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Cited by 258

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Affirmative Action in Higher Education: How Do Admission and Financial Aid Rules Affect Future Earnings?

Arcidiacono, Peter·2005·Econometrica

Abstract

This paper addresses how changing the admission and financial aid rules at colleges affects future earnings. I estimate a structural model of the following decisions by individuals: where to submit applications, which school to attend, and what field to study. The model also includes decisions by schools as to which students to accept and how much financial aid to offer. Simulating how black educational choices would change were they to face the white admission and aid rules shows that race-based advantages had little effect on earnings. However, removing race-based advantages does affect black educational outcomes. In particular, removing advantages in admissions substantially decreases the number of black students at top-tier schools, while removing advantages in financial aid causes a decrease in the number of blacks who attend college.

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Crossref · 10.1111/j.1468-0262.2005.00627.x

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Metadata

Authors
Arcidiacono, P.
Venue
Econometrica
Published
2005-09
Volume
73
Issue
5
Pages
1477-1524
ISSN
  • 0012-9682 (ISSN-L)
  • 1468-0262
OpenAlex
W2103560305
Cited by
258
References
2
FWCI
25.44
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Affirmative Action in Higher Education: How Do Admission and Financial Aid Rules Affect Future Earnings? https://app.cochat.ai/papers/10.1111/j.1468-0262.2005.00627.x
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Arcidiacono, P. (2005). Affirmative Action in Higher Education: How Do Admission and Financial Aid Rules Affect Future Earnings? Econometrica. https://doi.org/10.1111/j.1468-0262.2005.00627.x

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.