Social Science Research 52 (2015) 465–478
Contents lists available at ScienceDirect
Social Science Research
journal homepage: www.elsevier.com/locate/ssresearch
Field of study variation throughout the college pipeline
and its effect on the earnings gap: Differences between
ethnic and immigrant groups in Israel
Sigal Alon ⇑
Tel-Aviv University, Israel
a r t i c l e
i n f o
Article history:
Received 27 January 2014
Revised 19 February 2015
Accepted 19 March 2015
Available online 28 March 2015
Keywords:
Field of study
Undermatching
College pipeline
Earnings gap
Ethnic inequality
Israel
a b s t r a c t
This study demonstrates the analytical leverage gained from considering the entire college
pipeline—including the application, admission and graduation stages—in examining the
economic position of various groups upon labor market entry. The ﬁndings, based on data
from three elite universities in Israel, reveal that the process that shapes economic
inequality between different ethnic and immigrant groups is not necessarily cumulative.
Field of study stratiﬁcation does not expand systematically from stage to stage and the
position of groups on the ﬁeld of study hierarchy at each stage is not entirely explained
by academic preparation. Differential selection and attrition processes, as well as
ambition and aspirations, also shape the position of ethnic groups in the earnings hierarchy
and generate a non-cumulative pattern. These ﬁndings suggest that a cross-sectional
assessment of ﬁeld of study inequality at the graduation stage can generate misleading
conclusions about group-based economic inequality among workers with a bachelor’s
degree.
Ó 2015 Elsevier Inc. All rights reserved.
1. Introduction
In most Western countries, participation in all levels of the education system has been increasing for decades, as
has the overall level of academic credentials. Yet there is a mismatch in many countries between the decline in
racial/ethnic and gender gaps in educational attainment, and the persistence of earnings gaps. This is possible partly
because high-status groups are able to secure an education that, although quantitatively similar in terms of years of
schooling, is qualitatively superior—either by type of institution or ﬁeld of study—which, in turn, leads to better
economic returns (Alon, 2009; Lucas, 2001). Recent evidence suggests that college major is the most important
determinant of future earnings, even after controlling for ability (Arcidiacono, 2004; Roksa and Levey, 2010). In fact,
the disparity in returns across college majors rivals the college wage premium (Altonji et al., 2012).1 Consequently ﬁeld
of study variation accounts for much of the racial/ethnic and gender gaps in starting salaries among college graduates
⇑ Address: Department of Sociology and Anthropology, Tel Aviv University, Ramat Aviv, Tel Aviv 69978, Israel.
E-mail address: [email protected]
Altonji et al. (2012) ﬁnd that the gap in wage rates between male electrical engineering and general education majors is as large as the gap between college
and high school graduates.
1
http://dx.doi.org/10.1016/j.ssresearch.2015.03.007
0049-089X/Ó 2015 Elsevier Inc. All rights reserved.
466
S. Alon / Social Science Research 52 (2015) 465–478
(Rumberger and Thomas, 1993; Weinberger, 1998). The potential of using ﬁeld of study variation to explain economic
inequality is thus considerable.
Field of study inequality can be observed throughout the college pipeline, especially in the application, admission and
graduation stages. Variation in occupational and ﬁeld of study aspirations exists among high school students due to
disparities in economic resources, K-12 academic preparation, knowledge about college majors and occupations, and
ambition and maturity (Schneider, 2009). In countries where applicants are asked to ranked their ﬁeld of study preferences
at the application stage—such as in Israel, Australia and most European countries—it is possible to study the variation in
college major choice set, even before college entry, as prospective applicants must list their preferred majors and rank them
accordingly. Hällsten (2010), for example, ﬁnds that college applicants in Sweden from service class backgrounds are more
likely to choose majors with high potential earnings than those from manual labor class backgrounds. Other studies concur
that social background affects the choice of ﬁeld of study, although the direction of the effect is not clear (see, for example,
Reimer and Pollak (2005) on Germany and Van de Werfhorst et al. (2001) on the Netherlands). In such settings, where the
application and admission stages are major-speciﬁc, the admissions decision, which determines ﬁeld of study, can contract
or expand group inequality.
Even in countries where college applicants do not generally choose a major at the application stage, as in the U.S., preferences regarding ﬁelds of study (and future occupations) shape applicants’ decisions about educational attainment
(Altonji et al., 2012; Schneider and Stevenson, 1999). However, ﬁeld of study preferences cannot be observed directly at
the application stage in these settings—rather, the only information that surveys and college administrative data provide
is regarding ‘‘intent to major’’ and ‘‘declared major.’’ As a result, the research in the U.S. tends to focus on inequality in applicants’ choice set of colleges, not majors or ﬁelds of study. For example, several studies have focused on the phenomenon of
college undermatching—that is, when a high school graduate attends a college that is less selective than what her academic
achievement indicates—which tends to be more widespread among minority and low-SES students (Bowen et al., 2009;
Hoxby and Avery, 2012). At the same time, we know little about ﬁeld of study matching, and about whether there are class,
gender, or ethnic differences in the ﬁelds of study that students consider, either before or after enrollment in college.
But the effect of ﬁeld of study on the formation of economic inequality does not end after enrollment in a major (whether
upon admission or later in college). Fields of study vary in their grading norms, curricular structures, and social and academic
climates, all of which affect persistence (Alon and Gelbgiser, 2011; Des Jardins et al., 2002; Hearn and Olzak, 1981; Leppel,
2001; Xie and Shauman, 2003). For example, stricter and more competitive academic climates, which are typically associated
with lucrative occupations, can result in higher dropout rates.2 Moreover, competitive environments and a lack of diversity
can inhibit the academic performance of students who are minorities in these ﬁelds by exacerbating both the pressure to perform well, and the fear of conﬁrming stereotypes that they will not perform well, a process known as ‘‘stereotype threat’’
(Jayanti and Lynch, 2012; Steele and Aronson, 1995; Steele, 1997).3 Some students change majors or drop out of college altogether, both of which contribute to the emergence of group differences in ﬁelds of study among graduates. If the dropout patterns in majors vary by ethnicity, gender or class, then the predictions regarding group economic inequality will morph from
enrollment to graduation.
Although ﬁeld of study inequality tends to transform throughout the stages of the college pipeline, there is no study that
tracks this process—from the applicant’s major choice set to the type of degree attained—and that quantiﬁes its implications
for economic inequality. There are studies that examine inequality in the diploma type of graduates upon entering the labor
market, but these studies overlook the impact of earlier stages in the college pipeline on this outcome (e.g., Carnevale et al.,
2011). Alternatively, there are studies that demonstrate group differences in the ﬁeld of study choice sets of applicants, but
they do not track how ﬁeld of study-related economic inequality changes from the choice stage to degree attainment (e.g.,
Hällsten, 2010). Finally, there are studies that assess the transformation in ﬁelds of study by group after enrollment, either
due to dropping out or transferring, but the declared major is the typical starting point, rather than the ﬁelds in the major
choice set (e.g., Alon and Gelbgiser, 2011; Arcidiacono et al., 2012; Riegle-Crumb and King, 2010).
