Alon, Sigal. 2011. “The Diversity Dividends of a Need-blind and Color-blind Affirmative Action Policy” Social Science Research, 40(6):1494-1505.

TheDiversityDividendsofaNeed‐blindandColor‐blindAffirmativeActionPolicy

Sigal Alon

Tel-Aviv University

Abstract

In the early to mid-2000s, four flagship Israeli selective universities incorporated a need-blind and color-blind affirmative action policy into their admissions practices. The program, which gives an edge in admission to academically borderline applicants from disadvantaged backgrounds, emphasizes structural disadvantages, such as neighborhood socioeconomic status and high school rigor. The current investigation assesses whether this policy design increases geographic, economic and demographic diversity within the student population, and whether the diversification effects are uniform across departments of different levels of selectivity. The results, based on administrative data from the four universities, show that the policy infuses the entire student body, including the departments regarded as bastions of privilege, with under-represented and disadvantaged populations. The paper discusses the universal relevance of the findings to the ongoing discussion of admission regimes, diversity and equality of opportunity.

May 2010

This research was supported by grants #200800120 and #200900169 from the Spencer Foundation. I thank Erez Garnai for research assistance. Please direct all correspondence to: Sigal Alon, Department of Sociology and Anthropology, Tel Aviv University, Ramat Aviv, Tel Aviv 69978, Israel. [salon1@post.tau.ac.il]

1

The continuing public discontent with race-conscious admissions in the U.S.--echoing

similar claims in India, South Africa and other countries--and the ban on such preferences

in several states have motivated the search for race-neutral admission alternatives. The

past decade brought the implementation of percent plans in Texas, California and Florida,

alongside growing interest in propositions based on class preference. However, the

shortcomings of these propounded alternatives (Bowen et al. 2005; Espenshade and

Radford 2009; Kane 1998; Kurlaender and Grodsky 2010; Tienda 2010; The U.S.

Commission on Civil Rights 2002), together with the rising class inequality in higher

education in recent decades (Alon 2009), suggest that the quest for innovative race-

neutral ways to increase demographic, socioeconomic and geographical diversity at

selective postsecondary institutions is not over.

The current study contributes to the rich scholarship on admission regimes and

preferences by evaluating a unique policy of class-based affirmative action used at four

flagship Israeli universities since the early to mid-2000s. The program promotes

preferential treatment of academically borderline applicants from disadvantaged

backgrounds. The most theoretically attractive aspect of this policy, however, is its

distinctive design: neither the students' financial constraints nor their national or ethnic

origins are considered. The emphasis, rather, is on students‘ structural disadvantages,

such as neighborhood socioeconomic status and high school rigor.

In Israel, like in other countries, socioeconomic and national/ethnic origin

inequalities are intertwined with spatial inequality. Consequently, this program serves as

a natural experiment that tests the extent of this entanglement. Specifically, it

demonstrates whether and how much a need-blind and color-blind affirmative action

2

policy can increase geographic, economic and demographic diversity within a student

population, and whether the diversification effects are uniform across different levels of

selectivity. The conclusions reached here about the effectiveness of an existing program

of class-based affirmative action, and the scope of its dividends in terms of diversity,

have far-reaching significance for the design of preferential treatment policies, especially

in light of the controversy concerning admission practices in the U.S. The universal

relevance of these findings is considered, while taking into account the differences

between the U.S. and Israeli postsecondary systems, as well as in the judicial standing of

preferential treatment policies.

DIVERSITY AND ALTERNATIVE ADMISSION MODELS Diversification of selective colleges and universities is necessary because they are

bastions of privilege (Alon 2009; Alon and Tienda 2007; Bowen and Bok 1998; Bowen

et al. 2005; Karabel 2005; The National Association for College Admission Counseling

(NACAC) 2006). U.S. Justice Powell, the pivotal opinion in the Bakke case, outlined the

educational foundation for the "diversity rationale." He argued that a diverse student body

broadens the range of viewpoints collectively held by those students and subsequently,

allows a university to provide an atmosphere that is "conducive to speculation,

experiment and creation--so essential to the quality of higher education." Since then,

ample research has demonstrated the educational benefits of campus diversity (see

Milem, Chang, and Antonio 2005). A rise in student body diversity is associated with

better learning experiences for all students and more opportunities for interaction with

students from different backgrounds. This increases students‘ tolerance to a wide range of

3

viewpoints and enhances their cognitive and identity development, preparing them for

better participation in a democratic society.

Justice Powell concluded that race-conscious admissions practices, when

narrowly tailored, serve a compelling educational interest. The U.S. Supreme Court

ruling in 2003, in the two cases regarding the University of Michigan, enacted this

rationale but made it clear that affirmative action should be a temporary remedy. The

practice of affirmative action in higher education admissions was thus deemed

permissible because it is believed to yield educational benefits by assembling a student

body with diverse talents and perspectives (Gurin et al. 2002). Yet, despite evidence of

the demographic diversification effects of race-conscious admissions on elite campuses

(Alon and Tienda 2005; 2007; Bowen and Bok 1998), there is growing opposition to

these practices. One of the main criticisms of the preferential treatment of racial and

ethnic minorities in admissions is that they promote applicants who are ―not deserving‖

because they take into account group identity rather than individual circumstances

(Clayton and Tangri 1989). The rising discontent has led to public referenda against the

use of race-sensitive admissions, such as Proposition 209 in California, Initiative I-200 in

Washington State, and, most recently, Proposal 2 in Michigan and Arizona Proposal 107.

It has also resulted in judicial bans on racial preferences in higher education admissions

in Texas and Florida, as well as numerous lawsuits against affirmative action practices in

higher education (Gratz v. Bollinger [2003] and Grutter v. Bollinger [2003] are the most

notable).

