EFFECTS OF PEERS AND RANK ON COGNITION, - Specialized Research AI at MIT

EFFECTS OF PEERS AND RANK ON COGNITION,
PREFERENCES, AND PERSONALITY

Utteeyo Dasgupta, Subha Mani, Smriti Sharma, and Saurabh Singhal*

Abstract—We exploit the variation in admission cutoffs across colleges at
a leading Indian university to estimate the causal effects of enrolling in a
selective college on cognitive attainment, economic preferences, and Big
Five personality traits. Using a regression discontinuity design, we ﬁnd
that enrolling in a selective college improves university exam scores of
the marginally admitted women and makes them less overconﬁdent and
less risk averse, while men in selective colleges experience a decline in
extraversion and conscientiousness. We ﬁnd differences in peer quality and
rank concerns to be driving our ﬁndings.

Introduction

COGNITIVE ability, completed years of schooling, and

test scores have long been considered important deter-
minants of success in life (Hanushek & Woessmann, 2008;
Oreopoulos & Salvanes, 2011). However, there is now in-
creasing evidence that suggests economic preferences and
socioemotional traits like self-control, risk appetite, and com-
petitiveness to be as important in determining educational
attainment, labor market outcomes, and overall well-being
(Almlund et al., 2011; Buser, Niederle, & Oosterbeek, 2014;
Jaeger et al., 2010).

College is an important milestone that is believed to de-
velop both cognitive and socioemotional aspects of an indi-
vidual’s human capital. Consequently, there is great emphasis
on enrolling in selective colleges that are expected to provide
high-achieving peers, better teachers, and stronger alumni
networks and serve as a signal for higher ability. Experiencing
such an environment for three or four years is likely to shape
one’s broader skill set. The literature on school and college
quality reports both positive and nonsigniﬁcant effects of ex-
posure to more selective educational institutions on academic
outcomes (Abdulkadiro˘glu, Angrist, & Pathak, 2014; Ajayi,

Received for publication April 11, 2017. Revision accepted for publication

July 20, 2020. Editor: Asim I. Khwaja.

∗Dasgupta: Fordham University, IZA, and GLO; Mani: Fordham Univer-
sity, Population Studies Center at the University of Pennsylvania, IZA, and
GLO; Sharma: Newcastle University, IZA, and GLO; Singhal: Lancaster
University and IZA.

We thank Kehinde Ajayi, Felipe Barrera-Osorio, Leah Boustan, Ma-
tias Busso, Raissa Fabregas, Arya Gaduh, Maia Guell, Ingo Isphording,
Asim Khwaja, David McKenzie, Abhijeet Singh, Bertil Tungodden, Lore
Vandewalle, and three anonymous reviewers for useful feedback. We also
thank seminar participants at Copenhagen, Choice Lab, UPenn, Columbia,
Fordham, GeorgiaTech, Hunter, UConn, 3ie, Indian School of Business,
Monash, Rutgers, Shiv Nadar, and conference participants at NEUDC,
IGC-ISI India Conference, ASSA, Leuven, York, and Nordic Conference
in Development Economics for comments. We are grateful to the staff at
colleges of University of Delhi for lending their support. Neha Agarwal,
Riju Bafna, Piyush Bhadani, Tanya Gupta, Aishwarya Joshi, Japneet Kaur,
and Anshul Yadav provided excellent research assistance. We acknowl-
edge support from Fordham University, International Growth Center–India
Central, UNU-WIDER, and Newcastle University. These institutions had
no involvement in study design, data collection, analysis, or interpretation.
This paper was previously circulated as “Cognitive, Socioemotional and
Behavioral Returns to College Quality.”

A supplemental appendix is available online at https://doi.org/10.1162/

rest_a_00966.

2014; Jackson, 2010; Lucas & Mbiti, 2014; Pop-Eleches &
Urquiola, 2013; Saavedra 2009; Sekhri, 2020). Interestingly,
it remains mostly silent on the accompanying behavioral re-
sponses and underlying mechanisms that may explain these
mixed results. For instance, being in a more selective edu-
cational institution can also present a challenge for students
who have a low ordinal rank relative to their peers. Students’
perceptions of self-abilities based on relative rank could lead
to behavioral responses that may dilute or negate the overall
gains from attending a more selective educational institution
(Elsner & Isphording, 2017; Murphy & Weinhardt, 2020;
Pop-Eleches & Urquiola, 2013).

The objective of this paper is to examine the returns from
exposure to a selective college on academic outcomes, as
well as on measures of risk taking, competitiveness, over-
conﬁdence, and Big Five personality traits.1 To the best of
our knowledge, this is the ﬁrst paper to causally identify the
effects of enrolling in a more selective college on socioemo-
tional and behavioral aspects of human capital accumulation.
In doing so, we use rich student-level data in a regression
discontinuity design to address the selection problem arising
from sorting, that is, high-achieving students self-select into
more selective colleges while low-achieving students sort into
less selective colleges.

We analyze data from the University of Delhi (DU), one of
the top public universities in India, to estimate the returns to
college quality across a range of colleges with varying levels
of selectivity that are all within the same educational context.
Admission into colleges within the DU system is based on
the incoming cohorts’ average scores on the high school exit
exam. This gives rise to college-discipline-speciﬁc admission
cutoffs that determine an individual’s eligibility to enroll in a
speciﬁc discipline in a college. We exploit students’ inability
to manipulate this admission cutoff and compare outcomes
of students just above the cutoff to those just below the cutoff
to estimate the causal impact of enrolling in a more selective
college.

Value-added models of learning will predict better aca-
demic and nonacademic outcomes for students just above
the cutoff enrolled in more selective colleges. The company
of more able peers can allow richer learning opportunities,
provide a more dynamic environment for group interactions,

1That personality is malleable in adolescence and young adulthood is
now well accepted (Borghans et al., 2008; Specht, Egloff, & Schmukle,
2011). While cognitive ability, typically measured by IQ, is relatively stable
after age 10, there is evidence that negative and positive experiences can
have an impact on behavior and personality (Chuang & Schechter, 2015;
Schurer, Kassenboehmer, & Leung, 2018). A recent literature ﬁnds that
socioemotional skills measured after varying lengths of program exposure
(8–36 months), can in fact be shaped by soft skills interventions (Acevedo
et al., 2017; Adhvaryu, Kala, & Nyshadham, 2018; Campos et al., 2017).

The Review of Economics and Statistics, May 2022, 104(3): 587–601
© 2020 UNU-WIDER. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
https://doi.org/10.1162/rest_a_00966

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THE REVIEW OF ECONOMICS AND STATISTICS

and serve as a motivation to work harder to keep up with
the competition (Jain & Kapoor, 2015; Feld & Zölitz, 2017).
However, the marginal students, those just above the cutoff,
are also the worst off relative to their peer group (“small ﬁsh
in a big pond”), while those just below the cutoff are rela-
tively better than their peers (“big ﬁsh in a small pond”). The
marginally admitted student has a lower relative rank among
her peer group that could lower her academic self-concept
resulting in a detrimental or zero impact on not just her fu-
ture academic performance but also her behavior and per-
sonality (Marsh et al., 2008).2 Therefore, students above the
cutoff face trade-offs between the positive effects of higher-
ability peer environments and negative effects of low relative
rank (Cicala, Fryer, & Spenkuch, 2018; Elsner & Isphording,
2017, 2018; Fabregas, 2017; Murphy & Weinhardt, 2020).
Consequently, the net effects of enrolling in a more selective
college could go in either direction.3

We combine data from a series of incentivized tasks and
socioeconomic surveys administered to over 2,000 under-
graduate students at different colleges of DU to examine the
returns to enrollment in more selective college environments.
The ﬁrst outcome of interest is academic attainment as mea-
sured by scores on standardized university-level exams. Next,
we examine impacts on economic preferences such as com-
petitiveness, overconﬁdence, and risk elicited using incen-
tivized tasks. The ﬁnal set of outcomes deals with the Big Five
traits (Openness to Experience, Conscientiousness, Extraver-
sion, Agreeableness, and Emotional Stability), a broadly ac-
cepted taxonomy of personality traits.4

Several interesting ﬁndings emerge from our analysis.
First, enrollment in a selective college leads to gains in scores

2The evidence on rank effects being more prevalent in more heterogeneous
student ability environments is not conclusive. In the education psychology
literature, Marsh et al. (2008) show that rank concerns are likely to prevail
across different settings, and even in groups of gifted students. In economics,
Elsner and Isphording (2017) ﬁnd that ordinal rank concerns hold in cohorts
with both high and low variance in ability. These results could also be linked
to the literature on the effect of heterogeneity in peer ability on student
achievement and effort, and the ﬁndings appear mixed. For example, Carrell,
Sacerdote, and West (2013) and Booij, Leuven, and Oosterbeek (2017) ﬁnd
student achievement to be higher in low-variance peer ability settings. Lyle
(2009) ﬁnds that high variance in peer ability increases student achievement.
3This could also explain the mixed evidence on peer effects in education
with some studies ﬁnding positive peer effects and others documenting
nonlinear or no effects (Sacerdote, 2011).

