Estimating the Effects of College Characteristics over the Career Using Administrative Earnings Data,

Abstract

We estimate the labor market effect of attending a highly selective college, using the College and Beyond (C&B) Survey linked to Detailed Earnings Records from the Social Security Administration (SSA).This paper extends earlier work by Dale and Krueger (2002) that examined the relationship between the college that students attended in 1976 and the earnings they self-reported in 1995 on the C&B follow-up survey.In this analysis, we use administrative earnings data to estimate the effects of several college characteristics (that are commonly used as proxies for college quality) for a more recent cohort: students who entered college in 1989.We also estimate these effects for the 1976 cohort, but over a longer time horizon (from 1983 through 2007).The effects of college characteristics on earnings are sizeable for both cohorts in regression models that control for variables commonly observed by researchers, such as student SAT scores.However, when we partially adjust for unobserved student ability by controlling for the average SAT score of the colleges that students applied to, our estimates of the effects of college characteristics fall substantially and are generally indistinguishable from zero.There were notable exceptions for certain subgroups.For black and Hispanic students and for students who come from families with less parental education, the effects of college characteristics are large, even in models that adjust for unobserved student characteristics.Finally, contrary to expectations, our results do not suggest the effects of college characteristics increased for students who entered college more recently because estimates are similar for the 1976 and the 1989 cohort.*We thank the Mellon Foundation for financial support; Ed Freeland for obtaining permission of participating colleges; Matthew Jacobus and Licia Gaber Baylis for skilled computer programming support; Mike Risha of SSA for merging the C&B data with SSA data and for tirelessly running our programs; Lawrence Katz, Sarah Turner, and Mark Dynarski for helpful comments on an earlier