States that have banned race-conscious admission practices have since undertaken

alternative approaches. Texas, Florida and California, for example, have adopted various

4

forms of percent plans, programs that admit a fixed percentage of each school‘s class

rank distribution. The percent plans were designed to broaden college access across

economic, demographic, geographic and social groups in the absence of race-based

preferences. The cumulative evidence from Texas, California, and Florida indicates that a

uniform admission regime cannot increase ethno-racial diversity, even in predominantly

minority and residentially segregated states, without race-sensitive outreach to students at

high schools with low college-going rates and without generous financial support

(Grodsky and Kurlaender 2010; Tienda 2010; The U.S. Commission on Civil right,

2002).

Under the Texas H.B. 588 law, for example, applicants who graduate in the top

decile of their senior class are guaranteed admission to the public postsecondary school

of their choice regardless of their standardized test scores. Although the enactment of this

Plan in Texas was followed by an increase in racial and ethnic diversity in the state‘s

flagship institutions, Harris and Tienda (2010) show that the effect was the result of

changes in the size and composition of high school graduation cohorts rather than the

policy. Moreover, while the plan was successful in broadening geographic diversity

(Long Saenz and Tienda 2010), it failed to augment socioeconomic diversity (Koffman

and Tienda 2008; Tienda, Alon, and Niu 2010).1 Another problem with the uniform

admission regime that Texas put in place is that the University of Texas at Austin (UT-

Austin) became saturated with applicants eligible for automatic admission, thus limiting

1 Likewise, the ban on the consideration of race in admissions in California resulted in a decline in minority

enrollment at the most selective University of California campuses and at some of the more selective

California State University schools (Grodsky and Kurlaender 2010). The Talented 20 plan in Florida did

not produce racial and ethnic diversity at the two most selective campuses in the state‘s university system,

the University of Florida (UF) and Florida State University (FSU), since it benefitted white and Asian

students more than blacks and Hispanics (The U.S. Commission on Civil Rights 2002).

5

the university‘s ability to craft a class and achieve its institutional goals (Tienda 2010).2

Moreover, critics of the law, mostly suburban whites, continue to argue that the plan

gives unjust advantage to ―undeserving‖ applicants, claiming that low-ranked students

from academically rigorous high schools are often more qualified than those from low

performing high schools who are guaranteed college admission. Thus, despite the initial

appeal of a uniform admission regime, the Texas Top Ten Percent policy has become just

as controversial as the race-conscious system it replaced (Tienda 2010).

As the voices objecting to racial preferences became louder in recent years,

another race-neutral alternative to diversifying the collective college student body has

been proposed: preference plans based on socioeconomic class, either in addition to or as

an alternative to race-conscious admissions (Bowen et al. 2005; Kahlenberg 1996). Since

the poor of all races are seen as more deserving than blacks (Skrentny 1996) the concept

of class-based preference is compelling and enjoy strong public support (Carnevale and

Rose 2004). Moreover, giving an edge in admission to applicants from modest

backgrounds to the most selective institutions is expected to advance socioeconomic

diversity and social mobility. Nonetheless, class-based preferential policies remain

theoretical in the U.S. Since they have never been implemented (except for sporadic

experiments), the evidence on their consequences is based on statistical simulations rather

than real initiatives (Bowen et al. 2005; Espenshade and Radford 2009; Kane 1998). The

statistical simulations suggest that class-based preferences cannot replace race-sensitive

admissions because income and parental educational attainment are not good substitutes

for race (Bowen et al. 2005; Espenshade and Radford 2009; Kane 1998). In addition,

2 After the Top Ten Percent bill was revised by the Texas legislature in 2009, UT-Austin was required to

fill only 75 percent of its freshman slots with eligible seniors.

6

whereas policies based on racial preferences include the pool of socioeconomically

strong minorities, class-based policies are limited, it is argued, by the small pool of

qualified applicants from socioeconomically disadvantaged backgrounds.

Noteworthy is the fact that these simulations use a narrow definition of ―class,‖

based solely on parental education and income. Kahlenberg (1996), however, in his

thorough conceptualization of class preferences, maintains that the fairest and most apt

method for implementing class-based affirmative action should take into account

household net worth, the quality of secondary education, neighborhood influences, and

family structure; it is also the most reliable option because the abundance of information

required lowers the chances of abusing the system.3 Yet it seems that this

multidimensionality, however justified, complicates the implementation of class

preferences tremendously. Administering such a comprehensive plan demands the taxing

tasks of determining the relevant indicators, and then collecting, verifying and weighting

a wide array of sensitive information.4 Consequently, such a design for class-based

preferential policy may not prove practical.

The Israeli affirmative action plan at elite institutions, however, takes a different

route for determining class: it targets individuals from disadvantaged neighborhoods and

high schools. The theoretical foundation of this design is rooted in the long sociological

tradition that highlights the effects of social structures, such as neighborhoods and

schools, on youth achievements and educational outcomes (Ainsworth 2002; Brooks-

3 Kahlenberg (1996) suggests three methods for implementing class-based affirmative action. The simplest

is based on family income alone, while the second includes the three factors at the basis of the common

definition of socioeconomic status: parental income, education, and occupation. The third, and most

sophisticated, alternative takes into account individual as well as structural determinants of inequality. 4 This was demonstrated by the year-long experience of UCLA‘s School of Law with class-based

admission preferences in 1996 (Sander 1997).