4These preferences and traits have been identiﬁed to explain a range of
labor market outcomes. Competitiveness can explain gender gaps in wages
(Niederle & Vesterlund, 2007). Overconﬁdence affects entrepreneurial en-
try (Koellinger, Minniti, & Schade, 2007). Recent work from developing
countries also shows a link between these skills and indicators of labor
force participation, performance, and skill accumulation (Dasgupta et al.,
2015; Nordman, Sarr, & Sharma, 2019; Sharma & Tarp, 2018). Finally, a
spate of recent papers also ﬁnds that soft skills embedded training programs
can inﬂuence labor market performance (through effects on socioemotional
traits). Adhvaryu et al. (2018) ﬁnd an on-the-job soft skills training pro-
gram for Indian female garment workers to have led to gains in worker
productivity, possibly through improvements in extraversion and forward-
looking behavior. Acevedo et al. (2017) ﬁnd that a soft skills embedded
vocational training resulted in higher levels of soft skills and higher em-
ployment for women in the Dominican Republic. Campos et al. (2017) ﬁnd
that a psychology-based personal initiative program for microenterprise
owners in Togo led to higher proﬁts and adoption of business practices.

on standardized university-level exams for marginally admit-
ted women, and their higher attendance rates are possibly
driving this effect. Second, exposure to more able peer envi-
ronments in these selective colleges makes women less risk
averse and less overconﬁdent. Third, we ﬁnd that marginally
admitted men experience a signiﬁcant decline in extraversion
and conscientiousness as compared to their counterparts in
less selective colleges, representing small ﬁsh in a big pond
effects. Fourth, we ﬁnd suggestive evidence that the returns
to enrolling in selective colleges vary by college quality, with
men’s personality traits being more susceptible to concerns
over low relative ranks at the top end of the college quality
distribution. Finally, we do not ﬁnd signiﬁcant variation in
measures of teacher quality across colleges implying differ-
ences in peer quality and rank concerns to be driving our
results.

Our ﬁndings are consistent with recent work on related
topics. For instance, Murphy and Weinhardt (2020) exploit
idiosyncratic variation in cohort composition among primary
school children in the United Kingdom to ﬁnd that students
with the same ability but higher relative rank perform signif-
icantly better in secondary school. Applying a similar iden-
tiﬁcation strategy to U.S. data, Elsner and Isphording (2017)
ﬁnd that students with higher ordinal rank are more likely to
complete high school and enter and graduate from college. El-
sner and Isphording (2018) also ﬁnd that low relative rank in-
creases the likelihood of engaging in risky and violent behav-
ior, and they attribute this to diminished future expectations
and perceived status arising from lower ordinal rank. Fabre-
gas (2017), using data from Mexico City middle schools, also
ﬁnds that students who are just above the cutoff express lower
perseverance and aspirations to attend college. Interestingly,
the effects we observe for behavior and personality traits are
larger than those for standardized university exam scores.
This is in line with ﬁndings in Sacerdote (2011): the peer
effects in higher education are greater on social outcomes
related to memberships in sorority/fraternity, smoking, and
drinking than on academic achievement. Overall, our ﬁndings
contribute to understanding the gender-differentiated cogni-
tive and noncognitive returns to postsecondary education.

The rest of the paper is organized as follows. The insti-
tutional setting and college admissions process at the Uni-
versity of Delhi, sampling strategy, and data are described in
section II. The empirical strategy is outlined in section III.
All results and robustness checks are presented in section IV.
Concluding remarks follow in section V.

II. Background and Data

University of Delhi (DU) is one of India’s top public uni-
versities and offers three-year undergraduate education to ap-
proximately 160,000 full-time students. DU consists of 79
colleges, each offering degrees in multiple disciplines such
as science, commerce, arts, and humanities. Each college is
an independent entity with its own campus, faculty, students,
and teaching conducted within the colleges. However, the

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EFFECTS OF PEERS AND RANK ON COGNITION, PREFERENCES, AND PERSONALITY

589

curriculum and all exams for each discipline are determined
centrally by DU and are identical across all colleges. Teacher
salaries are also the same across colleges in DU. These are
unique features of DU; in most other settings, these factors
vary across educational institutions.

A. College Admissions Process

College admissions for most disciplines in DU are based
on the student’s high school exit exam score computed as
the average of best of four out of ﬁve subjects, including
language.5 In the ﬁrst two weeks of June each year, students
apply using the Common Pre-Admission Form where they
state their high school exit exam scores and select the colleges
and the disciplines within the colleges they wish to apply
to. The application costs INR 100 (approximately 1.5 USD).
This form can be purchased and submitted at multiple centers
across Delhi, thereby minimizing any time costs arising from
traveling to several colleges.

After the applications period is complete, based on capac-
ity constraints and the incoming cohort’s average score, each
discipline within a college announces the cutoff scores that
determine admission into the speciﬁc college and discipline.6
All applicants above the cutoff in the discipline are eligible
for admission in the college discipline. Since there is excess
demand for high-quality colleges, the cutoffs for these col-
leges are signiﬁcantly and systematically higher than the low-
quality colleges. If there are vacancies, colleges announce a
second list with lower cutoffs. This process continues for
several rounds as colleges gradually lower their cutoffs until
all spots are ﬁlled.7 As expected, the more selective colleges
ﬁll their seats within the ﬁrst couple of rounds, while the
less selective ones sequentially lower their cutoffs, taking
at times up to ten rounds to ﬁll their seats. As a result, the
DU college admission process creates an environment where
students who enroll in more selective colleges are exposed
to high-achieving peers as compared to students enrolled in
less selective colleges.

Sampling Strategy

Our study was conducted from January to March 2014.
We constructed our sample in the following manner. First,
to ensure representativeness along the distribution of college

quality, we obtained the list of all 79 colleges afﬁliated with
DU. Second, we drew a list of 58 colleges that offer disci-
plines in commerce and/or economics.8 These 58 colleges
can be further categorized into daytime coeducational col-
leges (32), daytime women-only colleges (17), and evening
coeducational colleges (9). Of the 32 daytime coeducational
colleges, we further exclude 7 colleges that offer too few dis-
ciplines or use any criteria other than high school exit exam
scores for admissions, resulting in a list of 25 target colleges.
After considering admission cutoffs for each of these 25 col-
leges for three years (2011–2013) and budget constraints,
we identiﬁed 18 colleges that had consistently ranked cut-
offs across the three years for economics and commerce, of
which we could implement our study in 15 colleges with
varying cutoffs.

We focus on the two disciplines of economics and com-
merce for a number of reasons, in addition to cost consid-
erations. First, enrollment in economics and commerce is
usually higher than in most other disciplines. For example,
in DU in 2011, the total enrollment in the ﬁrst year for eco-
nomics and commerce was over 10,200 students, accounting
for 28% of total student intake for honors disciplines.9 Sec-
ond, economics and commerce have higher cutoffs across
all colleges as compared to other popular disciplines such as
history, political science, mathematics, and English. To illus-
trate, in our sample of ﬁfteen colleges, in 2011, the average
cutoff for commerce and economics is 91%. The average cut-
offs for other disciplines are history (74%), political science
(75.8%), mathematics (82.8%), and English (77.13%). Third,
and importantly, admission into economics and commerce is
based solely on high school exit exam scores, facilitating the
regression discontinuity design, while for some other disci-
plines, the admission process entails a combination of written
entrance exams, high school exit exam scores, and interviews.
We also examine whether colleges in our sample are rep-
resentative of the remaining colleges in DU in terms of their
selectivity. Figure A1 in the online appendix shows that the
distribution of cutoffs in economics and commerce in our
sample of 15 colleges overlaps with those of the remaining
43 of the 58 colleges, and the Kolmogorov-Smirnov tests do
not reject the null of equal distributions (p-value = 0.922
and 0.941 for economics and commerce, respectively), sug-
gesting that our sample of colleges is representative of the
remaining colleges in DU.

5In India, after a common high-stakes exam in grade 10, in the last two
years of high school, students select into one of the following academic
tracks, each of which has four subjects and a language: science, commerce,
and humanities. At the end of grade 12, they take the high school exit exam,
which varies by track. College admissions often require a certain high school
track. For example, students applying for undergraduate degrees in science
should have had a science track. Commerce and economics disciplines
require applicants to have studied mathematics in their high school tracks.
6These cutoffs are publicly available at http://www.du.ac.in/index.php?

id=664.

