Working Paper: NBER ID: w30364
Authors: Lelys I. Dinarte Diaz; Maria Marta Ferreyra; Sergio S. Urza; Marina Bassi
Abstract: 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 other 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, estimates indicate a role for quality determinants to shrink the quality gap among programs. These findings have implications for the design and replication of high-quality SCPs, their regulation, and the development of information systems.
Keywords: short-cycle higher education programs; quality determinants; academic outcomes; labor market outcomes
JEL Codes: I2; J24
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| quality determinants (L15) | student outcomes (A21) |
| teaching numerical competencies (A21) | labor market outcomes (J48) |
| providing labor market information (J20) | labor market outcomes (J48) |
| quality determinants (L15) | dropout rates (I21) |
| quality determinants (L15) | formal employment (J46) |
| quality determinants (L15) | wages (J31) |