Nearby College Enrollment

• In Event: Excellence in Education Research: Early-Career Scholars and Their Work

Abstract

Living away from home during the college years is seen by many as a rite of passage, signaling the first steps toward financial independence and adulthood (Mulder & Clark, 2002). This decision, however, is not necessarily based on merely adding geographic distance between parents’ home and the selected institution of higher education (IHE). Some people’s decisions to move are driven by investment reasons (e.g. higher entry level salary after graduation conditional on institutional selectivity and prestige) in addition to the usual consumer reasons (e.g. enjoying a strong NCAA Division I football team). The decision to move, then, involves a trade-off. Students may think that while migration to attend college is more expensive, they will get a solid consumer return, a solid investment return, or perhaps even both. Some students may, however, abandon the idea of traveling if the expected costs outweigh the future returns. The latter scenario assumes, though, that most students have a clear understanding of the many variables needed for evaluating the pros and cons of moving away to attend college. A perhaps more realistic depiction of the decision process followed by students is that they may still decide to move when such decision would not necessarily translate into better investment returns. For example, if students who migrated enrolled in institutions that resemble the selectivity and prestige of some of their nearby colleges, their decision to move may potentially result into simply spending significantly more resources, incurring significantly larger student loan debt, and possibly attaining similar outcomes compared to those associated with nearby college enrollment (NCE).

The proposed study will test whether non-nearby college enrollment (non-NCE) —broadly defined as migrating away from home to attend college– is associated with better educational and financial outcomes when compared to the outcomes resulting from NCE. The relevance of this question is not only justified given the assumed (yet untested) positive effects of non-NCE on students’ outcomes, but also due to its financial implications. Even after factoring other variables affecting costs (e.g. institutional financial aid and selectivity) into the college choice process, living away from home during the college years not only implies higher tuition and fees, along with forgone earnings, but also increases in cost of living expenses, rent, supplies, and travel and transportation (Turley, 2009), likely resulting in a more expensive pathway through college and potential greater reliance upon student loans to finance these elevated costs.

Counter-factual and conceptual framework:
Despite the simplicity of asking whether non-NCE positively affects students’ outcomes, its answer requires complex modeling procedures. This estimation requires one to observe the same student i in two counterfactual scenarios (Rubin, 2005). In the first, student i decides to attend college locally at time t. In the second, the same student i moves away from home to enroll in college also at time t. Once time has passed and the outcomes associated with each scenario are captured at time t + n (where n is the time allowed to pass), one would simply need to observe the difference from each case in order to evaluate which decision rendered better results (Caliendo & Kopeinig, 2008; Holland, 1986; Rosenbaum & Rubin, 1983). This estimation, however, is impossible (Holland, 1986; Rubin, 2005). A student, and her/his family, has only one chance to make such a decision with no other option but to accept its consequences. Since we cannot observe the same individual in the two states (NCE and non-NCE), we need to compare NCE students against non-NCE students to try to capture NCE effects.

In the absence of statistical correction comparing these students is likely to render biased results as students belonging to each group are likely to be systematically different. The source of these differences is due to the fact that decisions to move do not happen on a level playing field. Investment in non-NCE is constrained by availability of different forms of capital (financial, social, and cultural —Bourdieu, 1986). Students from wealthier backgrounds, who are able to afford non-NCE, would probably be more likely to achieve better educational and occupational outcomes than students who could not afford to move. This increased likelihood of success, however, is not necessarily the result of having moved away from home to attend college but the result of greater sources of support associated with higher SES that enabled such movement in the first place. To account for these differences, human capital (Becker, 1962; Mincer, 1958) and stratification (Bourdieu, 1986) theories will guide the modeling rationale and the selection of a multidimensional and multilevel set of individual, institutional, and state level factors that may have influenced or even enabled participants’ decisions to move. Table 1 includes the variables accounting for potential systematic differences in sources of support and resources between NCE and non-NCE students that will be used in the models.

Because this proposal builds upon the counterfactual reference framework, which acknowledges potential systematic differences between NCE and non-NCE students that may be driving the variation of the outcomes of interest, the modeling procedure will rely on two quasi-experimental techniques that account for observable —propensity score matching (PSM, Rosenbaum & Rubin, 1983)– and unobservable —the Heckman control function (Heckman, 1979)– differences before measuring the effects of NCE on academic and financial outcomes. Once these quasi-experimental techniques have created comparable groups, researchers would be in a better position to address whether non-NCE rendered better outcomes, thus enabling them to inform families about the most likely outcome resulting from each scenario. While both PSM and Heckman will render average treatment effect on the treated (ATET) of choosing non-NCE, heterogeneity of treatment effect on the treated (HTET) depending on socioeconomic status (SES) will be also measured. Accounting for this potential HTET is important given that SES may modify the ATET. That is, high SES students may benefit from non-NCE, whereas low SES students may not. Based on Gabler et al. (2009) the implementation of HTE will rely on (a) interacting treatment effect with SES status (treatment-by-covariate interaction) and (b) running disaggregated models conditional on SES status (subgroup analysis).

Research Purpose:
The purpose of the study is to provide students, their families, and decision makers with evidence about the most likely outcomes resulting from their college selection as a function of proximity, thus testing for the practical relevance of the extra investment associated with moving further away to enroll in college.

The models will rely upon a unique dataset built from several sources of information. The student level indicators will be taken from ELS (2002 to 2012). Institutional level variables will be retrieved both from ELS and IPEDS, and state level indicators will be retrieved from the National Association of State Student Grant & Aid Programs (NASSGAP) and the U.S. Census Bureau. Due to the potential influence of state and regional tuition reduction agreements on moving decisions (Cooke & Boyle, 2011; Zhang & Ness, 2010), information about state members from each regional compact and number of participating programs will be gathered from official websites (see MSEP, 2014; NEBHED, 2014; SREB, 2014; WICHE, 2014).

Author

• Manuel S. Gonzalez Canche, University of Georgia