In this study, I combine the various pieces of the puzzle regarding the formation of ethnic-based ﬁeld of study inequality,
from application through graduation. I use universities’ administrative data regarding the major choice sets of applicants (a
ranked list of preferences), the admissions decisions of the universities, and the type of diplomas that graduates attain. The
Israeli university setting is especially conducive for assessing ﬁeld of study inequality throughout the college pipeline
because Israeli applicants apply to speciﬁc majors (including those that lead to most professional degrees) and departments
within colleges and universities, so that both the application and admission stages are major speciﬁc. Another advantage is
the ability to quantify the economic consequences of ﬁeld of study inequality. I use data on the monthly salary that a graduate in a certain ﬁeld can anticipate upon labor market entry from the Israeli Central Bureau of Statistics, which draws from
the administrative records of the state tax authorities. Moreover, because the bachelor’s admissions process is formulaic
(based entirely on a composite academic score), the current study can fully replicate each applicant’s major-speciﬁc
admissibility, and net out the effect of prior academic credentials on group variation by ﬁeld of study. This unique setting
2
For example, the strict and competitive academic climates in STEM ﬁelds can result in higher dropout rates (DiPrete and Buchmann, 2013; Hearn and Olzak,
1981; Suresh, 2006; Xie and Shauman, 2003).
3
The lack of diversity among professors and role models may generate an environment in which students who are underrepresented in these departments—
females, ethnic minorities, and the socioeconomically underprivileged—ﬁnd it harder to ﬂourish (Fisher and Margolis, 2002; Hearn and Olzak, 1981; Margolis
et al., 2008; Price, 2010; Xie and Shauman, 2003).
S. Alon / Social Science Research 52 (2015) 465–478
467
enables me to assess whether ﬁeld of study variation throughout the college pipeline contributes to the persistent economic
inequality between ethnic and immigrant groups in Israel. Moreover, this analytical framework allows me to link the ethnicity-based variation among graduates to differences to academic preparation, ﬁeld of study aspirations (which can lead
to either undermatching or overmatching), and/or to group differences in college performance. The insights gained from this
investigation are used to develop general inferences about the formation of ﬁeld of study-related economic inequality.
2. The economic and educational gaps in Israeli society
The Israeli population is divided along ethnic lines. The ﬁrst cleavage is between Israeli Jews, who account for approximately 80 percent of the population, and Israeli Arabs, who make up the rest. This investigation focuses on the Jewish population because Arabs have distinct patterns of participation in both the higher education system and the labor market.4 The
Jewish population in Israel is divided along ethnic lines: ‘‘Ashkenazi,’’ those of European and/or American origin, and ‘‘Mizrachi,’’
those with roots in Asia and/or Africa. Before 1948, there were several immigration waves to Israel from both America and
Europe, with the ﬁrst major wave starting in 1882.5 The establishment of the State of Israel in 1948 brought with it massive
waves of Jewish immigrants, who continued to arrive throughout the 1950s. These waves consisted mainly of refugees from
Europe, on the one hand, and those from Asia and Africa, on the other. In the early 1990s, there was another massive immigration
wave, dominated by immigrants from the former Soviet Union (FSU).6 They consist of about 15 percent of the Jewish population.
The social and cultural assimilation of the European and American immigrants in Israeli society was smoother, in general,
than that of the immigrants from Asia and Africa. Consequently, an earnings hierarchy was institutionalized among the
Jewish population of Israel, one in which the Ashkenazi Jews are at the top of the socioeconomic ladder, the Mizrachi
Jews are at the bottom, and Jews of mixed ethnicity occupy the space in the middle (Cohen et al., 2007; Dahan et al.,
2002; Yaish, 2001). Within each category, second- and third-generation immigrants rank higher than the ﬁrst generation,
and the ethnic earnings gaps are smaller among women than among men.
The economic differential between Ashkenazi and Mizrachi Jews has largely been attributed to the gaps in educational
attainment between these groups. Recent studies indicate a gradual convergence in the ethnicity-based educational attainment gap in Israel over time, as measured mainly by mean years of schooling and the share of group members holding a
bachelor’s degree (Friedlander et al., 2002; Haberfeld and Cohen, 2007; Okun and Friedlander, 2005). Yet, the ethnic gaps
in annual earnings among male bachelor’s degree holders in Israel widened (Cohen and Haberfeld, 1998; Haberfeld and
Cohen, 2007). For example, while the ratio of the percentage of native-born Mizrachi men with a bachelor’s degree to their
Ashkenazi counterparts climbed steeply between 1992 and 2001 (from .25 to .45), the respective ratio of monthly earnings
rose only slightly, from .64 to .68 (Haberfeld and Cohen, 2007).
Moreover, an ethnicity-based gap in earnings exists even among the younger cohorts of the college-educated population.
In analyzing several Israeli Income Surveys, I found that, between 2005 and 2008, second-generation Mizrachi men between
the ages of 27 and 29 who had attended an institution of higher education earned a monthly salary that was 83 percent that
of second-generation Ashkenazi men in the same age cohort. During the same period, Mizrachi women of the same age
cohort earned 94 percent of what their Ashkenazi peers earned. Foreign-born men who immigrated to Israel from the
FSU enjoyed the highest pecuniary returns (106 percent that of Ashkenazi men), while the salary of male immigrants from
other parts of the world was only 83 percent that of Ashkenazi men.
This pattern of mismatch between educational attainment and earnings is not unique to Israel. For example, in the US the
gap in earnings determinants, such as education, between black and white women has been on the decline since the early
1980s. At the same time, the gap in earnings between these two groups has persisted (Altonji and Blank, 1999; Blau and
Beller, 1992). In addition to the possibility of rising discrimination, several other theories have been offered to explain this
paradox. One of them, put forward by Juhn et al. (1991), posits that education quality is a key factor in the slowdown of the
black–white wage convergence.7 Following this logic, Cohen et al. (2007) speculate that group-based differences in the quality
of education, such as differences in institution type and/or ﬁeld of study, may be one of the reasons that the ethnicity-based
gaps in earnings persist in Israel.
As of 2013, the higher education system in Israel is made up of about 70 postsecondary institutions that can be grouped
into two tiers. In the ﬁrst tier, there are six research universities. In the second tier, there are many new non-selective
degree-granting academic colleges, which are largely the product of the massive expansion of the Israeli higher education
system that began in 1995.8 In both tiers applicants apply to speciﬁc majors within each institution. Given that youth of
4
As a result of geographical segregation, labor market structure, differences in human capital, traditions and discrimination, Arabs have higher
unemployment rates than Jews. Those who work are concentrated in a few industrial sectors and occupations and earn less than their Jewish counterparts
(Yashiv and Kasir, 2009). Because of these restricted labor market opportunities, Arab students tend to be concentrated in a few ﬁelds of study. In 2006, for
example, Arabs accounted for about eight percent of degrees that the ﬁrst-tier universities awarded overall, but for about 20 percent of the degrees awarded in
medicine and 20 percent of the degrees awarded in paramedical studies. In contrast, they received only four percent of the degrees awarded in engineering.
