# The Disparate Impacts of College Admissions Policies on Asian American
Applicants

Joshua Grossman
Stanford University  Sabina Tomkins
University of Michigan  Lindsay Page
Brown University  Sharad Goel
Harvard University

###### Abstract

There is debate over whether Asian American students are admitted to selective
colleges and universities at lower rates than white students with similar
academic qualifications. However, there have been few empirical investigations
of this issue, in large part due to a dearth of data. Here we present the
results from analyzing 685,709 applications from Asian American and white
students to a subset of selective U.S. institutions over five application
cycles, beginning with the 2015–2016 cycle. The dataset does not include
admissions decisions, and so we construct a proxy based in part on enrollment
choices. Based on this proxy, we estimate the odds that Asian American
applicants were admitted to at least one of the schools we consider were 28%
lower than the odds for white students with similar test scores, grade-point
averages, and extracurricular activities. The gap was particularly pronounced
for students of South Asian descent (49% lower odds). We trace this pattern in
part to two factors. First, many selective colleges openly give preference to
the children of alumni, and we find that white applicants were substantially
more likely to have such legacy status than Asian applicants, especially South
Asian applicants. Second, after adjusting for observed student
characteristics, the institutions we consider appear less likely to admit
students from geographic regions with relatively high shares of applicants who
are Asian. We hope these results inform ongoing discussions on the equity of
college admissions policies.

## Introduction

Over the last several decades, questions have been raised over whether
selective colleges in the U.S. discriminate against Asian American applicants
in admissions decisions (Chun and Zalokar, 1992; Takagi, 1992; Espenshade et
al., 2004; Espenshade and Radford, 2009; Long, 2004; Park, 2019; SFFA v.
Harvard, 2019; Arcidiacono et al., 2022; Gelman et al., 2019). In the 1980s,
Brown and Stanford formed committees to audit their own admissions policies
and practices (Chun and Zalokar, 1992; Takagi, 1992). Brown found evidence of
discrimination in its admissions process; Stanford did not find clear evidence
of bias, but could not fully explain its lower acceptance rates of Asian
American applicants relative to white students. A 1990 report by the U.S.
Department of Education’s Office of Civil Rights (OCR) investigated
allegations that Harvard capped the number of Asian American students it
admitted (Chun and Zalokar, 1992). OCR found no evidence of an Asian quota,
but concluded that Asian American applicants were less likely to be admitted
than white students with similar academic qualifications. OCR further found
that this disparity largely disappeared once recruited athletes and the
children of alumni (“legacies”) were excluded from its analysis, suggesting
the gap in acceptance rates was driven by Harvard’s stated preference for
admitting students from these two groups (Hurwitz, 2011; Park, 2019; Chetty et
al., 2023). Most recently, in a 2023 decision, the Supreme Court ruled that
Harvard engaged in unconstitutional racial balancing, holding the Asian
American share of admitted students to approximately 20%—though Harvard denied
doing so. In the more than 30 years since the OCR investigation, there have
been limited third-party, applicant-level empirical analyses of potential
discrimination in college admissions decisions against Asian American
applicants. Over this time span, both the demographics of the United States
and the educational landscape have changed substantially. Asian American
representation among K–12 public school students has more than doubled,
increasing from 3% in 1993 to 7% in 2020 (Nowicki, 2022), and the overall
admission rate to Harvard has dropped from 18% in 1990 to 5% in 2020 (Lee,
1993; Fu and Kim, 2020). These changes suggest a need to reexamine college
admissions policies for potential disparate impacts on Asian American
applicants.

Here we analyze 685,709 first-year college applications submitted by 292,795
Asian American and white students to a subset of U.S. institutions with
relatively low admit rates and relatively high yield rates. All of the
applications we consider were submitted via a national postsecondary
application platform over five application cycles, from the 2015–2016 cycle to
the 2019–2020 cycle.111Each of the institutions we consider receives the
majority of first-year applications from students applying via the application
platform (Table LABEL:fig:n_apps_by_year).  We exclude students who attend a
high school outside of the United States or who report primary citizenship
outside of the United States. Given the complex patterns of immigration and
marked heterogeneity in experiences across subgroups, we disaggregate our
analysis by three regions of origin self-reported by the Asian American
applicants in our dataset: South Asia, East Asia, and Southeast Asia.222Once
applicants indicate being “Asian”, they have the option to select one or more
of 9 countries of origin; they can additionally indicate being “Other East
Asian”, “Other South Asian,” or “Other Southeast Asian”. We classify the
listed countries as follows: China, Japan, and Korea as East Asia; India and
Pakistan as South Asia; and the Philippines, Vietnam, Cambodia, and Malaysia
as Southeast Asia. 3% of Asian applicants select countries that span multiple
regions. In these cases, we randomly assign one of the spanned regions. 2% of
Asian applicants do not select a country of origin. These students are
excluded from the analysis.  To preserve confidentiality, we focus on broader
patterns rather than on individual institutions, and we report aggregate
results across the combined set of colleges and universities we consider. In
particular, our main outcome of interest is whether applicants were admitted
to at least one of these institutions. One limitation of our analysis is that
we do not directly observe admissions decisions, and so we infer these
decisions based on enrollment choices, as described below.

After excluding students who we infer to be recruited athletes, we estimate
that South Asian applicants had 49% lower odds of admission to the subset of
schools we consider than white applicants with comparable test scores, high
school grade-point averages, and extracurricular activities. We estimate that
both East Asian and Southeast Asian applicants had 17% lower odds of admission
to these schools. After additionally adjusting for whether a student applied
early to any considered college or university, the student’s high school, and
whether the student is a legacy applicant, we estimate that Southeast Asian
students were accepted at similar rates to white students, and that East Asian
students had 10% lower odds of admission than white students. But, we estimate
that South Asian applicants still had 30% lower odds of acceptance to these
institutions than white students after adjusting for all available information
in our data. We note, however, that we do not have access to all materials
submitted by and about applicants, such as essays, letters of recommendation,
alumni interviews, and admission officer ratings. Finally, we explore how the
relative share of Asian American and white enrollees might change at the
colleges and universities we consider under various hypothetical admission
policies. Under a policy that admits students solely on the basis of
standardized test scores and participation in extracurricular activities—and
holding fixed the combined number of enrolled Asian American and white
students—we estimate that enrollment of South Asian students and East Asian
students would increase substantially, while the number of Southeast Asian
students would remain approximately the same.

Concerns about the disparate impacts of college admissions policies on Asian
American students are often entangled with discussions about affirmative
action (West-Faulcon, 2016; Park et al., 2023; Hughes et al., 2016; Takagi,
1992; Kim, 2022; Karabel, 2005; Gersen, 2017; Antonovics and Sander, 2013;
Gelman et al., 2019). At their core, however, these two issues—affirmative
action and differences in the admission rates of similarly qualified white and
Asian American students—are conceptually distinct. In particular, during the
time period we consider, institutions could have admitted Asian American
applicants at rates comparable to similarly qualified white students while
still giving preference to applicants from groups underrepresented in higher
education.333As of 2023, explicit racial preferences in college admissions are
no longer legally permissible (Students for Fair Admissions, Inc. v. President
and Fellows of Harvard College, 2023).

## Data description

Our analysis is based on applications submitted through a national
postsecondary application platform. The data we use contain detailed,
anonymized information on each student, including race and gender;
standardized test scores (ACT and/or SAT); high school grade-point average
(GPA); Advanced Placement (AP) exam scores; structured descriptions of their
extracurricular activities (e.g., the number of hours they spent participating
in various clubs or sports); the location and other characteristics of the
high school they attended; whether their parents attended college, and, if so,
the colleges they attended; whether they received an application fee waiver (a
proxy for financial need); the set of colleges to which they applied via the
platform; and whether they applied early action or early decision to any of
the institutions we consider (Table LABEL:fig:observed-unobserved-vars). If a
student took the SAT, we convert their SAT score to an equivalent ACT score to
facilitate comparisons between applicants and aid interpretation.444If
students took both the ACT and SAT, or took either test more than once, we
choose the highest ACT-equivalent score achieved on a single test.  Although
we have quite detailed individual-level data, we do not have access to the
full set of application materials, including student essays, letters of
recommendation, or intended major. We also do not have access to internal
college evaluations, such as interviewer ratings.

We approximate admissions decisions by first inferring enrollment decisions.
We infer enrollment by observing the school to which a high school counselor
sent a student’s official high school transcript, information that is
collected by the platform. (NB: official transcripts typically are required by
colleges to formalize acceptance decisions.) We then infer that students were
admitted to at least one of the schools we consider if, and only if, they sent
a transcript to (i.e., ultimately enrolled in) one of those schools. This
inference rests on an assumption that students who were admitted to at least
one of the schools we consider ultimately attended one of those schools. While
imperfect, three points suggest this process yields results that are suitably
accurate for our purposes. First, we assessed the quality of our enrollment
inference by matching 5,000 randomly selected applicants to the schools we
consider to be their true enrollments as reported by the National Student
Clearinghouse. We find that the estimated precision of our enrollment
inference strategy is 97% with an estimated recall of 91%. We further find
that accuracy is comparable across race groups (see the Methods section in the
Appendix). Second, the schools we consider have relatively high yield rates,
suggesting that admission to these schools is strongly correlated with
enrollment. Finally, we find qualitatively similar results with an estimation
strategy that holds under the weaker assumption that enrollment is independent
of race, conditional on acceptance and other observed student characteristics
(see the Estimating Admission Rates section in the Appendix for details).

Our study pool is comprised of 685,709 applications submitted by 292,795
students to the colleges and universities we consider in the 2015–2016 through
the 2019–2020 application cycles. We include Asian and white applicants who
attended a U.S. high school, excluding students from high schools for which we
cannot reliably infer college enrollment (see the Methods section in the
Appendix and Table LABEL:fig:summary-stats-reliable-transcript). We cannot
identify athletic recruits with certainty, but we exclude from our sample
students who appear to be athletic recruits based on the timing of their
applications and their reported extracurricular activities (see the Methods
section in the Appendix). Within our study pool, 36% of applicants self-
identify as Asian, with 51%, 15%, and 34% of these students self-identifying
as East Asian, Southeast Asian, and South Asian, respectively. Finally, we
supplement our data from the platform with public high school data from the
Common Core of Data (CCD), private high school data from the Private School
Universe Survey (PSS), and rurality data at the ZIP code level from the
Economic Research Service of the U.S. Department of Agriculture.

## Results

Among applicants to the colleges and universities we consider, we estimate
that 16% of East Asian, 8% of Southeast Asian, and 10% of South Asian students
were admitted to at least one of these institutions, compared to 12% of white
applicants. While these aggregate admissions rates differ by race and
ethnicity, they do not account for differences in qualifications across
groups. For example, Asian American applicants had, on average, higher
standardized test scores than white applicants (Table LABEL:fig:summary-stats-
by-race). As a first step to account for these differences, in Figure 1 we
show estimated admissions rates by standardized test score for Asian American
applicants and white applicants. We find that Asian American students were
admitted at consistently lower rates than white applicants with comparable
test scores, with the largest gap for South Asian applicants. For instance,
among applicants with an ACT (or ACT-equivalent) score of 34—placing them in
the 99th percentile of test takers—we estimate that 16% of white students were
admitted compared to 9% of South Asian students, a relative gap of 43%.