7As cutoffs drop between admission rounds, it is possible for students to
move up to colleges where they are now eligible. In our sample, 26.5% of
the students switched colleges during the admission process, of whom 94%
moved to a more selective college. We discuss this further in section IVC.

8The remaining 21 colleges offer only specialized disciplines such as

pharmacy, nursing, homeopathy, physical and sports education, and art.

9A concern with focusing on economics and commerce may be that of
discipline-speciﬁc, gender-based selection effects. Based on data obtained
under the Right to Information Act, we calculate the share of female students
across all colleges within DU enrolled in the ﬁrst year in 2011 in a variety of
disciplines (table A1 in the online appendix). The share of women exceeds
50% in the arts disciplines and is just below 50% in the science disciplines.
This is consistent with previous evidence in the literature on gender-based
selection across disciplines (see Buser et al., 2014 and papers cited therein).
Notably, we do not ﬁnd the share of female students in economics and
commerce to be outliers, implying that discipline-speciﬁc selection effects
at the time of entry into DU are unlikely to be a pressing concern for our
analysis.

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THE REVIEW OF ECONOMICS AND STATISTICS

Further, a wealth of evidence suggests that colleges in DU
are among the most favored choices for economics and com-
merce. India Today, a well-known Indian magazine, pub-
lishes an annual ranking of the top ﬁfty colleges across
the country for various disciplines. This list is based on a
perceptions-based survey and factual survey. In 2011 and
2012 (the years of admission for the sample of students in our
survey), for the categories of commerce and arts (economics
falls within arts category), several colleges of DU feature in
the top ﬁfty colleges across India.10 Similarly, the National
Institutional Ranking Framework (NIRF), a recent initiative
by the Indian Ministry of Human Resource Development,
ranks higher education institutions across the country on a
range of parameters. According to the latest data for 2019 for
undergraduate programs in arts and commerce, eleven of top
twenty colleges are in DU.11

In the region of Delhi and neighboring states, DU is the
leading university offering these nontechnical disciplines.
Other public universities in the area offering similar disci-
plines are quite few, much smaller, and are not considered
as reputable (Borker, 2017). Private universities are substan-
tially more expensive than DU and not as competitive. In
the NIRF, none of the other high-ranking colleges in Delhi
are non-DU and no other high-ranking colleges are in close
proximity of Delhi. It is also expensive to relocate to a differ-
ent city, especially as most colleges have limited on-campus
housing facilities.12 Further, nationally representative data
such as the Indian Census and National Sample Surveys show
that migration among youth is low for education and accounts
for only a small share of the migrant stream. Most migrants
move within state (Chandrasekhar & Sharma, 2014). This
suggests that among those who narrowly fail to get admitted
into more selective DU colleges, a less selective college in
DU is likely to be preferable to attending other universities
in Delhi and surrounding states.13

C. Data

We collected data on approximately 2,000 second- and
third-year students enrolled in economics and commerce dis-
ciplines in these ﬁfteen colleges. To conduct the surveys
during class hours, we obtained approval from the college

principals, and collaborated with teachers at these colleges
to determine the speciﬁc session timings. Upon arriving in
the classrooms, teachers introduced the research team, and
students were told that we would be conducting a decision-
making study and survey, that participation was voluntary,
and that they would be monetarily compensated for their time.
In the ﬁrst part of the study, we conducted incentivized
experiments to elicit economic preferences. First, to capture
subjects’ competitiveness and overconﬁdence, we used a sim-
ple number-addition task (similar to Bartling et al., 2009).
After a practice session, participants had to predict their per-
formance in advance and also choose between a piece-rate
and tournament compensation scheme. Under the piece-rate
scheme, INR 10 was paid for every correct answer. Under
the tournament scheme, INR 20 was paid for every correct
answer if the subject outperformed a randomly selected stu-
dent of DU who had solved the questions earlier.14 We de-
ﬁne competitiveness as a dummy that takes a value 1 if the
subject chose the tournament compensation scheme and 0 if
the subject chose the piece-rate compensation scheme. As in
Dasgupta et al. (2017), we deﬁne overconﬁdence as the ratio
of the predicted performance to the student’s performance in
the actual task.15

Second, to measure risk preferences, we used the Gneezy
and Potters (1997) investment task. In this, subjects allocated
a portion of their endowment (INR 150) to a risky lottery and
set aside the remainder. If they won the lottery based on a
die roll, the invested amount was tripled, and they also got
any amount they set aside. Conversely, if they lost the lottery,
they received only the amount that was set aside. We deﬁne
risk preference as the proportion allocated to the risky lottery
in the investment game.

In the second part of the study, we implemented a socio-
economic survey that collected details on family background
characteristics, school and college information, academic
performance, and participation in extracurricular activities.
To measure cognitive attainment, we collected data on stan-
dardized university exam scores.16 To measure personal-
ity traits, we administered the ten-item Big Five inventory
(Gosling, Rentfrow, & Swann, 2003) that consists of the fol-
lowing traits. Openness to Experience captures a tendency
to be open to new aesthetic, cultural, or intellectual experi-
ences. Conscientiousness refers to a tendency to be organized,

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10The survey methodology is available at https://www.indiatoday.in/
india/best-colleges/story/best-colleges-in-india-2012-methodology-1050
20-2012-06-08. 2011 rankings available at: https://www.indiatoday.in/best
colleges/2012/compare-college/2011-commerce-arts. 2012 rankings at:
https://www.indiatoday.in/bestcolleges/2012/compare-college/2012-com
merce-arts.

11NIRF 2019 rankings are available at https://www.nirﬁndia.org/2019/

CollegeRanking.html.

12Borker (2017) ﬁnds that 72% of DU students are from Delhi and live
with their parents. In 2016, almost 80% of applicants to DU were from Delhi
and the neighboring states of Uttar Pradesh and Haryana: https://www
.hindustantimes.com/delhi/50-delhi-university-aspirants-from-delhi-this-
year/story-oWvwZH76uFK7DpgWP0OlCP.html.

13We conducted a survey of approximately 300 grade 12 students across
eleven high schools in Delhi in 2019 and found that DU is the top choice
for over 93.3% of them.

14We implemented a pilot version of this game where forty students from
DU had participated, and their performance is used for comparison in the
tournament wage scheme.

15Our measure of overconﬁdence falls in the category of overestimation
(Moore & Healy, 2008). Our competition design as well as the nonincen-
tivized belief elicitation is similar to Dasgupta et al. (2015, 2017), and
Kamas and Preston (2012). Further, since we were already paying for the
real effort task, we did not incentivize the belief elicitation in line with Karni
and Safra (1995).

16We also administered a ten-item Raven’s (progressive) matrices test,
which is acknowledged as a measure of ﬂuid intelligence (e.g., see the
review in Dean, Schilbach, & Schoﬁeld, 2019). We ﬁnd a strong positive and
signiﬁcant relationship between performance on Raven’s test and university
exam scores, ruling out concerns about these exams reﬂecting rote-learning
skills alone. See table A2 in the online appendix.

EFFECTS OF PEERS AND RANK ON COGNITION, PREFERENCES, AND PERSONALITY

591

responsible, and hard working. Extraversion relates to an out-
ward orientation of one’s interests and energies oriented to-
ward the outer world of people, characterized by sociability.
Agreeableness is related to the tendency to act in a cooper-
ative and unselﬁsh manner. Emotional Stability (opposite of
Neuroticism) is predictability and consistency in emotional
reactions with absence of rapid mood changes.

Overall, we conducted sixty sessions with approximately
35 subjects per session. Each session lasted about 75 min-
utes. No feedback was provided between or after the tasks.
All subjects received a show-up fee of INR 150. The average
additional payment was INR 230. All subjects participated
only once in the study. To minimize wealth effects, addi-
tional payments were based on one of the randomly chosen
incentivized tasks. Instructions for the incentivized tasks are
available in online appendix B.

III. Empirical Speciﬁcation and Sample Description

A. Empirical Speciﬁcation

For estimating the returns to college quality, we ﬁrst group
colleges based on their relative selectivity. We use admission
cutoffs, as exogenously announced by the individual colleges,
as the criteria to sort the ﬁfteen colleges in our sample into
four ordered categories ranging from 1 (highest rank) to 4
(lowest rank). As a result, colleges with similar cutoffs ap-
pear under the same group/rank. In table A3 in the online
appendix, for each of the four ranks, we present the means
and standard deviations of cutoffs within each rank. As ex-
pected, the average cutoffs are greater in the higher-ranked
colleges. Further, the cutoffs appear to show greater disper-
sion as one moves down the ranks. This is not surprising as
less selective colleges are likely to have more heterogeneity
than more selective colleges. A similar pattern emerges if we
examine the means and standard deviations of high school
exit exam scores within a rank. Overall, table A3 shows that
students who perform similarly in high school exit exams are
grouped within each rank.