5
The majority came from the Russian Empire, with smaller numbers arriving from countries in the Middle East, such as Syria and Yemen.
6
There were also two small waves of immigration from Ethiopia during this period.
7
They found that between 1979 and 1987, almost the entire wage gap between racial groups at the same education levels was attributable to differences in
education quality.
8
Between 1994/95 and 2010/11, the total number of undergraduate students more than doubled, from 86,000 to 183,000. This was mostly due to the
addition of second-tier institutions (although the number of undergraduate students attending the six universities also increased).
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S. Alon / Social Science Research 52 (2015) 465–478
Mizrachi origin are more likely than their Ashkenazi counterparts to attend the second-tier non-research colleges, the expansion of the higher education system has helped narrow the gap in bachelor’s degree attainment (Ayalon, 2008). That said,
because the economic returns on degrees from second-tier colleges are lower than the returns on degrees from the major universities (Suzzman et al., 2007), the ethnic wage gap persists. Furthermore, within the ﬁrst-tier universities, Mizrachi Jews are
under-represented in highly selective ﬁelds. For example, in 2006/07, Mizrachi university graduates were over-represented in
the humanities and social sciences, but under-represented in (Science, Technology, Engineering and Mathematics (STEM) (Israel
Central Bureau of Statistics (ICBS), 2009).
This study examines the proposition that the ethnic variation in majors may explain the ethnicity-based gaps in earnings,
even among the younger cohorts of the college-educated population. The empirical investigation tracks the variation in ﬁeld
of study among different ethnic origin groups in Israel, quantiﬁes the economic implications of this process in terms of
expected salary, and considers several factors that may account for the variation in ﬁelds of study.
3. Data and methods
3.1. Database
The empirical investigation utilizes the institutional administrative records of the three largest ﬁrst-tier Israeli universities—Tel Aviv University (TAU), The Hebrew University (HUJI), and Ben-Gurion University (BGU) (see Alon (2011) for
details). Due to the over-time changes in the demographic composition of the college-age population, as described below,
I limit the analyses to the applicant cohorts from 1999 to 2002. During this period, these three universities received
90,000 applications. About 63,000 applicants were admitted and 45,000 students eventually enrolled. Seventy-three percent
of the latter graduated from one of these three universities.
3.2. Variables
3.2.1. Ethnic origin
The conventional classiﬁcation scheme for ethnicity among the Jewish population in Israel, used in previous research and
ofﬁcial statistics, is based on either continent of birth or father’s continent of birth, using the dichotomy of Asia/Africa
(Mizrachi) and Europe/America (Ashkenazi).9 However, because this scheme recognizes only ﬁrst- and second-generation
immigrants, and ignores the possibility of mixed ethnicity, it is inadequate for describing the contemporary college-age population, which, as time lapses from the major immigration waves of the late 1940s and 1950s, is increasingly comprised of
third-generation Jews—that is, native-born Israeli Jews whose parents were also born in Israel.10 Among individuals who
applied to the three universities between 1999 and 2008 the pattern is clear: the share of third-generation applicants
(native-born Israeli Jews with two native-born parents) is surging. In 1999, about 54 percent of the applicant pool to university
was third generation; by 2008, this share was 69 percent.11 Given this trend, and in order to keep the share of the third-generation group—whose exact ethnicity is impossible to discern—as small as possible, this analysis is limited to the older cohorts in
the sample, those who applied between 1999 and 2002. This also ensures that each ethnic origin category is more homogeneous
in terms of time of immigration to Israel, and allows for a longer observation window through which to follow the applicants
until graduation.
As mentioned above, the other problem with the conventional classiﬁcation of ethnicity is that it does not reliably reﬂect
individuals of mixed ethnicity and mixed generations, because it ignores the ethnicity and generation of the mother.12 Given
the rise in ethnic intermarriage, this is a growing population, especially among the native born (Okun and Khait-Marelly, 2008;
Gshur and Okun, 2003; Okun, 2004). Fortunately, the university administrative data has information about the ethnic origin of
both parents of applicants. Thus, I created a measure for ethnic origin that takes into account both mixed-ethnicity status and
immigrant generation, which is presented in Table 1. Thirty-three percent of applicants between 1999 and 2002 were third
generation on both sides and thus their ethnicity cannot be classiﬁed. Yet, this classiﬁcation distinguishes between applicants
who are ‘‘veteran’’ Ashkenazi (those who are third-generation on one side and second-generation from Europe/America on the
other) and second-generation Ashkenazi (individuals whose parents both immigrated from Europe or America). Likewise,
Mizrachi applicants, those with roots in Asia/Africa, are classiﬁed as veteran Mizrachi or second-generation Mizrachi. Twenty
percent of the applicant body were ﬁrst-generation Jewish immigrants, the majority of which was part of the massive immigration wave from the FSU that began in the early 1990s, while about a quarter of the ﬁrst-generation pool from non-FSU countries
came from the US.
9
This scheme distinguishes between native-born Israeli Jews with a native-born father; native-born Israeli Jews with a father born in Asia or Africa; nativeborn Israeli Jews with a father born in Europe or America; immigrants from Asia or Africa; and immigrants from Europe or America.
10
Today, almost 40 percent of the age cohort of potential university applicants is third-generation Jews (Israel Central Bureau of Statistics (ICBS), 2010).
11
At the same time, the share of second-generation applicants (native-born Israeli Jews with one or both parents born outside of Israel) declined from 25 to
12 percent. Due to immigration from the former Soviet Union throughout the 1990s, the share of ﬁrst-generation immigrant applicants remained stable at
approximately one-ﬁfth.
12
For example, individuals classiﬁed as third generation (native-born Israeli Jews with a native-born father) could have a mother who is either third or second
generation. Moreover, the mother could be a native-born Israeli, from Asia/Africa, or from Europe/America.
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S. Alon / Social Science Research 52 (2015) 465–478
Table 1
A classiﬁcation of ethnic origin of the Israeli universities applicant pool, 1999–2002.
Origin group
Generation
Applicants
1999–2002 (%)
Two native-born parents
One native-born parent and one from Asia or Africa (veteran ‘‘Mizrachi’’)
One native-born parent and one from Europe or America (veteran ‘‘Ashkenazi’’)
Both parents from Asia or Africa (2nd generation ‘‘Mizrachi’’)
Both parents from Europe or America (2nd generation ‘‘Ashkenazi’’)
One parent from Europe or America and one from Asia or Africa (mix ethnicity)
Immigrants from Former Soviet Union (FSU) countries
Immigrants from other countries
3rd
2nd–3rd
2nd–3rd
2nd
2nd
2nd
1st
1st
33.2
9.5
13.8
10.6
9.1
3.2
13.7
6.9
3.2.2. Field of study economic hierarchy
To quantify the economic consequences of ﬁeld of study inequality by ethnic origin, I use data from the Israeli Central
Bureau of Statistics, which draws from the administrative records of the state tax authorities. Speciﬁcally, I use the monthly
salary of university graduates during their ﬁrst years in the labor market following graduation, by ﬁeld of study and by
institution (ICBS, 2012).13 Altogether, there was institution-speciﬁc data on expected earnings for 39 ﬁelds of study. The data
was obtained for four cohorts of university graduates, from 2000 through 2003, during their ﬁrst two years in the labor market,
separately for men and women.14 I merged this information on ﬁeld of study expected salary with the data, assigning each
applicant, admit, and graduate the corresponding projected salary by his or her ﬁeld of study at each stage. In the case of double
majors, the salary assigned was that of the major with the highest salary (at each stage).15 In sum, each woman in the sample
was assigned, at each stage—application, admission, enrollment and graduation—the average monthly salary of female
graduates in her corresponding ﬁeld and institution. Similarly, each male was assigned, at each stage, the salary of male graduates in his ﬁeld/institution.