7

Gunn et al. 1997; Massey and Denton 1993; McLeod and Edwards 1995; Pebley and

Sastry 2004; Sampson et al. 1997; Sampson et al. 1999; South and Crowder 1999; Wilson

1987, 1996). This neighborhood/school-based need-blind and color-blind affirmative

action policy capitalizes on the overlap between spatial boundaries and categorical

inequality. That is, bad neighborhoods and failing schools are populated with

categorically disadvantaged groups, such as racial and ethnic minorities, recent

immigrants, and the poor (Blau 1977; Massey 2007). Thus, to the extent that there is

sufficient overlap between systems of inequality, this scheme has the potential to yield

wide-ranging diversity dividends. The next section provides a description of the

guidelines and operation of the plan.

A NEED- AND COLOR-BLIND AFFIRMATIVE ACTION POLICY AT

SELECTIVE ISRAELI UNIVERSITIES

The higher education system in Israel is mostly public. As of 2007/08, there were 75

institutions in two tiers. In the first tier, there are six research universities. These

universities generally rely on a formulaic selection process for bachelor‘s admissions

based entirely on an academic index, calculated by taking a weighted mean of an

individual‘s matriculation diploma grades and psychometric test score (similar to an SAT

score). 5

The admission cutoff points are not institution-wide but rather major-specific;

the annual number of applicants to each program within each university; thus, within

each institution there are departments that vary in their selectivity level.

5 The second tier consists of degree-granting non-research academic colleges, and specialized institutions or

branches of foreign universities. At the academic colleges, admission is based solely on the matriculation

diploma, and is therefore substantially less selective than admission to the universities.

8

Even after the spectacular expansion of the Israeli higher education system in 1995,6

underprivileged populations remained under-represented in most of the first-tier

universities. In order to address the issues of diversity and access, the four most selective

and internationally recognized universities--Tel-Aviv University, The Hebrew

University, Ben-Gurion University and The Technion--incorporated a comprehensive and

standardized program of class-based affirmative action into their admission practices in

the early to mid-2000s.7

The implementation of the policy involves three stages. First, socioeconomic

eligibility is established outside of the universities. Applicants to all four universities are

given the option to complete and submit a standard application for preferential treatment

(substantiated with documents). The form is examined by a centralized nonprofit

organization that weights the information and creates an index of socioeconomic

disadvantage, and in turn, reports each applicant‘s score to the universities. The score

ranges from 0 to 85 and the threshold for socioeconomic eligibility used by the

universities is 30 points. Eligibility is determined according to three parameters, all of

which are based on an applicant‘s high school years: the structure of opportunity

(neighborhood and high school attended); family socioeconomic status (parental

education and family size); and individual and/or family adverse circumstances.8 The

weighting algorithm, not known to the applicants, makes it almost impossible to pass the

30-point threshold without a structural disadvantage since the two structural factors

6 The total number of undergraduate students tripled from 58,000 in 1990/91 to 168,000 in 2007/08, mostly

due to the addition of 2nd

tier instiutions. The number of undergraduate students attending the six

universities also rose (from 49,000 to 67,000, respectively). 7 The other two institutions, Bar-Ilan University and Haifa University, did not adopt such a program. Both

schools are less selective than the other four universities. 8 ‗Adverse circumstances‘ applies if the applicant is an orphan, an immigrant, divorced, a single parent,

suffers from a health disability, experienced the death of a sibling, or has divorced parents. The definition

also includes any applicant that has a parent with a disability or chronic illness.

9

account for about 50 percent of the eligibility score. Thus, applicants from a poor

community who attended the only high school in the vicinity automatically pass the

threshold for socioeconomic eligibility. Notably, information about an applicant‘s

national or ethnic origin and financial situation is neither requested nor considered.

In the second stage, academic eligibility is determined for those who passed the

threshold for socioeconomic eligibility. The eligible pool consists of applicants with

borderline achievements, those whose academic index score is around 0.5-1.0 standard

deviations below the major-specific cutoff point of admissibility. The third, and final,

stage is the admission decision. It is important to note here that the admission process is

non-mechanistic: that is, admission is not guaranteed to applicants that pass the first two

stages of eligibility and, as such, the departments have the discretion to pick and choose

their students.9 To prevent the saturation of certain majors with AA-eligible admits, the

cap on the share of affirmative action admits in each department is 5 percent of each

department‘s entering class.

The focus on structural inequality facilitates the administration and

implementation of the program because an applicant's place of residence and high school

are easily available on public record. It also expands the pool of qualified applicants,

which is one of the key problems cited in the literature of class-based affirmative action.

Moreover, by eliminating the need to verify an applicant‘s financial standing, the

program is less invasive of privacy, more reliable, and reduces the likelihood of

manipulation. Yet, feasibility is not a goal in itself; the main issue is whether targeting

9 All the applicants are grouped into one pool; the only difference is that eligible students may have a small

edge over non-eligible applicants who are similarly academically situated. Moreover, the exact

socioeconomic eligibility score of applicants above the 30-point threshold has no bearing on the admission

decision.

10

structural determinants of disadvantage yields geographic, demographic and economic

diversity dividends. The answer to this question will depend on the level of overlap

between spatial boundaries and categorical inequality in Israel.

THE OVERLAP BETWEEN SYSTEMS OF INEQUALITY IN ISRAEL

Due to the ethnic and socioeconomic diversity of its population, Israel provides the

perfect setting for studying categorical inequality. There are two main demographic

cleavages in Israel. The first is along national lines, between Israeli Jews and Arabs.

Seventy-six percent of Israel's population is comprised of Jews whereas 20 percent

consists of Arabs, the majority of which are Muslims. The second cleavage exists along

ethnic lines within the Jewish population, between Jews of European and American

origin and Jews of Asian and African origin. Immediately after the establishment of the

State of Israel in 1948, the Jewish population of the state grew several folds from

numerous and massive waves of immigration from both America and Europe (mostly

Holocaust survivors), and Asia and Africa. In recent years, the most significant

immigration wave totaled one million Jews, most of whom arrived from the Former

Soviet Union between 1989 and 1995.10

Yet as time lapses from the major immigration

waves of the 1940s and the 1950s, the share of first- and second-generation immigrants in

the population is declining, especially among the college age population.

