Among applicants to the Ivy-11 colleges, we estimate that 16% of East Asian, 8% of Southeast Asian, and 10% of South Asian students ultimately attended one of these institutions, compared to 12% of white applicants. While these aggregate attendance 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 S3). As a first step to account for these differences, in Fig. 1 we show estimated attendance rates by standardized test score for Asian American applicants and white applicants. With the exception of East Asian applicants at the highest test scores, we find that Asian American applicants to Ivy-11 schools ultimately attended one of these schools 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 applicants attended compared to 9% of South Asian applicants, a relative gap of 43%.
Figure 1
Estimated rate of attendance at any of the Ivy-11 colleges we consider as a function of standardized test score, for Asian American applicants and white applicants in the study pool. Asian American applicants typically attended at lower rates than white applicants with identical test scores, with the largest gap for South Asian students. Among the students in our study pool who attended an Ivy-11 and 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 201828. Point sizes are proportional to the number of applicants in each group.
Standardized test scores are but one factor among many that impact admissions decisions and subsequent enrollment outcomes. 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 attendance rates, we fit a series of nested logistic regression models of the following form:
$$\begin{aligned}&\Pr (Y_i=1) = \text {logit}^{-1}( \beta _0\! +\! \beta _S \mathbbm {1}_S\! +\! \beta _E \mathbbm {1}_E\! +\! \beta _{SE} \mathbbm {1}_{SE}\! +\! X_i \beta _X ), \end{aligned}$$
where \(Y_i\) is a binary variable indicating whether applicant i attended any Ivy-11 college; \(\mathbbm {1}_S\), \(\mathbbm {1}_E\), 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 attendance 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 S13–S15).
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 S5–S12). 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 attendance 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 attendance 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).
Table 1 Estimated conditional odds of attendance at an Ivy-11 college for Asian American applicants in the study pool relative to white applicants.
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 attendance rates for all three Asian subgroups we consider. Figure 2 helps explain this result. The top panel of the figure shows estimated attendance rates for Asian American applicants and white applicants conditional on test scores and legacy status, which we define in this figure to mean an applicant had at least one parent who attended an Ivy-11 as an undergraduate, and the student applied to the Ivy-11 institution(s) that their parent(s) attended. For a given test score, we estimate that applicants—both white and Asian American—with legacy status at an Ivy-11 were more than twice as likely to attend an Ivy-11 than applicants without legacy status. In the bottom panel of Fig. 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 attendance rates conditional on test score and legacy status are similar across race and ethnicity, white students appear to benefit from being substantially more likely to have legacy status.
The higher estimated attendance rates that we observe for legacy applicants may stem from either higher admission rates or higher yield rates. Though we can only speculate, it seems likely that both factors play a role. Further, to the extent that our results reflect disparities in admissions decisions, these findings may in theory be driven in part from the potentially greater social capital of legacy students, rather than explicit preferences for legacy applicants. We note, however, that Model 5 adjusts for whether an applicant had a parent who attended a top-50 institution (based on the 2019 U.S. News rankings) not included in the subset of colleges on which we focus, or attended an Ivy-11 college 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 legacy status specifically, rather than the more generalized impacts of high social capital.
Figure 2
Estimated rate of attendance at any of the Ivy-11 colleges 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 an Ivy-11 as an undergraduate are more than twice as likely to attend 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 legacy status.
Figure 3
For each U.S. state, estimated rate of attendance at any Ivy-11 college for non-legacy white 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. We report attendance rates of non-legacy white applicants to better isolate the impact of geography on attendance from the potential impacts of legacy status and race itself. Larger point sizes indicate a higher number of high-scoring white and Asian applicants from the state. 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 attendance rates for non-legacy white applicants with high scores.
Finally, we examine the relationship between estimated attendance rates and geography. For each state, Fig. 3 displays the estimated attendance 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. When computing attendance rates, we limit to non-legacy white applicants to adjust for the the potential effects of legacy and race on enrollment and therefore better isolate the impact of geography. 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 attendance 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 (Fig. S2). 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 (Figs. S1 and S3). Table S27 displays the data used to construct Fig. 3.
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 attendance 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 attendance gap between Southeast Asian and white applicants largely disappears, though we still find that white applicants have higher estimated odds of attendance 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—and student enrollment choices may also be impacted by factors unobserved in our data.
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. To simplify this exercise, we assume that students admitted to an Ivy-11 school ultimately attend an Ivy-11 school. 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 enrollment, as shown in Fig. S4), 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 Fig. 4 shows the estimated share of attendees in our data from the three Asian subgroups of interest. The rest of the figure shows the estimated share of attendees 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 enrollees30. 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 results in fewer Asian American attendees than the ACT variant.
The final two categories we consider investigate outcomes under hypothetical policies that maintain both the current number of attendees from each state and the total number of attendees 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 admitted under the hypothetical policies matches the historical enrollment numbers. With these added legacy and geographic constraints, the share of Asian American attendees is smaller than under the unconstrained analogs, as expected given our results above. But, even with these constraints, the number of Asian American attendees across policies is still similar to or larger than the status quo.
Figure 4
Estimated enrollment of Asian American students at the Ivy-11 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.
