Student support and academic performance: experiences at private universities in Mexico

Abstract

Financial aid to students in tertiary education can contribute to human capital accumulation through two channels: increased enrollment and improved student performance. We pay particular attention to the latter channel, and study its quantitative importance in the context of a student support program from the Sociedad de Fomento a la Educación Superior (Society for the Promotion of Higher Education) (SOFES) implemented at private universities in Mexico. Administrative data provided by SOFES are analyzed using a regression‐discontinuity design. The advantage of the regression‐discontinuity method is that it represents a natural experiment with randomly assigned treatment so that selection issues are minimized. The empirical results suggest that this financial aid package (loans and scholarships) contributes to better academic performance.

Keywords:

• higher education
• financial aid
• academic performance

Acknowledgements

This paper was written while the first author was consultant at the World Bank, on leave from the CPB Netherlands Bureau for Economic Policy Analysis. The authors thank Bruce Chapman, Luis Crouch, Bas van der Klaauw, Vicente Paqueo, Dinand Webbink, and participants at a World Bank seminar and a CPB seminar for helpful comments, and thank Martín Cervantes, Alejandra Diez de Sollano, David Montano Román, and Sergio Ghigliazza Ramos from SOFES in Mexico City for their kind help and hospitality.

Notes

1. The empirical relevance of credit market imperfections as a barrier to access to higher education is questioned in advanced economies (see, for example, Heckman and Carneiro 2003; Kane 1995; Dynarski 2003; Cameron and Taber 2000). However, in middle‐income countries with large income inequalities and limited public student support, such as Mexico, difficult access to credit is a real problem with potentially strong implications for higher education enrollment.

2. An elaborate description of the SOFES program is available in the World Bank’s project appraisal document (World Bank 1998).

3. As the information is double‐checked by SOFES, the data quality in the SOFES database is high. There are, however, a few peculiarities and we cleaned the data in the following way. We dropped missing cases on study field (14 observations), students in a Master program or PhD program (2317 observations), extreme observations on GPAs (<6 and >10; 2254 observations), extreme reported income levels (lower than 1000 pesos per month and larger than 50000 pesos per month; 1109 observations), very low reported credit levels (<0.2; 74 observations), very high reported levels of ENF (>0.7; 1006 observations), very low reported levels of the socio‐economic stratification index (<2.1 on a scale from 2.1 to 29; eight observations), and very high reported levels of additional financial support (>30000 pesos; 17 observations). This data cleaning reduces the number of observations from 10,124 to 6102. We tried a similar analysis for Master students (2286 subjects) but were often left with too few observations to properly apply the RD approach. Finally, we only include people who are studying at the moment of data collection (June 2003). The data used in this paper are a random sample of the total SOFES database.

4. If our research design would be based on a comparison of student performance between students with and without SOFES support, some additional types of selection could arise. First, financial aid is not a random event, but the outcome of a deliberate application process. Before entering our analysis as a SOFES recipient, several decisions by students could influence the sample. Students applying for SOFES support are well informed about the terms and repayment conditions. In particular, unmotivated students are not likely to apply, as the consequences of default can be severe. We do not have an indicator for the student’s motivation. When SOFES students are more motivated than the average student, we might find a positive treatment effect on student performance while this is actually a selection effect. This source of selection is ruled out in the RD strategy if we assume that this type of motivation does not vary systematically with the percentage of financial aid for which the student is eligible. Second, students without financial support may actually have unsuccessfully applied for a SOFES loan. To the extent that rejected SOFES applicants more closely resemble accepted SOFES applicants than the group that did not apply, the treatment and control group become more similar in observed and unobserved characteristics. This leads to a downward bias of the treatment effect. It is likely that this bias is small, as the coverage of SOFES is still limited. Finally, renewal of SOFES loans is contingent upon completion of the previous term of the program. This means that the students who lost SOFES eligibility due to low performance may appear in the control group. Selection of this type creates upward bias of the treatment effect.

5. Because of small numbers of observations, we cannot exploit the discontinuity at DS = 0.50. For similar reasons we use a broader band width at DS = 0.30.

6. As shown in Table 1, advised scholarships are also linked to the student’s ENF. Actual scholarships can deviate from advised scholarships, but we have data on actual scholarships, and these data are used in the regression analysis.