Figure 1:  _Estimated rate of admission to at least one of the selective
institutions we consider as a function of standardized test score, for Asian
American applicants and white applicants in the study pool. Asian American
applicants typically were admitted at lower rates than white applicants with
identical test scores, with the largest gap for South Asian students. Among
admits in our study pool who report ACT or SAT scores, 93% have ACT (or ACT-
equivalent) scores at or above 32. Percentiles are derived from all students
who took the ACT in 2018(ACT, Inc., 2018). Point sizes are proportional to the
number of applicants in each group._

Standardized test scores are one factor among many that colleges consider when
determining whom to admit. Additional criteria that we are able to observe
include high school grade-point average (GPA), participation in
extracurricular activities, legacy status, and the state in which each
applicant’s high school is located. To understand the extent to which these
other considerations may explain the observed disparities in admissions rates,
we fit a series of nested logistic regression models of the following form:

$\displaystyle\Pr(Y_{i}=1)=\text{logit}^{-1}(\beta_{0}\\!+\\!\beta_{S}\mathbbm{1}_{S}\\!+\\!\beta_{E}\mathbbm{1}_{E}\\!+\\!\beta_{SE}\mathbbm{1}_{SE}\\!+\\!X_{i}\beta_{X}),$

where $Y_{i}$ is a binary variable indicating whether applicant $i$ was
admitted to any college or university we consider; $\mathbbm{1}_{S}$,
$\mathbbm{1}_{E}$, and $\mathbbm{1}_{SE}$ indicate whether the applicant
identified as South Asian, East Asian, or Southeast Asian, respectively; and
$X_{i}$ is a vector of additional covariates (e.g., test scores and GPA) that
we vary across models, with $\beta_{X}$ the corresponding vector of
coefficients. Our key coefficients of interest are $\beta_{S}$, $\beta_{E}$,
and $\beta_{SE}$, which yield estimates of the gap in admissions rates between
white applicants and Asian American applicants in the three Asian subgroups
that we consider. We find similar results if we fit separate models comparing
white applicants to applicants in each Asian subgroup individually (Tables
LABEL:fig:robustness-east-asian–LABEL:fig:robustness-southeast-asian).

Table 1 shows, for nine models that include different subsets of control
variables, the fitted coefficients for each of the three Asian subgroups (see
also Tables LABEL:fig:basket-year-model-covariates–LABEL:fig:location-model-
covariates). Coefficients are exponentiated for ease of interpretation as odds
ratios. The first model includes only fixed effects for the application season
and the subset of colleges (or application “basket”) to which the student
applied—among the full set of colleges we consider—facilitating comparisons
among groups of students who applied in the same year and to the same subset
of colleges. The corresponding coefficients are thus akin to raw admissions
odds ratios across groups, without adjusting for differences in applicant
credentials.

The second and third models in Table 1 additionally adjust for measures of
academic preparation, including SAT/ACT alone (Model 2) and, additionally,
GPA, AP test scores, and SAT II subject test scores (Model 3). These academic-
preparation models corroborate the visual pattern in Figure 1: we estimate
that Asian American students—especially South Asian students—had substantially
lower odds of admission than white students with similar test scores and
related academic credentials. These disparities largely persist when we
progressively adjust for extracurricular activities (Model 4); gender and
family characteristics, like whether the student received an application fee
waiver (Model 5); and whether the student applied early (Model 6).

Outcome: Inferred acceptance to at least one college or university we consider
Basket+Year SAT/ACT GPA+AP+SAT2 Activities Sex+Family Early App Legacy
Location+HS All (1) (2) (3) (4) (5) (6) (7) (8) (9) South Asian 0.66∗∗∗
0.56∗∗∗ 0.59∗∗∗ 0.51∗∗∗ 0.51∗∗∗ 0.52∗∗∗ 0.61∗∗∗ 0.60∗∗∗ 0.70∗∗∗ (0.01) (0.01)
(0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.02) Southeast Asian 0.64∗∗∗
0.73∗∗∗ 0.78∗∗∗ 0.83∗∗∗ 0.81∗∗∗ 0.84∗∗∗ 0.88∗∗∗ 0.94 1.02 (0.02) (0.02) (0.02)
(0.03) (0.03) (0.03) (0.03) (0.03) (0.04) East Asian 1.11∗∗∗ 0.86∗∗∗ 0.85∗∗∗
0.83∗∗∗ 0.79∗∗∗ 0.73∗∗∗ 0.90∗∗∗ 0.88∗∗∗ 0.90∗∗∗ (0.02) (0.01) (0.01) (0.02)
(0.01) (0.01) (0.02) (0.02) (0.02) Aggregated Asian 0.88∗∗∗ 0.74∗∗∗ 0.75∗∗∗
0.72∗∗∗ 0.69∗∗∗ 0.67∗∗∗ 0.79∗∗∗ 0.79∗∗∗ 0.85∗∗∗ (separate model) (0.01) (0.01)
(0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.02) Basket+Year X X X X X X X X X
SAT/ACT X X X X X X X X GPA+AP+SAT2 X X X X X X X Activities X X X X X X
Sex+Family X X X X X Early App X X Legacy X X Location+HS X X Observations
292,795 292,795 292,795 292,795 292,795 292,795 292,795 292,795 292,795 In-
sample AUC 0.65 0.75 0.79 0.81 0.82 0.85 0.83 0.84 0.88 White base rate 12%
12% 12% 12% 12% 12% 12% 12% 12% ∗p$<$0.05; ∗∗p$<$0.01; ∗∗∗p$<$0.001

Table 1:  _Estimated conditional odds of admission to at least one college or
university we consider for Asian American applicants in the study pool
relative to white applicants. Coefficients are estimated via logistic
regression, and are exponentiated for ease of interpretation as odds ratios.
After adjusting for test scores and extracurricular activities (Model 4),
South Asian students had 48% lower estimated odds of admission relative to
white students, with East Asian and Southeast Asian applicants exhibiting
smaller but statistically significant gaps (17% lower odds of admission).
These disparities appear to be explained in part by legacy preferences (Model
7) and geography (Model 8). The “Aggregated Asian” coefficients are computed
from separate models that do not separate students into Asian subgroups._

Next, with Model 7, we account for whether a student is the child of an alum.
After adjusting for legacy status—in addition to all of the above mentioned
factors—we see large reductions in the estimated disparities in acceptance
rates for all three Asian subgroups we consider. Figure 2 helps explain this
result. The top panel of the figure shows estimated admission rates for Asian
American applicants and white applicants conditional on legacy status and test
scores.555In Figure 2, we follow convention and define legacy status to mean
an applicant had at least one parent who attended one of the colleges or
universities we consider as an undergraduate, and the student applied to the
institution(s) that their parent(s) attended. In our regression models, we
additionally adjust for other familial connections to the included colleges
and universities, like a parent attending graduate school there or having two
parents with undergraduate degrees from the same school 666In prior work
examining the effect of legacy status on admission to elite institutions,
Hurwitz (2011) found that the magnitude of the legacy effect is larger in
models that account for the application components that we do not observe.
For a given test score, we estimate that applicants—both white and Asian
American—with legacy status were more than twice as likely to gain admission
than applicants without legacy status. In the bottom panel of Figure 2, we
present prevalence of legacy status among applicants with an ACT-equivalent
test score of 32 or above, mirroring the focus of the upper panel. Here, we
observe that white applicants were approximately three times more likely to
have legacy status than East Asian and Southeast Asian applicants, and almost
six times more likely than South Asian students. Thus, even though estimated
acceptance rates conditional on test score and legacy status were similar
across race and ethnicity, white students appear to benefit from being
substantially more likely to have legacy status.

In theory, the higher estimated admissions rates that we observe for legacy
applicants may stem both from admissions practices that favor the children of
alumni and from the potentially greater social capital of legacy students. We
note, however, that Model 5 adjusts for whether an applicant had a parent who
attended a top-50 institution (based on 2019 U.S. News rankings) not included
in the subset of colleges on which we focus, or attended one of the colleges
in our subset to which the student did not apply—proxies for having high
social capital distinct from legacy status specifically. The change in
disparities that we observe moving from Model 5 to Model 7 thus appears
attributable to the specific benefits of having legacy status, rather than the
more generalized benefits of high social capital.

Figure 2:  _Estimated rate of admission to at least one college or university
we consider for white applicants and Asian American applicants with high ACT
or SAT scores. Across test scores, we estimate that applicants with a parent
who attended one of the selective institutions we consider as an undergraduate
are more than twice as likely to be admitted than non-legacy applicants with
the same test scores. The bottom panel shows the proportion of applicants with
high test scores who have legacy status, disaggregated by race. High-scoring
white applicants are three to six times more likely to have legacy status than
high-scoring Asian American applicants, suggesting white applicants
disproportionately benefit from a boost in admission rates afforded to those
with legacy status._ Figure 3:  _For each U.S. state, overall estimated
admission rate to at least one institution among the subset of selective
schools we consider for white applicants and Asian applicants with an ACT-
equivalent score at or above 32, with the proportion of high-scoring white and
Asian applicants who identify as Asian on the horizontal axis. Point sizes are
proportional to the number of high-scoring white and Asian applicants from the
state who applied to one of the institutions we consider. The red least-
squares regression line is weighted by the same count of applicants. States
with a greater share of Asian American applicants have, on average, lower
estimated admission rates for high-scoring applicants._

Finally, we examine the relationship between estimated acceptance rates and
geography. For each state, Figure 3 displays the estimated admission rate of
high-achieving applicants—with ACT-equivalent scores of 32 or above—to the
fraction of applicants from that state who were Asian American. In computing
this proportion, we limit to white applicants and Asian American applicants,
and point sizes are proportional to the total number of high-scoring white and
Asian American applicants in each state. The negatively sloped regression line
shows that states with a larger fraction of Asian American applicants tended
to have lower estimated admission rates. Further, states with a higher
proportion of Asian American applicants tended to have higher average test
scores, suggesting the geographic trend is not driven by a gap in academic
achievement (Figure A2). This geographic pattern also persists when we exclude
applicants from California, and when we disaggregate the data to the level of
high school instead of state (Figures A1 and A3).

Model 8 in Table 1—which adjusts for location as well as academic and
extracurricular performance but not legacy status—shows that these apparent
geographic preferences account for much of the admissions gap between white
and Asian American applicants. Model 9, the last one we consider, adjusts for
all application information available to us, including both legacy status and
geography. After adjusting for this rich set of covariates, we see that the
estimated admissions gap between Southeast Asian and white applicants largely
disappears, though we still find that white students have higher estimated
odds of admission than otherwise similar East Asian and South Asian
applicants. It is unclear what may account for these remaining disparities,
though it bears repeating that admissions officers have access to more
complete application materials than do we, including letters of
recommendation, essays, and interview assessments.

We conclude our analysis by exploring how the relative share of Asian American
students at the institutions we consider might change under various
hypothetical admissions policies. In line with our analysis above, we restrict
our attention to white students and Asian American students. Specifically, we
hold fixed the combined number of students in these groups (approximately
mirroring historical admissions outcomes, as shown in Figure A4), and so any
increases in Asian American enrollment necessarily imply decreases in
enrollment of white students. Any exercise of this sort is inherently
speculative—in part because changes in admissions policies could alter
application behavior—but we still believe it is informative to gauge the
approximate magnitude of effects.

As a baseline, the top row of Figure 4 shows the estimated share of enrollees
in our data from the three Asian subgroups of interest. The rest of the figure
shows the estimated share of enrollees from these subgroups under eight
hypothetical admissions policies that are divided into four categories. In the
first category—which we call “top-$k$” policies—we imagine admitting students
with the highest ACT-equivalent scores, with ties broken randomly. In the
second category, “random above threshold,” we consider policies that randomly
admit students above an ACT-equivalent score $t$ such that admitted students
have a mean score equal to that of actual enrollees (Sandel, 2020). For both
of these categories we consider two variants: the “ACT” variant selects from
the entire applicant pool of the schools we consider, while the “ACT+ECs”
variant selects only from applicants with at least as many hours of reported
extracurricular (EC) activities over four years of high school as the median
of the hours reported by all enrollees. Under all four policies, we estimate
the same or larger shares of Asian American students compared to what we
observe in the data. Asian American students report, on average, fewer
extracurricular hours than white applicants, so the ACT+ECs policy variant
admits fewer Asian American applicants than the ACT variant.

The final two categories we consider investigate outcomes under hypothetical
policies that maintain both the current number of enrollees from each state
and the total number of enrollees with legacy. Specifically, we first divide
our historical data into 102 (2 x 51) cells consisting of legacy and non-
legacy applicants from each U.S. state and Washington, D.C.; we then in turn
apply each of the four policies described above to each of the 102 cells,
ensuring for each cell that the number of students enrolled under the
hypothetical policies matches the historical enrollment numbers. With these
added legacy and geographic constraints, the share of Asian American enrollees
is smaller than under the unconstrained analogs, as expected given our results
above. But, even with these constraints, the number of Asian American
enrollees across policies is still similar to or larger than the status quo.

