inequality than equalizing parental background. The importance of credit access falls when students can supply work to pay for college (Garriga and Keightley 2007). Although Colombia is a large developing economy, the market for student loans is very limited, covering only 7 percent of students in 2003 (ICETEX 2010). Lack of family resources, limited opportunities to work during college, and missing credit markets for student loans are clear impediments to college access in countries such as Colombia.

In those countries, tuition subsidies may therefore expand college access. Our paper complements the literature on Chile’s recent free college policies (Bucarey 2018) and England’s free college elimination (Murphy et al 2019). It also joins in the vast literature of college financial aid in the U.S. (cited in Section 8.5), including the recent literature on free community college and the so-called “Promise” programs implemented in multiple U.S. states.

# **3 Data and institutional environment**

In this section we describe our data sources, Colombia’s higher education environment, and our study cohorts. Appendix A.1 contains further details.

Our data consists of student- and program-level information drawn from three administrative datasets: Saber 11, SPADIES, and SNIES. The Saber 11 dataset contains students’ test scores at the national mandatory high school exit exam (also named Saber 11) as well as selfreported socio-economic information. Saber 11 is a standardized test that measures a student’s preparedness for higher education—reflecting not only her innate ability but also her primary and secondary education quality. We use it as a measure of student ability, broadly understood as academic readiness for higher education. Family income is reported in brackets defined relative to the monthly legal minimum wage (MW), which is equal to 381,000 Colombian pesos (COP) in 2005 (US$ 1 = 2,321 COP in 2005).

SPADIES tracks every higher education student. For each semester, it records the number of classes attempted and passed by the student, as well as her graduation or dropout date. It does not record the specific classes attempted, number of attempts per class, or class grades. SNIES contains program-level information, including institution, field, and tuition. We focus on bachelor’s programs, which capture approximately 80 percent of the country’s total higher education enrollment. Enrollment is quite evenly split between public and private institutions; public institutions are heavily subsidized and charge a considerably lower tuition than private institutions.

To analyze college enrollment, we focus on the 2005 high school cohort. We calculate deciles and quintiles of their ability distribution; in what follows, deciles and quintiles always refer to this distribution. For consistency with the model, we classify students into “student types” defined by combinations of student ability quintiles and family income brackets. Appendix table A1 shows the distribution of student types in this cohort. While a remarkable 70 percent of high school graduates come from the lowest two income brackets, less than 5 percent come from the top one. Not surprisingly, income and ability and strongly and positively correlated.

For our analysis of final outcomes and academic progression we focus on students from the 2006 college entry cohort that enroll in five-year bachelor’s programs within five years of finishing high school. Since every program requires a different number of classes for graduation, we normalize the requirement to 100 classes for every program to facilitate exposition.We assume 7