Next, for each rank, we compute the minimum score re-
quired for admission into the group. These cutoffs vary by
student type where students differ in their current discipline
(commerce and economics), academic track in high school
(science, commerce, and humanities), year of entry (2011 and
2012), and gender (men and women). For example, a student
seeking admission into economics, having studied science in
high school faces a different cutoff from a student who stud-
ied commerce in high school. Thus, for each rank of colleges,
we get a set of cutoffs that deﬁne the minimum score required
by each student type for admission into that college rank.

We then combine the cutoffs, ranks, and student data. For
our analysis, from an initial sample of approximately 2,000
students, we exclude all students whose admissions were not
based on their high school exit exam scores. This includes stu-
dents belonging to historically disadvantaged backgrounds
(Scheduled Castes, Scheduled Tribes, and Other Backward
Classes) for whom afﬁrmative action policies mandate a

ﬁxed number of seats (29.3%); students admitted on the ba-
sis of excellence in sports or other extracurricular activities
(4.8%); those who transferred across colleges after enroll-
ment or switched disciplines within a college (0.3%); and
those providing insufﬁcient information (1.3%). These ex-
clusions leave us with 1,331 students.

Since we are interested in estimating the returns to enroll-
ment in a more selective college group, we now construct
three samples using our sample of 1,331 students. In the
ﬁrst constructed sample, colleges in rank 1 are assigned to
the treated group/more selective colleges, and the remaining
colleges (in ranks 2, 3, and 4) are assigned as comparison
group/less selective colleges. In the next sample, colleges in
ranks 1 and 2 are assigned to the treated group, and the re-
maining colleges (in ranks 3 and 4) are assigned to the com-
parison group. Finally, a third sample is constructed where
colleges ranked 1, 2, and 3 are assigned to the treated group
and colleges in rank 4 are in the comparison group. Follow-
ing Abdulkadiro˘glu et al. (2014), Jackson (2010), and Pop-
Eleches and Urquiola (2013), we construct our ﬁnal analysis
sample by stacking the three samples together and estimate a
single average treatment effect measuring the impact of en-
rollment in a relatively selective college. The stacking method
has two advantages. First, it allows us to estimate the effect
of enrolling in a more selective college over the distribution
of college quality. Second, this methodology increases the
sample size and, consequently, power. Note that stacking our
sample can plausibly make a student appear at most three
times in the data. However, as we only use observations near
the cutoff for our analysis (i.e., within a 5 percentage point
window), it results in 868 students appearing more than once
in the ﬁnal analysis sample of 2,393 observations.

Of course, enrollment in a more selective college is en-
dogenous, as not all students who are eligible to enroll do so.17
To account for this, we use a fuzzy regression discontinuity
(RD) design where enrollment is instrumented by eligibility
to enroll in a more selective college (Lee & Lemieux, 2010).
In particular, we estimate the following set of instrumental
variable (IV) regressions where the ﬁrst-stage regression is

T Ri j = α0 + α1Ti j + α2di j + α3d 2
i j

+ α4di jTi j + α5d 2

i jTi j

K(cid:2)

l=6

αl Xli j + η j + δm + (cid:2)i j

(1)

and the corresponding second-stage regression is

Yi j = δ0 + δ1T Ri j + δ2di j + δ3d 2
i j

+ δ4di jT Ri j

+ δ5d 2

i jT Ri j +

K(cid:2)

l=6

δl Xli j + η j + δm + μi j,

(2)

17Similarly, we also have a few instances where students who are ineligible
for a more selective college are admitted to that college. Overall, in the
stacked sample used in the analysis, only 0.37% of the subjects who have
a negative distance from the cutoff are enrolled in a more selective college,
and approximately 8.85% of the subjects who have a positive distance from
the cutoff are enrolled in a less selective college.

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THE REVIEW OF ECONOMICS AND STATISTICS

where Yi j in equation (2) is the outcome variable of interest
for student i of type j. Equation (1) is a linear probability
model where T Ri j takes the value 1 if student i of type j is
treated, that is, enrolled in a more selective college. The run-
ning variable, di j, is computed as the difference between stu-
dent i(cid:3)s high school exit exam score and the relevant college
rank-speciﬁc cutoff faced by her type j. The instrument is a
dummy variable for eligibility, Ti j, that takes a value 1 if di j
is nonnegative, 0 otherwise. We allow for nonlinearity in the
relationship between the outcomes and the running variable
by including a quadratic speciﬁcation in the running vari-
able as well as allow the returns from college quality to vary
on each side of the cutoff by allowing interactions between
the TR dummy and di j and d 2
i j. Our regressions also include
cutoff ﬁxed effects (η j) where the cutoffs vary by student
types. This allows us to obtain the relevant counterfactual for
a student enrolled in the high-quality college: a student of
the same type (i.e., currently enrolled in the same discipline,
with the same high school academic track, same gender, and
same year of admission) who marginally missed the relevant
cutoff. To account for variation in the timing of the surveys,
we also include survey month ﬁxed effects (δm). We also in-
clude a vector of predetermined characteristics (X s) such as
mother’s education, father’s education, private school enroll-
ment, age, household income, and religion in the regressions,
to improve the precision of our estimates. Finally, μi j and (cid:2)i j
are i.i.d. error terms.

The coefﬁcient estimate on TR in equation (2) gives us the
local average treatment effect (LATE) of being enrolled in
a more selective college. As the literature on the effects of
school and college quality documents signiﬁcant heterogene-
ity by gender (e.g., Hastings, Kane, & Staiger, 2006; Jackson,
2010; Kling, Ludwig, & Katz, 2005), we also report our re-
sults for men and women separately.

Since the running variable is discrete, following Lee and
Card (2008), we cluster our standard errors with respect to
0.25 bins of the running variable.18 The choice of the band-
width is an important issue in RD analysis. Since we have
various outcome variables, we ﬁx the bandwidth to be 5 per-
centage points for the main analysis. In section IVC, we show
that our results are robust to using outcome-speciﬁc optimal
bandwidths.

As we wish to estimate the effects of enrolling in a more se-
lective college, the ideal sample would comprise students/DU
applicants who strictly prefer more selective colleges to the
less selective ones such that a score above (below) the rel-
evant cutoff would lead to admission in a more (less) se-
lective college. As explained in section IIA, DU follows a
decentralized admission process wherein applicants ﬁll in a
common form to indicate the college disciplines they wish
to apply to.19 This process does not gather the preferences

18The main results are robust to two-way clustering of the standard errors
at student and bin level, as in Cameron, Gelbach, and Miller (2011). Results
are available from the authors upon request.

19The student allocation mechanism in DU is different from the more com-
monly observed centralized mechanisms such as the Boston school choice

of the applicants over colleges and/or disciplines, and all we
observe is the current discipline that the student is enrolled
in, her high school exit exam score, and the cutoffs at the
time of admission. Nonetheless, with a ﬁxed supply of seats,
the higher cutoffs at colleges are a reﬂection of excess de-
mand for those seats. It is then reasonable to assume that the
average student prefers admission into a college with higher
cutoffs than one with lower cutoffs. We discuss this further in
section IVC.

Summary Statistics

In table 1, we present descriptive statistics for our sam-
ple. In panel A, we see that average score on standardized
university-level exams, our measure of academic attainment
during college, is 70% with no signiﬁcant gender differ-
ences. In panel B, we summarize choices in the incentivized
tasks: competitiveness, overconﬁdence, and risk. Thirty-one
percent of the subjects are considered competitive as they
choose the tournament payment scheme. The average stu-
dent is overconﬁdent as the ratio of the expected number of
correct answers to the number correctly solved in the ac-
tual task is 1.6, signiﬁcantly higher than 1. These ﬁndings
are also supported by other papers that ﬁnd that about one-
third of subjects choose the tournament wage scheme and
often irrationally overestimate their own abilities (Dasgupta
et al., 2015; Niederle & Vesterlund, 2007). Finally, the aver-
age investment of 46.6% in the risky asset is in the range of
44.67% to 70.86% observed for student populations (Char-
ness & Viceisza, 2016). The signiﬁcant gender differences
in competitiveness and risk aversion are in accordance with
previous work (see Niederle, 2016, for a review).

