course returns to educational mobility, we applied growth curve models to our longitudinal data (Singer and Willett, 2003). Growth curve models are multilevel models, typically used to model and account for patterns of change over time, focusing on between-individual variations in the growth process (Singer and Willett, 2003). Since our longitudinal data includes only eight birth cohorts, which covers too short a period to produce period effects, we modeled these growth curves by year, rather than by age, while nonetheless controlling for age in the models. An advantage of the year variable over age variable is that the N for each year is similar, yielding consistent estimates at the start and the end of each earnings trajectory.7 Thus, we transformed the data into years-in-person file, allowing up to a maximum of 19 observations per respondent (i.e., 1995–2013) and coded the year variable 0 for the initial observation in 1995 (the intercept). This specification implies that the coefficient for year represents the annual rate of change in average annual monthly earnings, while adding a year2 term to the model generates a parabola shape to the earnings trajectory. Fitting multilevel models to these data, we allow both the intercept and the slopes for time and time square in level one to vary between individuals who make level two. This analytic technique requires observing at least one time point for each respondent but does not require observing the same number of time points for each respondent (Bliese and Ployhart, 2002). With these models we estimate the underlying earnings trajectories of the four educational mobility groups. 3. Results We start by asking: How much and what type of intergenerational educational mobility is there in Israel? And, do all Israel's subpopulations share similar mobility rates and patterns? Fig. 1 displays marginal distributions of college graduates by sub-population and generation, as well as total intergenerational educational mobility rates by sub-populations. As well documented, Fig. 1 reveals a sharp increase in college graduates between generations for all sub-populations. The current generation is more than twice as likely to have a college degree than its parental generation (29% compared to 12%, respectively). This intergenerational increase in college graduates does not reflect the massive expansion of tertiary education in the 1990s, as our respondents entered tertiary education prior to this expansion. Instead, the very low level of education of the parental generation, particularly amongst Arabs and MizrahiJews, coupled with an increasing demand for an educated workforce in the industrializing economy – and perhaps a modest expansion of tertiary education – might better explain this intergenerational contrast in college attainment. Indeed, Fig. 1 reveals sharp ethno-religious differences in the intergenerational contrasts in college attainment, with 7:1 and 5:1 intergenerational ratios for Arabs and Mizrahi-Jews, respectively (14% compared to 2% among Arabs, and 21% compared to 4% among Mizrahi-Jews). Due to the very low level of education in their parental generations, Arabs and Mizrahi-Jews in the children generation still lag far behind their Ashkenazi counterparts in college attainment (14%, 21%, and 47%, respectively). These ethno-religious differences, moreover, mirror the hierarchical position of these group within the Israeli stratification structure, with Ashkenazi-Jews at the top and Arabs at the bottom. Finally, Fig. 1 does not display any significant gender differences in college attainment. The discrepancy in the marginal distributions of college graduates between generations necessitates some intergenerational educational mobility. Indeed, the last set of columns in Fig. 1 indicates that about 25% of all Israelis have experienced some sort of intergenerational educational mobility – a rather low level when compared to intergenerational class mobility (about 75%, Yaish, 2004). Also noteworthy is the finding that women are only slightly more educated than men and thus only slightly more mobile than men. More recent data will surely display different results, as women's educational attainment has dramatically superseded that of men's in recent years (Shavit, 2017). Focusing on TMR alone, however, says little about the pattern of mobility Israelis have experienced. To get a better understanding of this, we present a decomposition of TMRs into four mobility patterns, represented by our mobility groups. Fig. 2 graphs the results of this decomposition, and reveals that a major source for the relatively low intergenerational educational mobility rates (25% in Fig. 1) is the very high levels of intergenerational reproduction among those without college degree, that is the immobile low. The only sub-population with relatively low reproduction rates among this mobility group is the Ashkenazi (with 46%) – which, educationally, is the most mobile sub-population in Israel. In fact, the Ashkenazi-Jews seem the exceptional sub-population in all four mobility groups. Not surprisingly, Fig. 2 indicates that intergenerational educational mobility in Israel is mainly associated with upward, rather 7

Results did not change when we used age instead of year. These results can be obtained from the authors on request. 6

Social Science Research 83 (2019) 102302

L. Gabay-Egozi and M. Yaish

Fig. 1. College degree attainment (%) in Israel and TMR, by sub-populations (N = 11,769).

Fig. 2. TMR Decomposition to: Mobile Up, Immobile High, Mobile Down, and Immobile Low, by sub-populations (N = 11,769).

than downward mobility (21% compared to 4%, respectively). Nonetheless, one in six educationally mobile Israelis have experienced downward mobility (4% downwardly mobile in Fig. 2 out of 25% TMR in Fig. 1). A closer examination of Fig. 2 indicates that downward mobility is mostly prevalent among Ashkenazi-Jews. About one in five educationally mobile Ashkenazi-Jews has experienced downward mobility (8% downwardly mobile out of 36% TMR), compared to as little as 1 in 14 and 1 in 19 amongst Arabs and Mizrahi-Jews, respectively (1% of downwardly mobile out of 14% and 19% TMR, for Arabs and Mizrahi-Jews, respectively). The very low level of downward mobility among Arabs and Mizrahi-Jews is probably due to a floor effect related to the very low proportion of college graduates in their parental generations. In sum, we see here the advantage of studying absolute educational mobility because it enables a decomposition of mobility into upward and downward moves. Just as important, studying absolute mobility brought to the fore the domination of reproduction processes in the intergenerational educational mobility process in Israel, particularly among those without college degrees in both generations. Finally, there appear to be only negligible gender differences in the distribution of Israelis in the four mobility groups. It is interesting to examine next if these ethno-religious differences hold also after taking into account important individual and 7