Figure 4:  _Estimated enrollment of Asian American students at the
institutions we consider under eight hypothetical admissions policies, with
the top panel showing the actually observed demographic composition in our
historical data. In all cases, we consider only the subset of Asian American
students and white students, and so increases in Asian American enrollment
correspond to decreases in the enrollment of white students. In most
instances, the hypothetical policies we consider lead to an increase in
enrollment of Asian American students, including those that preserve the
number of legacy students and the number of enrollees from each state in the
historical data._

## Discussion

Based on a large-scale analysis of applications to a subset of selective U.S.
colleges and universities, it appears that that Asian American students were
less likely to be admitted than white students with comparable academic
credentials and extracurricular activities, a disparity that is particularly
pronounced for South Asian students. It further appears that much—though not
all—of this gap is attributable to admissions practices that favor the
children of alumni and apparent geographic preferences. These disparities
likely stem from a complex set of objectives that universities work to
balance, and are not necessarily driven by explicit or implicit racial
preferences. Nonetheless, our results prompt questions about the equitable
design of college admissions policies.

In our primary analysis, we excluded applicants who we inferred were recruited
athletes, under the assumption that filling sports teams is a hard constraint
for many universities, and that doing so involves qualitatively distinct
admissions criteria. We note, though, that athletic recruits are
disproportionately likely to be white rather than Asian American: in our study
pool, white applicants outnumber Asian American applicants by a factor of
about two to one, but among inferred recruits, white applicants outnumber
Asian American applicants by a factor of four to one. As a result, if we do
not proactively exclude recruited athletes from our analysis, we find an even
larger gap in the estimated admissions rates between Asian American students
and white students with comparable academic credentials (Tables
LABEL:fig:robustness-east-asian–LABEL:fig:robustness-southeast-asian).

Our results are subject to two key limitations. First, we have imperfect
information on college admissions decisions. In our analysis, we infer
admissions decisions from enrollment choices, where we assume that students
who applied to but did not ultimately attend one of the selective schools we
consider were not admitted to any of those schools. This assumption only
allows us to approximately reconstruct admissions decisions. However, given
the relatively high yield rates of the universities we consider, we believe
this assumption is suitably accurate for our analysis. Further, we find
qualitatively similar results under an alternative estimation strategy that
rests on the weaker assumption that enrollment decisions are independent of
race, conditional on acceptance and other observed student characteristics
(see the Estimating Admission Rates section in the Appendix). Finally, our
results remain largely the same if we eliminate any one school from our
analysis (Tables LABEL:fig:robustness-east-asian–LABEL:fig:robustness-
southeast-asian), suggesting the robustness of our results to the exact subset
of schools we consider.

Second, we do not have access to each student’s complete application
materials. Specifically, we do not observe a student’s intended major, essays,
teacher recommendations, transcripts, interview ratings, and admission officer
ratings. It is thus possible that students who we observe to have similar
academic and extracurricular credentials are in fact different in important
ways that are revealed in these other materials. We note, however, that
results made public through litigation suggest that—at least in the case of
Harvard’s admissions practices—the disparities we identify persist after
adjusting for several additional markers of academic and extracurricular
excellence, including admission officer ratings of each applicant’s academics,
extracurriculars, teacher recommendations, and counselor recommendations (cf.
Figure 6.1 in (Students for Fair Admissions, Inc. v. President and Fellows of
Harvard College, 2017), Model 4 to Model 5, Table B.7.1 and B.7.2 show
coefficients).777Expert testimony provided in the Harvard case indicates that
disparities in admission rates at Harvard are reduced after adjusting for
admission officers’ assessments of an applicant’s “personal qualities” and
admission officers’ “overall rating” of an applicant. There is worry, however,
that assessments of “personal qualities” are more subjective than ratings of
academic and extracurricular achievements, are less clearly connected to
merit, and may be influenced by implicit or explicit racial biases. Further,
“overall ratings” are so closely tied to the final admissions decision, that
we would expect adjusting for them would mask any disparities (Jung et al.,
2018).  Further, Kim (2022) finds that Asian American and white college
applicants with similar academic credentials receive letters of recommendation
that are “broadly similar in content and tone.”

Discussions of college admissions practices impacting Asian Americans often
revolve around affirmative action. But, as we noted at the start, these issues
are conceptually distinct. In theory, one can both implement affirmative
action policies that maintain the share of students on campus from groups that
are underrepresented in higher education while simultaneously admitting Asian
American students at the same rate as white students with similar academic and
extracurricular credentials. In such a case, we would expect the number of
enrolled white students to decrease, not the number of racial minorities.
During the time period we examined, affirmative action was widely used for
shaping the diversity of college campuses, meaning the scenario described
above was an option available to college administrators. Thus, at the very
least, our results shed light on past admissions choices and their
consequences for Asian American college applicants. Now that affirmative
action is legally prohibited, institutions will need to reconsider how
applicants are evaluated in order to ensure equitable admissions processes and
to maintain diverse campuses. For example, existing decision-making processes
that afford preference to the children of alumni appear to not only
disadvantage Asian Americans but also other racial minorities (Figure A5).
Looking ahead, we hope our findings facilitate ongoing discussions about the
design and implementation of equitable admissions policies.

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##  Appendix A Appendix

Methods

  * •

The first subsection describes the data filters used to construct the study
pool.

  * •

The second subsection describes how a record of a sent transcript is deemed as
a reliable or unreliable signal of enrollment.

  * •

The third subsection describes our validation of sent transcripts as a signal
of enrollment using true records of enrollment from the National Student
Clearinghouse.

  * •

The fourth subsection describes how we attempt to identify potential athletic
recruits among the applicant pool.

“Estimating Admission Rates” summarizes a complementary analysis contingent on
the weaker assumption that enrollment is independent of race, conditional on
acceptance and other observed student characteristics.

Table LABEL:fig:n_apps_by_year shows the proportion of publicly reported
applications reported by the institutions in the main analysis that were
submitted via the national postsecondary application platform.

Table LABEL:fig:summary-stats-reliable-transcript shows summary statistics for
applicants from high schools with reliable and unreliable records of sent
transcripts.

Table LABEL:fig:summary-stats-by-race shows summary statistics for white, East
Asian, South Asian, and Southeast Asian applicants in the study pool.

Table LABEL:fig:observed-unobserved-vars lists relevant covariates observed
and unobserved by the authors and by the application platform.

Tables LABEL:fig:basket-year-model-covariates through LABEL:fig:location-
model-covariates list and describe the covariates used in the main model
specifications.

Tables LABEL:fig:robustness-east-asian through LABEL:fig:robustness-southeast-
asian describe robustness checks of the main model specification.

Tables LABEL:fig:replication_data_white through
LABEL:fig:aggregated_replication_data show the number of applicants in various
coarsened groups defined by race and ethnicity, test score, legacy status,
region of the country, and enrollment signal.

Figures A1 through A3 show robustness checks of the state-level analysis in
the main text.

Figures A4 and A5 support the hypothetical policy results in the main text.

## Methods

### Data filtering

We begin our core analysis with the 551,292 South Asian, East Asian, Southeast
Asian, and white students who submitted at least one application to one of the
selective schools we consider via the national postsecondary application
platform in the 2015–2016 application cycle through the 2019–2020 cycle. We
then filter to the 449,564 applicants who attended a high school in a U.S.
state or the District of Columbia, and who did not report citizenship outside
of the United States. We next limit to the 297,417 applicants who attended
high schools with official transcript sends that, to the best of our
knowledge, accurately reflect an intention to enroll. Finally, for our main
analysis, we restrict to the 292,795 applicants who, to the best of our
knowledge, are not athletic recruits.

### Identifying high schools with reliable transcript sending behavior

Typically, when an applicant intends to enroll in a particular college to
which they were admitted, their high school must submit an official transcript
to the college. Many high schools use the same portal to submit official
transcripts, and the platform observes when an applicant’s transcript is sent
via this portal. While the platform does not observe acceptances or
enrollments, high school transcript sends serve as a highly accurate
enrollment proxy for the subset of applicants who meet the following
conditions:

  * •

First, the applicant’s high school must use the transcript sending platform.
In other words, we only include applicants whose high school sent at least one
transcript via the platform in the same year the applicant applied.

  * •

Second, transcript sends must be targeted to specific colleges. If a high
school counselor does not track the intended enrollment of a particular
applicant, they may indiscriminately sent final transcripts to every college
to which the applicant submitted an application.

We define a high school’s transcript sending behavior as “reliable” in a given
year if the high school submits the same number of transcripts as applications
for fewer than 5% of applicants who submit at least two applications. In this
definition, we do not consider applicants who submit one application and one
transcript, as we cannot reliably guess whether the student intended to enroll
or the transcript was sent indiscriminately by their counselor. In Tables
LABEL:fig:robustness-east-asian-LABEL:fig:robustness-southeast-asian, we
replicate the main results with thresholds other than 5%, finding
qualitatively similar results.

Among the applicants from high schools who exhibit reliable transcript sending
behavior, we further exclude the applicants who submitted the same number of
transcripts as applications and submitted at least two applications, since
these students’ counselors likely sent the transcripts indiscriminately.

### Verifying transcript send enrollment signal with NSC data

Using a stratified random sample of 5,000 enrollments obtained from the
National Student Clearinghouse (NSC), we find that our enrollment heuristic
has nearly perfect precision (97%) and high, but not perfect, recall (91%).
The 5,000 sampled applicants were selected from the study pool, which includes
only those applicants whose high school met the threshold for reliable
transcript sending behavior in the given application year.

Among the first stratum of 2,500 applicants with a transcript sent to at least
one of the selective schools we consider, we were able to match 2,336 (93%) to
NSC enrollments. We find that 2,271 actually enrolled in one of the selective
schools we consider within a year of admission. Thus, the estimated precision
of the enrollment proxy is 97%. Precision is nearly identical across race
groups: white students have a precision of 97%, while Asian American students
have a precision of 97.1%. Precision is also similar across application years,
high school states, and application fee waiver status.

We are ultimately interested in the likelihood of admission to our specific
set of selective schools. Thus, the transcript sending signal is arguably
superior to knowledge of actual enrollment, as an admitted student with the
intention to enroll may later decide not to enroll. If we were to use the
student’s true enrollment as a signal of admission to one of the selective
schools, we would falsely assume that the student was not admitted to one of
the selective schools. Thus, the true precision of our enrollment signal may
be even higher than 97%.

For the second stratum of 2,500 applicants who applied to one of the selective
institutions we consider but did not send a transcript to one of these
institutions, we were able to match 2,294 (92%) to NSC enrollments. 43 (2%) of
the 2,294 matched applicants ended up enrolling at one of the selective
institutions we consider within a year of applying. We attribute this
discrepancy to a number of potential factors: students may be admitted off the
waitlist at one of the selective schools we consider; individual counselors
may not use the transcript sending portal even if the rest of the high school
uses the portal; or students may transfer to one of the selective schools we
consider after initially being rejected. To determine the source of the
discrepancy, we disaggregate by whether the matched applicant sent a
transcript to any school on the platform.

1,247 of the 2,294 matched applicants sent a transcript to a school on the
platform outside of the specific set of selective schools we consider. Of
these 1,247 applicants, 13 actually ended up enrolling at one of these
selective schools within a year of applying, but 10 of these 13 enrolled first
at the school to which they sent a transcript. We assume that these 10
students transferred to one of the selective schools we consider after
initially being rejected, so we exclude them from the error calculation, as
our proxy for rejection is assumed to be correct for the application year. The
remaining three applicants may have been admitted off the waitlist at one of
the schools we consider after initially committing to a different school. In
the study pool, 139,888 applicants sent a transcript to a school on the
platform outside of the specific set of colleges and universities we consider.
Thus, from these applicants, we estimate 139,888*(3/1,247) = 337 unobserved
enrollments at the considered schools.

The remaining 1,057 of the 2,294 matched applicants did not send a transcript
to any school on the platform. 30 (3%) of the 1,057 applicants ended up at one
of the selective schools we consider. We attribute these 30 enrollments to the
idiosyncratic counselor behavior described above. In the study pool, 117,138
applicants did not send a transcript to any school on the platform. Thus, we
estimate 117,138*(30/1,057) = 3,325 unobserved enrollments from these
applicants.

In sum, in the study pool, we observe 35,769 enrollments to the selective
schools we consider. We estimate 337 + 3,325 = 3,662 unobserved enrollments.
Thus, our estimated recall is 35,769/(35,769+3,662) = 91%. Given that our
estimated recall is based on discrepant enrollments of only 33 matched
applicants, we cannot meaningfully evaluate recall across groups.

