Diversifying Society's Leaders? Determinants and Causal Effects of Admission

This paper studies why children from high-income families are more likely to attend Ivy-Plus colleges (Ivy League, Stanford, MIT, Duke, Chicago — 12 colleges total) and whether attending these colleges causally improves post-college outcomes. The authors construct a de-identified panel dataset linking federal income tax records, Department of Education college attendance data, College Board and ACT test scores, and application and admissions records from several Ivy-Plus and flagship public colleges covering approximately 2.4 million students across entering classes from 1998–2015.

The central finding on the input side is that students from families in the top 1% of the income distribution (income above $611,000) are 2.3 times more likely to attend an Ivy-Plus college than middle-class students (defined as the 70th–80th percentiles of the national parental income distribution, approximately $91,000–$114,000) with comparable SAT/ACT scores. Two-thirds of this gap is attributable to higher admissions rates at Ivy-Plus colleges for high-income applicants; conditional on SAT/ACT scores, top-1% applicants are 58% more likely to be admitted than middle-class applicants. The remaining third splits between differences in application rates (roughly 20% of the total attendance gap) and matriculation rates (roughly 12%). In contrast, admissions rates at flagship public colleges are essentially uncorrelated with parental income conditional on test scores.

Three admissions practices drive the high-income admissions advantage at Ivy-Plus colleges. First, legacy preferences: legacy applicants from the top 1% are admitted at more than five times the rate of non-legacy applicants with comparable test scores, demographics, and admissions ratings; children of alumni of a given Ivy-Plus college are not more likely to be admitted to other Ivy-Plus colleges, confirming that legacy status is not merely a proxy for unobservable credentials. Legacy preferences account for 52 of the estimated 168 “extra” top-1% students per average Ivy-Plus class (enrollment ~1,650). Second, non-academic ratings: students from the top 1% have markedly stronger non-academic credentials (extracurricular activities, leadership ratings) partly because they disproportionately attend private high schools whose students receive higher non-academic ratings despite no higher academic ratings; this accounts for 35 additional extra top-1% students. Third, athletic recruitment: the share of recruited athletes rises from 5% among admitted students from the bottom 60% to 13% among those from the top 1%, accounting for 27 additional extra top-1% students.

On the output side, the authors estimate causal effects of attending an Ivy-Plus college using a new research design based on waitlisted applicants. The key identification assumption is that idiosyncratic variation in admissions decisions across waitlisted applicants at one Ivy-Plus college is uncorrelated with admissions decisions at other Ivy-Plus colleges — which the authors verify empirically. Under this assumption, comparisons of admitted vs. rejected waitlisted applicants identify causal effects for marginal students. The marginal student who attends an Ivy-Plus college instead of the average flagship public is approximately 50% more likely to reach the top 1% of the earnings distribution at age 33, nearly twice as likely to attend a highly-ranked graduate school, and 2.5 times as likely to work at a prestigious firm. Attending an Ivy-Plus college increases mean earnings by $101,000 at age 33 relative to a counterfactual mean of $143,000 at state flagships. Effects are concentrated in the upper tail of earnings — the impact on reaching the top quartile is small and statistically insignificant, while impacts on reaching the top 1% far exceed what a constant percentage treatment effect would predict. Effects are larger for students with weaker fallback options (i.e., whose home-state colleges channel fewer students to the top 1%).

Critically, the three credentials driving the high-income admissions advantage — legacy status, athletic recruitment, and high non-academic ratings — are uncorrelated with or negatively correlated with post-college success once the college attended is held constant. Academic credentials (SAT/ACT scores, academic ratings) remain highly predictive of outcomes.

Counterfactual simulations show that eliminating all three high-income admissions preferences and replacing those slots with students having the same test score distribution would increase enrollment from the bottom 95% of the parental income distribution by 8.8 percentage points — comparable in magnitude to the effect of race-based affirmative action on Black and Hispanic enrollment shares. Such a policy would have small effects on monetary leadership outcomes (e.g., Fortune 500 CEO share from bottom-95% families rises by only 0.4 pp, because Ivy-Plus graduates are a small fraction of all top earners) but larger effects on non-monetary leadership positions: the share of senators from the bottom 95% would rise by 1.7 pp and the share of Supreme Court justices by 5.4 pp. With need-affirmative policies (giving low-income students preferences comparable to those currently given to legacy applicants), the share of Supreme Court justices from families in the bottom 60% would rise by 17.5 pp. These predictions assume that the causal share of Ivy-Plus attendance in explaining observational differences in leadership outcomes is the same as that estimated for early-career outcomes, and they ignore general equilibrium effects.

Q: How much more likely are top-1% students to attend an Ivy-Plus college than middle-class students with the same test scores? A: Students from families in the top 1% (income above $611,000) are 2.3 times more likely to attend an Ivy-Plus college than students from the 70th–80th percentile of the parental income distribution (approximately $91,000–$114,000) with comparable SAT/ACT scores. This “missing middle” pattern is stable across entering classes from 1998 to 2018 and persists after controlling for race and ethnicity.