Differences in national and ethnic origin shape Israel‘s stratification system.

There is a clear hierarchy, which has persisted over the past fifty years, in the levels of

educational attainment, occupational status, and earnings: Jews of European and

American descent are at the top of the socioeconomic ladder followed by Jews of Asian

10

This wave represents 15 percent of the population. There were also two small waves of immigrants from

Ethiopia during this period.

11

and African origin, while the Arab citizens of Israel occupy the bottom echelons of the

socioeconomic hierarchy (Cohen and Haberfeld 1998; Haberfeld and Cohen 1998, 2007;

Lewin-Epstein and Semyonov 1986, 1992; Semyonov and Tyree 1981; Yaish 2001). In

Israel, like in other countries, this categorical inequality is coupled with spatial

inequality, creating an important contour of stratification.

Israel‘s development towns are the paragon of the overlap of systems of

segregation (Alon 2004a; Spilerman and Habib 1976; Yiftachel 2000). The Israeli

government established these geographically peripheral towns in order to absorb, in

particular, the massive influx of Jews from Asia and Africa during the 1950s (Khazzoom

2005). In 2006, more than 90 percent of these localities ranked in the bottom half of the

localities' socioeconomic index (ICBS 2006), a clear indication that the disadvantaged

status of these towns has not changed much over the decades.11

Their unbalanced

demography has also persisted: more than half of the residents in these towns in 1995

were of Asian/African origin (first and second generation) as opposed to about a third in

the general population (ICBS 1995). Such coupling also occurs in the Arab localities,

deprived areas with high shares of poor, unskilled residents. In 2006, eighty percent of

the independent Arab villages and towns ranked in the bottom third of the socioeconomic

index (ICBS 2006). Moreover, most of the Arab population lives in the geographical

periphery (about half resides in the northern part of Israel), far from the main

metropolitan areas of core economic activity. The spatial segregation of recent

immigrants, who are mostly from the Former Soviet Union, is less pronounced because

11

The index ranges from 1 to 10. Localities are classified according to the socioeconomic level of the

population, which is determined by the financial resources of the residents, housing, standard of living,

schooling and education, employment profile, various types of socio-economic distress, and demographic

characteristics.

12

they, unlike the immigrants before them, were free to choose where to live. In fact, as of

1995, there is an overrepresentation of recent immigrants in strong localities (ICBS

1995). Nevertheless, these new immigrants may still benefit from the affirmative action

plan since it is likely that most do not reside in the affluent parts of these localities, and

because recent immigration status is also considered an indicator of socioeconomic

eligibility.

The implications of the spatial stratification of Israel for its postsecondary

education system are blatant: in 2006, the share of university students from affluent

localities (ranked in the top third of the socioeconomic index) was about 28 percent, but

only 6 percent of students were from localities in the bottom third (ICBS 2006).

Consequently, a policy that spotlights spatial and school inequality should, theoretically,

stretch the diversity dividends to economic status and national and ethnic origin. Thus,

Arabs, Jews of Asian or African origin, and the poor are likely overrepresented in the

affirmative action-eligible pool of students compared to the regular pool. Recent

immigrants may also be overrepresented due to their incomplete economic and social

assimilation in Israel. This hypothesis, however, demands empirical testing because even

in the development towns and Arab localities where segregation is uttermost, not all

residents are underprivileged or belong to an ethnic or national minority.

This investigation sheds light on the effect of the Israeli affirmative action

initiative on the contours of diversification in selective university campuses. Given that

applications and admissions to the universities are major-specific, it is critical to not only

examine the magnitude of the plan‘s diversity dividends, but also their distribution across

selectivity levels. Hence, the study asks: Where does the plan have its greatest

13

diversifying influence? Is it on the more selective programs, where demand and academic

requirements are high and, consequently, where disadvantaged groups are most under-

represented? Or, alternatively, is the plan most effective among the least selective majors,

where the under-representation of groups is smallest?

DATA AND METHODS

Database and sample

I obtained institutional administrative data from four chief Israeli universities—Tel-Aviv

University (TAU), The Hebrew University (HUJI), The Technion (TEC), and Ben-

Gurion University (BGU)—for periods ranging from ten to twelve consecutive years

(circa 1997 to 2008).12

During this period, the fields of study at the four universities

received 1,135,286 applications from 348,869 applicants (each applicant can apply to

multiple majors or apply in different years). Of the applicants, 60 percent were admitted

to at least one of the fields they applied to. Ultimately, 163,610 students enrolled at the

four institutions during this period.

The preferential policy began in 2001 at HUJI; 2003 at TAU;13

2004 at TEC; and

2006 at BGU. Consequently, the data yields eight years of admission observations under

the AA regime at HUJI, six at TAU, five at TEC, and three at BGU.14

The analytical

sample contains around 180,000 observations of applicants and 72,000 of students who

12

The total tally is: 12 years for Tel Aviv University (1997-2008), 11 years for the Technion and Hebrew

University (1998-2008) each, and 10 years for Ben-Gurion University (1999-2008). 13

TAU‘s faculties of law and social sciences have experimented with the affirmative action program since

1997. However, due to the small scale of this local initiative (168 students), I deleted these observations

and do not consider these years under the AA regime. 14

The analyses are limited to students under age 28 due to the different admissions criteria for older

students and the fact that the affirmative action plan has the same age cap. Also excluded are 230 students

who benefited from a relatively new program at TAU, which does not require the submission of test scores

for applicants from disadvantaged schools who ranked at the top of their class. In addition, 290 students of

Ethiopian origin who benefit from other types of preferential treatment are omitted from the analyses.

