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Learning and Earning: An Approximation to College Value Added in Two Dimensions

Evan Riehl, Juan E. Saavedra, Miguel Urquiola


This chapter is a preliminary draft unless otherwise noted. It may not have been subjected to the formal review process of the NBER. This page will be updated as the chapter is revised.

Chapter in forthcoming NBER book Productivity in Higher Education, Caroline M. Hoxby and Kevin Stange, editors
Conference held May 31–June 1, 2016
Forthcoming from University of Chicago Press

This paper explores the implications of measuring college productivity in two different dimensions: earning and learning. We compute system-wide measures using administrative data from the country of Colombia that link social security records to students’ performance on a national college graduation exam. In each case we can control for individuals’ college entrance exam scores in an approach akin to teacher value added models. We present three main findings: 1) colleges’ earning and learning productivities are far from perfectly correlated, with private institutions receiving relatively higher rankings under earning measures than under learning measures; 2) earning measures are significantly more correlated with student socioeconomic status than learning measures; and 3) in terms of rankings, earning measures tend to favor colleges with engineering and business majors, while colleges offering programs in the arts and sciences fare better under learning measures.

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This chapter first appeared as NBER working paper w22725, Learning and Earning: An Approximation to College Value Added in Two Dimensions, Evan Riehl, Juan E. Saavedra, Miguel Urquiola
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