Arenson, K. 2008. “Applications to U.S. Colleges Are Breaking Records.” New York Times, January 17th, Section: America.Google Scholar
Avery, C. 2009. “The Effects of College Counseling on High-Ability, Low-Income Students: Results of a Pilot Study with a Randomized Control Trial.” NBER Working Paper No. 16359.Google Scholar
Bettinger, E., B. T. Long, P. Oreopoulos, and L. Sanbonmatsu. 2012. “The Role of Simplification and Information in College Decisions: Results from the H&R Block FAFSA Experiment.” Quarterly Journal of Economics 27(3):1205–42.Web of ScienceCrossrefGoogle Scholar
Black, S., and A. Sufi. 2002. “Who Goes to College? Differential Enrollment by Race and Family Background.” NBER Working Paper No. 9310.Google Scholar
Bowen, W., M. Chingos, and M. McPherson. 2009. Crossing the Finish Line. Princeton, NJ: Princeton University Press.Google Scholar
Bucks, B. 2004. “Affirmative Access Versus Affirmative Action: How Have Texas’ Race-Blind Policies Affected College Outcomes?” Working Paper.Google Scholar
Card, D. 1993. “Using Geographic Variation in College Proximity to Estimate the Return to Schooling.” NBER Working Paper No. 4483.Google Scholar
Dale, S., and A. Krueger. 2002. “Estimating the Payoff to Attending a More Selective College: An Application of Selection on Observables and Unobservables.” Quarterly Journal of Economics 117(4):1491–527.CrossrefGoogle Scholar
DesJardins, S. L., D. A. Ahlburg, and B. P. McCall. 2006. “An Integrated Model of Application, Admission, Enrollment, and Financial Aid.” Journal of Higher Education 77(3):381–429.CrossrefGoogle Scholar
Dillon, E., and J. Smith. 2013. “The Determinants of Mismatch Between Students and Colleges.” NBER Working Paper No. 19286.Google Scholar
Fu, C. 2013. “Equilibrium Tuition, Applications, Admissions and Enrollment in the College Market.” PIER Working Paper No. 12-013.Google Scholar
Hoxby, C., and C. Avery. 2013. “The Missing ‘One-Offs’: The Hidden Supply of High-Achieving, Low Income Students.” NBER Working Paper No. 18586.Google Scholar
Hoxby, C., and S. Turner. 2013. “Expanding College Opportunities for High-Achieving, Low Income Students.” Stanford Institute for Economic Policy Research Discussion Paper No. 12-014.Google Scholar
Linsenmeier, D., H. Rosen, and C. Rouse. 2002. “Financial Aid Packages and College Enrollment Decisions: An Econometric Case Study.” NBER Working Paper No. 9228.Google Scholar
Liu, A. Y.-H., R. G. Ehrenberg, and J. Mrdjenovic. 2007. “Diffusion of Common Application Membership and Admissions Outcomes at American Colleges and Universities.” NBER Working Paper No. 13175.Google Scholar
Lochner, L., and E. Moretti. 2011. “Estimating and Testing Non-Linear Models Using Instrumental Variables.” NBER Working Paper No. 17039.Google Scholar
Loury, L. 2006. “All in the Extended Family: Grandparents, Aunts, and Uncles and Educational Attainment.” Tufts University Economics Department Working Paper.Google Scholar
National Center for Education Statistics. 2010. Digest of Education Statistics. U.S. Department of Education.Google Scholar
Pallais, A. 2013. “Small Differences That Matter: Mistakes in Applying to College.” NBER Working Paper No. 19480.Google Scholar
Thaler, R., and C. Sunstein. 2008. Nudge: Improving Decisions about Health, Wealth, and Happiness. New Haven, CT: Yale University Press.Google Scholar
Van der Klaauw, W.. 2002. “Estimating the Effect of Financial Aid Offers on College Enrollment: A Regression-Discontinuity Approach.” International Economic Review 43(4):1249–1287.CrossrefGoogle Scholar
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.
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.
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.