This measure of ﬁeld of study expected salary is appropriate for the research question at hand because, of all types of
wage data, it is closest to the actual information that applicants/students have before/during college (Hällsten, 2010;
Wiswall and Zafar, 2013). That is, applicants/students do not know how much they will earn after labor market entry. At
best, they may know the average salary among recent graduates in certain ﬁelds (Manski, 1983). The current analysis
exposes group differences that can be attributed to between-major variance in earnings. If there are differences in labor market treatment or preferences between ethnic groups within each major exist, then the gaps revealed in this study are an
underestimation of the actual gaps between the groups.16
The expected salary ranges from 5000 to 20,000 New Israeli Shekels (NIS), for the graduates of three universities who
began their studies between 1999 and 2002 (see Table 2).17 There are substantial gaps in economic value in the labor market
between ﬁelds of study. Leading the pack in terms of starting monthly salary are the graduates of various engineering programs,
the computer sciences, exact sciences, pharmaceutical studies and economics.18 At the bottom are the graduates of several ﬁelds
in the humanities and social sciences. There is a sizable gender divide: the potential starting salary, on average, of males with
bachelor’s degrees is more than 11,000 NIS, while that of their female counterparts is about 7000 NIS. This is not just the result
of ﬁeld of study variation between men and women, but also of the gender gap within ﬁelds: that is, the salary of male
graduates is higher than that of females, even when their diplomas are in the same ﬁeld and from the same institution. The
analyses of the ethnic differences in this study are conducted separately for men and women using their corresponding earnings
data.
3.2.3. Academic score
This score is calculated by taking a weighted mean of an individual’s matriculation diploma grades (similar to AP grades)
and psychometric test score (similar to an SAT score). I standardized the score for each institution so that each applicant is
ranked relative to the academic score of the relevant institution’s applicant pool (an institution-speciﬁc percentile
distribution).
13
I divided the annual earnings by the number of months that the graduates in each ﬁeld of study were employed, in order to adjust for differences in labor
supply.
14
Graduates who pursued advanced degrees were omitted, as were those younger than 21 or older than 35 years of age.
15
Students and graduates can have a double major, and applicants can include more than one major in their major choice set.
16
The salary reﬂects not only the value of the ﬁeld of study, but also factors such as sector, industry, geographic location, preferences,
employability, unobserved characteristics and discrimination. To be sure, all these aspects are captured in the average ﬁeld of study expected salary,
but they do not interfere with the assessment of the divide in potential earnings because graduates of all origins are assigned the same
ﬁgure.
17
The NIS are in 2004 prices, and the salary of graduates from corresponding departments across all three institutions was averaged. This is only for the
purpose of Table 2. All the analyses are based on the major-institution-speciﬁc data.
18
I deleted applicants to medicine and dentistry from the sample. The wage data reported for these ﬁelds is inaccurate because, due to the length of study of
these ﬁelds, these students had not yet obtained their professional licenses by the time the data was gathered from the tax authorities.
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S. Alon / Social Science Research 52 (2015) 465–478
Table 2
The expected salary by ﬁeld of study of graduates (NIS in 2004 prices),
1999–2002, average across all three institutions.
Major
Males
Mean
Females
Mean
Electrical Engineering
Computer Sciences
Industrial & Management Engineering
Mathematics
Physics
Chemical Engineering
Mechanical Engineering
Engineering – other
Law
Physiotherapy & Occupational Therapy
General Studies [Social Sciences]
Political sciences & International relations
Nursing
Economics
Statistics
Pharmacy
Bio-Medical Engineering
Management
Communication
History & Philosophy
Architecture
Chemistry
Geography
Accounting
Nutrition sciences
Sociology
Art
Psychology
Agricultural sciences
Social work
Geology
Regional studies & foreign languages
Medical Laboratory Sciences
Education
Biology
Jewish studies
Hebrew, Linguistics & Literature
Communication Disabilities
General Studies [humanities]
Mean
19,187
16,695
14,371
12,913
12,526
12,109
11,804
11,788
10,988
10,760
10,469
10,265
10,181
10,085
10,082
10,032
9243
9237
9006
8936
7576
7565
7535
7478
7438
7416
7315
7097
6984
6973
6844
6835
6545
6326
6056
5598
5468
–
–
11,633
17,169
14,992
12,937
10,415
8094
10,689
10,734
9087
8394
6006
6277
6618
7884
8150
7693
8475
8178
7207
6348
6652
5750
6380
5620
6417
4833
5969
5326
4900
5571
5632
5478
5428
4891
5019
5407
4797
5323
5348
7825
7024
4. Results
4.1. The ethnic gap in ﬁeld of study expected salary among university graduates
By the time university graduates in Israel enter the labor market, there is already an ethnic gap in earning potential due to
variation in the type of diplomas attained. Table 3 (Panel A) presents the expected salary of university graduates based on the
value of their degrees, by ethnic origin group. The ethnic gaps, which are strikingly similar among both male and female
graduates, reveal a tripartite hierarchy. Leading the pack are graduates who immigrated from the FSU, together with veteran
Ashkenazi graduates.19 The middle tier consists of second-generation or third-generation Mizrachi graduates as well as secondgeneration Ashkenazi graduates. The expected monthly salary, by major, of male graduates in these groups is about 95–96 percent that of FSU-born immigrants (94–95 percent for females).20 At the bottom are ﬁrst-generation male and female immigrants
from other (non-FSU) countries: their expected salary, by major, is only 91 percent that of their FSU-born counterparts. These
ﬁndings demonstrate that the documented ethnic gaps in earnings among employees with a college diploma are partly shaped
by ﬁeld of study variation upon graduation. Both factors—country of origin and immigrant generation—are involved in determining economic hierarchy. Given that earning trajectories often depend on starting wages and vary by occupation, these initial
19
Males from the FSU graduated from ﬁelds in which graduates earn, on average, more than 12,000 NIS upon labor market entry (for FSU females the average
salary was 7300 NIS).
20
This may be an underestimation of the ethnic gaps in expected salary among university graduates in Israel because the third-generation group may lump
together those with the highest and lowest expected salaries.
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S. Alon / Social Science Research 52 (2015) 465–478
Table 3
The ﬁeld of study expected salary of university graduates by ethnic origin group.