Q: How is the overall attendance gap decomposed into application, admissions, and matriculation? A: Differences in admissions rates explain two-thirds of the gap in Ivy-Plus attendance between top-1% and middle-class students conditional on test scores. Of the estimated 168 “extra” top-1% students per average Ivy-Plus class, 87 come from higher admissions rates for non-recruited athletes, 27 from athletic recruitment, and the remaining slack from application rate differences (accounting for roughly 20% of the overall attendance gap) and matriculation differences (roughly 12%).

Q: How large is the admissions advantage for top-1% applicants at Ivy-Plus colleges? A: Conditional on SAT/ACT scores, applicants from the top 1% are 58% more likely to be admitted to Ivy-Plus colleges than middle-class applicants. Students from the top 0.1% are 2.5 times more likely to be admitted than middle-class applicants with comparable test scores. At flagship public colleges, admissions rates are essentially constant across the income distribution conditional on test scores.

Q: What is the magnitude of legacy preferences and how is it established that legacy is not just a proxy for other credentials? A: Legacy applicants from the top 1% are admitted at more than five times the rate of otherwise comparable non-legacy applicants at the college their parents attended. The paper isolates the legacy effect by showing that children of alumni at a given Ivy-Plus college are only slightly more likely to be admitted at other Ivy-Plus colleges — and the predicted counterfactual admissions rate for legacy students at other colleges closely matches their actual admissions rate — confirming that legacy status is not merely a proxy for other unobservable credentials. Legacy applicants constitute 2.5% of the overall applicant pool but over 9% of top-1% applicants.

Q: How do non-academic credentials differ by parental income, and what drives the difference? A: Top-1% applicants have markedly stronger non-academic ratings (measuring extracurricular participation and leadership traits) compared with other applicants, while the share achieving high academic ratings is essentially constant across the income distribution. Students from the top 1% are much more likely to have attended private high schools, whose applicants receive substantially higher non-academic ratings than students from public high schools with the same SAT/ACT scores. Non-academic ratings account for 35 of the estimated 168 extra top-1% students per Ivy-Plus class.

Q: What is the research design for estimating causal effects, and what is the key identification assumption? A: The authors focus on applicants who are waitlisted at a given Ivy-Plus college and compare those ultimately admitted versus rejected from the waitlist. The key identification assumption is that if different colleges’ admissions committees make correlated assessments of underlying student merit but uncorrelated idiosyncratic admissions errors, then residual variation in admissions outcomes for waitlisted applicants at one college is orthogonal to students’ long-run potential. The authors validate this empirically by showing that waitlist admission at one Ivy-Plus college is uncorrelated with admissions decisions and internal ratings at other Ivy-Plus colleges.

Q: What are the causal effects of attending an Ivy-Plus college on post-college outcomes? A: For the marginal student (one who attends an Ivy-Plus college instead of the average flagship public), attending an Ivy-Plus college increases the probability of reaching the top 1% of the earnings distribution at age 33 by approximately 50%, nearly doubles the probability of attending an elite graduate school, and increases the probability of working at a prestigious firm by approximately 2.5 times. Mean earnings at age 33 increase by $101,000 (relative to a counterfactual mean of $143,000 at state flagships). Effects on reaching the top quartile of earnings are small and statistically insignificant, while effects at the very top tail are disproportionately large.

Q: Why do the findings differ from Dale and Krueger (2002) and related studies finding little effect of selective college attendance on earnings? A: The authors replicate the matriculation design of Dale and Krueger (comparing outcomes conditional on the set of colleges to which students were admitted) and obtain estimates statistically indistinguishable from their waitlist design — the research designs are not the source of disagreement. Instead, the differences arise because (1) the authors have direct college fixed effects rather than relying on average test scores as a proxy for college quality, and (2) the authors focus on upper-tail outcomes (top 1% earnings, elite graduate schools, prestigious firms) rather than log mean earnings, where Ivy-Plus colleges have their largest effects.

Q: Are the credentials that drive the high-income admissions advantage — legacy, athlete status, high non-academic ratings — predictive of better post-college outcomes? A: No. Recruited athletes, students with higher non-academic ratings, and legacy students have equivalent or lower chances of reaching the upper tail of the income distribution, attending an elite graduate school, or working at a prestigious firm than comparable Ivy-Plus applicants once the college attended is held constant. By contrast, SAT/ACT scores and academic ratings are highly positively predictive of all three post-college outcome measures.

