Understanding the income gradient in college attendance in Mexico: The role of heterogeneity in expected returns

Abstract

Differences in college enrollment between poor and rich are striking in Latin America. Explanations such as differences in college preparedness and credit constraints have been advanced. An alternative explanation could be differences in information sets between poor and rich, for example, about career opportunities, translating into different expected returns to college. Poor people might expect low returns and thus decide not to attend or they might face high (unobserved) costs that prevent them from attending despite high expected returns. I use data on people's subjective expectations of returns to address this identification problem. I find that poor individuals require higher expected returns to be induced to attend college than individuals from rich families. Testing predictions of a model of college attendance shows that poor individuals are particularly responsive to changes in direct costs, which is consistent with them being credit constrained. Performing counterfactual policy experiments, I find that a sizeable fraction of poor individuals would change their decision in response to a reduction in direct costs and that these individuals at the margin have expected returns that are as high or higher than the individuals already attending college.

Credit constraints in higher education in a context of unobserved heterogeneity

“…This is important since previous reported evidence utilizes methods such OLS and instrumental variables, which may conduct to misleading conclusions or results which heavily depend upon the considered instrument (Heckman, Urzúa and Vytlacil, 2006). 1 Unlike previous studies, this paper tests the existence of credit constraints in higher education access by comparing the actual economic returns of individuals who attended to it with those of who are at the margin of attending to it, in the context of unobserved heterogeneity models (Willis and Rosen, 1979;Card, 1994Card, , 1995Taber, 2001, Heckman andVytlacil, 2005;Heckman, Urzúa and Vytlacil, 2006;Vytlacil, 2010, 2011;Kaufmann, 2012). In order to identify individuals, and following Carneiro, Heckman and Vytlacil (2011), our empirical strategy consists in simulating marginal changes in di¤erent policies so we can identify who are at the margin of attending or not to a higher education institution.…”

“…On the one hand, there are articles focused on assessing the e¤ect that …nancial aid has on enrollment (Kane, 1996(Kane, , 2007Cameron and Heckman, 2001;Lochner and Monge-Naranjo 2011;Rau , Rojas and Urzúa, 2013). On the other hand, we …nd studies that consider the economic returns of higher education (Carneiro and Heckman, 2002;Cameron and Taber, 2004;Kaufmann, 2012). The …rst group analyzes if the access to …nancial aid positively a¤ects higher education enrollment, a result that may shed light on the existence of credit constraints.…”

“…Articles focusing on economic returns such as Cameron and Heckman (2001), Cameron and Taber (2004) and Kaufmann (2012), …nd no evidence of credit constraints on higher education access in United States and Mexico. The …rst two articles suggest that ability constraints (or long run constraints) rather than credit constraints (or short run constraints) are the main determinants behind the decision of attending to a higher education institution.…”

“…The …rst two articles suggest that ability constraints (or long run constraints) rather than credit constraints (or short run constraints) are the main determinants behind the decision of attending to a higher education institution. Kaufmann (2012) uses a survey of economic returns that individuals expect to have in the future in order to identify the existence of credit constraints in Mexico, concluding that those who are at the margin of attending or not to a higher education institution would have larger economic returns than those who are attending, implying the presence of credit constraints.…”

From LATE to MTE: Alternative methods for the evaluation of policy interventions

“…After that, we turn to MTE estimation as a more informative way of exploiting a continuous instrument, which aims at identifying a 3 Applications in economics of education range from estimating the effects of child care attendance on child performance (Felfe and Lalive, 2015, Noboa-Hidalgo and Urzúa, 2012, and Cornelissen, Dustmann, Raute, and Schönberg, 2016, the effects of secondary schooling attendance on earnings (Carneiro, Lokshin, Riado-Cano, and Umapathi, 2015), the effects of advanced high school mathematics education on earnings (Joensen and Nielsen, 2016), the effects of mixed-ability schools on long-term health (Basu, Jones, and Rosa Dias, 2014), the effects of alternative breast cancer treatments on medical costs (Basu, Heckman, Navarro-Lozano, and Urzúa, 2007), and the returns to attending college (see e.g. Carneiro, Heckman, and Vytlacil, 2011 for the U.S., Balfe, 2015 for the U.K., Kamhöfer, Schmitz, andWestphal, 2015, for Germany, andNybom, 2014, for Sweden as well as Kaufmann, 2014, on the role of credit constraints in Mexico).…”