Social Science Research 83 (2019) 102302

L. Gabay-Egozi and M. Yaish

Fig. 3. Ethno-religion members in the four intergenerational mobility groups (mean adjusted predicted probabilities, with 95% CIs) (N = 11,769).

family background characteristics. That is, does ethnicity per se determine the intergenerational educational mobility patterns of Israelis, or is it simply a reflection of the socio-demographic strengths and weaknesses of the various ethno-religious sub-populations? To address this question, we applied a multinomial logit model to the data to estimate how gender and ethno-religious groups are selected into each of the four mobility groups, net of other individual and family background characteristics. The parameter estimates of this model are presented in Appendix B, and indicate statistically significant ethno-religious differences but not gender differences in the selection into the four mobility groups. To facilitate interpretation of these rather complex parameters, Fig. 3 shows means adjusted predicted probability of the selection of each ethno-religious group to the four mobility groups. Note that the y-axis of the upper set of graphs runs from zero to 0.9, while that of the bottom set runs only from zero to 0.04. The pattern that emerges from Fig. 3 replicates the results presented in Fig. 2, prior to the introduction of controls for individual and family background characteristics. In particular, it shows that for all sub-populations alike, upward mobility is the dominant mobility pattern, with 20–30 percent of each sub-population in the mobile up group. Similarly, the reproduction of education among those without a college degree in both generations is by far the most dominant pattern for all Israelis, with 70–80 percent of each subpopulation in the immobile low group. When ethno-religious differences are the focus of the analysis, the pattern presented in Fig. 3 indicates that even after adjusting for background characteristics, the Ashkenazi sub-population stands out with the most advantageous educational mobility pattern. Compared to Mizrahi-Jews and Arabs, Ashkenazi-Jews are more likely to experience upward mobility, and less likely to end up without a college degree if their parents do not have a degree (immobile low). This suggests that the (im)mobility patterns of MizrahiJews and Arabs are the least advantageous. Interestingly, also studies of intergenerational class mobility in Israel from the same period reveal a somewhat disadvantaged mobility pattern for Mizrahi-Jews and Arabs relative to that of Ashkenazi-Jews (Yaish, 2004). At the same time, Ashkenazi-Jews are also the most likely sub-population to experience downward educational mobility, reflecting the relatively high proportion of Ashkenazi parents with college education. Studying intergenerational educational mobility tables in Israel, we are able to show how detrimental lack of parental education was for their offspring's educational attainment. In particular, in Israel reproduction processes are mainly associated with lack of education. That is, parents without college degrees tend to reproduce themselves intergenerationally much more than parents with college degrees. This scenario was not anticipated in the theoretical discussion that led us to the first hypothesis. Accordingly, we expected much stronger reproduction forces at the top of the educational hierarchy. Just as important, the very strong tendency for reproduction at the bottom of the educational hierarchy also runs contrary to the second hypothesis, which postulates a much weaker educational reproduction as selection criteria become more meritocratic. Finally, we find some interesting differences between Israel's sub-populations. On the one hand, whereas Mizrahi-Jews and Arabs tend to reproduce their disadvantages intergenerationally (immobile low), Ashkenazi-Jews are more likely to reproduce their educational advantages (immobile high). On the other hand, the Ashkenazi-Jews greater total mobility rates result from relatively more upward as well as downward mobility. These important distinctions between upward mobility and immobility and downward mobility and immobility cannot be observed when the focus of 8

Social Science Research 83 (2019) 102302

L. Gabay-Egozi and M. Yaish

Table 3 OLS models on earnings. Snapshot estimates of average annual monthly (ln) earnings—1995-2000, by gender (N = 10,447).

Intergenerational educational groups [immobile low] Mobile up Immobile high Mobile down Ethnicity [Mizrahi-Jews] Ashkenazi-Jews Arabs Age in 1995 Immigrant Education history [HS Academic track] HS Vocational track Unknown HS track & less than HS Family background No father Household SEI Household size Constant Adjusted R2 N ∗

p < 0.05,

p < 0.01,

∗∗

∗∗∗

Males

Females

0.265*** (0.022) 0.237*** (0.037) −0.035 (0.048)

0.345*** (0.022) 0.266*** (0.036) −0.042 (0.049)

0.034 (0.021) −0.262*** (0.029) 0.049*** (0.004) −0.054 (0.032)

−0.019 (0.021) −0.280*** (0.033) 0.016*** (0.004) −0.014 (0.031)

0.045* (0.021) −0.047∼ (0.025)

−0.079*** (0.021) −0.132*** (0.025)

−0.005 (0.048) 0.003*** (0.001) −0.026*** (0.005) 7.620*** (0.113)

−0.065 (0.045) 0.003*** (0.001) −0.015** (0.006) 7.936*** (0.117)

0.166 5227

0.139 5220

p < 0.001.