Group (sorted)
Panel A: Graduates
Males
Immigrants from FSU
One native-born parent and one from Europe or America (veteran ‘‘Ashkenazi’’)
One parent from Europe or America and one from Asia or Africa (mix ethnicity)
Two native-born parents
Both parents from Asia or Africa (2nd generation ‘‘Mizrachi’’)
One native-born parent and one from Asia or Africa (veteran ‘‘Mizrachi’’)
Both parents from Europe or America (2nd generation ‘‘Ashkenazi’’)
Immigrants – others
Panel B: Applicants
Major’s expected
salary (males)
% from
FSU Imm.
Major’s expected
salary (males)
% from
FSU Imm.
12,123
11,942
11,667
11,551
11,547
11,531
11,517
10,988
100
99
96
95
95
95
95
91
13,982
13,175
12,951
12,897
12,967
13,076
12,900
12,206
100
94
93
92
93
94
92
87
% from
FSU Imm.
Major’s expected
salary (females)
% from
FSU Imm.
Females
Major’s expected
salary (females)
Immigrants from FSU
One native-born parent and one from Europe or America (veteran ‘‘Ashkenazi’’)
One native-born parent and one from Asia or Africa (veteran ‘‘Mizrachi’’)
Two native-born parents
One parent from Europe or America and one from Asia or Africa (mix ethnicity)
Both parents from Asia or Africa (2nd generation ‘‘Mizrachi’’)
Both parents from Europe or America (2nd generation ‘‘Ashkenazi’’)
Immigrants – others
7375
7095
6997
6991
6971
6912
6899
6711
100
96
95
95
95
94
94
91
8435
7907
7915
7808
7799
7795
7752
7491
100
94
94
93
92
92
92
89
Note: based on major-institution speciﬁc averages; the most lucrative major for each graduate/applicant.
gaps—when university graduates ﬁrst embark on their labor market careers—can translate into lifelong differences in economic
well-being.
To better understand how graduates from different ethnic groups ended up in advantaged or disadvantaged positions on
the economic hierarchy, the following analyses track the formation of economic stratiﬁcation throughout the college pipeline from the high school years to college graduation. To help with tracking this inequality, two types of evidence are presented simultaneously: the expected salary of each group at various stages (gross gaps as well as adjusted for academic
achievements), and the transition probabilities from high school graduation until the attainment of a bachelor’s degree.
4.2. Application stage: Field of study choice set
Applicants to Israeli universities can list several ﬁelds of study on their university applications, but must rank them
according to preference. How important are economic returns in shaping the ﬁeld of study choices of applicants? To compare
the magnitude of the effects of several characteristics of a major on inclusion in the choice set, I use the McFadden’s choice
model, which is intended speciﬁcally for individual decisions that are at least partly based on observable attributes
(McFadden, 1974).21 In this conditional ﬁxed-effects logit regression, the dependent variable is the most lucrative major listed
by each applicant, and the key independent variable is the major’s expected salary.22 The speciﬁcation compares the magnitude
of this effect to the effect of other characteristics of a major: academic rigor (average standardized composite score among
admits), percent female (percent of females among enrolled students), and graduation rate (the percent of enrolled students
that graduate). All variables are standardized to facilitate the interpretation of the results.
Table 4 presents the results for males and females. Although drawing causal inferences from this speciﬁcation is not
straightforward—either because the correlation between some of the attributes of a major (see correlation matrix at the bottom of Table 4) or because of misspeciﬁcation (omitted characteristics of a major)—it seems that economic returns are a
determinant of a major’s desirability for male applicants. An increase of one standard deviation in a major’s expected salary
(about 4000 NIS) doubles the likelihood that a major will be chosen by a male applicant (Model 1: exp (.709)); this holds
even when all of a major’s characteristics are considered (Model 5). The effect of economic returns on women’s decision
making is more subtle (exp (.125) = 1.13; one SD is about 2000 NIS), and not very different from the effects of other characteristics of a major. This is in line with prior evidence that demonstrates that while expected earnings are an essential
component in the selection of a college major, women are less inﬂuenced by this factor than men are (Alon and DiPrete,
2015; Montmarquette et al., 2002). Male and female applicants may weight factors differently when choosing what to study,
but the key question for this study is, within each sex group, are there ethnic differences in the effect of a major’s pecuniary
returns, and, if so, what are the economic implications of this disparity?
21
To ﬁt this model, the data were converted into person-major conﬁgurations, generating a row for each alternative (39 ﬁelds of study) for each applicant
(N = 90,000). The analysis is based on 3,348,108 person-major observations.
expðzij cÞ
22
ð1Þ Prðyi ¼ jjzi Þ ¼ P j
; j ¼ 0; 1; . . . ; J.
j¼1
expðzij cÞ
472
S. Alon / Social Science Research 52 (2015) 465–478
Table 4
Conditional logit model: the effect of major’s characteristics on major choice (most lucrative major), 1999–2002 applicants.
Variables (Standardized)
Males
Females
(1)
(2)
Major’s expected salary (M/F) 0.709***
(0.00350)
Major’s academic rigor
(3)
0.533***
(0.00547)
0.788***
(0.00517)
Major’s percent females
Major’s graduation rate
Observations (person-major)
1,380,825
Correlations Matrix
1
1.
2.
3.
4.
1
0.472
0.659
0.2147
Expected salary
Academic rigor
Female percent
Graduation rate
2
1
0.3325
0.4117
3
1
0.156
(4)
(5)
(6)
(7)
(8)
(9)
(10)
0.563***
0.125***
(0.00687) (0.00396)
0.00296
0.0110*
(0.00720)
(0.00444)
0.233***
0.0228***
(0.00832)
(0.00443)
0.191***
0.0251**
0.176***
(0.00567) (0.00771)
(0.00479)
1,967,283
0.187***
(0.00666)
0.178***
(0.00575)
0.0244***
(0.00709)
0.231***
(0.00640)
4
1
1
2
3
4
1
2
3
4
1
0.4614
0.693
0.1681
1
0.2694
0.4529
1
0.2027
1
Standard errors in parentheses.
*
p < 0.05.
**
p < 0.01.
***
p < 0.001.
Table 5
The transition probabilities from high school graduation until the attainment of a bachelor’s degree.
a
b
Cohorta
Universities applicant poolb
1
HS-PSE
2
Application-Admission
3
Admission-Enrollment
4
Enrollment-Graduation
5
Application-Graduation
Males
Immigrants from FSU
Veteran ‘‘Ashkenazi’’
Immigrants – others
0.42
0.64
0.53
0.57
0.74
0.68
0.77
0.71
0.72
0.65
0.71
0.64
0.29
0.38
0.32
Females
Immigrants from FSU
Veteran ‘‘Ashkenazi’’
Immigrants – others
0.47
0.72
0.58
0.58
0.73
0.65
0.77
0.67
0.70
0.76
0.76
0.69
0.34
0.37
0.32
Source: Israel Income Surveys 2005–2008; 1976–1981 birth cohorts.
Source: Universities administrative data applicant’s cohorts 1999–2002.