### Identifying potential athletic recruits

In order to field competitive athletic teams, universities often recruit
students with exceptional athletic ability. University admission offices may
have a hard constraint of filling athletic teams with a sufficient number of
talented student athletes. Admissions decisions for student athletes are
primarily the choice of athletic coaches, who are incentivized to offer
admission to recruits with the greatest athletic ability who meet or exceed
the minimum academic qualifications for admission.888See “Before Recruiting in
Ivy League, Applying Some Math” [New York Times, 2011] and “A Little Secret:
Athletics at the Most Selective Colleges and Universities in the Nation:
College and University Admissions, Part III” [Huffington Post, 2011].
Further, student athletes are typically admitted early.999See “Varsity
athletes, admissions and enrollment at top colleges” [Washington Post, 2019]
We thus attempt to exclude students who, to our knowledge, may be athletic
recruits, as the admission process for student athletes differs considerably
from that of typical applicants. As a robustness check, we repeat the main
analysis without excluding potential recruits, finding qualitatively similar
results (Tables LABEL:fig:robustness-east-asian-LABEL:fig:robustness-
southeast-asian).

While we do not observe true athletic recruitment status, we have access to
detailed information about each applicant’s extracurricular participation. We
know not only the extracurricular activities of each applicant, but also the
number of years participated in each activity during high school, the order in
which those activities are reported in their application, and whether the
applicant intends to continue participation in the activity in college. We
assume that student athletes will list their athletic participation as the
first activity in their application. We further assume that student athletes
will have participated in their first-listed sport during all four years of
high school, and that they intend to continue participating in their first-
listed sport in college. Finally, we assume that student athletes will only
apply to one college or university in an early round, and that they will
always send a transcript to that one institution.

Among the 40,391 white and Asian American applicants who submitted a
transcript to one of the institutions we consider in the main analysis and who
also attended a high school in the U.S. with reliable transcript-sending
behavior, 4,622 applicants were potential recruits (11%). While we cannot
formally verify that these students were actually recruited by the schools we
consider, 11% is in line with public estimates of the fraction of selective
university enrollees who are student athletes.101010As above, see “Varsity
athletes, admissions and enrollment at top colleges” [Washington Post, 2019]

## Estimating Admission Rates

In our main analysis, we assume that students who were admitted to one of the
selective institutions we consider ultimately attended one of those
institutions. In this way, we could infer admissions decisions from enrollment
choices—which we can in turn accurately impute by looking at the institution
to which a student sent their final high school transcript. Here we describe
an alternative estimation strategy that holds under the weaker assumption that
enrollment choices are independent of race conditional on acceptance and other
observable student characteristics.

Denote by $A$ the event that a particular applicant is admitted to one of the
schools we consider, where $A=1$ if the applicant is admitted and $A=0$ if the
applicant is not admitted. Denote by $E$ the analogous enrollment event.
Finally, denote by $R$ the race of the applicant, and by $W$ a set of non-race
covariates. Now, suppose we are interested in comparing the admission
probability of an applicant with race $R$ and another applicant of race
$R^{\prime}$ with identical non-race covariates $W$. We can express this
comparison as a risk ratio:

$\displaystyle\frac{\Pr(A=1\mid W,R^{\prime})}{\Pr(A=1\mid W,R)}.$

Without observing admission outcomes, the above ratio cannot be estimated
directly. But, suppose we assume that $E\perp\\!\\!\\!\perp R\mid A=1,W$. In
other words, conditional on acceptance and all observed non-race covariates,
the decision to enroll in one of the considered institutions is independent of
race. Then,

$\displaystyle\Pr(E=1\mid W,R)$ $\displaystyle=\Pr(A=1\mid
W,R)\cdot\Pr(E=1\mid A=1,W,R)$ $\displaystyle=\Pr(A=1\mid W,R)\cdot\Pr(E=1\mid
A=1,W),$

and so

$\displaystyle\Pr(A=1\mid W,R)$ $\displaystyle=\frac{\Pr(E=1\mid
W,R)}{\Pr(E=1\mid A=1,W)}.$

Applying this result to the acceptance risk ratio:

$\displaystyle\frac{\Pr(A=1\mid W,R^{\prime})}{\Pr(A=1\mid W,R)}$
$\displaystyle=\frac{\Pr(E=1\mid W,R^{\prime})}{\Pr(E=1\mid
A=1,W)}\cdot\frac{\Pr(E=1\mid A=1,W)}{\Pr(E=1\mid W,R)}$
$\displaystyle=\frac{\Pr(E=1\mid W,R^{\prime})}{\Pr(E=1\mid W,R)}.$

Thus, by assuming that enrollment is independent of race conditional on
acceptance and non-race covariates, we can estimate the acceptance ratio using
only data on enrollment. Averaging over $W$, we have

$\displaystyle{{\sum}}_{W}\frac{\Pr(A=1\mid W,R^{\prime})}{\Pr(A=1\mid
W,R)}\cdot\Pr(W)={{\sum}}_{W}\frac{\Pr(E=1\mid W,R^{\prime})}{\Pr(E=1\mid
W,R)}\cdot\Pr(W).$ (1)

Importantly, the right-hand side of Eq. (1) can be estimated directly from
enrollment choices, as done with the main models in our analysis (Table 1). In
particular, take $R$ to be white students and $R^{\prime}$ to be, in turn, the
three Asian subgroups we consider. Then, after adjusting for test scores, GPA,
and extracurricular activities (i.e., by using Model 4 in the main text), we
estimate that the average acceptance ratio is 0.58 for South Asian applicants,
0.85 for East Asian applicants, and 0.89 for Southeast Asian applicants. These
estimates align with the results we report in Table 1, corroborating our main
analysis.

Table A1:  Approximate proportion of all publicly reported applications to the selective schools we consider that were submitted via the application platform, by academic season. The share of applications submitted via the platform has decreased in recent years as alternative platforms have become more popular.  | Academic year | Proportion
---|---|---
1 | 2015-2016 | 99%
2 | 2016-2017 | 98%
3 | 2017-2018 | 96%
4 | 2018-2019 | 93%
5 | 2019-2020 | 92%

Table A2:  Summary statistics for the ‘Included’ applicants who attend high schools with reliable transcript-sending behavior, the ‘Excluded’ applicants who do not, and the combined set of ‘All’ applicants. On average, the ‘Included’ applicants submit more applications, apply early with a greater likelihood, are more likely to have legacy status, have higher standardized test scores, have more extracurricular hours, are more likely to play sports, are less likely to use application fee waivers, are more likely to attend urban and private high schools, and have smaller graduating class sizes. We re-run the main regression by inversely weighting the probability that a given applicant attends a high school with reliable transcript behavior, finding qualitatively similar results (Tables LABEL:fig:robustness-east-asian-LABEL:fig:robustness-southeast-asian, ‘Reweighted’ model variant).  Variable | All | Included | Excluded
---|---|---|---
Tot. applicants | 444,420 | 292,795 | 151,625
Prop. sent transcript | 10% | 12% | 7%
Prop. white | 64% | 64% | 66%
Prop. Asian American | 36% | 36% | 34%
Prop. East Asian | 17% | 18% | 15%
Prop. South Asian | 12% | 12% | 12%
Prop. Southeast Asian | 6% | 6% | 8%
Mean num. apps submitted anywhere | 7.9 | 8.4 | 7.1
Mean num. apps submitted to subset | 2.3 | 2.3 | 2.2
Prop. applied early | 39% | 42% | 34%
Prop. w/ legacy | 6% | 7% | 3%
Mean ACT score | 32.1 | 32.4 | 31.4
Prop. unreported ACT | 14% | 14% | 13%
Mean standardized GPA | 0.8 | 0.8 | 0.8
Prop. unreported GPA | 14% | 16% | 10%
Mean num. AP tests | 4 | 4.1 | 3.8
Median activity hours | 3196 | 3236 | 3107
Median sports hours | 480 | 540 | 400
Prop. female | 53% | 53% | 54%
Prop. first generation | 14% | 12% | 19%
Prop. using fee waiver | 15% | 12% | 20%
Prop. rural HS | 6% | 4% | 9%
Prop. private HS | 22% | 27% | 13%
Median grad. class size | 333 | 319 | 366
Prop. from California | 17% | 16% | 19%
Prop. from Texas | 5% | 4% | 6%
Prop. from Florida | 3% | 3% | 4%
Prop. from New York | 13% | 15% | 9%

Table A3:  Summary statistics for the race and ethnicity groups included in the analysis. White applicants are more likely to have legacy status than Asian applicants, have a greater number of extracurricular hours, on average, and are more likely to attend smaller and private high schools. East and South Asian applicants have, on average, higher standardized test scores and take more AP tests than white and Southeast Asian applicants.  Variable | All | White | Asian | E Asian | S Asian | SE Asian
---|---|---|---|---|---|---
Tot. applicants | 292,795 | 186,079 | 106,716 | 53,856 | 36,389 | 16,471
Prop. sent transcript | 12% | 12% | 13% | 16% | 10% | 8%
Prop. white | 64% | 100% | 0% | 0% | 0% | 0%
Prop. Asian American | 36% | 0% | 100% | 100% | 100% | 100%
Prop. East Asian | 18% | 0% | 50% | 100% | 0% | 0%
Prop. South Asian | 12% | 0% | 34% | 0% | 100% | 0%
Prop. Southeast Asian | 6% | 0% | 15% | 0% | 0% | 100%
Mean num. apps |  |  |  |  |  |
submitted anywhere | 8.4 | 8.1 | 9 | 9 | 9.5 | 7.7
Mean num. apps |  |  |  |  |  |
submitted to subset | 2.3 | 2.1 | 2.8 | 3 | 2.9 | 2.3
Prop. applied early | 42% | 41% | 44% | 50% | 42% | 33%
Prop. w/ legacy | 7% | 10% | 3% | 4% | 2% | 3%
Mean ACT score | 32.4 | 32.2 | 32.9 | 33.3 | 32.8 | 31.4
Prop. unreported ACT | 14% | 17% | 10% | 9% | 11% | 11%
Mean standardized GPA | 0.8 | 0.8 | 0.8 | 0.8 | 0.7 | 0.8
Prop. unreported GPA | 16% | 16% | 15% | 16% | 15% | 14%
Mean num. AP tests | 4.1 | 3.6 | 5 | 5.3 | 5.1 | 4
Median activity hours | 3236 | 3384 | 2975 | 3131.7 | 2862 | 2688
Median sports hours | 540 | 728 | 240 | 318 | 162 | 240
Prop. female | 53% | 52% | 53% | 54% | 51% | 56%
Prop. first generation | 12% | 9% | 16% | 18% | 10% | 25%
Prop. using fee waiver | 12% | 8% | 19% | 19% | 14% | 30%
Prop. rural HS | 4% | 5% | 1% | 2% | 1% | 2%
Prop. private HS | 27% | 31% | 20% | 20% | 17% | 23%
Median grad. class size | 319 | 280 | 400 | 400 | 403 | 372
Prop. from California | 16% | 11% | 24% | 26% | 16% | 31%
Prop. from Texas | 4% | 3% | 5% | 4% | 7% | 5%
Prop. from Florida | 3% | 3% | 2% | 1% | 3% | 3%
Prop. from New York | 15% | 14% | 15% | 17% | 13% | 12%

Table A4:  Variables observed by the national postsecondary application platform and the authors, only the platform, and neither the platform nor the authors.  |  Platform and our data | Platform, but not our data | Neither platform nor our data
---|---|---|---
1 |  Unique applicant identifier | Full name | Athletic recruitment eligibility
2 |  Gender | High school transcript(s) | True admission outcome(s)
3 |  Race, ethnicity, and region(s) of origin | Academic honors | True enrollment outcome(s)
4 |  Age | Letters of recommendation | Ratings of admission officers
5 |  Citizenship status | Essays and written responses | Alumni interview ratings
6 |  High school name and location | Intended career | Official test scores
7 |  High school graduation date | College-specific fields (e.g., major) | Family income and assets
8 |  Self-reported test scores |  |
9 |  Self-reported GPA, GPA weighting, and class rank |  |
10 |  Highest educational attainment of parents |  |
11 |  Institutions attended and degrees obtained by parents |  |
12 |  Extracurricular categories, years participated, hours participated per year, leadership positions, and free text description |  |
13 |  Application submission status at individual colleges |  |
14 |  Application timing (e.g., restrictive early action) |  |
15 |  Application fee waiver status at individual colleges |  |
16 |  Receipt(s) of official transcript submission to individual colleges sent via the platform |  |