analysis is on the intergenerational educational association alone, as most educational mobility studies are. Following, we examine life course earnings returns to the four educational mobility patterns identified by our four mobility groups. Prior to that, however, and in order to highlight the advantages of the life course perspective, we begin with a snapshot view on the association between the four educational mobility groups and earnings at entry to the labor force. Table 3 displays parameter estimates from OLS models predicting a six-year average (ln) monthly earnings from 1995 to 2000 separately by gender. In these models interaction terms between ethno-religious sub-populations and mobility groups are not present because they do not achieve any acceptable statistically significant level.8 The implication of this is that the three ethno-religious groups share similar returns to educational mobility. This result is consistent with previous research in Israel that net of education, Israel's ethnic groups have similar economic returns to education (cf. Haberfeld and Cohen, 2007). The finding that the three ethno-religious groups share similar returns to educational mobility does not imply that they share similar levels of earnings. On the contrary, Table 3 indicates that Arab men and women pay a rather large earnings penalty simply for being Arabs rather than Jews (26% and 28%, for men and women, respectively). The disadvantaged economic position of Arabs in the Israeli labor market, economy and ultimately stratification structure is of little surprise here as it is well documented in the literature (Haberfeld and Cohen, 2007; Yaish, 2004). Also of little surprise is the finding that amongst Jewish men and women ethnicity has no direct effect on earnings. Note especially, however, the parameter estimates of the rate of returns to educational mobility groups, presented at the top of Table 3. It is evident from these parameters that Israeli men and women without a college degree (immobile low and mobile down) do not get any pay premium on their parental education, as differences in parameter sizes between the immobile low (parents without a degree) and the mobile down (parents with a degree) are not statistically significant for either men or women. Similarly, among men the relatively high college premium (mobile up and immobile high) cannot be attributed to their parental college degree or lack thereof. The difference in pay premium between those from degree holder origins (immobile high) and non-degree origins (mobile up) (24%–27%) in Table 3 is not statistically significant (p = 0.459). Among women, however, this difference (35%–27%) is

8 The formal model selection tests, indicating that the ethno-religious—mobility group interaction terms do not improve the fit of the models presented in Table 3 are available from the authors on request.

9

Social Science Research 83 (2019) 102302

L. Gabay-Egozi and M. Yaish

Fig. 4. Mean adjusted linear predictions of (ln) earnings (with 95% CIs) derived from the OLS models in Table 3 (Snapshot estimates of average annual monthly (ln) earnings—1995-2000), by mobility groups, ethno-religion groups and gender (N = 10,447).

statistically significant (p = 0.025), suggesting that upwardly educationally mobile women might be more selected on unobservables than multi-generation graduate women. As shown below, however, predicted snapshot earnings for both men and women with a degree do not differ by parental education, implying that differences in observed characteristics amongst female graduates between the mobile up and the immobile high offset the positive effect of parental lack of college degree. In sum, parents’ education has little effect, if any, in determining earnings, as postulated by human capital theory. Based on the models presented in Table 3, Fig. 4 displays means adjusted linear predictions of monthly average ln earnings at labor market entry (1995–2000), by cross classifying parental and respondent's education, ethno-religion group and gender. As expected, for all sub-populations involved, predicted earnings is higher for college degree holders than for those without a degree. More important, however, within degree holders as well as within those without a degree, predicted earnings do not vary by parental education. Noteworthy is the relative disadvantaged position of Jewish women and Arab men. Mizrahi women, Ashkenazi women, and Arab men share similar adjusted earnings with each other as well as with Mizrahi and Ashkenazi men without a degree. In this context, Arab women suffer a double disadvantage, as their adjusted earnings are the lowest when they do not hold a college degree, or similar to that of Ashkenazi and Mizrahi women without a degree when holding a college degree. The crucial point to be made here is that for neither sub-population does parental education impinge on earnings, as postulated by hypothesis 3 above. Taking a snapshot perspective to studying earnings returns to intergenerational educational mobility patterns, our results support the human capital argument that parental education does not determine individual's earnings. Nevertheless, parental education might hold a long-term effect, as it represents psychological, social and economic resources that might cumulate. Highly educated parents know the educational system, and hence can guide their children on what (fields) and where (institutes) to study. They also have more social capital than less educated parents, which can assist in the transition of their offspring into the labor market. Thus, the question remains, Has intergenerational educational mobility consequences for long-term earnings trajectories? To address this question we applied growth curve models to our longitudinal data. These are multilevel models in which personyears are nested in individuals, and are designed to estimate the underlying earnings trajectories of individuals in the four educational mobility groups, net of potential confounders. Thus, by adding a set of cross level interaction terms between year and year square in level 1 and intergenerational mobility groups in level 2, we can estimate for each mobility group the earnings in 1995 (mobility group main effect), the earnings growth rate (mobility group X year interaction terms), and its parabolic shape (mobility group X year2). As in previous analyses, we are also interested in differences in these trajectories across sub-populations, and thus we extended the analysis further by gender and interact the above effects with ethno-religious categories. As before, the best fitting models in these analyses (not reported) indicate that the ethno-religious—mobility group interaction terms do not reach acceptable statistically significant level. Appendix C presents the results of the full models, separately by gender, while Table 4 focuses on the sets of effects that, as explained above, produce the earnings trajectories of interest, visualized in Fig. 5.9 Starting with the earnings trajectories for men, the left pane in Fig. 5 illustrates very clearly how small differences in earnings in 1995 tend to fan out over time in a way that brings to bear not only respondent's education but also parental education. Consider first the 9 For brevity we display the earnings trajectory of an average Israeli man or woman, ignoring ethno-religious differences. According to the models in Appendix C, however, the earnings trajectories of the three ethno-religious groups should have similar shapes but different starting levels.