In panel C, we summarize subjects’ Big Five personality
traits. Subjects report a higher score on agreeableness, con-
scientiousness, and openness to experience than they do for
extraversion and emotional stability. Women are more ex-
trovert, conscientious, and agreeable, and less emotionally
stable than males. Schmitt et al. (2008) note similar gender
differences in personality traits across several cultural con-
texts. Finally, in panel D, we present descriptive statistics on
background characteristics. The average age of the students
is close to 20. Over 90% are Hindus (the dominant religion
in India), 85% attended a private high school, and 75% to
78% have either a highly educated mother or father (college
degree or higher). A third of the sample comes from low-
income households (those earning less than INR 50,000 per
month or INR 600,000 per year).20

mechanism (Abdulkadiro˘glu et al., 2014), the student or college propos-
ing deferred acceptance mechanisms, or the top trading cycle mechanism
(Sönmez & Ünver, 2011), where students indicate preference rankings over
disciplines and colleges and a central body allocates students.

20According to the nationally representative India Human Development
Survey of 2011–2012, the average yearly income for upper-caste house-
holds is approximately INR 180,000 (appropriate reference group for our
analysis). This indicates that students in our sample belong to households
with a relatively higher socioeconomic status, and thus are not representa-
tive of the overall population.

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EFFECTS OF PEERS AND RANK ON COGNITION, PREFERENCES, AND PERSONALITY

593

TABLE 1.—SUMMARY STATISTICS

A: Cognitive attainment
University exam score

B: Economic preferences

Competitiveness

Overconﬁdence

Risk preference

C: Personality traits
Extraversion score

Agreeableness score

Conscientiousness score

Emotional stability score

Openness to experience score

D: Background characteristics

Age

Religion

Private School

Income

Mother’s Education

Father’s Education

Full Sample
(1)

70.44
(7.39)

0.31
(0.46)
1.64
(1.22)
46.59
(19.08)

4.77
(1.43)
5.20
(1.16)
5.31
(1.26)
4.54
(1.38)
5.42
(1.12)

19.66
(0.86)
0.92
(0.27)
0.85
(0.36)
0.30
(0.46)
0.75
(0.43)
0.78
(0.41)

Men
(2)

70.19
(7.43)

0.41
(0.49)
1.66
(1.20)
49.88
(21.71)

4.69
(1.43)
4.97
(1.16)
5.20
(1.29)
4.65
(1.40)
5.44
(1.10)

19.69
(0.86)
0.92
(0.28)
0.85
(0.36)
0.30
(0.46)
0.73
(0.44)
0.78
(0.41)

Women
(3)

70.64
(7.36)

0.24
(0.43)
1.63
(1.24)
43.99
(16.24)

4.83
(1.42)
5.38
(1.12)
5.40
(1.23)
4.45
(1.36)
5.41
(1.14)

19.65
(0.86)
0.93
(0.26)
0.84
(0.36)
0.31
(0.46)
0.77
(0.42)
0.79
(0.41)

Difference
(4)

−0.45

0.17***

0.03

5.89***

−0.14**

−0.41***

−0.20***

0.20***

0.03

0.04

−0.01

0.01

−0.01

−0.04**

−0.00

Religion is an indicator variable for being a Hindu; income is an indicator variable for monthly family income being below Rs 50,000; mother’s and father’s education are indicator variables for tertiary education;
private school is an indicator variable for graduation from a private high school. Personality traits’ scores range from 0 to 7. For second- and third-year students, we have the average exam scores based on three semesters
and ﬁve semesters respectively. Sample restricted to +/−5 window around the cutoff. Signiﬁcant at ∗10%, ∗∗5%, and ∗∗∗1%.

C. Testing the Validity of the RD Design

The RD model relies on two assumptions: (a) there is no
precise manipulation of the assignment variable around the
cutoff, and (b) the probability of being enrolled in a more
selective college is discontinuous at the cutoff.

Features of

the DU admission process

rule out
manipulation-related concerns. First, admission depends on
scores on high school exit exams that follow a double-blind
grading procedure, making manipulation difﬁcult, if not out-
right impossible. Second, at the time of application to DU
colleges, students are not aware of the precise cutoffs that will
determine admissions that year. Based on historical trends,
students may have an estimate of the cutoff range, but it is
only after students apply to the colleges that cutoffs are deter-
mined and announced. Since the rule for determining these
cutoffs is not public knowledge, students cannot perfectly
predict future cutoffs. Overall, it is virtually impossible for
students to precisely manipulate the side of the college cutoff
they will ultimately fall on.21 This inability to control the as-

21We also conducted the density test proposed by Cattaneo, Jansson, and
Ma (2020) and do not reject the null hypothesis that the density is smooth
around the cutoff (p-value = 0.13).

signment variable around the cutoff also implies that pretreat-
ment variables would be similar around the cutoff. We next
formally check for discontinuities in predetermined (pretreat-
ment) background characteristics such as mother’s education,
father’s education, private high school enrollment, age, in-
come, and religion by estimating the following reduced-form
regression,

Xi j = β0 + β1Ti j + β2di j + β3d 2
i j

+ β4di jTi j

+ β5d 2

i jTi j + η j + δm + υi j,

(3)

where X is the vector of predetermined background char-
acteristics and the right-hand side variables are as deﬁned
in equations (1) and (2). The results from these regressions
are presented in the online appendix table A4. We ﬁnd that
the impact of the treatment indicator, that is, being eligi-
ble to enroll in a more selective college on the predeter-
mined variables, is mostly small and never signiﬁcantly dif-
ferent from 0, conﬁrming the validity of the RD design. The
corresponding graphical representations are provided in ﬁg-
ures A2 to A4 in the online appendix. However, as we do
not have student-level panel data, we are unable to rule out

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THE REVIEW OF ECONOMICS AND STATISTICS

FIGURE 1.—FIRST-STAGE RELATIONSHIP

TABLE 2.—FIRST-STAGE DISCONTINUITY

TABLE 3.—AVERAGE PEER QUALITY

Without controls

Observations
With controls

Observations

Full Sample
(1)

0.680***
(0.066)
2,393
0.681***
(0.063)
2,368

Males
(2)

0.698***
(0.086)
1,059
0.702***
(0.082)
1,043

Females
(3)

0.626***
(0.078)
1,334
0.629***
(0.074)
1,325

Av. grade 12 score

Observations
Av. grade 10 score

Observations

Full Sample
(1)

2.477***
(0.244)
2,368
2.932***
(0.291)
2,361

Males
(2)

2.461***
(0.334)
1,043
2.597***
(0.440)
1,041

Females
(3)

2.713***
(0.369)
1,325
3.371***
(0.422)
1,320

This table shows the ﬁrst-stage discontinuity results using a ﬂexible second-order polynomial described
in the text. We control for mother’s education, father’s education, private school enrollment, age, income,
and religion in all speciﬁcations (see notes in table 1 for variable deﬁnitions). All regressions also include
cutoff and month of survey ﬁxed effects. Standard errors clustered at 0.25 bins of the centered high school
exit exam score level are reported in parentheses. Signiﬁcant at ∗10%, ∗∗5%, and ∗∗∗1%.

This table reports instrumental variable estimates using the ﬂexible second-order polynomial described
in the text. We control for mother’s education, father’s education, private school enrollment, age, income,
and religion in all speciﬁcations (see notes in table 1 for variable deﬁnitions). All regressions also include
cutoff and month of survey ﬁxed effects. Standard errors clustered at 0.25 bins of the centered high school
exit exam score level are reported in parentheses. Signiﬁcant at ∗10%, ∗∗5%, and ∗∗∗1%.

discontinuities in pretreatment outcome variables around the
cutoff.

Next, we check if the probability of enrollment in a more
selective college is indeed discontinuous at the cutoff. This
is also proof of a strong ﬁrst-stage regression, necessary for
obtaining valid estimates in the second stage. In ﬁgure 1, we
plot the proportion of students enrolled in a more selective
college in each 0.25 bin against the distance from the cutoff.
This is done for the pooled sample and then separately for men
and women. In all three panels, we see a clear discontinuity
in the probability of enrolling in a more selective college at
the cutoff, indicating the appropriateness of the RD design.
A formal estimation of the ﬁrst-stage relationship between
enrollment in a selective college and eligibility is provided
in table 2. We ﬁnd that on average, students who are eligible
to enroll in a selective college are 68% more likely to do
so, indicating a strong revealed preference for more selective

colleges. We ﬁnd similar strong effects of the eligibility to
enroll in a selective college for both men and women. As
expected, compliance is not perfect, and hence, we use a fuzzy
RD design and in the sections that follow, present results from
the corresponding IV speciﬁcation discussed in equations (1)
and (2).