International students and students enrolled in various programs that combine military service and

academic studies are also not included in the sample.

14

enrolled in the above four universities after the affirmative action plan (AA regime) went

into effect. For the sake of parsimony, I report aggregated results and do not dwell on

between-institution variations.

Selectivity

To understand the choices and destinations of the AA applicants and admits, it is critical

to consider between-major and between-institution differences in the selectivity of

admissions. Since Israeli applicants apply to specific majors within each institution and,

thus, the admission decisions are major-institution-specific, the basic unit for the measure

of selectivity is the department. Altogether, the four institutions contained about 170

departments, representing, approximately, 50 different general fields of study. For each

department I calculated two selectivity indicators: first, competitiveness, defined as the

department admission rate (44 percent on average for the period between 1997 and

2008); and, second, academic rigor, determined by the mean test score of admits (the

overall 1997-2008 average is 636; to put this in perspective, the national average of all

test-takers in 2009 was 564).15

Next, I created an index of selectivity by calculating the

sum of the standardized scores of the admission rate and mean test score of each

department (ranges from -4.26 to 3. 44). The departments were then classified into

selectivity quintiles. Table 1 presents general statistics regarding this classification.

The results underscore the selective and competitive nature of most departments

at the four flagship Israeli universities. Classified as most selective are departments that,

on average, admitted 23 percent of applicants and whose mean test score was 698. The

15

These two measures do not completely overlap (correlation of -0.56). For instance, there are departments

with very low admission rates but that draw from an applicant pool with relatively low academic

credentials (e.g., nursing) and vice versa (e.g., physics).

15

results in the bottom panel of Table 1 indicate that 43 percent of first-time applicants in

1997-2008 sent an application to these majors.16

Among the most selective departments

are the vocational/professional fields, many of which are in the medical sciences17

or in

the engineering/computer sciences (but also architecture and psychology). It is important

to note that these most selective professional programs are longer (between 4-6 years)

than the typical three-year bachelor‘s programs. The bottom quintile has an admission

rate of 72 percent, on average, and mean test score of 573 (similar to the national average

test score); only four percent of applicants in 1997-2008 sought admission to these

departments. All departments classified as least selective are the humanities.

Variables

Appendix A provides definitions for all variables.

AA eligibility. This is a dichotomous outcome that indicates whether a student

applied for preferential treatment and passed the 30-point threshold.

Locality. I identified several theoretically relevant indicators based on student

locality of residence. They are: whether the locality is classified as a development town;

its ranking on the localities socioeconomic index; and its geographic region.18

Origin. I created five mutually exclusive indicators for student origin: (a) second

generation Asia-Africa: native-born Israeli Jews with at least one parent from Asia or

Africa, making up 12 percent of the students in the AA regime; (b) second generation

Europe-America: native-born Israeli Jews with both parents from Europe or America, or

only one parent from Europe or America if the other parent is a native-born Israeli (15

16

Since applicants can apply to multiple majors and students can have dual majors, each individual is

classified according to the most selective major she applied to, was admitted to, and enrolled in. 17

Included are medicine, dentistry, pharmacology, occupational therapy, communication disabilities, and

physiotherapy. 18

The database does not contain neighborhood- and school-level information.

16

percent); (c) Israel: third-generation immigrants, that is, native-born Israeli Jews with

two native-born parents (36 percent); (d) first-generation Jewish immigrants from Asia-

Africa or Europe-America (17 percent); and (e) Arab (8 percent). Twelve percent of the

sample lacked information about their origin, mainly because applicants in one of the

institutions were not asked about their parents‘ place of birth. Yet, since I was able to

identify Arabs and the origin of first-generation immigrants in this institution, I decided

to include its observations in the analytical sample, but report the share of missing data in

the origin variable. Consequently, the results may underestimate the share of second-

generation students.

Economic need. This indicator is based on students‘ receipt of institutional need-

based grants during their first year of university studies (there are no direct aid provisions

by the state). This is a relative measure of pecuniary constraints: within each institution in

a given year, the allocation of grants is solely need-based, and, as a result, distinguishes

between the more and less needy students. About 18 percent of the students in the AA

regime received need-based financial aid. Because this information comes from

administrative records and is not self-reported, it is an attractive measure of student‘s

financial constraints. It is worth emphasizing that in Israel, unlike in the U.S., decisions

regarding eligibility for affirmative action and financial aid are orthogonal: the former is

strictly need-blind and made by an external non-profit organization, while the latter is

made by universities only after a student has been admitted.19

Also noteworthy is the low

and uniform tuition at all Israeli public institutions,20

which renders a unique opportunity

19

Racial preference policies in the U.S. expand educational opportunities for under-represented minorities

not only because of the edge that they provide in admissions, but also because they imply better financial

aid packages for minorities (Alon 2001; Alon 2004b). 20

The tuition in 2009, for example, was 9,351 NIS, which is approximately $2,500.

17

to measure the non-pecuniary effect of affirmative action initiatives on economic

diversity.

Analytical Strategy

In order to estimate the program‘s diversification effect on the overall composition of the

student body, and the distribution of this effect across levels of selectivity, I compare the

diversity of the AA-eligible and the regular student pools during the AA regime. 21

The

diversity in the regular pool captures the counterfactual diversity level, that is, the level of

diversity that would exist if the affirmative action plan were not in place. The analyses

take into account the two factors that determine the magnitude of the overall

diversification effect on student body composition: first, the prevalence of AA-eligible

students in the relevant student body; and second, the dissimilarity between the AA-

eligible and regular pools in terms of key characteristics.