The results suggest that ethnic gaps in ﬁeld of study among both males and females are already present at the application
stage and, in fact, are larger among applicants than among graduates. Panel B of Table 3 provides a tabulation of ﬁeld of study
expected salary of university applicants, based on the ﬁeld that each applied to, by ethnic origin group. The results show that
the tripartite hierarchy in expected salary that exists among graduates is more pronounced among applicants. Among graduates, leading the pack at the admissions stage are FSU immigrants while immigrants from other countries chose majors that
put them at the bottom of the hierarchy.
In sum, the economic hierarchy among university graduates is already manifest in applicants’ ﬁeld of study preferences.
Even though economic returns are an important factor in choosing a university major, not all applicants translate this desire
into application choices. Understanding the ethnic variation in major choice sets, and, consequently, the position of groups
on the ﬁeld of study economic hierarchy at the application stage, can go a long way toward explaining economic inequality
among graduates. To this end, I will consider three explanations: selection into higher education, academic credentials, and
aspirations. For the sake of parsimony, I focus on the distinct pathways of three ethnic groups: FSU immigrants (top of hierarchy); other ﬁrst-generation immigrants (bottom of hierarchy); and veteran Ashkenazis (top of hierarchy).
4.2.1. Selection into the postsecondary education system
One explanation for the ethnic variation in ﬁeld of study choice sets at the application stage is variation in the likelihood
of attending college. To estimate selection into higher education, I use the Israel Income Survey and focus on cohorts similar
to those in the university applicant pool.23 Table 5 (column 1) reports the transition probability from high school to the
postsecondary system for each group. High school graduates who are veteran Ashkenazi are the most likely of all groups to
23
Israel Income Surveys from 2005 through 2008.
S. Alon / Social Science Research 52 (2015) 465–478
473
0
Adjusted composite score [within each inst]
40
60
80
100
20
Composite academic score (percentile)
Immigrants from FSU
Immigrants - others
Veteran ''Askenazi''
Fig. 1. The distribution of the composite academic score (percentile), applicants, 1999–2002.
enter the postsecondary education system, with 64 percent of males and 72 percent of females doing so. This high rate likely
captures the quality of their high school academic preparation, the economic resources at their disposal, access to information
about higher education and the labor market, and guidance by parents, teachers, counselors, and others (Alon, 2009; Schneider,
2009). Conversely, FSU immigrants have the lowest rate of continuation: only 42 percent of males and 47 percent of females
pursue an education after high school. Given this low rate, it is likely that only the best within this group—either in terms of
observed characteristics (academic achievements) or unobserved characteristics (motivation and ambition)—make it to the university applicant pool. The continuation rate of immigrants from non-FSU countries is somewhere in between, with 53 percent
of males and 58 percent of females enrolling in college. Taken together, these results suggest that the lower rate of transition
into higher education (positive selection) of FSU immigrants is likely one of the reasons behind their lucrative major choice set.
4.2.2. Academic credentials
It seems, however, that there is an even bigger culprit behind the high ﬁeld of study economic ranking of veteran
Ashkenazis than selection into higher education: namely, disparities in prior academic preparation. Fig. 1 displays the distributions of the academic composite scores (based on test scores and grades) of all applicants by ethnic group (the pattern
was similar for both sex groups). This boxplot presentation reveals that veteran Ashkenazi applicants are the group with the
highest prior achievements, on average. The group with the weakest academic scores is FSU immigrants (together with
second-generation Mizrachi applicants whose results are not shown). The gaps are substantial: about half of veteran
Ashkenazi applicants attained a score above the 60th percentile while only a quarter of FSU immigrants reached that threshold. Given the low academic standing of FSU immigrants, their lucrative major choice set is surprising—even their peers who
immigrated from other countries, and who applied to ﬁelds with the lowest economic returns, have much better academic
scores, on average. Taken together, these results reveal that the strong academic credentials of veteran Ashkenazi graduates
are, as expected, an important factor in their high ranking in the expected earnings distribution upon labor market entry.
Neither high school grades nor test scores, however, can explain the top application stage position of FSU immigrants or
the bottom position of immigrants from non-FSU countries.
4.2.3. Aspirations and orientation
Applicants’ occupational and academic aspirations may provide the missing piece of the puzzle regarding the position of
the two immigrants groups on the ﬁeld of study economic hierarchy at the application stage. FSU immigrants’ top position
may reﬂect predilection toward STEM ﬁelds, which are typically selective and lucrative. About 35 percent of male FSU immigrant applicants had some kind of engineering major, which tend to top the list of starting salaries, as the most lucrative ﬁeld
in their major choice sets. At the same time, about 30 percent of male veteran Ashkenazi applicants had an engineering ﬁeld
in their choice sets, which puts them closer to other second- and third-generation groups at the application stage, compared
to their superior position at the graduation stage. Among male immigrants from non-FSU countries—the group at the bottom
of the expected earnings distribution as early as the application stage—only 20 percent chose an engineering ﬁeld at application time. A humanities ﬁeld was the most lucrative major in the choice set of 13 percent of this group, compared to less than
4 percent among FSU males. The pattern among female applicants is similar.
To reveal the effect of economic aspirations and orientations on ﬁeld of study choice set, net of prior academic achievements, I regress ﬁeld of study salary on ethnic origin for applicants, admits and graduates, separately for men and women,
controlling for an individual’s (standardized) academic composite score (veteran Ashkenazi is the omitted category). The
results, presented in Tables 6a and 6b, demonstrate that the application stage economic disadvantage of some third- and
second-generation groups, which arises from ﬁeld of study choices, disappears, or even reverses in sign, when we take into
474
S. Alon / Social Science Research 52 (2015) 465–478
Table 6a
Ethnic gaps in ﬁeld of study average monthly salary (in NIS), 1999–2002 (reference group: veteran ‘‘Ashkenazi’’), Males.
MALES
Applicants
Variables
(1)
Gross
806.6⁄⁄⁄
(82.47)
Immigrants – others
968.6⁄⁄⁄
(99.29)
Two native-born parents
278.0***
(66.31)
One native-born parent and one from Asia or Africa (veteran ‘‘Mizrachi’’)
98.78
(89.43)
Both parents from Asia or Africa (2nd generation ‘‘Mizrachi’’)
207.7*
(86.51)
Both parents from Europe or America (2nd generation ‘‘Ashkenazi’’)
274.7**
(87.84)
One parent from Europe or America and one from Asia or Africa (mix ethnicity) 224.2
(130.9)
Academic score
Immigrants from FSU
Missing acad. score
Constant
Observations
R-squared
13,175***
37,940
0.010
Admits
Graduates
(2)
Net
(3)
Gross
(4)
Net
(5)
Gross
(6)
Net
1690⁄⁄⁄
(76.02)
276.5⁄⁄
(90.94)
75.24
(60.07)
381.7***
(81.13)
691.2***
(78.95)
67.78
(79.55)
220.3
(118.6)
67.18***
(0.762)
1049***
(55.70)
67.84
(94.05)
930.2⁄⁄⁄
(108.7)
275.8***
(69.96)
353.8***
(96.44)
378.2***
(91.74)
311.6***
(92.83)
199.1
(139.6)
746.8⁄⁄⁄
(82.18)
457.3⁄⁄⁄
(94.76)
134.7*
(60.50)
154.3
(83.55)
555.1***
(80.05)
76.86
(80.30)
285.5*
(120.8)
74.23***
(0.793)
688.2***
(65.98)
181.0
(137.3)
953.6⁄⁄⁄
(164.4)
390.7***
(101.8)
410.6**
(138.5)
394.9**
(133.3)
424.8**
(136.4)
275.1
(205.6)
864.2⁄⁄⁄
(119.8)
440.9⁄⁄
(143.4)
209.4*
(88.22)
156.7
(120.3)
588.8***
(116.6)
162.2
(118.3)
124.4
(178.3)
75.14***
(1.146)
1176***
(107.8)
9290***
11,588***
26,463
0.004
7099***
11,942***
13,456
0.004
7452***
0.188
0.255
0.252
Standard errors in parentheses.