Table A5:  Variables includes in Model 1, ‘Basket+year’.  | Covariate |  Additional description
---|---|---
1 | Intercept |
2 | South Asian |  Applicant identifies as South Asian
3 | Southeast Asian |  Applicant identifies as Southeast Asian
4 | East Asian |  Applicant identifies as East Asian
5 | Year by college fixed effects |  Term for each combination of selective college applied to and application year, e.g., ‘College X 2016’

Table A6:  Variables included in Model 2, ‘SAT/ACT’. Variables from Model 1 are also included.  | Covariate |  Additional description
---|---|---
1 | Equivalent ACT Composite Score |  If SAT score reported, converted to equivalent ACT score
2 | Equivalent ACT Composite Score Squared |
3 | Missing ACT Score |  Student did not report an ACT or SAT score

Table A7:  Variables included in Model 3, ‘GPA+AP+SAT2’. Variables from all prior model are also included. Standardization is by high school-year using all applicants observed by the platform. We standardize by subtracting the sample mean and dividing by the sample standard deviation. Standardized values for high school-years with only one observation are coded as 0. Standardized values are capped at 3 and floored at -3.  | Covariate |  Additional description
---|---|---
1 | Standardized GPA |  GPA standardized by high school and year
2 | Missing Cumulative GPA |  Student did not report a GPA
3 | Standardized ACT |
4 | Std. Num. AP |  Standardized number of AP tests taken
5 | Std. Num. Passed AP |  Standardized number of AP tests with a reported score of 3 or higher
6 | Std. Num. 5 AP |  Standardized number of AP tests with a reported score of 5 (maximum)
7 | Std. Num. SAT Subject |
8 | Std. Num. SAT Subject 700 |  Standardized number of SAT subject tests with a score of at least 700
9 | Std. Num. Science AP |
10 | Std. Num. History AP |
11 | Std. Num. Math AP |
12 | Std. Num. English AP |
13 | Std. Num. Language AP |
14 | Std. Num. Social Science AP |
15 | Std. Num. Arts AP |
16 | Std. Num. Science SAT Subject |
17 | Std. Num. History SAT Subject |
18 | Std. Num. Math SAT Subject |
19 | Std. Num. English SAT Subject |
20 | Std. Num. Language SAT Subject |
21 | Took Art Studio Art 2D Design AP |
22 | Took Art Studio Art 3D Design AP |
23 | Took Art Studio Art Drawing AP |
24 | Took Biology AP |
25 | Took Biology Ecological SAT Subject |
26 | Took Biology Molecular SAT Subject |
27 | Took Calculus AB AP |
28 | Took Calculus BC AP |
29 | Took Calculus BC AB Subscore Grade AP |  Reported a Calculus AB subscore for AP Calculus BC
30 | Took Chemistry AP |
31 | Took Chemistry SAT Subject |
32 | Took Computer Science A AP |
33 | Took Economics Macroeconomics AP |
34 | Took Economics Microeconomics AP |
35 | Took English Language Composition AP |
36 | Took English Literature Composition AP |
37 | Took Environmental Science AP |
38 | Took European History AP |
39 | Took French Language AP |
40 | Took French Reading SAT Subject |
41 | Took French With Listening SAT Subject |
42 | Took German Language AP |
43 | Took German Reading SAT Subject |
44 | Took German With Listening SAT Subject |
45 | Took Government Politics Comparative AP |
46 | Took Government Politics United States AP |
47 | Took History Of Art AP |
48 | Took Human Geography AP |
49 | Took Italian Language Culture AP |
50 | Took Italian Reading SAT Subject |
51 | Took Latin AP |
52 | Took Latin Reading SAT Subject |
53 | Took Latin Literature AP |
54 | Took Latin Vergil AP |
55 | Took Literature SAT Subject |
56 | Took Math Level 1 SAT Subject |
57 | Took Math Level 2 SAT Subject |
58 | Took Music Theory AP |
59 | Took Music Theory Aural Subscore AP |
60 | Took Music Theory Nonaural Subscore AP |
61 | Took Physics SAT Subject |
62 | Took Physics 1 AP |
63 | Took Physics 2 AP |
64 | Took Physics B AP |
65 | Took Physics C Electricity Magnetism AP |
66 | Took Physics C Mechanics AP |
67 | Took Psychology AP |
68 | Took Research AP |
69 | Took Seminar AP |
70 | Took Spanish Language AP |
71 | Took Spanish Literature AP |
72 | Took Spanish Reading SAT Subject |
73 | Took Spanish With Listening SAT Subject |
74 | Took Statistics AP |
75 | Took US History SAT Subject |
76 | Took United States History AP |
77 | Took World History AP |
78 | Took World History SAT Subject |
79 | Took Writing SAT Subject |

Table A8:  Variables included in Model 4, ‘Activities’. Variables from all prior model are also included.  | Covariate |  Additional description
---|---|---
1 | Archery |  8 covariates per sport: Log total number of hours participated in sport, binary indicator for leadership role in sport, binary indicator for four years of high school participation in sport, and binary indicator for leadership and four year participation in sport, with separate covariates for JV/Varsity participation and Club participation
2 | Badminton |
3 | Baseball |
4 | Basketball |
5 | Bowling |
6 | Boxing |
7 | Cheerleading |
8 | Cricket |
9 | Crosscountry |
10 | Diving |
11 | Equestrian |
12 | Fencing |
13 | Field Hockey |
14 | Football |
15 | Golf |
16 | Gymnastics |
17 | Handball |
18 | Ice Hockey |
19 | Indoor Track |
20 | Judo |
21 | Lacrosse |
22 | Other Sport |
23 | Outdoor Track |
24 | Racquetball |
25 | Rifle |
26 | Rowing Crew |
27 | Rugby |
28 | Sailing |
29 | Skiing |
30 | Soccer |
31 | Softball |
32 | Squash |
33 | Swim |
34 | Sync swimming |
35 | Table Tennis |
36 | Tennis |
37 | Track and field |
38 | Triathlon |
39 | Volleyball |
40 | Water polo |
41 | Weight lifting |
42 | Wrestling |
43 | Academic |  4 covariates per activity type: Identical to sports, but without the JV/Varsity or Club designation
44 | Art |
45 | Career Oriented |
46 | Volunteering |
47 | Computer/Technology |
48 | Cultural |
49 | Dance |
50 | Debate/Speech |
51 | Environmental |
52 | Family Responsibilities |
53 | Foreign Exchange |
54 | Foreign Language |
55 | Journalism/Publication |
56 | Junior ROTC |
57 | LGBT |
58 | Music Instrumental |
59 | Music Vocal |
60 | Other Activity |
61 | Religious |
62 | Research |
63 | Robotics |
64 | School Spirit |
65 | Science/Math |
66 | Student Govt/Politics |
67 | Theater/Drama |
68 | Work Paid |

Table A9:  Variables included in Model 5, ‘Sex+Family’. Variables from all prior model are also included.  | Covariate |  Additional description
---|---|---
1 | Male |
2 | Received Platform Fee Waiver |  Received an income-eligibility fee waiver for any school applied to via the platform
3 | Received Subset Platform Fee Waiver |  Received an income-eligbility fee waiver at any school among the selective schools we consider
4 | Received Subset Member Fee Waiver |  Received a fee waiver directly from the considered school (not necessarily related to income)
5 | Highest Parent Educ. is High School |
6 | Highest Parent Educ. is Some College |
7 | Highest Parent Educ. is 4 year College Degree |
8 | Highest Parent Educ. is Graduate School |
9 | Highest Parent Educ. is Unknown |
10 | Top 50 Non Subset Legacy Undergrad 1 |  First listed parent attended a Top 50 university defined by U.S. News in 2019 outside of the schools we consider as an undergraduate
11 | Top 50 Non Subset Legacy Undergrad 2 |
12 | Top 50 Non Subset Legacy Grad 1 |
13 | Top 50 Non Subset Legacy Grad 2 |
14 | No App Subset Legacy Undergrad |  Either parent was an undergraduate at a considered schools that the applicant did not apply to
15 | No App Subset Legacy Grad |

Table A10:  Variables included in Model 6, ‘Early App’. Variables from Models 1 through 5 are also included.  | Covariate |  Additional description
---|---|---
1 | Early Application Subset |  Applied to a considered school under restrictive early action
2 | Early Decision Subset |

Table A11:  Variables included in Model 7, ‘Legacy’. Variables from Models 1 through 5 are also included.  | Covariate |  Additional description
---|---|---
1 | Subset Double Legacy Undergrad |  Both parents were undergraduates at the same considered school to which the student applied
2 | Subset Double Legacy Grad |
3 | Subset Double Legacy Mixed |  One parent was an undergraduate and the other parent was a graduate student at the same considered school to which the student applied
4 | Subset Single Legacy Undergrad |  Exactly one parent was an undergraduate at a considered school to which the student applied
5 | Subset Single Legacy Grad |
6 | Subset Two Separate Legacy Undergrad |  Each parent was an undergraduate at a considered school to which the student applied and both attended a different considered school
7 | Subset Two Separate Legacy Grad |

Table A12:  Variables included in Model 8, ‘Location+HS’. Variables from Models 1 through 5 are also included, except for basket-year fixed effects, which are redundant with the included state-year-basket fixed effects.  | Covariate |  Additional description
---|---|---
1 | Log Graduating Class Size |
2 | Prop. Students Applying Platform |  Proportion of students in the graduating class who submitted at least one application via the platform
3 | Prop. Free Reduced Lunch |
4 | Missing Prop. Free Reduced Lunch |  Unknown proportion of students receiving free or reduced lunch in high school
5 | Is Private |  Attended a private high school
6 | Unknown Public/Private |  Unknown classification of high school as public or private
7 | Is Parochial |  Attended a parochial high school
8 | Top 100 Private |  Top 100 private school according to 2022 Niche Rankings
9 | Top 100 Public |  Top 100 public school according to 2022 U.S. News rankings
10 | School Offers AP/SAT2 Fixed Effects |  For each of the AP and SAT subject tests identified above, did at least one applicant in the high school-year report a score for it?
11 | Rurality |  Terms for U.S. Census Rurality Code
12 | ZIP3 Fixed Effects |  Terms for first three digits of high school zip code
13 | State-year-basket Fixed Effects |  Terms for each combination of considered college applied to, high school state, and year of application, e.g., ‘College X 2016 California’
14 | Log State ACT Rank |  Logarithm of the within state-year ranking of applicant’s ACT score