10

Social Science Research 83 (2019) 102302

L. Gabay-Egozi and M. Yaish

Table 4 Selected coefficients from mixed models on average annual monthly (ln) earnings, 1995–2013 (year in person), by gender. Men (N = 76,355)

Entrance (main effect) 1. Immobile low 2. Mobility up 3. Immobile high Growth rate (by year) 1. Immobile low 2. Mobility up 3. Immobile high Curve (by year2) 1. Immobile low 2. Mobility up 3. Immobile high Women (N = 78,258)

Entrance (main effect) 1. Immobile low 2. Mobility up 3. Immobile high Growth rate (by year) 1. Immobile low 2. Mobility up 3. Immobile high Curve (by year2)v 1. Immobile low 2. Mobility up 3. Immobile high

p < 0.05,

∗

p < 0.01,

∗∗

∗∗∗

vis-a-via

vis-a-via

vis-a-via

2. Mobility up

3. Immobile high

4. Mobility down

−0.061*

0.090* 0.151***

0.129* 0.190*** 0.039

−0.079***

−0.125*** −0.046***

−0.038*** 0.041*** 0.087***

0.003***

0.005*** 0.002***

0.001** −0.002** −0.004***

vis-a-via

vis-a-via

vis-a-via

2. Mobility up

3. Immobile high

4. Mobility down

−0.279***

−0.119*** 0.160***

0.044 0.323*** 0.163**

−0.032***

−0.061*** −0.030***

−0.005 0.027* 0.057***

0.001***

0.003*** 0.002***

0.000 −0.001*** −0.003***

p < 0.001.

Fig. 5. Predicted mean adjusted growth curves derived from the mixed models in Table 4 (average annual monthly (ln) earnings, 1995–2013), by gender.

earnings trajectories of the two college degree holder groups: immobile high and mobile up. Table 4 indicates that on entrance to the labor market in 1995, first time graduates earn about 15% more than their multi-generation graduate counterparts, possibly due to being positively selected on unobservables. However, as time progresses the earnings trajectory of the multi-generation graduates supersedes that of the first generation graduates due to about 5% higher annual earnings growth rate, as anticipated by hypotheses H4 and H7. When the two groups without college degree (immobility low and mobility down) are examined, a similar pattern appears. The earnings trajectory of the mobile down group grows at about 4% higher annual rate compared to that of the immobile low group, as we expected by hypotheses H6 and H5. This is a clear depiction of the ‘glass floor effect’ (Reeves and Howard, 2013; McKnight, 2015), according to which educated parents use their resources to guarantee a smooth landing for their educationally downwardly 11

Social Science Research 83 (2019) 102302

L. Gabay-Egozi and M. Yaish

mobile offspring. We see here a clear payoff to the life course perspective, and a clear support to the four cumulative advantage scenarios depicted by hypotheses H4—H7. For women, parameter estimates in Table 4 and the graph in the left pane in Fig. 5 also display fanning out earnings trajectories for the two college degree holder groups. On entrance to the labor market, first generation graduate women earn about 16% more than their multi-generation graduate counterparts, while with time the earnings trajectory of the multi-generation graduates supersedes that of the first generation graduates, due to about 3% higher annual earnings growth rate. At the same time, the earnings trajectories of the two groups without college degree have similar starting point (b = 0.044, n.s.) and growth rate (b = −0.005, n.s.), implying that they do not fan out with time. This implies that the ‘glass floor effect,’ so effective in the case of Israeli men, does not work in the case of Israeli women. One possible explanation why women without college degree whose parents have college degree do not benefit from the ‘glass floor effect’ or from a compensatory advantage is that Israeli women without a college degree tend to occupy a very narrow range of low-skill low-pay jobs. This being the case, their privileged background has a limited, if at all, scope to affect earnings. To summarize, our analyses show that within a snapshot perspective, earnings returns to intergenerational educational mobility follow the human capital scenario. Thus, only individuals’ own education determines earnings. Acknowledging that parental education might be an advantage that has a long-term effect, we show how intergenerational educational mobility shapes earnings trajectories in ways that confirm the four cumulative advantage scenarios summarized in Table 1. 4. Conclusions There are a number of reasons why researchers should consider education in addition to other scales (e.g., occupation prestige or income) when studying intergenerational mobility. For one, the information on education is relatively easy to collect and is less prone to measurement errors than income or occupation. This is especially true when respondents are asked to report on their parental income or occupation. Education, moreover, is a relatively reliable measure of socio-economic standing because it is less affected by own life-cycle events, as most individuals attain their final educational degree in their twenties. By contrast, income and occupational prestige are affected by economic cycles, family formation and dissolution processes as well as by age. Finally, and perhaps most importantly, results have consistently shown that within the Origins-Education-Destination triangle, for those individuals with a college degree, the origin-destination association is approaching null, representing nearly complete equality of opportunity (Hout, 1984; Yaish, 2004; Vallet 2004; Torche, 2011; Breen and Jonsson, 2007; Breen and Luijkx 2007). This finding and the centrality of parental education in the education attainment process suggest that intergenerational educational mobility is a major force shaping stratification processes in society. Nevertheless, educational stratification research mostly focuses on the intergenerational educational association (relative mobility), obscuring young adults' actual life experiences of upward and downward mobility and immobility. This study, by contrast, contributes to the literature by focusing on absolute intergenerational educational mobility. Studying absolute intergenerational educational mobility in Israel, we show that intergenerational educational reproduction entails different things for the different subpopulations. For Mizrahi-Jews and Arabs it mostly means reproduction of disadvantages, while for Ashkenazi-Jews it means reproduction of advantages. Furthermore, we have shown that Ashkenazi-Jews’ greater mobility rates result from both upward and downward mobility. Focusing on the intergenerational educational association, as most previous research does, conceals these interesting results. This paper further contributes to the literature by testing a theoretical formulation derived from the cumulative advantage mechanism about enduring life course effects of educational mobility on earnings. Accordingly, advantages and disadvantages cumulate to generate growing effects on earnings over the life course. We argue here that parental education is one such advantage, which cumulates on an individual's own education in producing earnings trajectories. Parental education is often equated with social and economic resources that bear significantly on their offsprings' long-term attainment process. Highly educated parents have more knowledge of the educational system, they are better equipped to guide their children on what (fields) and where (institutes) to study, and they possess more social capital that can ease the transition from school to work. Focusing on intergenerational educational mobility, then, we argue that parental education affects life course earnings, net of own education. This is not a trivial, nor a wellknown argument. Just as important, this argument runs contrary to human capital theory. Our results provide empirical support for this argument, as they show how the earnings trajectories of individuals in the four mobility groups have fanned out over the life course – chiefly in the case of Israeli men – as expected by the four cumulative advantage scenarios. Whereas college graduates' earnings curve lines have a high start, over time those who were raised by college educated parents show a steeper upward curve (cumulative advantage) compared to those whose parents do not have a college degree (offsetting advantage). Similarly, for those who did not earn a college degree, college educated parental background provides a moderate upward earnings curve over the years (compensatory advantage) compared to the earnings curve of those whose parents do not have a college degree (cumulative disadvantage). The compensatory advantage mechanism (‘glass floor effect’), so effective in the case of Israeli men, however, does not seem to work in the case of Israeli women. We suggest that this is mainly due to a homogeneous unrewarding occupational structure that women without a college degree share, regardless of their parental education. The significance of these results crystallizes when contrasted with our findings on the association between educational mobility earnings using the snapshot approach. Accordingly, returns to intergenerational educational mobility are not determined by parental education, as is depicted by the human capital theory. Thus, we argue, human capital theory might be an appealing explanation for earnings trajectories differentiated by own education, the cumulative advantage mechanisms explain the same respecting both own and parental education. 12

