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The Fast Track to New Skills
determinants—such as providing labor market information and being deemed of high quality by the regulating authorities—as well as some characteristics, such as program size and HEI type, contribute to students’ academic and labor market outcomes.
Defining and Measuring SCP Quality Challenges of Measuring SCP Quality Measuring the quality of higher education is challenging for a couple of reasons. First, there is little agreement over what is expected of higher education or how to measure quality in a standardized way. Moreover, the measures in a country are usually determined by data availability in its higher education information system. A second challenge is related to whether quality should be measured through student outcomes or program value added. The distinction between outcomes and value added, outlined in this book’s introduction and chapter 2, helps clarify this challenge. Consider the wage earned by a program’s graduate immediately upon graduation. The wage constitutes the outcome, determined by studentlevel inputs (ability, effort, and other background characteristics), peers’ characteristics, and program-level inputs. The program’s contribution to the student’s wage, net of the contributions made by the student herself and her peers, is the program’s value added. Estimating the value added of an SCP requires detailed individual-level data on all elements of the production function that could affect the graduate’s wage. Unfortunately, this level of detail in higher education administrative data is difficult to obtain from the countries in the region. Some countries do not collect these data. Others do, but gaining access to these data is an enormous challenge as it usually contains confidential individual level information.1 Collecting the data and facilitating their access remains a key task in LAC. Due to the lack of data or the complexity of getting access to the data, this chapter follows an alternative approach, which is described in detail in Dinarte et al. (2021). The chapter uses the data reported by program directors to the WBSCPS on program infrastructure, curriculum and training, engagement with industry, costs and financing, faculty, and additional practices, as well as data on other characteristics of the programs, institutions, and students. Further, the chapter uses data collected by the WBSCPS on average academic and labor market outcomes for graduates at the program level, including dropout rates, extra time to graduate, formal employment, and wages. Throughout, the term “determinant” refers to practices (for example, providing labor market information to students), inputs (for example, workshops for practical training), or input characteristics (for example, the percentage of faculty with more than five years of experience working in industry) that programs can choose and that could potentially affect graduates’ outcomes. Using WBSCPS data for the five countries covered in the survey—Brazil (the states of Ceará and São Paulo), Colombia, the Dominican Republic, Ecuador, and