Table A13:  Robustness checks of the main specification. Each variant of the main specification lists the corresponding value of the exponentiated East Asian coefficient for each of the nine models in the main analysis. Exponeniated coefficients are qualitatively similar across all specifications. Detailed descriptions of each variant are on the next page. Variant | Region |  White base rate |  Basket + year |  SAT / ACT |  GPA + AP + SAT2 |  ECs |  Sex + Fam. |  Early app |  Legacy |  Loc. + HS |  All
---|---|---|---|---|---|---|---|---|---|---|---
Main model | E. Asian |  12% |  1.11 |  0.86 |  0.85 |  0.83 |  0.79 |  0.73 |  0.90 |  0.88 |  0.90
E. Asian and white | E. Asian |  12% |  1.12 |  0.87 |  0.84 |  0.81 |  0.78 |  0.73 |  0.89 |  0.88 |  0.89
Include recruits | E. Asian |  13.8% |  1.08 |  0.85 |  0.83 |  0.86 |  0.83 |  0.76 |  0.92 |  0.91 |  0.90
2015 only | E. Asian |  12.8% |  1.06 |  0.81 |  0.79 |  0.77 |  0.74 |  0.68 |  0.84 |  0.84 |  0.85
2016 only | E. Asian |  12.4% |  1.10 |  0.82 |  0.82 |  0.78 |  0.74 |  0.68 |  0.85 |  0.81 |  0.83
2017 only | E. Asian |  11.9% |  1.09 |  0.88 |  0.86 |  0.86 |  0.81 |  0.75 |  0.92 |  0.90 |  0.91
2018 only | E. Asian |  11.2% |  1.15 |  0.91 |  0.88 |  0.85 |  0.82 |  0.76 |  0.92 |  0.92 |  0.94
2019 only | E. Asian |  11.9% |  1.16 |  0.89 |  0.88 |  0.87 |  0.81 |  0.74 |  0.91 |  0.90 |  0.90
East coast only | E. Asian |  13.8% |  1.20 |  0.93 |  0.88 |  0.86 |  0.82 |  0.73 |  0.94 |  0.87 |  0.87
California only | E. Asian |  10.5% |  0.89 |  0.66 |  0.81 |  0.80 |  0.77 |  0.76 |  0.86 |  0.90 |  0.95
Real ACT/GPA | E. Asian |  11.5% |  1.18 |  0.90 |  0.88 |  0.86 |  0.79 |  0.73 |  0.88 |  0.89 |  0.90
ACT $\geq$ 27 | E. Asian |  13.1% |  1.12 |  0.86 |  0.84 |  0.82 |  0.76 |  0.71 |  0.86 |  0.87 |  0.89
Remove legacy | E. Asian |  9.7% |  1.30 |  1.00 |  0.96 |  0.94 |  0.90 |  0.82 |  0.91 |  0.99 |  0.90
Transcript senders | E. Asian |  18.7% |  1.36 |  1.06 |  0.99 |  0.95 |  0.89 |  0.82 |  1.00 |  0.92 |  0.93
Regular decision | E. Asian |  7.6% |  0.97 |  0.77 |  0.80 |  0.79 |  0.73 |  0.71 |  0.81 |  0.81 |  0.87
No transcript thres. | E. Asian |  8.6% |  1.19 |  0.90 |  0.87 |  0.85 |  0.82 |  0.76 |  0.92 |  0.90 |  0.91
20% transcript thres. | E. Asian |  11.8% |  1.12 |  0.87 |  0.85 |  0.84 |  0.80 |  0.74 |  0.90 |  0.89 |  0.90
0% transcript thres. | E. Asian |  12.2% |  1.11 |  0.86 |  0.84 |  0.83 |  0.79 |  0.72 |  0.89 |  0.88 |  0.89
Reweighted | E. Asian |  12% |  1.15 |  0.88 |  0.86 |  0.85 |  0.79 |  0.73 |  0.88 |  0.90 |  0.91
Leave one out max | E. Asian |  12.1% |  1.13 |  0.88 |  0.87 |  0.86 |  0.81 |  0.75 |  0.91 |  0.89 |  0.91
Leave one out min | E. Asian |  10.9% |  1.05 |  0.81 |  0.81 |  0.79 |  0.75 |  0.71 |  0.85 |  0.83 |  0.87

Detailed descriptions of each model variant:

  * •

The ‘E. Asian and white’ variant fits the main specification only on East
Asian and white applicants in the study pool, mimicking the effect of
interacting race with each variable in the main model.

  * •

The ‘Include recruits’ specification does not remove applicants who we believe
may be recruited athletes.

  * •

The ‘2015 only’ model fits the main model on only the 2015-2016 academic year
application data, with a similar interpretation for the other variants whose
names end in ‘only’.

  * •

The ‘Real ACT/GPA’ model excludes applicants who do not report an ACT/SAT
score and/or a high school GPA.

  * •

The ‘ACT $\geq$ 27’ model removes applicants with an equivalent ACT below 27,
as very few enrollees at the schools we consider have ACT scores below 27.

  * •

The ‘Remove legacy’ model removes legacy applicants from the study pool,
following a similar model choice in the Harvard v. SFFA court case.

  * •

The ‘Transcript senders’ model includes only those applicants who sent a
transcript to a specific college on the platform. These applicants have the
strongest enrollment signal, as the precision of our transcript heuristic is
97%.

  * •

The ‘Regular decision’ model excludes applicants who applied early to only one
college, sent a transcript to that college, and did not apply anywhere else.
This is a likely signal of enrollment at the school to which the student
applied early.

  * •

The ‘No transcript thres.’ allows all high school-years to be included in the
analysis, and only excludes students with at least one application who sent
the same number of transcripts as applications. The ‘20% transcript thres.’
model allows only applicants from high school-years for which less than 20% of
applicants who submitted more than one application sent the same number of
transcripts and applications. This model also removes all students with more
than one application who sent the same number of transcripts as applications.
The ‘0% transcript thres.’ model does not allow high-school years with any
applicants who submitted more than one application and sent the same number of
transcripts and applications.

  * •

The ‘Reweighted’ model reweights the main model by the inverse likelihood that
the given applicant attended a high school-year where no more than 5% of
applicants with more than one application sent the same number of transcripts
as applications. The corresponding propensity model is fit using the same
covariates as Model 9, excluding the state-year-basket fixed effects.

  * •

The ‘Leave one out’ variants assess the sensitivity of the Asian region
coefficients to the particular set of schools considered in the analysis. The
exponentiated coefficients of the ‘Leave one out max’ and ‘Leave one out min’
variants are derived from fitting the 9 models $n_{\text{schools}}$ times,
where $n_{\text{schools}}$ is the number of selective schools considered in
the main analysis. To preserve confidentiality, the exact value of
$n_{\text{schools}}$ is not provided. For each of the 9 model specifications,
we report the maximum and minimum observed values of the exponentiated Asian
region coefficient across $n_{\text{schools}}$ datasets. Each dataset excludes
application data from one of the selective schools considered in the main
analysis.

Table A14:  Robustness checks of the main specification. Each variant of the main specification lists the corresponding value of the exponentiated South Asian coefficient for each of the nine models in the main analysis. Exponeniated coefficients are qualitatively similar across all specifications. Model variants are described in the caption of the corresponding figure for the exponentiated East Asian coefficient.  Variant | Region |  White base rate |  Basket + year |  SAT / ACT |  GPA + AP + SAT2 |  ECs |  Sex + Fam. |  Early app |  Legacy |  Loc. + HS |  All
---|---|---|---|---|---|---|---|---|---|---|---
Main model | S. Asian |  12% |  0.66 |  0.56 |  0.59 |  0.51 |  0.51 |  0.52 |  0.61 |  0.60 |  0.70
S. Asian and white | S. Asian |  12% |  0.64 |  0.55 |  0.58 |  0.49 |  0.49 |  0.51 |  0.60 |  0.60 |  0.71
Include recruits | S. Asian |  13.8% |  0.62 |  0.53 |  0.55 |  0.52 |  0.52 |  0.53 |  0.61 |  0.61 |  0.69
2015 only | S. Asian |  12.8% |  0.63 |  0.53 |  0.57 |  0.50 |  0.49 |  0.50 |  0.59 |  0.59 |  0.69
2016 only | S. Asian |  12.4% |  0.65 |  0.53 |  0.57 |  0.49 |  0.48 |  0.50 |  0.58 |  0.56 |  0.67
2017 only | S. Asian |  11.9% |  0.67 |  0.58 |  0.62 |  0.53 |  0.53 |  0.54 |  0.63 |  0.62 |  0.71
2018 only | S. Asian |  11.2% |  0.65 |  0.56 |  0.59 |  0.51 |  0.51 |  0.52 |  0.61 |  0.59 |  0.68
2019 only | S. Asian |  11.9% |  0.69 |  0.58 |  0.62 |  0.53 |  0.52 |  0.52 |  0.61 |  0.61 |  0.68
East coast only | S. Asian |  13.8% |  0.64 |  0.56 |  0.59 |  0.51 |  0.50 |  0.51 |  0.60 |  0.59 |  0.68
California only | S. Asian |  10.5% |  0.66 |  0.49 |  0.64 |  0.57 |  0.55 |  0.60 |  0.68 |  0.65 |  0.79
Real ACT/GPA | S. Asian |  11.5% |  0.70 |  0.58 |  0.61 |  0.53 |  0.51 |  0.53 |  0.60 |  0.61 |  0.69
ACT $\geq$ 27 | S. Asian |  13.1% |  0.68 |  0.56 |  0.60 |  0.52 |  0.50 |  0.52 |  0.60 |  0.60 |  0.69
Remove legacy | S. Asian |  9.7% |  0.78 |  0.65 |  0.68 |  0.59 |  0.60 |  0.60 |  0.61 |  0.70 |  0.69
Transcript senders | S. Asian |  18.7% |  0.81 |  0.69 |  0.69 |  0.58 |  0.57 |  0.58 |  0.68 |  0.64 |  0.73
Regular decision | S. Asian |  7.6% |  0.60 |  0.52 |  0.58 |  0.53 |  0.51 |  0.52 |  0.60 |  0.59 |  0.68
No transcript thres. | S. Asian |  8.6% |  0.69 |  0.58 |  0.60 |  0.53 |  0.53 |  0.55 |  0.63 |  0.62 |  0.71
20% transcript thres. | S. Asian |  11.8% |  0.66 |  0.56 |  0.60 |  0.52 |  0.51 |  0.53 |  0.61 |  0.61 |  0.70
0% transcript thres. | S. Asian |  12.2% |  0.67 |  0.57 |  0.60 |  0.52 |  0.52 |  0.53 |  0.62 |  0.62 |  0.70
Reweighted | S. Asian |  12% |  0.67 |  0.56 |  0.59 |  0.52 |  0.51 |  0.53 |  0.60 |  0.62 |  0.71
Leave one out max | S. Asian |  12.1% |  0.67 |  0.57 |  0.60 |  0.52 |  0.51 |  0.53 |  0.62 |  0.61 |  0.71
Leave one out min | S Asian |  10.9% |  0.64 |  0.53 |  0.57 |  0.51 |  0.50 |  0.52 |  0.59 |  0.59 |  0.68

Table A15:  Robustness checks of the main specification. Each variant of the main specification lists the corresponding value of the exponentiated Southeast Asian coefficient for each of the nine models in the main analysis. Exponeniated coefficients are qualitatively similar across all specifications. Model variants are described in the caption of the corresponding figure for the exponentiated East Asian coefficient.  Variant | Region |  White base rate |  Basket + year |  SAT / ACT |  GPA + AP + SAT2 |  ECs |  Sex + Fam. |  Early app |  Legacy |  Loc. + HS |  All
---|---|---|---|---|---|---|---|---|---|---|---
Main model | SE Asian |  12% |  0.64 |  0.73 |  0.78 |  0.83 |  0.81 |  0.84 |  0.88 |  0.94 |  1.02
SE Asian and white | SE Asian |  12% |  0.64 |  0.73 |  0.76 |  0.80 |  0.78 |  0.82 |  0.85 |  0.90 |  0.98
Include recruits | SE Asian |  13.8% |  0.60 |  0.69 |  0.72 |  0.81 |  0.80 |  0.83 |  0.87 |  0.92 |  0.99
2015 only | SE Asian |  12.8% |  0.64 |  0.76 |  0.79 |  0.83 |  0.82 |  0.88 |  0.90 |  1.01 |  1.15
2016 only | SE Asian |  12.4% |  0.62 |  0.71 |  0.75 |  0.82 |  0.78 |  0.79 |  0.86 |  0.91 |  0.98
2017 only | SE Asian |  11.9% |  0.64 |  0.74 |  0.79 |  0.84 |  0.83 |  0.85 |  0.91 |  0.97 |  1.04
2018 only | SE Asian |  11.2% |  0.64 |  0.72 |  0.77 |  0.82 |  0.80 |  0.85 |  0.87 |  0.94 |  1.06
2019 only | SE Asian |  11.9% |  0.67 |  0.75 |  0.80 |  0.84 |  0.79 |  0.81 |  0.85 |  0.92 |  0.99
East coast only | SE Asian |  13.8% |  0.63 |  0.73 |  0.76 |  0.81 |  0.79 |  0.80 |  0.86 |  0.84 |  0.91
California only | SE Asian |  10.5% |  0.60 |  0.65 |  0.84 |  0.90 |  0.88 |  0.95 |  0.98 |  1.04 |  1.17
Real ACT/GPA | SE Asian |  11.5% |  0.69 |  0.81 |  0.85 |  0.88 |  0.82 |  0.85 |  0.88 |  0.96 |  1.03
ACT $\geq$ 27 | SE Asian |  13.1% |  0.68 |  0.75 |  0.81 |  0.85 |  0.80 |  0.84 |  0.87 |  0.95 |  1.04
Remove legacy | SE Asian |  9.7% |  0.76 |  0.86 |  0.90 |  0.95 |  0.90 |  0.92 |  0.91 |  1.04 |  1.04
Transcript senders | SE Asian |  18.7% |  0.85 |  0.98 |  1.02 |  1.05 |  1.00 |  1.04 |  1.10 |  1.02 |  1.11
Regular decision | SE Asian |  7.6% |  0.70 |  0.79 |  0.86 |  0.91 |  0.84 |  0.85 |  0.91 |  0.97 |  1.05
No transcript thres. | SE Asian |  8.6% |  0.62 |  0.72 |  0.76 |  0.82 |  0.81 |  0.85 |  0.88 |  0.95 |  1.04
20% transcript thres. | SE Asian |  11.8% |  0.65 |  0.74 |  0.79 |  0.84 |  0.82 |  0.85 |  0.89 |  0.95 |  1.03
0% transcript thres. | SE Asian |  12.2% |  0.64 |  0.74 |  0.79 |  0.84 |  0.81 |  0.85 |  0.88 |  0.94 |  1.02
Reweighted | SE Asian |  12% |  0.71 |  0.83 |  0.88 |  0.94 |  0.88 |  0.91 |  0.94 |  1.05 |  1.13
Leave one out max | SE Asian |  12.1% |  0.66 |  0.76 |  0.82 |  0.88 |  0.84 |  0.88 |  0.92 |  0.95 |  1.05
Leave one out min | SE Asian |  10.9% |  0.58 |  0.64 |  0.68 |  0.72 |  0.69 |  0.75 |  0.75 |  0.82 |  0.90