Social Science Research 83 (2019) 102302

L. Gabay-Egozi and M. Yaish

Declarations of interest None. Funding This work was supported by the Israel Science Foundation grant (ISF Grant no. 575/14) to the second author. Acknowledgements We thank the editor of Social Science Research and two anonymous reviewers for their valuable comments and suggestions. An earlier version of this paper was presented at the 2017 PAA meeting in Chicago, April 2017, and at the ISA-RC28 meeting in NYC, August 2017. We thank participants in these meetings for helpful comments. We also thank Shira Offer for valuable comments and suggestions. The usual disclaimer applies. Appendix A. Means (standard deviations) and proportions of respondents' characteristics by mobility group Scale range (min, max)

All

Immobile low

Mobile up

Immobile high

Mobile down

Female

0

1

Respondent's age

25

32

Ethnicity Mizrahi-Jews

0.51 (0.50) 28.11 (2.27)

0.50 (0.50) 28.15 (2.28)

0.53 (0.50) 28.12 (2.27)

0.54 (0.50) 27.79 (2.18)

0.48 (0.50) 27.99 (2.31)

0

1

45.80

***

0

1

37.80

Arabs

0

1

16.40

Immigrant

0

1

High school tracking Academic track

0.08 (0.28)

0.12 (0.33) 0.85 (0.36) 0.03 (0.16) 0.21 (0.41)

0.16 (0.37) 0.79 (0.41) 0.05 (0.21) 0.27 (0.45)

***

Ashkenazi-Jews

0.54 (0.50) 0.26 (0.44) 0.21 (0.41) 0.06 (0.24)

0

1

43.18

***

0

1

37.14

Unknown track & less than HS

0

1

19.68

Family background No father

0.46 (0.50) 0.24 (0.43) 0.30 (0.46)

***

Vocational track

0.33 (0.46) 0.46 (0.50) 0.23 (0.42)

0

1

8

100

0.03 (0.18) 38.52

***

Household SEI

0.04 (0.19) 45.03

Household size

2

14

(20.36) 5.82 (2.18)

(16.66) 6.23 (2.32)

11769 100.00

7870 66.87

8,135 (5,914) 10,447 100.00

7,000 (4,807) 6,747 64.58

Ns % 'Snapshot' earnings 1995–2000 Ns %

201

66,666

*** ***

*** ***

*** ***

**

0.40 (0.49) 0.50 (0.50) 0.10 (0.30) 0.06 (0.24)

***

0.65 (0.48) 0.23 (0.42) 0.12 (0.32)

***

0.05 (0.22) 49.13

*

(18.21) 5.12 (1.69)

*** *** ***

***

*** ***

2489 21.15 ***

10,233 (6,870) 2,361 22.60

*

0.80 (0.40) 0.12 (0.32) 0.09 (0.28) 0.03 (0.17) 75.32

** *** *** ***

***

0.03 (0.16) 41.54

(13.69) 4.74 (1.21)

(15.93) 4.87 (1.55)

970 8.24

440 3.74

10,911 (7,553) 945 9.04

***

8,330 (6,756) 394 3.77

*** *** ***

*** **

*** ***

***

Note: Asterisks in Column k indicate statistically significant (P < 0.05) differences between the means shown in Columns k and k+1 (K = 4, 5, 6). Asterisks in Column 7 indicate statistically significant differences between the means shown in Columns 7 and 4. ∗ p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001.