Q: How much could changing admissions practices diversify Ivy-Plus enrollment and subsequently society’s leadership? A: Eliminating legacy preferences, non-academic rating weights, and the differential recruitment of high-income athletes — and filling those slots with students having the same test score distribution as the current class — would increase enrollment from families in the bottom 95% of the parental income distribution by 8.8 percentage points, a magnitude comparable to race-based affirmative action’s effect on Black and Hispanic enrollment shares. For leadership positions, predicted effects are small for monetary outcomes (Fortune 500 CEOs from the bottom 95% would increase by only 0.4 pp) but larger for positions where Ivy-Plus graduates are a larger share: senators from the bottom 95% would increase by 1.7 pp and Supreme Court justices by 5.4 pp. A stronger need-affirmative policy (giving low-income students preferences equivalent to current legacy preferences) would increase the share of Supreme Court justices from the bottom 60% by 17.5 pp.

Q: How are “elite” and “prestigious” employers defined in this study? A: Elite firms are defined as those that disproportionately employ Ivy-Plus graduates relative to flagship public graduates, pulling firms from the top of that ratio ranking until 25% of Ivy-Plus attendee employment is accounted for. Prestigious employers are defined by the residual of that ratio after controlling for the firm’s predicted top-1% income probability — they are firms that disproportionately employ Ivy-Plus graduates conditional on their salaries, capturing high-status jobs that do not necessarily lead to the highest earnings. The paper validates this algorithmic approach against external rankings (Vault.com for law and consulting firms; Scimagoir for hospitals), finding substantial overlap.

Q: How are treatment effect estimates adjusted for heterogeneity in students’ fallback options? A: Causal effects of Ivy-Plus attendance are much larger for students with weaker fallback options — specifically, students whose home-state flagship colleges channel fewer students to the top 1% of earnings. The authors exploit this heterogeneity to estimate the treatment effect for the marginal student who actually switches from a flagship public to an Ivy-Plus college. This heterogeneity also implies that the average causal effect across all admitted students may differ from the effect for the marginal admitted student.

Q: What share of the overrepresentation of top-1% families at Ivy-Plus colleges is attributable to pre-application factors versus admissions practices? A: Of the 245 “extra” top-1% students in an average Ivy-Plus class relative to an unconditionally income-neutral benchmark, 77 (31%) are attributable to the higher test scores of top-1% students (a pre-application factor). The remaining 168 (69%) reflect higher attendance rates conditional on test scores, of which the large majority is attributable to admissions practices (legacy, non-academic ratings, athletic recruitment) rather than application or matriculation rate differences.

Ivy-Plus colleges: The twelve highly selective private colleges comprising the eight Ivy League institutions plus Stanford, MIT, Duke, and the University of Chicago — the focus group of the study, which together account for more than 10% of Fortune 500 CEOs, a quarter of U.S. senators, and three-fourths of Supreme Court justices appointed in the last half century despite enrolling less than 0.5% of Americans.

Missing middle: The pattern by which attendance rates at Ivy-Plus colleges conditional on SAT/ACT scores are lowest for students from the middle class (70th–80th percentile of the parental income distribution, approximately $91,000–$114,000) — lower than both the top 1% and, slightly, the bottom 40% — producing a non-monotone income gradient in attendance.

Legacy preference: An admissions advantage given to applicants whose parent(s) obtained an undergraduate degree from the college to which the student is applying. In the paper’s data, legacy applicants from the top 1% are admitted at more than five times the rate of non-legacy applicants with comparable test scores, demographics, and admissions ratings; the preference is college-specific (children of alumni are only slightly more likely to be admitted at other Ivy-Plus colleges).

Waitlist research design: The paper’s primary identification strategy for causal effects, which exploits idiosyncratic variation in admissions decisions among waitlisted applicants. The design’s validity rests on the empirical finding that waitlist admissions at one Ivy-Plus college are uncorrelated with admissions decisions and internal ratings at other Ivy-Plus colleges, implying that residual variation conditional on being on the waitlist is orthogonal to students’ long-run potential outcomes.

Prestigious employers: Firms defined by the paper’s algorithm as disproportionately employing Ivy-Plus graduates conditional on those firms’ predicted top-1% income probability — capturing high-status employment that does not necessarily lead to the highest earnings (e.g., prominent law firms, consulting firms, elite hospitals). Validated against external rankings (Vault.com, Scimagoir).

Non-academic ratings: Numerical scores assigned by admissions officers measuring aspects of an application outside academic achievement, such as extracurricular activities and leadership traits. In the paper’s data, non-academic ratings differ substantially by parental income — particularly because top-1% applicants disproportionately attend private high schools whose students receive higher non-academic ratings — while academic ratings do not differ across the income distribution.

Surrogate index: A prediction of later earnings outcomes (specifically, probability of reaching the top 1% at age 33 and mean income rank) constructed from individuals’ graduate school attendance and employer fixed effects at ages 22–25, used to extend the outcome window for cohorts observed only early in their careers. The approach follows the terminology and methodology of Athey et al. (2019).