Table A16:  Aggregated counts of white applicants and admits across groups defined by geography, equivalent ACT score, and legacy status. Admission is proxied by observing whether a final transcript is sent to one of the schools we consider. “ZIP 1” refers to the first digit of the student’s high school ZIP code. To preserve confidentiality, legacy and non-legacy applicant cell counts with fewer than 50 applicants are redacted, along with the corresponding count of admits. Further, legacy and non-legacy admit cell counts of 0 are redacted, along with the corresponding count of applicants.  Race | ZIP 1 | ACT | Non-legacy applicants | Non-legacy admits | Legacy applicants | Legacy admits
---|---|---|---|---|---|---
White | 0 | 32 | 3,321 | 199 | 411 | 102
White | 0 | 33 | 4,652 | 409 | 705 | 166
White | 0 | 34 | 5,201 | 746 | 942 | 357
White | 0 | 35 | 4,380 | 1,058 | 980 | 449
White | 0 | 36 | 1,600 | 609 | 451 | 306
White | 1 | 32 | 3,236 | 286 | 285 | 77
White | 1 | 33 | 4,455 | 516 | 622 | 214
White | 1 | 34 | 5,023 | 1,002 | 849 | 373
White | 1 | 35 | 4,093 | 1,198 | 898 | 538
White | 1 | 36 | 1,374 | 555 | 404 | 295
White | 2 | 32 | 1,618 | 77 | 155 | 21
White | 2 | 33 | 2,408 | 159 | 252 | 33
White | 2 | 34 | 2,770 | 285 | 385 | 119
White | 2 | 35 | 2,298 | 461 | 410 | 177
White | 2 | 36 | 868 | 252 | 184 | 108
White | 3 | 32 | 1,148 | 49 | 59 | 14
White | 3 | 33 | 1,630 | 111 | 103 | 23
White | 3 | 34 | 1,949 | 207 | 157 | 47
White | 3 | 35 | 1,717 | 253 | 151 | 74
White | 3 | 36 | 708 | 191 | 78 | 45
White | 4 | 32 | 1,022 | 37 |  |
White | 4 | 33 | 1,400 | 78 | 77 | 13
White | 4 | 34 | 1,521 | 128 | 82 | 29
White | 4 | 35 | 1,364 | 185 | 81 | 32
White | 4 | 36 | 464 | 78 |  |
White | 5 | 32 | 548 | 22 |  |
White | 5 | 33 | 768 | 59 |  |
White | 5 | 34 | 821 | 55 | 54 | 16
White | 5 | 35 | 745 | 111 | 65 | 31
White | 5 | 36 | 270 | 64 |  |
White | 6 | 32 | 1,082 | 52 | 65 | 17
White | 6 | 33 | 1,606 | 100 | 95 | 19
White | 6 | 34 | 1,890 | 193 | 156 | 52
White | 6 | 35 | 1,729 | 258 | 135 | 43
White | 6 | 36 | 716 | 132 | 75 | 42
White | 7 | 32 | 692 | 31 |  |
White | 7 | 33 | 924 | 36 |  |
White | 7 | 34 | 1,263 | 106 | 78 | 21
White | 7 | 35 | 1,143 | 148 | 85 | 35
White | 7 | 36 | 494 | 105 | 54 | 31
White | 8 | 32 | 786 | 36 | 59 | 8
White | 8 | 33 | 1,052 | 72 | 83 | 19
White | 8 | 34 | 1,148 | 117 | 101 | 36
White | 8 | 35 | 995 | 152 | 91 | 34
White | 8 | 36 | 334 | 76 | 53 | 29
White | 9 | 32 | 2,418 | 107 | 285 | 47
White | 9 | 33 | 3,396 | 195 | 431 | 88
White | 9 | 34 | 3,969 | 416 | 583 | 175
White | 9 | 35 | 3,298 | 530 | 584 | 217
White | 9 | 36 | 1,108 | 276 | 243 | 127

Table A17:  Aggregated counts of Asian American applicants and admits across groups defined by geography, equivalent ACT score, and legacy status. “ZIP 1” refers to the first digit of the student’s high school ZIP code. To preserve confidentiality, legacy and non-legacy applicant cell counts with fewer than 50 applicants are redacted, along with the corresponding count of admits. Further, legacy and non-legacy admit cell counts of 0 are redacted, along with the corresponding count of applicants.  Ethnicity | ZIP 1 | ACT | Non-legacy applicants | Non-legacy admits | Legacy applicants | Legacy admits
---|---|---|---|---|---|---
Asian | 0 | 32 | 1,109 | 56 | 51 | 12
Asian | 0 | 33 | 1,988 | 146 | 86 | 20
Asian | 0 | 34 | 3,050 | 353 | 181 | 50
Asian | 0 | 35 | 4,037 | 948 | 209 | 104
Asian | 0 | 36 | 2,538 | 1,027 | 86 | 56
Asian | 1 | 32 | 1,596 | 104 |  |
Asian | 1 | 33 | 2,363 | 265 | 73 | 27
Asian | 1 | 34 | 3,126 | 487 | 115 | 55
Asian | 1 | 35 | 3,204 | 985 | 165 | 85
Asian | 1 | 36 | 1,569 | 733 | 90 | 63
Asian | 2 | 32 | 628 | 19 |  |
Asian | 2 | 33 | 1,067 | 44 |  |
Asian | 2 | 34 | 1,513 | 154 | 51 | 8
Asian | 2 | 35 | 1,996 | 432 | 97 | 51
Asian | 2 | 36 | 1,103 | 358 |  |
Asian | 3 | 32 | 432 | 9 |  |
Asian | 3 | 33 | 754 | 41 |  |
Asian | 3 | 34 | 1,147 | 95 |  |
Asian | 3 | 35 | 1,347 | 227 |  |
Asian | 3 | 36 | 887 | 297 |  |
Asian | 4 | 32 | 269 | 10 |  |
Asian | 4 | 33 | 434 | 23 |  |
Asian | 4 | 34 | 730 | 72 |  |
Asian | 4 | 35 | 915 | 172 |  |
Asian | 4 | 36 | 604 | 162 |  |
Asian | 5 | 32 | 143 | 7 |  |
Asian | 5 | 33 | 193 | 15 |  |
Asian | 5 | 34 | 288 | 30 |  |
Asian | 5 | 35 | 375 | 73 |  |
Asian | 5 | 36 | 233 | 85 |  |
Asian | 6 | 32 | 412 | 10 |  |
Asian | 6 | 33 | 616 | 33 |  |
Asian | 6 | 34 | 876 | 77 |  |
Asian | 6 | 35 | 1,125 | 162 |  |
Asian | 6 | 36 | 739 | 231 |  |
Asian | 7 | 32 | 321 | 9 |  |
Asian | 7 | 33 | 604 | 27 |  |
Asian | 7 | 34 | 987 | 80 |  |
Asian | 7 | 35 | 1,529 | 213 |  |
Asian | 7 | 36 | 1,040 | 250 |  |
Asian | 8 | 32 | 222 | 10 |  |
Asian | 8 | 33 | 327 | 21 |  |
Asian | 8 | 34 | 512 | 58 |  |
Asian | 8 | 35 | 553 | 128 |  |
Asian | 8 | 36 | 344 | 122 |  |
Asian | 9 | 32 | 2,100 | 70 | 68 | 4
Asian | 9 | 33 | 3,473 | 163 | 126 | 25
Asian | 9 | 34 | 5,348 | 379 | 205 | 56
Asian | 9 | 35 | 6,692 | 877 | 255 | 83
Asian | 9 | 36 | 3,523 | 771 | 132 | 59

Table A18:  Aggregated counts of South Asian applicants and admits across groups defined by geography, equivalent ACT score, and legacy status. Admission is proxied by observing whether a final transcript is sent to one of the schools we consider. “ZIP 1” refers to the first digit of the student’s high school ZIP code. To preserve confidentiality, legacy and non-legacy applicant cell counts with fewer than 50 applicants are redacted, along with the corresponding count of admits. Further, legacy and non-legacy admit cell counts of 0 are redacted, along with the corresponding count of applicants.  Ethnicity | ZIP 1 | ACT | Non-legacy applicants | Non-legacy admits | Legacy applicants | Legacy admits
---|---|---|---|---|---|---
South Asian | 0 | 32 | 422 | 8 |  |
South Asian | 0 | 33 | 883 | 42 |  |
South Asian | 0 | 34 | 1,314 | 106 |  |
South Asian | 0 | 35 | 1,561 | 277 |  |
South Asian | 0 | 36 | 894 | 285 |  |
South Asian | 1 | 32 | 516 | 28 |  |
South Asian | 1 | 33 | 737 | 65 |  |
South Asian | 1 | 34 | 968 | 148 |  |
South Asian | 1 | 35 | 928 | 244 |  |
South Asian | 1 | 36 | 366 | 155 |  |
South Asian | 2 | 32 | 264 | 4 |  |
South Asian | 2 | 33 | 460 | 21 |  |
South Asian | 2 | 34 | 615 | 47 |  |
South Asian | 2 | 35 | 808 | 135 |  |
South Asian | 2 | 36 | 372 | 100 |  |
South Asian | 3 | 32 | 185 | 4 |  |
South Asian | 3 | 33 | 363 | 14 |  |
South Asian | 3 | 34 | 551 | 32 |  |
South Asian | 3 | 35 | 594 | 86 |  |
South Asian | 3 | 36 | 365 | 101 |  |
South Asian | 4 | 32 | 128 | 3 |  |
South Asian | 4 | 33 | 198 | 8 |  |
South Asian | 4 | 34 | 352 | 33 |  |
South Asian | 4 | 35 | 424 | 70 |  |
South Asian | 4 | 36 | 240 | 48 |  |
South Asian | 5 | 32 | 55 | 3 |  |
South Asian | 5 | 33 | 79 | 3 |  |
South Asian | 5 | 34 | 117 | 12 |  |
South Asian | 5 | 35 | 152 | 28 |  |
South Asian | 5 | 36 | 60 | 17 |  |
South Asian | 6 | 32 | 166 | 4 |  |
South Asian | 6 | 33 | 260 | 9 |  |
South Asian | 6 | 34 | 387 | 25 |  |
South Asian | 6 | 35 | 483 | 54 |  |
South Asian | 6 | 36 | 272 | 66 |  |
South Asian | 7 | 32 | 142 | 4 |  |
South Asian | 7 | 33 | 287 | 11 |  |
South Asian | 7 | 34 | 503 | 32 |  |
South Asian | 7 | 35 | 707 | 84 |  |
South Asian | 7 | 36 | 444 | 74 |  |
South Asian | 8 | 32 | 60 | 3 |  |
South Asian | 8 | 33 | 110 | 4 |  |
South Asian | 8 | 34 | 172 | 16 |  |
South Asian | 8 | 35 | 196 | 40 |  |
South Asian | 8 | 36 | 132 | 45 |  |
South Asian | 9 | 32 | 434 | 16 |  |
South Asian | 9 | 33 | 781 | 24 |  |
South Asian | 9 | 34 | 1,232 | 61 |  |
South Asian | 9 | 35 | 1,791 | 159 |  |
South Asian | 9 | 36 | 997 | 195 |  |