13

Social Science Research 83 (2019) 102302

L. Gabay-Egozi and M. Yaish

Appendix B. Multi-logit coefficients (standard errors in parentheses) of intergenerational educational mobility on the odds of accessing one of the intergenerational educational mobility (N = 11,769) Mobile up

Immobile high

Mobile down

Rather than Immobile low Female

−0.098 (0.051)

Ethnicity [Mizrachi-Jews] Ashkenazi-Jews

0.431*** (0.058) Arabs −0.068 (0.094) Age in 1995 −0.007 (0.011) Immigrant −0.183 (0.104) High school tracking history [Academic track] Vocational track −1.375*** (0.059) Unknown track & less than HS −1.190*** (0.074) Family background No father 0.081 (0.127) Household SEI 0.021*** (0.002) Household size −0.181*** (0.018) Constant −0.270 (0.348)

p < 0.05,

Mobile down

Rather than Immobile high

−0.066 (0.091)

−0.114 (0.112)

−0.033 (0.091)

−0.048 (0.121)

1.408*** (0.120) 0.002 (0.262) −0.066** (0.020) 1.349*** (0.137)

1.126*** (0.145) 0.374 (0.289) −0.036 (0.025) 1.678*** (0.148)

−0.977*** (0.121) −0.070 (0.266) 0.059** (0.020) −1.532*** (0.142)

−0.282 (0.171) 0.372 (0.360) 0.030 (0.027) 0.329* (0.142)

−1.872*** (0.124) −1.606*** (0.143)

−0.702*** (0.140) 0.205 (0.136)

0.497*** (0.128) 0.416** (0.148)

1.170*** (0.161) 1.811*** (0.162)

−0.149 (0.250) 0.105*** (0.003) −0.116** (0.038) −6.009*** (0.658)

−0.425 (0.336) 0.090*** (0.004) −0.108* (0.044) −6.918*** (0.799)

0.229 (0.246) −0.084*** (0.003) −0.065 (0.039) 5.739*** (0.661)

−0.276 (0.368) −0.015*** (0.004) 0.008 (0.050) −0.909 (0.883)

AIC BIC ∗

Mobile up

15984.6 16227.9

p < 0.01,

∗∗

∗∗∗

p < 0.001.

Appendix C. Coefficients from mixed models on average annual monthly wage (ln) over time 1995–2013, by gender

Year Year2 Intergenerational educational groups [Immobile low] Mobile up Immobile high Mobile down Mobile up * Year Immobile high * Year Mobile down * Year Mobile up * Year2 Immobile high * Year2 Mobile down * Year2 Ethnicity [Mizrahi-Jews] Ashkenazi-Jews Arabs

Male (N = 76,355)

Female (N = 78,258)

0.043*** (0.002) −0.001*** (0.0001)

0.037*** (0.002) −0.001*** (0.0001)

0.061* (0.025) −0.090* (0.040) −0.129* (0.052) 0.079*** (0.005) 0.125*** (0.007) 0.038*** (0.010) −0.003*** (0.0002) −0.005*** (0.0003) −0.001** (0.0005)

0.279*** (0.025) 0.119** (0.039) −0.044 (0.055) 0.032*** (0.005) 0.061*** (0.007) 0.005 (0.010) −0.001*** (0.0002) −0.003*** (0.0003) −0.0002 (0.0005)

−0.028 (0.020) −0.260*** (0.028)

0.034 (0.021) −0.306*** (0.031)

14

Social Science Research 83 (2019) 102302

L. Gabay-Egozi and M. Yaish Age in 1995 Immigrant High school tracking history [Vocational] Academic track Unknown track & less than HS Family background No father Household SEI Household size Constant AIC BIC ∗

p < 0.05,

∗∗

p < 0.01,

0.048*** (0.004) −0.071* (0.030)

0.017*** (0.004) 0.008 (0.030)

−0.043* (0.020) −0.131*** (0.022)

0.088*** (0.020) −0.073** (0.026)

−0.026 (0.046) 0.003*** (0.001) −0.025*** (0.005) 7.509*** (0.109)

−0.111* (0.043) 0.004*** (0.001) −0.016** (0.005) 7.728*** (0.113)

1514100.9 1514359.7

1488360.9 1488620.4

p < 0.001.

