Notes

across students depending on what they would choose if they did not enroll in an SCP.

Complementary conclusions emerged from the value-added analysis performed for Colombia. Although program-level contributions vary across fields, they vary even more within fields due to the variation in institution- and program-level characteristics. Moreover, the analysis of job vacancies indicated that there is not only a large demand for SCPs, but also a possible geographic mismatch between the demand for and supply of SCP graduates, as the firms demanding them are more geographically concentrated than the new SCP graduates. In other words, labor markets for SCP graduates are also heterogeneous across locations.

Although the wide variation in program-level outcomes and value-added contributions is concerning, it also gives policy makers the opportunity to understand what makes a program “good” or “pertinent.” The institution- and program-level characteristics that are available in administrative data sets are associated with a program’s value-added contribution. But other program characteristics—usually not measured in administrative data sets—may have an even stronger association. These characteristics include whether the program features internships, how it connects with the local labor market, how it relates to the private sector, and whether it offers a flexible class schedule. The World Bank Short-Cycle Program Survey collected data on these characteristics, which are used in chapter 4 to investigate their relationships with program outcomes and value added. Together with the evidence presented here and the evidence in chapter 3 on SCP supply, those results should provide rich and useful information to policy makers interested in understanding what makes an SCP “good,” and in expanding the supply of “good” programs.

Notes

1. Cedefop (2018). 2. Ryan (2001); Quintini and Manfredi (2009); Quintini, Martin, and Martin (2007). 3. Ryan (2001). 4. Hanushek et al. (2017). 5. Golsteyn and Stenberg (2017); Verhaest et al. (2018). 6. Arias et al. (2014); Arias, Evans, and Santos (2019). 7. Bassi et al. (2012). 8. Gonzalez-Velosa et al. (2015). 9. Bahr (2016); Liu, Belfield, and Trimble (2015); Dadgar and Trimble (2015); Dynarski,

Jacob, and Kreisman (2016); Bettinger and Soliz (2016); Jepsen, Troske, and Coomes (2014); Minaya and Scott-Clayton (2017); Stevens, Kurlaender, and Grosz (2015); Xu and Trimble (2016). 10. In the United States, a full-time student can earn an associate degree in two years. This requires general education courses. A certificate usually involves two or fewer years of

courses in a professional/technical field only. A bachelor’s degree lasts at least four years. 11. See, for instance, Heckman, Stixrud, and Urzúa (2006). 12. Bassi et al. (2012). 13. Ferreyra et al. (2017) 14. This section draws on the background paper by Kutscher and Urzúa (2020), written for this book. 15. The analysis was carried out only for countries where it was possible to distinguish between graduates of bachelor’s programs and SCPs. 16. Interestingly, the comparison of the Mincerian returns to SCP degrees relative to the alternative of bachelor’s dropouts over time suggests a general upward trend.

In Ecuador, El Salvador, and Paraguay, the estimated returns increased more than 30 percentage points between the early 2000s and the late 2010s; in Bolivia,

Honduras, and Uruguay, they increased between 10 and 30 percentage points; whereas in Argentina and Chile, the increase was less than 10 percentage points. Costa

Rica and Mexico are the only countries with decreasing returns during this period. 17. Other characteristics might contribute to the heterogeneity in returns. For example, there might be two groups of SCP graduates: “first-time learners,” working toward their first higher education degree, and “further learners,” seeking to add specific, technical skills via SCPs to their skills portfolio. One would expect these groups to have different profiles in terms of labor market outcomes, socioeconomic characteristics, and even the types of HEIs they attend. Given current data limitations, it is not possible to investigate this issue further. It remains an important topic for future research. 18. This section draws on the background paper by Ferreyra, Galindo, and Urzúa (2020) written for this book. This paper extends and generalizes the methodology used by

Mountjoy (2019). 19. These growth rates correspond to the net number of programs. The rates are calculated as the difference between the entry and exit rates reported in table 3.1, in chapter 3. 20. An important limitation is that enrollment data for SENA, which accounts for a large share of secondary enrollment in Colombia, is not available. Hence, “not enrolled” includes students who effectively obtain a high school diploma and do not enter the higher education system, plus those who enroll in SENA. 21. This section draws on the background paper by Ferreyra et al. (2020), written for this book. 22. Evidence for the United States also finds that longer programs make higher contributions (Jepsen, Troske, and Coomes 2014; Liu, Belfield, and Trimble 2015; Xu and

Trimble 2016; Bahr 2016). 23. These findings are similar to those of Minaya and Scott-Clayton (2019) for the United

States. 24. This section draws on the background paper by Galindo, Kutscher, and Urzúa (2021), written for this book.