Table A19:  Aggregated counts of Southeast Asian applicants and admits across groups defined by geography, equivalent ACT score, and legacy status. Admission is proxied by observing whether a final transcript is sent to one of the schools we consider. ”ZIP 1” refers to the first digit of the student’s high school ZIP code. To preserve confidentiality, legacy and non-legacy applicant cell counts with fewer than 50 applicants are redacted, along with the corresponding count of admits. Further, legacy and non-legacy admit cell counts of 0 are redacted, along with the corresponding count of applicants.  Ethnicity | ZIP 1 | ACT | Non-legacy applicants | Non-legacy admits | Legacy applicants | Legacy admits
---|---|---|---|---|---|---
Southeast Asian | 0 | 32 | 189 | 7 |  |
Southeast Asian | 0 | 33 | 253 | 21 |  |
Southeast Asian | 0 | 34 | 270 | 37 |  |
Southeast Asian | 0 | 35 | 263 | 69 |  |
Southeast Asian | 0 | 36 | 82 | 30 |  |
Southeast Asian | 1 | 32 | 208 | 9 |  |
Southeast Asian | 1 | 33 | 298 | 45 |  |
Southeast Asian | 1 | 34 | 293 | 41 |  |
Southeast Asian | 1 | 35 | 217 | 53 |  |
Southeast Asian | 1 | 36 | 92 | 38 |  |
Southeast Asian | 2 | 32 | 105 | 5 |  |
Southeast Asian | 2 | 33 | 145 | 3 |  |
Southeast Asian | 2 | 34 | 157 | 15 |  |
Southeast Asian | 2 | 35 | 135 | 24 |  |
Southeast Asian | 2 | 36 | 52 | 16 |  |
Southeast Asian | 3 | 32 | 78 | 2 |  |
Southeast Asian | 3 | 33 | 112 | 11 |  |
Southeast Asian | 3 | 34 | 128 | 16 |  |
Southeast Asian | 3 | 35 | 115 | 23 |  |
Southeast Asian | 3 | 36 |  |  |  |
Southeast Asian | 4 | 32 |  |  |  |
Southeast Asian | 4 | 33 | 57 | 4 |  |
Southeast Asian | 4 | 34 | 52 | 4 |  |
Southeast Asian | 4 | 35 | 57 | 12 |  |
Southeast Asian | 4 | 36 |  |  |  |
Southeast Asian | 5 | 32 |  |  |  |
Southeast Asian | 5 | 33 |  |  |  |
Southeast Asian | 5 | 34 |  |  |  |
Southeast Asian | 5 | 35 |  |  |  |
Southeast Asian | 5 | 36 |  |  |  |
Southeast Asian | 6 | 32 | 71 | 3 |  |
Southeast Asian | 6 | 33 | 108 | 7 |  |
Southeast Asian | 6 | 34 | 98 | 9 |  |
Southeast Asian | 6 | 35 | 80 | 9 |  |
Southeast Asian | 6 | 36 |  |  |  |
Southeast Asian | 7 | 32 | 70 | 2 |  |
Southeast Asian | 7 | 33 | 117 | 5 |  |
Southeast Asian | 7 | 34 | 109 | 9 |  |
Southeast Asian | 7 | 35 | 171 | 21 |  |
Southeast Asian | 7 | 36 | 88 | 18 |  |
Southeast Asian | 8 | 32 | 52 | 3 |  |
Southeast Asian | 8 | 33 | 53 | 5 |  |
Southeast Asian | 8 | 34 | 68 | 11 |  |
Southeast Asian | 8 | 35 | 58 | 11 |  |
Southeast Asian | 8 | 36 |  |  |  |
Southeast Asian | 9 | 32 | 470 | 17 |  |
Southeast Asian | 9 | 33 | 648 | 38 |  |
Southeast Asian | 9 | 34 | 814 | 56 |  |
Southeast Asian | 9 | 35 | 771 | 119 |  |
Southeast Asian | 9 | 36 | 249 | 55 |  |

Table A20:  Aggregated counts of East Asian applicants and admits across groups defined by geography, equivalent ACT score, and legacy status. Admission is proxied by observing whether a final transcript is sent to one of the schools we consider. “ZIP 1” refers to the first digit of the student’s high school ZIP code. To preserve confidentiality, legacy and non-legacy applicant cell counts with fewer than 50 applicants are redacted, along with the corresponding count of admits. Further, legacy and non-legacy admit cell counts of 0 are redacted, along with the corresponding count of applicants.  Ethnicity | ZIP 1 | ACT | Non-legacy applicants | Non-legacy admits | Legacy applicants | Legacy admits
---|---|---|---|---|---|---
East Asian | 0 | 32 | 498 | 41 |  |
East Asian | 0 | 33 | 852 | 83 | 61 | 18
East Asian | 0 | 34 | 1,466 | 210 | 124 | 34
East Asian | 0 | 35 | 2,213 | 602 | 156 | 81
East Asian | 0 | 36 | 1,562 | 712 | 65 | 47
East Asian | 1 | 32 | 872 | 67 |  |
East Asian | 1 | 33 | 1,328 | 155 | 57 | 23
East Asian | 1 | 34 | 1,865 | 298 | 82 | 37
East Asian | 1 | 35 | 2,059 | 688 | 111 | 57
East Asian | 1 | 36 | 1,111 | 540 | 62 | 46
East Asian | 2 | 32 | 259 | 10 |  |
East Asian | 2 | 33 | 462 | 20 |  |
East Asian | 2 | 34 | 741 | 92 |  |
East Asian | 2 | 35 | 1,053 | 273 | 62 | 33
East Asian | 2 | 36 | 679 | 242 |  |
East Asian | 3 | 32 | 169 | 3 |  |
East Asian | 3 | 33 | 279 | 16 |  |
East Asian | 3 | 34 | 468 | 47 |  |
East Asian | 3 | 35 | 638 | 118 |  |
East Asian | 3 | 36 | 480 | 182 |  |
East Asian | 4 | 32 | 103 | 3 |  |
East Asian | 4 | 33 | 179 | 11 |  |
East Asian | 4 | 34 | 326 | 35 |  |
East Asian | 4 | 35 | 434 | 90 |  |
East Asian | 4 | 36 | 348 | 109 |  |
East Asian | 5 | 32 | 66 | 2 |  |
East Asian | 5 | 33 | 80 | 7 |  |
East Asian | 5 | 34 | 131 | 13 |  |
East Asian | 5 | 35 | 186 | 41 |  |
East Asian | 5 | 36 | 156 | 62 |  |
East Asian | 6 | 32 | 175 | 3 |  |
East Asian | 6 | 33 | 248 | 17 |  |
East Asian | 6 | 34 | 391 | 43 |  |
East Asian | 6 | 35 | 562 | 99 |  |
East Asian | 6 | 36 | 435 | 153 |  |
East Asian | 7 | 32 | 109 | 3 |  |
East Asian | 7 | 33 | 200 | 11 |  |
East Asian | 7 | 34 | 375 | 39 |  |
East Asian | 7 | 35 | 651 | 108 |  |
East Asian | 7 | 36 | 508 | 158 |  |
East Asian | 8 | 32 | 110 | 4 |  |
East Asian | 8 | 33 | 164 | 12 |  |
East Asian | 8 | 34 | 272 | 31 |  |
East Asian | 8 | 35 | 299 | 77 |  |
East Asian | 8 | 36 | 181 | 73 |  |
East Asian | 9 | 32 | 1,196 | 37 | 53 | 3
East Asian | 9 | 33 | 2,044 | 101 | 92 | 19
East Asian | 9 | 34 | 3,302 | 262 | 163 | 45
East Asian | 9 | 35 | 4,130 | 599 | 198 | 65
East Asian | 9 | 36 | 2,277 | 521 | 100 | 43

Table A21:  Aggregated counts of legacy and non-legacy applicants and admits across ethnicity and ACT groups. Admission is proxied by observing whether a final transcript is sent to one of the schools we consider. To preserve confidentiality, legacy and non-legacy applicant cell counts with fewer than 50 applicants are redacted, along with the corresponding admit count. Further, legacy and non-legacy admit cell counts of 0 are redacted, along with the corresponding count of applicants.  Ethnicity | ACT | Non-legacy applicants | Non-legacy admits | Legacy applicants | Legacy admits
---|---|---|---|---|---
White | 32 | 15,871 | 896 | 1,415 | 294
White | 33 | 22,291 | 1,735 | 2,459 | 591
White | 34 | 25,555 | 3,255 | 3,387 | 1,225
White | 35 | 21,762 | 4,354 | 3,480 | 1,630
White | 36 | 7,936 | 2,338 | 1,613 | 1,021
South Asian | 32 | 2,372 | 77 |  |
South Asian | 33 | 4,158 | 201 | 70 | 11
South Asian | 34 | 6,211 | 512 | 123 | 42
South Asian | 35 | 7,644 | 1,177 | 159 | 78
South Asian | 36 | 4,142 | 1,086 | 73 | 43
Southeast Asian | 32 | 1,303 | 54 |  |
Southeast Asian | 33 | 1,825 | 144 | 51 | 7
Southeast Asian | 34 | 2,029 | 203 | 75 | 15
Southeast Asian | 35 | 1,904 | 345 | 81 | 32
Southeast Asian | 36 | 701 | 198 |  |
East Asian | 32 | 3,557 | 173 | 168 | 22
East Asian | 33 | 5,836 | 433 | 268 | 69
East Asian | 34 | 9,337 | 1,070 | 467 | 146
East Asian | 35 | 12,225 | 2,695 | 599 | 263
East Asian | 36 | 7,737 | 2,752 | 301 | 185

Figure A1:  For each U.S. state, estimated rate of admission to the selective
schools we consider for white applicants and Asian applicants with an ACT-
equivalent score at or above 32, with the proportion of high-scoring white and
Asian applicants who identify as Asian on the horizontal axis. Point sizes are
proportional to the number of high-scoring white applicants and Asian
applicants to the considered schools whose high school is located in the given
state. The red least-squares regression line is weighted by the same count of
high-scoring white and Asian American applicants from each state. The blue
line excludes applicants from California. States with a greater share of Asian
American applicants have, on average, lower admission rates for high-scoring
applicants. This pattern holds even if applicants from California are
excluded.

Figure A2:  For each U.S. state, mean equivalent ACT score among applicants
who reported an ACT score, with the proportion of high-scoring white and Asian
applicants who identify as Asian on the horizontal axis. Hawaii is excluded
from the plot due to its exceptionally high share of Asian American
applicants. Hawaii’s mean equivalent ACT score is 31.6. Point sizes are
proportional to the number of high-scoring white applicants and Asian
applicants to the considered schools whose high school is located in the given
state. The red least-squares regression line is weighted by the same count of
white and Asian American applicants from each state.

Figure A3:  For each high school in the study pool, rate of admission to any
of the selective schools we consider for white applicants and Asian American
applicants with an ACT-equivalent score at or above 32, with the proportion of
high-scoring white and Asian American applicants who identify as Asian
American on the horizontal axis. Point sizes are proportional to the number of
high-scoring white applicants and Asian applicants to the considered
institutions who attend the given high school. The red least-squares
regression line is weighted by the same count of high-scoring white and Asian
American applicants from the given high school. High schools with a greater
share of Asian American applicants have, on average, lower admission rates for
high-scoring applicants.

Figure A4:  Fraction of observed enrollments to the selective schools we
consider attributed to Asian American and white enrollees. The overall share
of enrollments attributed to Asian American and white enrollees decreases
slightly over the five years included in the analysis. The hypothetical
policies described in the main analysis assume that this share remains
approximately constant regardless of application year.

Figure A5:  The proportion of applicants with one or more parents who attended
one of the selective schools we consider as an undergraduate, by
race/ethnicity and fee waiver status. The pool of applicants in this figure is
the same as the main analysis, but does not apply the filters for race or
ethnicity. White applicants are the most likely to have legacy, and Asian
American applicants are the least likely.