∗∗∗

References Ayalon, Hanna, Yogev, Abraham, 2005. Field of study and students' stratification in an expanded system of higher education: the case of Israel. Eur. Sociol. Rev. 21 (3), 227–241. Bar-Haim, Eyal, Yaish, Meir, Yossi, Shavit, 2008. Up the down escalator: expansion and stratification in education. Israeli Sociol. 10 (1), 61–79. Bar-Haim, Eyal, Blank, Carmel, Shavit, Yossi, 2013. Changes in the stratification process in Israel, 1995-2008. In: State of the Nation Report. Dan Ben David, Jerusalem. Becker, Gary S., 1964. Human Capital: A Theoretical and Empirical Analysis, with Special Reference to Education. Columbia University Press, New York. Becker, Rolf, 2003. Educational expansion and persistent inequalities of education: utilizing subjective expected utility theory to explain increasing participation rates in upper secondary school in the Federal Republic of Germany. Eur. Sociol. Rev. 19 (1), 1–24. Bernardi, Fabrizio, 2014. Compensatory advantage as a mechanism of educational inequality a regression discontinuity based on month of birth. Sociol. Educ. 87 (2), 74–88. Bernardi, Fabrizio, Ballarino, Gabriele, 2016. Education, Occupation and Social Origin: A Comparative Analysis of the Transmission of Socio-Economic Inequalities. Edward Elgar Publ, Northampton, MA. Blau, Peter M., Duncan, Otis D., 1967. The American Occupation Structure. John Wiley & Sons, New York. Bliese, Paul, Ployhart, Robert, 2002. Growth modeling using random coefficient models: model building, testing, and illustrations. Organ. Res. Methods 5 (4), 362–387. Bolotin-Chachashvili, Svetlana, Shavit, Yossi, Ayalon, Hanna, 2002. Expansion and equality in Israeli higher education. Soziologia Israelit 2, 317–347. Boudon, Raymond, 1974. Education, Opportunity and Social Inequality: Changing Prospects in Western Society, first ed. John Wiley & Sons Inc, New York. Bourdieu, Pierre, 1984. Distinction: A Social Critique of the Judgement of Taste. Translated by R. Nice. Harvard University Press, Cambridge, MA. Bourdieu, Pierre, Passeron, Jean Claude, 1990. Reproduction in Education, Society and Culture. Translated by Richard Nice, second ed. Sage Publications, Thousand Oaks, CA. Bowles, Samuel, Gintis, Herbert, 1976. Schooling in Capitalist America: Education Reform and the Contradictions of Economic Life. Basic Books Inc, New York. Breen, Richard (Ed.), 2004. Social Mobility in Europe. Oxford University Press, Oxford. Breen, Richard, Ermisch, John, 2017. Educational Reproduction in Great Britain: A Prospective Approach. European Sociological Review 33 (4), 590–603. Breen, Richard, Goldthorpe, John H., 1997. Explaining educational differentials towards a formal rational action theory. Ration. Soc. 9 (3), 275–305. Breen, Richard, Jonsson, Jan O., 2007. Explaining change in social fluidity: educational equalization and educational expansion in twentieth-century Sweden. Am. J. Sociol. 112 (6), 1775–1810. Breen, Richard, Luijkx, Ruud, 2007. Social mobility and education: a comparative analysis of period and cohort trends in britain and Germany. In: Scherer, Stefani, Pollak, Reinhard, Otte, Gunnar, Gangl, Markus (Eds.), From Origin to Destination. Trends and Mechanisms in Social Stratification Research. Campus Verlag, Frankfurt. Cheng, Siwei, Brand, Jennie, E.Zhou, Xiang, Xie, Yu, 2018. Who Benefits First? Who Benefits Last? Economic Returns on College over the Life Cycle. Paper presented to the Population Association of America, Denver, Colorado April 2018. Cheng, Siwei, Tamborini, Christopher R., Kim, ChangHwan, Sakamoto, Arthur, 2017. Intra-cohort and Inter-cohort Trends in the Black-White Earnings Gap Among Men Using 40 Years of Administrative Data. In: Paper presented at the Population Association of America annual meeting; Chicago, illinois. Cohen, Yinon, Haberfeld, Yitchak, Kristal, Tali, 2007. Ethnicity and mixed ethnicity: educational gaps among Israeli-born Jews. Ethn. Racial Stud. 30 (5), 896–917. Dahan, Momi, Dvir, Eyal, Mironichev, Natalie, Shye, Samuel, 2003. Have educational gaps narrowed? Isr. Econ. Rev. 2, 37–69. Davies, Richard, Heinesen, Eskil, Holm, Anders, 2002. The relative risk aversion hypothesis of educational choice. J. Popul. Econ. 15 (4), 683–713. DiPrete, Thomas A., Eirich, Gregory M., 2006. Cumulative advantage as a mechanism for inequality: a review of theoretical and empirical developments. Annu. Rev. Sociol. 32 (1), 271–297. Erikson, Robert, Goldthorpe, John H., 1992. The Constant Flux. A Study of Class Mobility in Industrial Societies. Clarendon Press, Oxford. Friedlander, Dov, Okun, Barbara S., Eisenbach, Zvi, Elmakias, Lilach Lion, 2002. Immigration, social change and assimilation: educational attainment among birth cohorts of jewish ethnic groups in Israel, 1925-29 to 1965-69. Popul. Stud. 56 (2), 135–150. Ganzeboom, Harry B.G., Treiman, Donald J., Ultee, Wout C., 1991. Comparative intergenerational stratification research: three generations and beyond. Annu. Rev. Sociol. 17, 277–302. Goldthorpe, John H., 2016. Social class mobility in modern britain: changing structure, constant process. J. Br. Acad. 4, 89–111. Haberfeld, Yitchak, Cohen, Yinon, 2007. Gender, ethnic, and national earnings gaps in Israel: the role of rising inequality. Soc. Sci. Res. 36 (2), 654–672. Halsey, Albert H., Heath, Anthony F., Ridge, John M., 1980. Origins and Destinations: Family, Class and Education in Modern Britain. Clarendon Press, Oxford. Heller, Oren, 2017. Intergenerational Income Mobility in Israel. National Insurance Institute Research and Planning, Jerusalem, Israel. Hertz, Tom, Jayasundera, Tamara, Piraino, Patrizio, Sibel Selcuk, Smith, Nicole, Verashchagina, Alina, 2008. The inheritance of educational inequality: international comparisons and fifty-year trends. B. E. J. Econ. Anal. Policy 7 (2), 1–48.

