Lelys Dinarte-Diaz

Short-cycle higher education programs (SCPs), lasting two or three years, capture about a quarter of higher education enrollment in the world and can play a key role enhancing workforce skills. In this paper, we estimate the program-level contribution of SCPs to student academic and labor market outcomes, and study how and why these contributions vary across programs. We exploit unique administrative data from Colombia on the universe of students, institutions, and programs to control for a rich set of student, peer, and local choice set characteristics. We find that program-level contributions account for about 60-70 percent of the variation in student-level graduation and labor market outcomes. Our estimates show that programs vary greatly in their contributions, across and especially within fields of study. Moreover, the estimated contributions are strongly correlated with program outcomes but not with other commonly used quality measures. Programs contribute more to formal employment and wages when they are longer, have been provided for a longer time, are taught by more specialized institutions, and are offered in larger cities.

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Short-cycle higher education programs (SCPs) can play a central role in skill development and higher education expansion, yet their quality varies greatly within and among countries. In this paper we explore the relationship between programs’ practices and inputs (quality determinants) and student academic and labor market outcomes. We design and conduct a novel survey to collect program-level information on quality determinants and average outcomes for Brazil, Colombia, Dominican Republic, Ecuador, and Peru. Categories of quality determinants include training and curriculum, infrastructure, faculty, link with productive sector, costs and funding, and practices on student admission and institutional governance. We also collect administrative, student-level data on higher education and formal employment for SCP students in Brazil and Ecuador and match it to survey data. Using machine learning methods, we select the quality determinants that predict outcomes at the program and student levels. Estimates indicate that some quality determinants may favor academic and labor market outcomes while others may hinder them. Two practices predict improvements in all labor market outcomes in Brazil and Ecuador—teaching numerical competencies and providing job market information—and one practice—teaching numerical competencies—additionally predicts improvements in labor market outcomes for all survey countries. Since quality determinants account for 20-40 percent of the explained variation in student-level outcomes, quality determinants might have a role shrinking program quality gaps. Findings have implications for the design and replication of high-quality SCPs, their regulation, and the development of information systems.

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