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The B.E. Journal of Economic Analysis & Policy

Editor-in-Chief: Ludwig, Sandra / Schmitz, Hendrik

Ed. by Barigozzi, Francesca / Brunner, Johann / Fleck, Robert / Jürges, Hendrik / Mastrobuoni, Giovanni / Mendola, Mariapia / Requate, Till / de Vries, Frans / Wenzel, Tobias

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Volume 14, Issue 1


Volume 20 (2020)

Volume 6 (2006)

Volume 4 (2004)

Volume 2 (2002)

Volume 1 (2001)

The Effect of College Applications on Enrollment

Jonathan Smith
Published Online: 2013-12-25 | DOI: https://doi.org/10.1515/bejeap-2013-0002


This article investigates determinants of the number of four-year colleges to which students apply and how the number of applications affects their probabilities of enrollment. To estimate the effect on enrollment, I use a novel instrument: the adoption rate of the Common Application near a student’s home. I find that applying to one additional college increases students’ likelihood of enrollment, but only for those applying to very few colleges. Going from one to two applications and two to three applications increases students’ probabilities of enrollment by 40% and 10%, respectively. This is partially due to the increase in the probability of being accepted to some college but also due to the increase in the probability of choosing to enroll, conditional on being accepted.

Keywords: college enrollment; college applications

JEL Classification: I2; I23; I24; I28


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About the article

Published Online: 2013-12-25

U.S. Department of Education press release, June 24, 2009.

This 2004 statistic, calculated by the author using the Education Longitudinal Study of 2002, excludes students using early admissions or applying after graduating high school. It also excludes colleges that are open enrollment or for-profit.

The validity of the instrument is discussed in detail in Section 4.1.

ELS is US government restricted-use data that by law requires all observation counts to be rounded to the nearest 10.

Open enrollment and for-profit colleges are identified in ELS.

Early applications are not formally identified, so I eliminate those who apply to only one early decision (or early action) school and is accepted. Students may still avail themselves of early applications and either be rejected or be pushed into the non-early application pool. However, in 2003, only 17.7% of all four-year colleges offered early decision. In these colleges, the mean percentage of all applications received through early decision was 7.6% (Admission Trends Survey, NACAC, 2004). Therefore, it is a relatively small issue and moving forward, and I assume no students in the subsample utilize early decision.

ELS includes some data from IPEDS, but I merge in additional IPEDS data directly both from IPEDS and from the Delta Project, which is a cleaned version of IPEDS.

Early Decision: http://talk.collegeconfidential.com/college-search-selection/354075-list-colleges-early-action-early-decision-rolling-admissions.html; http://www.petersons.com.

Common Application: http://www.commonapp.org.

For one of many examples, see Arenson’s (2008) New York Times article.

A complete list of member schools can be found at: https://www.commonapp.org/CommonApp/Members.aspx.

Common Application schools often accept either the Common Application or their own application, with no stated preference. I cannot distinguish which type of application is used, just whether or not the Common Application is available.

Descriptive statistics on Common Application colleges and non-Common Application colleges are in Appendix.

Females earned 57% of all bachelor’s degrees in 2008–2009 (NCES 2010).

A linear probability model need not be used. All future results hold with a probit model.

In this sample, all else equal, students enroll in schools closer to home. See Card (1993) and Long (2004a) for more examples.

The first-stage results are in Table 3.

The instrument loses power, as the radius gets smaller and so I do not show those results.

Used a Wald Test.

The basic idea is that a Hausman Test is not appropriate if the true model is non-linear. The test allows for a non-linear OLS specification when there is only a single instrument. See Lochner and Moretti (2011) for details.

Given evidence that the OLS estimates cannot be rejected over the IV estimates, and since they are likely downward biased, this specification is easiest to interpret and conservative.

See Thaler and Sunstein (2008).


Citation Information: The B.E. Journal of Economic Analysis & Policy, Volume 14, Issue 1, Pages 151–188, ISSN (Online) 1935-1682, ISSN (Print) 2194-6108, DOI: https://doi.org/10.1515/bejeap-2013-0002.

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