*
p < 0.05.
**
p < 0.01.
***
p < 0.001.
Table 6b
Ethnic gaps in ﬁeld of study average monthly salary (in NIS), 1999–2002 (reference group: veteran ‘‘Ashkenazi’’), Females.
Females
Applicants
Variables
(1)
Gross
527.8⁄⁄⁄
(45.18)
Immigrants – others
416.1⁄⁄⁄
(55.65)
Two native-born parents
99.02*
(39.04)
One native-born parent and one from Asia or Africa (veteran ‘‘Mizrachi’’)
8.479
(50.55)
Both parents from Asia or Africa (2nd generation ‘‘Mizrachi’’)
111.8*
(48.94)
Both parents from Europe or America (2nd generation ‘‘Ashkenazi’’)
154.9**
(52.15)
One parent from Europe or America and one from Asia or Africa (mix ethnicity) 108.3
(74.13)
Academic score
Immigrants from FSU
Missing acad. score
Constant
Observations
R-squared
7907***
52,003
0.008
Admits
Graduates
(2)
Net
(3)
Gross
(4)
Net
(5)
Gross
(6)
Net
1126⁄⁄⁄
(43.37)
20.04
(52.83)
31.69
(36.62)
330.2***
(47.57)
430.2***
(46.36)
1.804
(48.94)
132.0
(69.59)
36.66***
(0.448)
869.0***
(32.03)
237.9⁄⁄⁄
(44.36)
384.6⁄⁄⁄
(53.78)
112.4**
(36.12)
56.95
(47.08)
211.0***
(45.26)
118.8*
(48.30)
158.6*
(68.89)
616.3⁄⁄⁄
(41.89)
177.9⁄⁄⁄
(50.45)
77.57*
(33.62)
204.7***
(43.97)
246.8***
(42.63)
9.715
(44.99)
58.42
(64.20)
31.20***
(0.424)
613.8***
(34.40)
280.4⁄⁄⁄
(61.47)
383.8⁄⁄⁄
(78.69)
103.8*
(51.91)
97.75
(66.71)
183.1**
(64.26)
195.9**
(69.29)
124.1
(98.71)
667.1⁄⁄⁄
(58.02)
210.8⁄⁄
(73.80)
54.51
(48.34)
193.0**
(62.34)
242.3***
(60.44)
73.07
(64.54)
78.79
(91.97)
31.53***
(0.592)
625.6***
(51.56)
6089***
7077***
36,070
0.005
5448***
7095***
18,762
0.006
5501***
0.128
0.138
0.138
Standard errors in parentheses.
*
p < 0.05.
**
p < 0.01.
***
p < 0.001.
account prior academic achievements (Model 2). A positive sign means that a group has economic aspirations (as observed in
the economic value of applicants’ ﬁeld of study choices) that exceed those of veteran Ashkenazi applicants with similar academic scores. Immigrants from the FSU apply to ﬁelds with the highest economic returns given their academic scores; their
expected salary at the application stage exceeds that of veteran Ashkenazi applicants by 1700 and 1100 NIS for males and
S. Alon / Social Science Research 52 (2015) 465–478
475
Males
14,500
14,000
13,500
NIS
13,000
12,500
12,000
11,500
11,000
10,500
10,000
Applicants
Admits
Immigrants from FSU
Graduates
Veteran “Ashkenazi”
Immigrants - others
Females
9,000
8,500
NIS
8,000
7,500
7,000
6,500
6,000
Applicants
Admits
Immigrants from FSU
Graduates
Veteran “Ashkenazi”
Immigrants - others
Fig. 2. The ﬁeld of study expected salary throughout the college pipeline, by ethnic origin group.
females, respectively. Interestingly, second-generation Mizrachi applicants also have high economic aspirations relative to
their academic qualiﬁcations; their edge in expected salary at the application stage is 690 and 430 NIS for males and females,
respectively.
These results demonstrate that an applicant’s test scores and grades do not constrain ﬁeld of study economic aspirations
and occupational ambitions in the same way across groups. The low academic credentials of FSU immigrants do not curb
their high ﬁeld of study economic aspirations while immigrants from other countries tend to aim lower than what their academic scores can match. Nonetheless, institutional admissions decisions can serve as a painful ‘‘reality check’’ for some
ambitious applicants, and thus can narrow or expand the ethnic variation in expected salary among applicants.
4.3. Admission decisions and matching
The admission stage is the point at which individual aspirations and academic credentials meet the academic requirements of ﬁelds of study. A group’s likelihood of admission is reported in Table 5 (column 2).24 FSU immigrants have the lowest
admission rate: only 57 percent of males and 58 percent of females were admitted to one of the majors that they listed.25
Veteran Ashkenazi applicants have the highest admission rate (74 and 73 percent for males and females, respectively), and
clearly the most realistic choice set, in that their academic achievements match the admission requirements of their choices.
The admissions decision substantially narrows the application stage economic gap between the FSU and veteran Ashkenazi
groups (among males the gap is eradicated). In fact, ethnic gaps in expected salary are smallest by the time that admissions
decisions are made. This is demonstrated in Fig. 2, which displays the ﬁeld of study expected salary for the three ethnic origin
groups selected, from the application stage through admissions and to graduation. Clearly, applicants from all groups aimed for
more lucrative majors than would admit them. Especially ambitious were male FSU immigrants: for them the plunge from
application to admission, in terms of a major’s expected salary, is steepest.
Taken together, the ethnic variation depicted in the application and admission stages provides important insights regarding the match between applicants’ academic and economic aspirations, on the one hand, and the academic requirements of
the ﬁelds of study they have listed, on the other. Clearly, the economic value of FSU immigrants’ choices generally overmatches, using Bowen et al.’s (2009) terminology, their academic and admission potential—that is, they apply to majors
24
25
I also gauged the ethnic gaps in admission likelihoods net of academic scores (results are available upon request).
They make up 12 percent of all male applicants, but only 9.5 percent of the male admit pool (the numbers for females are 15 and 12 percent, respectively).