RESULTS

The Prevalence of AA-eligible Students in the Student Body

The results in Table 2 show that since the affirmative action plan began, there were

179,000 applicants to the four universities. Six percent of applicants sought preferential

treatment (10,928 applicants had a score on the socioeconomic index), but only half of

them met the 30-point threshold for eligibility (three percent of applicants). About 55

percent of AA-eligible applicants were admitted compared to 57 percent of the regular

21

Since longitudinal data is available, a before and after comparison seems theoretically attractive for

assessing the plan‘s diversity dividends. However, the temporal trends in group representation during the

period of investigation undermine the feasibility of such a strategy. Most notable is the sharp decline in the

share of first- and second-generation immigrants within the student body (either from Asia-Africa or

Europe-America) and the increase in the share of students with native-born parents. This reflects a natural

demographic trend, resulting from the lapsed time from the major immigration waves to Israel.

18

applicants. Eventually, in the relevant years, 3.7 percent of the student body was AA-

eligible (N=2,651).

[Table 2 about here]

Yet the interesting question is: how do the two groups compare in their field of

study preferences as applicants (in terms of department selectivity), and their ultimate

destinations as students? An examination of the distribution of the two pools (AA-eligible

and regular) across quintiles of departmental selectivity (Table 3) shows that the major

choice sets of AA-eligible and regular applicants are quite similar. One in two of the AA-

eligible applicants included a most selective department in their application (comprising

3.4 percent of the applicant pool in this tier), while less than 2 percent chose a least

selective major (comprising 1.6 percent of the applicant pool in this tier). The widespread

attempts of this group to access the more selective fields of study indicate its high

ambition. However, more AA-eligible applicants are turned down from these departments

than are regular ones. Eventually, the share of AA-eligible admits at the most (and least)

selective departments is the smallest (only 2.4 percent) while their share is largest among

admits to the second, third and fourth quintiles of selectivity (3.3-3.5 percent). Given a

higher yield rate among the AA admits at the most selective departments compared to

regular admits (plausibly another sign of their ambition), their final share at this tier rises

to 3.2 percent. There share is the highest at the forth quintile (4.4 percent). In sum, the

preferential policy has the potential to enhance diversity in all selectivity echelons,

depending, of course, on the dissimilarities in key attributes between students admitted

without preferential treatment and those who received an edge. The following analysis

assesses these dissimilarities.

19

[Table 3 about here]

Dissimilarity Between Pools in Key Characteristics and the Plan’s Diversity Dividends

Table 4 provides evidence regarding differences in spatial location, ethnic origin and

economic status between the groups. The results capture a remarkable effect of spatial

diversity, yet it should be noted that the real spatial diversity is probably larger since the

data do not have neighborhood-level information, which is the level targeted by the plan.

Nine percent of the AA-eligible students came from development towns, compared to

only 2 percent in the regular pool. Overall, this represents an increase of 9 percentage

points in the share of students from development towns in the student body.22

Similarly,

the share of students from localities ranked in the bottom third of the localities SES index

among the AA-eligible pool is more than double that in the regular pool (12 vs. 5 percent,

respectively), indicating an increase of 4 percentage points. The comparisons of the other

localities are less straightforward because not all the neighborhoods and schools in those

localities are classified as disadvantaged. Even so, AA-eligible students were more likely

to hail from localities in the middle third of the localities SES index, and less likely to be

residents of strong localities (in the top third), than students admitted without preferences.

I also calculated the index of dissimilarity, which measures the evenness of the

distributions of the AA and regular students. The index of dissimilarity for the locality‘s

SES cluster is 0.28, implying that about a third of one of the two groups would have to

move to different localities in order to produce a distribution that matches that of the

other group. The index of dissimilarity for geographic areas is 0.23; most of the

difference in geographic diversity stems from the high share of AA students from remote

22

The growth rate of under-represented groups is calculated as follows: ((group share in the student body –

group share without AA students) / group share without AA students) * 100.

20

areas (most gains due to students from the northern part of Israel with some gains from

the south).

[Table 4 about here]

Table 4 also juxtaposes the ethnic composition of the two groups. The index of

dissimilarity in origin is 0.27, most of which stems from the higher share of students

classified as first- or second-generation Asia-Africa or Arabs in the AA-eligible pool.

About 21 percent of the AA-eligible students are Israeli-born of Asian or African origin,

while only 11 percent of the regular students are (growth rate of 4 percentage points).

Conversely, the share of regular admission students of European or American descent is

more than double the share among the AA-eligible students (15 vs. 7 percent). The

program also infuses the student body with Arab students: 25 percent of the students who

receive preferential treatment are Arabs, despite comprising only 8 percent of the general

student body. Furthermore, the AA-eligible pool contains a higher share of new

immigrants than the regular pool; most of this gain is due to immigrants of Asian or

African origin. Given the color-blind nature of the preferential policy this diversification

effect is quite remarkable. Finally, the strictly need-blind plan also taps into students with

economic constraints: fifty percent of the AA-eligible students received need-based

grants in their freshman year compared to just 18 percent of the regular students (growth

rate of 7 percentage points).

One of the most challenging aspects of any preferential policy is its ability to

diversify bastions of privilege. To examine whether the diversification effect reaches the

most selective departments, I replicated the analyses presented in Table 4 for different

selectivity tiers. For the sake of parsimony, Table 5 only reports the two indices of

21

diversification (the growth rate and the index of dissimilarity) for three tiers of

selectivity: the top tier (top quintile), the second tier (second and third quintiles), and the

bottom tier (fourth and bottom quintiles).23

The results demonstrate that the plan‘s

diversity dividends are the largest in the most selective departments because the baseline

share of disadvantaged groups in the top tier is very low. For example, in the top tier, the

share of students from development towns in the regular pool is lowest (1.3 percent,

result not shown), as expected, but the growth rate of such students is the largest (15

percentage points compared to 11 and 9 in the middle and bottom tiers). Likewise, the

indices of dissimilarity for the distributions of localities and origin are the largest in the

top tier. The growth rate of students receiving financial aid is also higher in the top tier (9

percentage points compare to 6 points in the other selectivity tier). In sum, the Israeli

color- and need-blind preferential policy infuses the student body of the four flagship

universities, including the bastions of privilege within them, with under-represented and

disadvantaged populations.