15

Social Science Research 83 (2019) 102302

L. Gabay-Egozi and M. Yaish

Hout, Michael, 1984. Status, autonomy, and training in occupational mobility. Am. J. Sociol. 89 (6), 1379–1409. Lareau, Annette, 1989. Home Advantage. Social Class and Parental Intervention in Elementary Education. Falmer Press, New York. McKnight, Abigail, 2015. Downward Mobility, Opportunity Harding and the ‘Glass Floor.’ Social Mobility and Child Poverty Commission. Centre for Analysis of Social Exclusion (CASE), London School of Economics. Mincer, Jacob, 1974. Schooling, Experience and Earnings. National Bureau of Economic Research, New York, NY. Need, Ariana, de Jong, Uulkje, 2001. Educational differentials in The Netherlands testing rational action theory. Ration. Soc. 13 (1), 71–98. Okun, Barbara S., Friedlander, Dov, 2005. Educational stratification among Arabs and Jews in Israel: historical disadvantage, discrimination, and opportunity. Popul. Stud. 59 (2), 163–180. Pfeffer, Fabian T., 2008. Persistent inequality in educational attainment and its institutional context. Eur. Sociol. Rev. 24 (5), 543–565. Plaut, Pnina O., Plaut, Steven S., 2015. Ethnic income disparities in Israel. Isr. Aff. 21 (1), 1–26. Reeves, Richard V., Howard, Kimberly, 2013. The glass floor: education, downward mobility, and opportunity hoarding. In: Center on Children and Families at Brookings. Rotman, Assaf, Shavit, Yossi, Shalev, Michael, 2016. Nominal and positional perspectives on educational stratification in Israel. Res. Soc. Stratif. Mobil. 43 (March), 17–24. Shavit, Yossi, 2017. Another look at persistent inequality in educational stratification in Israel. In: Arviv-Elyashiv, Rinat, Feniger, Yariv, Shavit, Yossi (Eds.), A Chance for a Change? Theory and Research on Equality of Educational Opportunities, 113–32. Tel-Aviv: Mofet. Shavit, Yossi, Blossfeld, Hans-Peter (Eds.), 1993. Persistent inequality: changing educational attainment in thirteen countries. Social Inequality Series. Westview Press, Boulder, Co. Shavit, Yossi, Arum, Richard, Adam, Gamoran (Eds.), 2007. Stratification in Higher Education: A Comparative Study. Stanford University Press, Stanford, California. Singer, Judith D., Willett, John B., 2003. Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. Oxford University Press, New York. Sobel, Michael E., 1981. Diagonal mobility models: a substantively motivated class of designs for the analysis of mobility effects. Am. Sociol. Rev. 46 (6), 893–906. Song, Xi, Cheng, Siwei, 2016. Linked lives, linked trajectories: intergenerational association of intragenerational income mobility. Presented at the Annual Meeting of the Population Association of America (PAA), Washington, DC, USA. Sorokin, Pitirim A., 1959. Social Mobility. The Free Press, Glencoe, IL. Teachman, Jay D., 1987. Family background, educational resources, and educational attainment. Am. Sociol. Rev. 52 (4), 548–557. Torche, Florencia, 2011. Is a college degree still the great equalizer? Intergenerational mobility across levels of schooling in the United States. Am. J. Sociol. 117 (3), 763–807. Treiman, Donald J., 1970. Industrialization and social stratification. In: Laumann, Edward O. (Ed.), Social Stratification: Research and Theory for the 1970s. BobbsMerrill, Indianapolis. Tyree, Andrea, 1981. Occupational economic status, ethnicity and sex in Israel: considerations in scale construction. Megamot 27 (1), 7–21. Vallet, Louis-André, 2004. Change in intergenerational class mobility in France from the 1970s to the 1990s and its explanation: an analysis following the CASMIN approach. In: Breen, Richard (Ed.), Social Mobility in Europe. Oxford University Press, Oxford. van de Werfhorst, Herman, 2011. Skill and education effects on earnings in 18 countries: the role of national educational institutions. Soc. Sci. Res. 40 (4), 1078–1090. VanDoorn, Majka, Pop, Ioana, Wolbers, Maarten H.J., 2011. Intergenerational transmission of education across european countries and cohorts. Eur. Soc. 13 (1), 93–117. Weisberg, Jacob, 1995. Returns to education in Israel: 1974 and 1983. Econ. Educ. Rev. 14 (2), 145–154. Yaish, Meir, 2004. Class Mobility Trends in Israeli Society, 1974-1991. Edwin Mellen Press, Lewiston. Yaish, Meir, Andersen, Robert, 2012. Explaining cross-national variation in social mobility: the role of economic and political context. Soc. Sci. Res. 41 (3), 527–538. Yaish, Meir, Gabay-Egozi, Limor, 2019. Intracohort trends in ethnic earnings gaps: the role of education. Socius: Socio. Res. Dyn. World 5, 1–14.

16