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S. Alon / Social Science Research 52 (2015) 465–478
associated with high salaries, but these majors require scores that are generally higher than this group’s academic qualiﬁcations. This pattern of overmatching—economic aspirations that exceed qualiﬁcations—is also found in high-achieving
applicants from traditionally weak groups, namely second- and third-generation Mizrachi applicants. The contrast in the
case of non-FSU ﬁrst-generation immigrants, who tend to choose ﬁelds with low expected salaries, even net of academic
scores, is glaring: this group’s economic aspirations greatly undermatch its academic potential, especially among those with
high academic achievements.26 In sum, applicants from groups at the bottom of the socioeconomic hierarchy are the most
likely to apply to ﬁelds of study that either over- or undermatch their academic qualiﬁcations, while those from strong socioeconomic groups (i.e., veteran Ashkenazi applicants) are least likely to display either overmatching or undermatching patterns
in their ﬁeld of study choices.
4.4. Persistence until degree attainment
The graduation stage is also important for understanding the positions of groups in the ﬁeld of study economic hierarchy
because not all students who matriculate persist until degree attainment. Moreover, among those who do persist, not all
obtain a degree in the ﬁeld that they ﬁrst enrolled in. Among male students, FSU immigrants have the highest dropout rate:
only 65 percent eventually attain a degree, compared to 71 percent of veteran Ashkenazi males (see Table 5, column 4). Only
29 percent of male FSU immigrants who applied to selective universities in Israel between 1999 and 2002 graduated from
the institution that they ﬁrst enrolled in, compared to 38 percent of veteran Ashkenazi applicants (Table 5, column 5) and 32
percent of immigrants from other countries. The gaps among females are smaller, and the persistence rate of female FSU
immigrants is slightly greater than that of female immigrants from other countries. Overall, the ethnic variation in expected
salary among females holds steady from the admission to graduation stages, while the variation among males expands
slightly after enrollment due to differential attrition rates (see Fig. 2 and Table 6).27
5. Discussion
This study demonstrates that ﬁeld of study variation is a key factor in explaining economic inequality among workers
with a bachelor’s degree. This area of research has great potential to shed light on earnings differentials by gender, racial,
ethnic, and immigration status in many countries, not least because we live in an era when the stratiﬁcation of the returns
to education continues to intensify. This study shows that ﬁeld of study inequality changes throughout the college pipeline,
from an applicant’s major choice set to the type of degree attained, and quantiﬁes the consequences of this process for groupbased economic inequality.
What leverage do we gain from considering the entire college pipeline for the purpose of understanding economic
inequality between groups? In a world where all groups had a cumulative trajectory from K-12 to the labor market, measuring ethnic-based economic inequality by ﬁeld of study at one stage—for example, upon graduation—would provide an
accurate picture of the economic position of groups. Consider, for example, veteran Ashkenazis in Israel, who have the highest expected salary among second- and third-generation university graduates. They are a paradigm of the process of cumulative advantage (DiPrete and Eirich, 2006): they have the best K-12 academic preparation; the ﬁnest understanding of the
higher education system, which results in ambitious, yet realistic, aspirations for ﬁeld of study choices; the greatest ability to
deal with the academic rigor of elite universities and the selective ﬁelds within them; and, consequently, the highest chances
of persisting until degree attainment. Ultimately, as a group, they have the best economic prospects in the labor market
because a large share of them obtains lucrative degrees. A cross-sectional assessment can accurately capture their advantaged position: at any window of observation—be it the application stage, after the admission decision, or at graduation
time—they are at the top of the hierarchy.
However, not all groups follow a cumulative trajectory. Consider, for example, FSU immigrants in Israel, who, like veteran
Ashkenazis, are at the top of the expected salary list at both the application stage and after graduation. A cross-sectional
glimpse at either stage would yield a misleading picture of a group that holds a highly advantaged position. But a more comprehensive look at the college process leads to a very different conclusion: the economic prospects of FSU immigrants as a
group are less than stellar, mainly because many of them do not attend college, and among those who do, a substantial share
drop out. They have high ambitions and occupational aspirations, but these do not fully compensate for their subpar academic preparation, which results in a low continuation rate into the postsecondary education system and a high attrition
rate from college. This nuanced understanding is only possible when we take into account the greater pipeline that leads
to a bachelor’s degree.
Moreover, if the process shaping ﬁeld of study inequality was cumulative throughout the college pipeline, we would
expect ﬁeld of study stratiﬁcation to expand systematically from stage to stage. But the formation of inequality in Israel
(and likely in other countries as well) is not cumulative, in that the gaps in expected salary among applicants shrink at
the admission stage but later expand during the college years due to attrition and differential selection processes. In order
26
High-achieving applicants in all other groups applied to more lucrative ﬁelds (based on a speciﬁcation that includes interaction terms between origin and
academic scores; results are available upon request).
27
In addition, what we see for males, but not for females, is that expected salary is higher among graduates than among admits. This suggests that attrition,
among all groups, is not random: those enrolled in more lucrative ﬁelds are more likely to graduate than those in less lucrative ﬁelds.
S. Alon / Social Science Research 52 (2015) 465–478
477
to interpret the economic position of various groups upon labor market entry and to understand the mechanisms that generate inequality in such a complex world, we need to employ a longitudinal perspective that incorporates the college pipeline as an integral part of the investigation.
As the ﬁndings in this study demonstrate, we can learn a great deal about the mechanisms that generate inequality from
studying ﬁeld of study choices. To some extent, the orientation towards ﬁeld of study is constrained and stratiﬁed by academic preparation in the K-12 system. But what this study reveals is that there are other factors that play a key role in the
development of the choice set and, thus, contribute to the non-cumulative pattern of ﬁeld of study inequality, including
orientation, preferences and aspirations, as well as knowledge about the higher education system and its link to labor market
outcomes (Alon and DiPrete, 2015; Schneider and Stevenson, 1999; Schneider, 2009). While applicants from groups at the
bottom of the socioeconomic hierarchy tend to exhibit under- or overmatching patterns (applying to majors with academic
thresholds that are lower or higher, respectively, than one’s academic scores) in terms of ﬁeld of study expected salary, the
aspirations of those from strong socioeconomic groups tend to match their admission potential. Thus, ﬁeld of study matching
proved itself to be not only a marker of socioeconomic advantage, but also an indicator of success in college and labor market
prospects. From this vantage point, mapping the decision making process behind the formation of the ﬁeld of study choice
set can yield important insights about the roots of gender, racial/ethnic, and class-based inequality.
Taken together, connecting the pieces of the puzzle that shape ﬁeld of study inequality—including K-12 academic preparation, ambitions, aspirations, application patterns, and differential selection and attrition processes during high school
and college—is useful for understanding the gaps in earnings among workers with a bachelor’s degree. Analyzing the college
pipeline, a strategic point in the school-to-work transition, helps determine to what extent wage inequality among university graduates is generated before, during, and after college, and helps devise policies aimed at eradicating persistent social
and economic gaps between ethnic and gender groups.
Acknowledgments
This research was supported by Grants #200800120 and #200900169 from the Spencer Foundation. I thank Ori Katz for
research assistance.
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