[Table 5 about here]

CONCLUSIONS

The Israeli policy for preferential treatment is a fascinating hybrid of race-conscious

admissions, class-based preferences, and percent plans. It is class-based, but, like race-

conscious admissions and percent plans, stresses group affiliation rather than individual

traits. The Israeli design shares its focus on spatial segregation and school inequality with

percent plans but targets these aspects more directly. Moreover, as in other forms of

affirmative action, admission is not guaranteed; thus, institutions enjoy discretion in

23

The detailed results are available from the author upon request.

22

crafting a class and the issue of institutional saturation, a major weakness of some forms

of percent plans, is circumvented.

The results of this study demonstrate that a need- and color-blind affirmative

action policy can increase the geographic, economic and demographic diversity of a

student population. Having such a policy in place at four selective Israeli universities

made the entire student body, even the echelons at the most selective departments, more

diverse than they otherwise would have been. All in all, the plan‘s focus on structural

determinants of disadvantage, such as geographic and school segregation, yields broad

diversity dividends.

These insights can enrich the ongoing discussion of admission regimes and

equality of opportunity in other countries. Obviously, between-country differences in

postsecondary systems preclude a straightforward duplication of this plan to other

nations. Nonetheless, the concept of basing preferences on school/neighborhood

affiliation is worth serious consideration, especially by elite postsecondary institutions in

the U.S., which are struggling to find race-neutral ways to increase diversity. Class-based

preferential treatment at these institutions is imperative in order to offset the widening

class gaps (Alon 2009; see also Bowen et al. 2005; Carnevale and Rose 2004;

Espenshade and Radford 2009; Kahlenberg 1996), and neighborhood/school-based

preference plans seem the most practical and feasible alternative. Wide-ranging diversity

dividends from such policies are likely not only because spatial segregation has a central

role in the formation of life chances in the U.S., but also because it overlaps with other

systems of inequality (Massey 2007). Such a policy can replace the banned racial

preference programs or supplement them in states where they are still permissible. (In the

23

former case it should be noted that, by default, any race-neutral policy cannot produce the

same level of racial diversity as racial preferences, as the data from simulations and the

implementation of percent plans can attest.24

)

Another issue to consider is the likely public reaction to such a policy in the U.S.

Despite greatly facilitating the administration of such a plan, the focus on group

indicators subjects it to the same criticisms often directed at race-conscious policies:

reverse discrimination and creaming. Nonetheless, class-based preferences may stir less

antagonism than plans that emphasize race because, similar to percent plans, they do not

rely on ascribed traits. To illustrate, Skrentny (1996) contends that there is no opposition

to group preferences per se, but rather to race-conscious admissions in particular because

blacks, unlike, say, veterans, are perceived as undeserving of the preferences.

Furthermore, since they apply to people of all races, such programs are probably

in legally safe waters in states that have banned racial preferences. Bleich and Conrad

(2010) reviewed several legal cases in these states and suggest that while quotas and

mechanistic racial affirmative action methods are prohibited, more limited consideration

of race that does not focus on individual characteristics is usually allowed. They argue (p.

32) that ―Potentially permissible programs in these untested waters would

include…admissions programs that promote diversity using other criteria, such as native

language or neighborhood demographics, that ultimately will have an effect on the racial

composition of the public work force or student body.‖ They conclude that significant

24

The U.S. Court of Appeals for the Fifth Circuit recently reinforced this point when they ruled to allow

the University of Texas (UT) to consider race in admissions in addition to the Top Ten Percent plan in

order to boost the level of diversity in its student body. The majority opinion supported UT‘s contention

that the percent plan is not a substitute for race-conscious university admission policies.

24

opportunities exist for creative minds to promote under-represented groups while still

respecting constitutional regimes that ban racial preferences.

Implementing such plans in elite institutions in the U.S. will require additional

tailoring to ensure their feasibility and success. For example, the non-mechanistic nature

of the admission process and the legality of such programs will be jeopardized if schools

do not erect a wall between the stages of determining SES eligibility and the admission

decisions, as is the case in the Israeli design. A possible solution would be to incorporate

structural preferences into a comprehensive full-file review of applicants; that is,

applicants‘ academic achievements should be interpreted together with their background

information, particularly place of residence and school quality. Furthermore, since class-

based preferences will expand the share of low-income admits, financial aid budgets will

have to be augmented in order to facilitate enrollment at elite and expensive institutions,

and ensure persistence and attainment of a bachelor‘s degree. Consequently, the actual

diversity dividends of such programs will also depend on the magnitude of the financial

resources dedicated to them. Nonetheless, given the appeal of a color-blind and need-

blind affirmative action program, and its potential to diversify bastions of privilege, this

is likely a worthwhile investment.

25

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Table 1: The selectivity index for academic departments (1997-2008)

Panel A: Admission rate and mean test scores

Average Mean

admission test

Selectivity level rate scores

Top quintile [33 departments] 23 698

2 quintile [33] 35 673

3 quintile [32] 38 636

4 quintile [33] 54 602

Bottom quintile [33] 72 573

Total 44 636

Panel B: The distribution of first-time applicants, admits, and students across selectivity

level

Share of

Selectivity level applicants admits students

Top quintile 43 19 20

2 quintile 25 24 24

3 quintile 16 23 23

4 quintile 12 23 23

Bottom quintile 4 11 10

N 370430 216632 158155

Note: selectivity level is missing for about one percent of the applicants.