IV. Results

A. Effects on Cognition, Economic Behavior, and Personality

Using the fuzzy RD design discussed in section IIIA, we
ﬁrst examine discontinuity in average peer quality in table 3.
We ﬁnd that the marginally admitted student is surrounded by
peers whose average score on the high school exit exam is 2.5
percentage points higher than peers of a comparable student
who just missed the cutoff (ﬁrst row of column 1). Columns 2

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EFFECTS OF PEERS AND RANK ON COGNITION, PREFERENCES, AND PERSONALITY

595

FIGURE 2.—COLLEGE QUALITY AND PEERS

This ﬁgure plots residual terms obtained by regressing average peer quality on cutoff ﬁxed effects against distance from the threshold.

and 3 show that both men and women in more selective col-
leges are surrounded by signiﬁcantly high-achieving peers.
This systematic difference in average peer ability is also evi-
dent when we consider performance on another pretreatment
achievement test. Students in India also take a similar high-
stakes exam at the end of grade 10. An analysis of our sam-
ple’s grade 10 scores in table 3 also points toward the higher
peer quality experienced by the marginally admitted student.
Figure 2 depicts the corresponding difference in peer quality.
Note that in addition to the increase in average peer quality,
the marginal student also has a lower ordinal rank in her peer
ability distribution.

Next, in table 4, we present the impacts of enrollment in a
more selective college on cognitive attainment (in column 1),
economic preferences (in columns 2–4), and personality traits
(in columns 5–9) for the pooled sample, men, and women in
panels A, B, and C, respectively. While curriculum and ex-
ams are the same within a discipline across colleges of DU,
marginal admission into a more selective college exposes stu-
dents to high-achieving peers and changes their relative posi-
tion in the peer ability distribution. Looking at the effects on
the standardized university-level exam scores for the pooled
sample in column 1 of panel A, we ﬁnd that compared to
students in less selective colleges, marginally admitted stu-
dents in more selective colleges experience a 1.127 percent-
age point increase in their average university exam scores.
Upon further examining these effects by gender, it is appar-
ent that this overall impact is driven by the signiﬁcant effects
on women’s test scores with no statistically signiﬁcant effect
for men (column 1, panels B and C). In particular, women

in more selective colleges on average score 2.8 percentage
points higher on the university exams relative to women in
less selective colleges, resulting in about 4% improvement
over the comparison group’s mean of 69%. Our ﬁnding that
women make signiﬁcant academic gains from exposure to
more able peer environments with little or no accompanying
effects on men has also been found in other studies (Angrist,
Lang, & Oreopoulos, 2009; Hastings et al., 2006; Jackson,
2010). Further, we show later in section IVB that women (but
not men) enrolled in more selective colleges are almost 32
percentage points more likely to have higher attendance rates
than their counterparts in less selective colleges. This gender
difference in attendance rates is likely to explain the observed
gender gap in academic returns to more selective college and
peer environments.

We also estimate the returns to enrollment in a selective col-
lege on the three measures of economic preferences: compet-
itiveness, overconﬁdence, and risk preference. The results are
reported in columns 2 to 4 of table 4. Pooled results indicate
that the marginally admitted student experiences a decline in
overconﬁdence with no signiﬁcant effects on competitiveness
and risk preferences. On disaggregating the sample by gen-
der, we observe overconﬁdence among marginally admitted
women reduces by 0.53 SD. Our results for overconﬁdence
show that marginal women in the more selective colleges ex-
perience a decline in overconﬁdence, and conversely, women
below the cutoff, who are relatively high-achieving compared
to their peers, become more overconﬁdent. We hypothesize
that the marginal female students in more selective colleges
who are the small ﬁsh in a big pond may update their beliefs

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TABLE 4.—RETURNS TO COLLEGE QUALITY: MAIN RESULTS

Economic Preferences

Personality Traits

University
Emotional Openness to
Exam Score Competitiveness Overconﬁdence Preference Extraversion Agreeableness Conscientiousness Stability Experience
(5)

Risk

(1)

(9)

(7)

(3)

(8)

(2)

(4)

(6)

A: Full Sample
Enrolled in a

selective college

Observations

B: Males

Enrolled in a

selective college

Observations

C: Females

Enrolled in a

selective college

Observations
β[male] = β[female]
p-value

1.127*
(0.681)
[0.098]
2,346

−0.654
(1.401)
[0.641]
1,030

2.790**
(1.279)
[0.029]
1,316

0.116
(0.214)
[0.643]
2,365

0.191
(0.259)
[1]
1,043

0.083
(0.226)
[0.311]
1,322

−0.288**
(0.125)
[0.068]
2,335

−0.135
(0.224)
[1]
1,035

−0.533*
(0.295)
[0.077]
1,300

0.153
(0.188)
[0.643]
2,359

−0.256
(0.262)
[1]
1,038

−0.285**
(0.139)
[0.258]
2,331

−0.487**
(0.194)
[0.031]
1,021

0.662*** −0.287
(0.252)
(0.193)
[1]
[0.004]
1,310
1,321

0.037
(0.114)
[0.508]
2,318

−0.016
(0.188)
[1]
1,013

0.057
(0.242)
[1]
1,305

−0.262
(0.205)
[0.369]
2,340

−0.562**
(0.221)
[0.031]
1,029

0.077
(0.258)
[1]
1,311

0.166
(0.120)
[0.369]
2,329

−0.012
(0.250)
[1]
1,018

0.380
(0.263)
[1]
1,311

−0.034
(0.135)
[0.508]
2,328

−0.045
(0.119)
[1]
1,018

−0.026
(0.358)
[1]
1,310

0.131

0.684

0.365

0.002

0.571

0.84

0.012

0.346

0.964

This table reports instrumental variable estimates using the ﬂexible second-order polynomial described in the text. We control for mother’s education, father’s education, private school enrollment, age, income, and
religion in all speciﬁcations (see notes in table 1 for variable deﬁnitions). All regressions also include cutoff and month of survey ﬁxed effects. Standard errors clustered at 0.25 bins of the centered high school exit
exam score level are reported in parentheses. Signiﬁcant at ∗10%, ∗∗5%, and ∗∗∗1%. Sharpened q-values are reported in brackets.

about their ability as they are surrounded by peers who are
academically higher achieving than them.22

We also ﬁnd that women enrolled in more selective col-
leges invest 0.66 SD more in the investment game, thereby
being less risk averse than their female counterparts in the
less selective colleges. To the extent that women are more
risk averse than men, and this gender gap in risk preferences
has implications for occupational choice and other economic
decision making, this result suggests that enrollment in more
selective colleges may result in a narrowing of this gender
gap. Speciﬁcally, as per the expected utility theory frame-
work and given the nature of the investment task (Gneezy
& Potters, 1997) used to elicit risk preferences, in this task,
only a risk-neutral person, or a person behaving under the
expected value maximization (EV) criteria should choose to
invest his or her entire endowment into the risky lottery. How-
ever, a risk-averse decision maker depending on his or her risk
parameter would invest less than the full amount in the lot-
tery. Consequently, a decrease in risk-averse behavior, that
is, allocating a greater proportion of the endowment to the
risky asset, can be interpreted as subjects getting closer to
risk-neutral behavior, and/or choosing according to the EV
criteria in the task. Since overconﬁdence is positively and risk
aversion is negatively related to competitiveness, a decline in
risk aversion and overconﬁdence could plausibly explain why
we do not observe any signiﬁcant effects on competitiveness.
Further, we ﬁnd no signiﬁcant effects on male behavior.

The last set of estimates pertains to personality: Big Five
traits of openness to experience, conscientiousness, extraver-

22Note that this does not necessarily imply that those at the top of the
distribution will also start overestimating their ability and become overcon-
ﬁdent to the same extent. Therefore, conceptually it does not imply that
there is a zero-sum game within a college for overconﬁdence.

sion, agreeableness, and emotional stability (see columns 5–
9, table 4). In the pooled sample, we ﬁnd that enrollment in
a more selective college negatively affects extraversion by
0.28 SD with no effect on other traits. Extraversion and con-
scientiousness among marginally admitted men reduces by
0.48 SD and 0.56 SD, respectively. Taken together, these es-
timates for male students suggest a diminished self-concept
stemming from their lower academic position within their
college rank, resulting in negative effects on economically
valuable personality traits, capturing small ﬁsh in a big pond
effects. Murphy and Weinhardt (2020) also ﬁnd men to be
inﬂuenced more signiﬁcantly on account of rank concerns.
We ﬁnd similar results using alternative measures. In results
reported in table A5 in the online appendix, membership in
college-level societies, another measure of extrovert behav-
ior, is also lower among men enrolled in more selective col-
leges. Similarly, we also ﬁnd that men at the margin of ad-
mission in more selective colleges report lower grit, which is
highly correlated with conscientiousness. We also observe a
decline in openness to experience and agreeableness for men,
though neither is statistically signiﬁcant. In light of ﬁndings
that show that conscientiousness and extraversion matter for
academic performance (Lundberg, 2013), the adverse effects
on these personality traits for the marginally admitted men
might explain why we observe no gains in exam scores for
them.23 Finally, it is also possible that exposure to being in a
selective college may affect some socioemotional skills with

23Due to a modest sample size, some of our coefﬁcients are impre-
cisely estimated and we are unable to reject the null of equality in coefﬁ-
cients between men and women. Gender differences are signiﬁcant for out-
comes related to risk preferences (p-value = 0.002) and conscientiousness
(p-value = 0.012), but not for university exam scores (p-value = 0.131),
overconﬁdence (p-value = 0.365), and extraversion (p-value = 0.571).

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597

A: Males

Enrolled in a selective college

Observations

B: Females

Enrolled in a selective college

Observations

High Attendance
(1)

−0.128
(0.086)
1,043

0.315**
(0.126)
1,325

TABLE 5.—PATHWAYS

Student Response

Relatively Less
Attendance
(2)

Teachers

External Tutorial
(3)

Class Cancelled
(4)

Student-Teacher
Ratios
(5)

0.322***
(0.090)
1,043

−0.110
(0.136)
1,325

0.032
(0.095)
1,043

−0.018
(0.108)
1,325

0.136
(0.123)
1,043

0.019
(0.150)
1,325

0.205
(0.538)
1,043

−0.539
(0.967)
1,325

This table reports instrumental variable estimates using the ﬂexible second-order polynomial described in the text. We control for mother’s education, father’s education, private school enrollment, age, income, and
religion in all speciﬁcations (see notes in table 1 for variable deﬁnitions). All regressions also include cutoff and month of survey ﬁxed effects. Standard errors clustered at 0.25 bins of the centered high school exit
exam score level are reported in parentheses. Signiﬁcant at ∗10%, ∗∗5%, and ∗∗∗1%.

a lag and become prominent only in the long run, such that
our effects are underestimated.

It is possible that impacts on measured outcomes differ by
length of exposure. To examine this, we allow for the effects
of college quality to vary by student cohorts (second and third
year) and ﬁnd that the main results in table 4 do not vary by
cohort. These results are reported in table A6 in the online
appendix.

B. Pathways

Owing to the design of the admissions process in colleges at
DU, we have so far shown and argued that differences in peer
quality and relative rank in the peer distribution are driving
our main results. In this section, we explore a variety of other
potential channels that could explain our main ﬁndings.

In column 1 in table 5, we examine differences in atten-
dance rates.24 We construct a binary variable for high at-
tendance that takes a value 1 if subjects report having class
attendance rates of 75% and higher and 0 if attendance is
below 75%. We ﬁnd that while there is no signiﬁcant differ-
ence for men in the probability of high attendance, women
enrolled in selective colleges have a greater probability of
high attendance than women in less selective colleges. This
indicates that they are present in class more often and there-
fore have an opportunity to learn from and engage with their
peers, making it one of the competing explanations for gains
on cognitive and behavioral outcomes. This ﬁnding ﬁts in
with the general observed pattern of women having better
study habits than men (Angrist et al., 2009; Hastings et al.,
2006).

Next, we examine the attendance of subjects relative to
their classmates. In column 2, we construct an outcome vari-
able that takes the value 1 if the subject attended classes
less often than his or her classmates. We ﬁnd that marginally
admitted men are more likely to skip classes than their class-

24Since attendance is self-reported, presence of random measurement er-

ror in this outcome variable is likely to bias the standard errors upward.

mates in less selective colleges. This points toward weakened
self-concept among men on account of their lower academic
position in the college, potentially indicating higher mental
or psychic costs of investing effort. Elsner and Isphording
(2017) also ﬁnd a similar effect in that students with lower
ordinal rank are more likely to be absent from classes.

Subjects could also experience learning gains due to com-
plementary investments in education in the form of exter-
nal private tutorials and remedial classes. These can improve
test scores independent of the college and peer environment.
However, as shown in column 3 of table 5, we do not ﬁnd any
discontinuity in the probability of using external tutorials for
either males or females.

Differences in indicators of teacher quality and presence
could also matter for students’ academic and nonacademic
outcomes (Hoffmann & Oreopoulos, 2009; Jackson, 2018).
As a measure of teacher presence, we asked students if teach-
ers frequently cancelled classes. Results in column 4 show
no discontinuity in the probability of classes being cancelled.
Finally, results in column 5 indicate that the student-teacher
ratio, an additional measure of teaching quality, also does not
vary around the cutoff.

Finally, there might be unobserved differences across col-
leges in student-teacher interactions (such as informal in-
class tests and levels of teacher attention and feedback) that
may affect students’ perception about their ability and rank.
It is difﬁcult to get information on these nuanced student-
teacher interactions. Even if we assume these to be more
prevalent in more selective colleges, it is not clear if the feed-
back reafﬁrms or mitigates students’ concerns about relative
rank. Thus, the net effect of such unobserved student-teacher
interactions is ambiguous.

C. Robustness

In this section, we discuss a number of robustness checks.
A crucial concern relates to sample selection bias such that
applicants who narrowly fail to get admitted into selective
colleges in DU may withdraw from DU to seek admission

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THE REVIEW OF ECONOMICS AND STATISTICS

in other non-DU colleges or universities instead of taking
admission in a less selective DU college. Thus, those who
remain and choose a lower-ranking college in DU may be
systematically different in terms of their behavior and so-
cioemotional skills from those who exit from DU, inducing
selection into the comparison group. In the absence of data
on applications, which could have plausibly allowed us to
identify such attrition during the admissions process (i.e., dis-
couraged applicants at the margin), we surveyed 298 grade
12 students across eleven schools in New Delhi in 2019 who
were on the verge of entry into higher education and collected
information on their intentions for higher education such as
colleges and universities, they are interested in applying to
and attending, the Big Five traits, and background character-
istics. We use this survey to conduct bounding exercises.

We ﬁnd DU to be the top choice for an overwhelming share
(93.3%) of the high school sample. Among these students
who intend to apply to DU (our pool of potential applicants
to DU), only 4% are potential attritors, that is, they state that
if they do not get admission into the top-rank colleges, they
will also decline admissions to lower-ranking DU colleges
and seek admission elsewhere. Importantly, we ﬁnd that the
decisions to not apply to DU and to exit DU in the event of not
getting into their preferred college are not correlated with any
of the Big Five personality traits (see table A7 in the online
appendix).

Nevertheless, we construct bounds for our treatment effects
by modifying the procedure of Lee (2009) in the RD context.
To construct the lower (upper) bounds on the treatment, we
trim the top (bottom) 4% of the dependent variable in the
treatment colleges and re-run our main regressions. These
results are reported in online appendix tables A8 and A9 for
men and women respectively. Our estimated treatment effects
calibrated using the 4% attrition rate in the school survey are
similar to the main results reported in table 4.25

The second concern relates to the possibility of type I error
that increases with the number of outcomes tested. We use
the method in Anderson (2008) to correct the standard errors
for multiple hypotheses testing by families of outcomes. Our
results are largely robust to this correction, and the sharpened
q-values are reported in brackets in table 4.

Third, the presence of differential participation in our study
around the cutoff would bias our estimates. Using administra-
tive data on class sizes obtained under the Right to Informa-
tion Act, we calculate the share of students who participated
in our study. The average participation rate is 58% in our sam-
ple. We ﬁnd no evidence of differential participation around

25We thank the editor for this suggestion. We also conduct bounding
exercises assuming 7%, 10%, and 15% attrition to examine the sensitivity
of our estimates. As shown in online appendix tables A8 and A9, the lower
bounds are statistically signiﬁcant for all male and female outcomes at 7%,
10%, and 15% attrition rate (except for exam score for women). While
we present both upper- and lower-bound estimates, the lower bounds may
be more relevant for us if the “marginally disappointed” individuals (with
higher cognitive ability, extraversion, conscientiousness, overconﬁdence,
and risk) were more likely to seek out non-DU alternatives for college
admission, creating sample selection in the comparison group.

the cutoff, thereby alleviating participation-related selection
concerns (table A10 in the online appendix).

Fourth, students who move across colleges during the ad-
missions process could be systematically different from those
who could have potentially moved, but did not, that is, those
with high school exit exam scores exceeding the required
cutoff but currently enrolled in a comparison college, raising
selection-related concerns. We ﬁnd no difference between
movers and potential movers in terms of the predetermined
characteristics, with movers being negligibly older (table A11
in the online appendix), attenuating the aforementioned
concerns.