Table(s)

Table 2: First-time applicants, admits and students at four Israeli universities

by AA status, AA regime

AA Status Share of Share of

Total Regular AA

AA

request

AA-

eligible

N Applicants 178713 167785 10928 6.1

Meet SES eligibility (30+ points

threshold) 178713 173105 5608

3.1

N admits 101703 98630 3073

3.0

N Students 72191 69540 2651 3.7

Note: the AA plan started in 2001 in HUJI; 2003 in TAU; 2004 in TEC; and 2006 in BGU.

Table 3: The distribution of first-time applicants, admits, and students across selectivity level,

by AA status, AA regime

AA Regular Share

Selectivity level % % of AA-eligible

Applicants

Top quintile [33 majors] 49.3 45.3 3.4

2 quintile [33] 26.4 26.1 3.2

3 quintile [32] 13.5 14 3.1

4 quintile [33] 9.2 11.4 2.6

Bottom quintile [33] 1.6 3.2 1.6

Admits

Top quintile 15.6 20.4 2.4

2 quintile 26.5 24.7 3.3

3 quintile 23.7 21.9 3.3

4 quintile 27.1 23.8 3.5

Bottom quintile 7.2 9.2 2.4

Students

Top quintile 19.1 21.9 3.2

2 quintile 26.2 25.1 3.8

3 quintile 21.1 21.4 3.6

4 quintile 27.4 22.9 4.4

Bottom quintile 6.2 8.8 2.6

Note: selectivity level is missing for about one percent of the applicants.

Table 4: Characteristics of students at the four institutions by status of AA-eligibility, AA

regime

Entire student body

Growth rate

3 Index of

AA REG Total Dissimilarity

% %

Locality characteristics1

Development town 9.1 2.3 2.5 9%

Locality SES cluster

0.28

Bottom 12.3 5.4 5.6 4%

Middle 73.8 64.6 64.9 0%

Top 13.9 30 29.5 -2%

100% 100% 100%

Geographic region

0.23

Jerusalem 19 14 14.2 1%

North 22.3 9.5 9.9 4%

Haifa 8.1 12.2 12.1 -1%

Center 17.7 29 28.6 -1%

Tel-Aviv 15 22.8 22.5 -1%

South 18.1 12.5 12.7 2%

100% 100% 100%

Origin

0.27

Asia-Africa 2nd gen2 21.3 11.4 11.8 4%

Europe-America 2nd gen2 6.8 15.1 14.8 -2%

Israel2 23.7 36.2 35.7 -1%

New immigrants - Total 19.7 17 17.1 1%

Asia-Africa 1st gen 4.6 1.4 1.5 7%

Europe-America 1st gen 13.7 12.6 12.6 0%

Missing 1.4 3.1 3

Arab 25.2 7.8 8.4 8%

Missing 3.4 12.5 12.2

100% 100% 100%

Economic need

Need-based financial aid recipient 50.3 18.2 19.4 7%

N 2,651 69,540 72,191

1) Information on locality SES cluster and geographic region is missing for 25 and 17 percent of the students,

respectively, but there are no differences between AA-eligible and regular students.

2) Based on data from three institutions.

3) The growth rate is calculated as [(%group_x TOT - %group_x REG)/%group_x REG]*100

Table 5: The diversity dividends of the preferential policy by selectivity quintiles, AA regime

Departmental selectivity tiers

Top Tier

Second Tier

Bottom Tier

(Top quintile )

(2+3 quintiles)

(4+5 quintiles)

Growth

rate Index of

Growth

rate Index of

Growth

rate Index of

Dissimilarity Dissimilarity Dissimilarity

Locality characteristics

Development town 15%

11%

9%

Locality SES cluster

0.31

0.27

0.25

Bottom 6%

6%

2%

Geographic region

0.27

0.25

0.17

North 5%

5%

4%

South 4%

1%

1%

Origin

0.34

0.25

0.29

Asia-Africa 2nd gen 3%

4%

2%

Asia-Africa 1st gen 11%

0%

10%

Arab 8%

9%

9%

Economic need

Need-based financial aid recipient 9% 6% 6%

Appendix A: Variables'

definitions

Variable Definition

AA-eligible Whether a student applied for preferential treatment and passed the 30-point threshold

Locality: Development town The student’s locality is classified as a Development town. These towns were established

in the 1950s to absorb the massive immigration waves of Jews from Asia and Africa

Locality: SES cluster The classification of local authorities by the socioeconomic level of the population is based on:

financial resources of the residents; housing; standard of living; schooling and education;

employment profile; various types of socio-economic distress; and demographic characteristics.

The index ranges from 1-10.

Low-cluster locality ranked at the bottom third of socioeconomic index (ranks 1-3)

Mid-cluster locality ranked at the middle third of socioeconomic index (ranks 4-7)

Top-cluster locality ranked at the top third of socioeconomic index (ranks 8-10)

Locality: Geographic region The classification of local authorities by their geographic location

Jerusalem

North

Haifa

Center

Tel-Aviv

South

Origin

Asia-Africa 2nd gen Native-born Israeli Jews to at least one parent from Asia-Africa

Europe-America 2nd gen Native-born Israeli Jews to two parents from Europe-America or one parent of

European-American origin the second native-born Israeli

Israel Native-born Jews to both native-born parents

New immigrants - Total First generation immigrants

1st gen - Asia-Africa First generation immigrants from Asia-Africa

1st gen - Europe-

America First generation immigrants from Europe-America

Arab Arab

Need-based financial aid

recipient Student received need-based financial-aid in the first year.

The student's most selective

department The department's index of selectivity is the sum of the standardized scores of admission rate

and admits’ mean test scores. Each applicant/admit/student is classified to the most selective

major she applied to; admitted to; and enrolled in.