Fifth, while we have some differences in sample sizes
across regressions in table 4 due to nonresponse (in the range
of 0.1% to 2% across all outcomes), our results are robust
to limiting the sample to those respondents for whom we
have data on all the outcomes (see table A12 in the online
appendix).26

Sixth, we show that the LATE estimates reported earlier in
table 4 are robust to (i) excluding the predetermined controls,
(ii) using triangular weights that assign greater weights to ob-
servations closer to the cutoff instead of rectangular weights,
and (iii) using outcome-speciﬁc optimal bandwidths as pre-
scribed by Calonico, Cattaneo, and Titiunik (2014) (see tables
A14 and A15 in the online appendix).

Next, another concern could be that the pools of applicants
might have been different across treatment and comparison
colleges during the admissions process. In the survey, we
also collected data on the colleges students had applied to.
We provided students with a list of seventeen colleges (of
which ﬁfteen were our sample colleges) and asked them to
indicate all colleges they had applied to. While this may be
subject to recall bias since at least two years had elapsed
since admission, we use these data in the following manner.
We construct a variable applicant that takes a value 1 for all
students currently enrolled in treatment colleges as well as
for any student from the comparison college who also applied
to the treatment college, and 0 otherwise. We ﬁnd that 87.6%
of individuals enrolled in the comparison colleges had also
applied to the treatment colleges. Our main results in table 4
are robust to limiting the sample to these “applicants” (table
A16 in the online appendix).

Finally, in estimating the returns to college quality, we also
implicitly assume that students prefer being in a more selec-
tive college to a less selective one. We now show that our
results are largely robust to relaxing this assumption. In the
survey, we asked students to rank a subset of the sample col-
leges as they would have at the time of admission. We use
these data in the following way. While constructing each of
our RD samples, we limit our sample to students who strictly
rank all the treated colleges higher than the comparison col-
leges and do not rank any of the comparison colleges at least

26We also ﬁnd the probability of missing data on the outcomes is not
systematic around the cutoff except for males’ overconﬁdence (table A13
in the online appendix).

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599

TABLE 6.—HETEROGENEOUS RETURNS TO COLLEGE QUALITY: BY COLLEGE RANKS

Economic Preferences

Personality Traits

University
Emotional Openness to
Exam Score Competitiveness Overconﬁdencea Preference Extraversion Agreeableness Conscientiousness Stabilitya Experience
(5)

Risk

(3)

(1)

(7)

(9)

(2)

(6)

(8)

(4)

A: Men in Rank 1
Enrolled in a

selective college

Observations

B: Women in Rank 1

Enrolled in a

selective college

Observations

−0.408
(2.991)
306

1.473
(1.473)
494

Enrolled in a

C: Men Excluding Rank 1 Cutoffs
−3.868
(4.397)
724

selective college

Observations

D: Women Excluding Rank 1 Cutoffs

Enrolled in a

selective college

Observations

6.380
(4.826)
822

−0.461
(0.293)
310

0.021
(0.189)
497

0.452
(0.610)
733

0.779
(0.573)
825

−0.351***
(0.098)
307

0.275
(0.260)
309

−0.341
(0.329)
305

−0.292*
(0.168)
492

0.641
(0.539)
728

−1.464
(1.105)
808

0.467** −0.356*
(0.213)
(0.190)
493
497

−1.031
(0.663)
729

−0.765**
(0.361)
716

1.633**
(0.804)
824

0.358
(0.791)
817

−0.016
(0.235)
303

−0.234
(0.197)
493

−0.056
(0.327)
710

1.342
(1.123)
812

−0.694**
(0.284)
308

−0.160
(0.164)
304

−0.427**
(0.203)
304

0.166
(0.299)
494

−0.299
(0.437)
721

−0.060
(0.570)
817

0.051
(0.395)
494

0.637
(0.648)
714

1.527
(1.207)
817

−0.219
(0.266)
492

0.416
(0.388)
714

0.749
(1.245)
818

This table reports instrumental variable estimates using the ﬂexible second-order polynomial described in the text. We control for mother’s education, father’s education, private school enrollment, age, income, and
religion in all speciﬁcations (see notes in table 1 for variable deﬁnitions). All regressions also include cutoff and month of survey ﬁxed effects. Standard errors clustered at 0.25 bins of the centered high school exit
exam score level are reported in parentheses. Signiﬁcant at ∗10%, ∗∗5%, and ∗∗∗1%.

a Due to multicollinearity, estimates reported in panel A are from ﬂexible linear regressions.

as high as any of the treated colleges. While the sample now
is limited and there is bound to be some recall error, we ﬁnd
that the effects on most economic preferences and personality
traits continue to hold (table A17 in the online appendix).

D. Heterogeneity

The existing literature has mainly studied effects of enroll-
ment in top educational institutions (Abdulkadiro˘glu et al.,
2014; Hoekstra, 2009) or average effects of enrolling in rel-
atively more selective institutions using data from a range
of institutions (Jackson, 2010; Lucas & Mbiti, 2014: Pop-
Eleches & Urquiola, 2013). However, returns to educational
quality may be nonlinear and vary across the quality distribu-
tion. For example, Hoekstra et al. (2018) examine schools of
varying selectivity in China and ﬁnd effects stemming from
enrollment present in only the most elite schools.

In a similar vein, in table 6 we examine if behavioral re-
sponses to college and peer environments differ depending on
how selective the college is. For this purpose, we reestimate
our regressions separately examining the effect of enrolling
in a rank 1 (most selective) colleges in panels A and B, and
the effects of enrolling in ranks 2 and 3 (less selective) col-
leges, that is, excluding rank 1 college cutoffs in panels C and
D. The returns to college quality may vary across these two
samples as the scope for improvement based on peer learning
may be lower in rank 1 colleges. Further, the adverse effects
of lower relative rank on academic self-concept may be more
acutely felt in the more selective colleges.

We ﬁnd that enrolling in a rank 1 college reduces con-
scientiousness, openness to experience, and overconﬁdence
among marginally admitted males and increases risk taking
and reduces overconﬁdence and extraversion for women. In

contrast, we ﬁnd that excluding rank 1 college cutoffs only
reduces extraversion for men and increases risk taking among
women. Overall, the results suggest that men are more likely
to be susceptible to relative rank concerns in the most selec-
tive colleges, which results in negative effects on personality
and behavior reported in panel A compared to panel C, table
6. On the other hand, for women, the results in panels B and
D remain largely similar. However, these results should be in-
terpreted with some caution as we lack the statistical power
to conduct a ﬁner analysis.

V. Conclusion

The existing empirical work on the returns to college qual-
ity has largely focused on test scores as outcomes of human
capital and generated mixed evidence. Scant attention has
been paid to underlying economic preferences and socioemo-
tional traits—facets of human capital that recent research has
documented as being important for one’s economic progress.
In this paper, our aim has been to ﬁll this critical gap by
examining the effects of college selectivity on cognitive, be-
havioral, and socioemotional outcomes, using data collected
from a large sample of students at a leading Indian univer-
sity. Exploiting the variation in college admission cutoffs, we
compare students just above the cutoff with those just below
the cutoff to determine the causal impact of enrollment in
a selective college, where they are surrounded by relatively
high-achieving peers and have a lower relative rank in their
peer group. We ﬁnd that marginally admitted female students
in more selective colleges experience improvements in scores
on standardized university exams. In terms of behavior and
personality, we ﬁnd that women just above the cutoff become
less risk averse and less overconﬁdent. On the other hand, men

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600

THE REVIEW OF ECONOMICS AND STATISTICS

in these colleges experience declines in extraversion and con-
scientiousness, pointing toward a weakened self-concept due
to a lower relative rank in their peer group, capturing the
small ﬁsh in a big pond effect. Further, we are also able to
show that variations in college qualities stem mainly from
variations in peer qualities (and rank) with no differences in
teacher presence or student-teacher ratios around the cutoff.
Some caveats remain. First, while our study shows that the
effects of selective colleges are not unequivocally positive
for the outcomes we consider in the short run, it is impor-
tant to bear in mind that in the long run, elite colleges are
still likely to lead to higher wages, access to well-connected
alumni networks, and better marriage prospects. Second, it
is possible that exposure to being in a selective college may
affect some socioemotional skills only in the long run, such
that our effects are underestimated. Third, while our study
does not encompass the entire population of DU students,
to the extent the admissions process is similar to that for
economics and commerce, our overall framework on peer
quality and rank concerns should matter in a similar way for
other disciplines as well. However, it should be noted that as
DU is one of the premier universities in India, its students
are not representative of the average Indian college student.
Although this study is unable to comment on the long-run
effects on personality traits and labor market outcomes, it
should encourage follow-up work that can can shed light on
longer-term impacts of such peer effects.

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