Choosing College in the 2000s: An Updated Analysis Using the Conditional Logistic Choice Model

Abstract

In this paper I investigate the college enrollment decisions of a nationally representative cohort of students who first attended in the mid-2000s. I find that while cost, distance, and match continued to be important in the choice between colleges, characteristics of the most-likely college choice appear less important in the choice of whether to enroll at all when controlling for student characteristics and local labor market conditions. Subpopulation analyses on students with high SAT scores and students with low family income, two groups that remain the focus of many financial aid policies, indicate some differences in the way these particular students chose college. Extending prior work by modeling discrete steps in the enrollment decision process—application and enrollment conditional on application—I find choice characteristics were most significant in the application stage. These results support other research that shows students may self-select out of potentially better college matches due to lack of information about actual costs or limited geographic opportunity.

Appendix

In this study, characteristics of the most-likely college (or nearest public two-year institution) were not consistently predictive of whether a student would enroll in college within two-years of high school graduation. In particular, the parameter on distance, which was negatively correlated with the choice between colleges and negatively signed across logistic models, was only statistically significant at the 5% level in two of eight models. Student-specific characteristics such as SAT percentile, family income, parental education, and gender as well as local unemployment rates were more consistently predictive of enrollment.

In the discussion section of the paper, I note that while these results suggest that distance was less important for students when choosing whether to attend college, it may also be the case that correlation between model covariates masks underlying realities about college access. If students are not sorted randomly in relation to postsecondary options, but instead are clustered based on observable characteristics such as family income, parental education level, and race/ethnicity, then it may be that controlling for these characteristics in a regression framework offers misleading insights into the relationship between distance and enrollment. To investigate whether distances to the closest public postsecondary institution are related to the likelihood of college enrollment, I proposed a thought experiment and method for carrying it out using synthetic data. I describe the procedure and results below.

I first use U.S. Census data to construct a population of synthetic students, each one representing a single census tract in the lower 48 states and created using its population characteristics. Specifically, synthetic students represent the “modal” person from the tract in that their characteristics are assigned using modal values of tract demographics. For example, in a tract in which women outnumber men, the student becomes female; in tracts where the plurality of residents have some college education, the student is assigned a parental education level of some college, and so on. Because Census data do not give SAT scores, I generate four students for each tract, each with a different SAT percentile—30th, 50th, 70th, or 90th—representing different levels of college readiness. As with the ELS cohort students, I first predict each synthetic student’s most-likely college to attend and then, using the characteristics of this college as well as those of the student, the likelihood that they will enroll in college. I generate four sets of predictions in total, one for each SAT percentile.

Figure 1 shows the heterogeneity in these predictions across the distribution of SAT score percentiles. Each map, one for each level of SAT percentile, shades census tracts according to the probability that its “modal” simulated student will enroll in college within two-years of high school graduation. Each tract is coded blue for predicted probabilities above than 0.5—more likely than not to attend within two-years of earning a high school diploma—and red for those below. Darker shades show predictions that are statistically significant ($p < 0.05$). The maps in Fig. 1 show variation in the likelihood of enrollment, despite the fact that all synthetic students within each simulation are equally college ready in terms of their SAT scores. Simulation models suggest that even for synthetic students with SAT scores in the 90th percentile (bottom right map in Fig. 1), that is, those most likely to benefit from attending college and earning a degree, the odds of enrollment change depending on location.

Fig. 1

Visualization of predicted probability that a synthetic modal student at the census tract level will enroll in college within two-years of earning high school diploma/GED based on characteristics of his/her most-likely college choice. Shades of blue represent predicted probabilities greater than 0.5 (more likely than not to attend); shades of red represent predicted probabilities less than 0.5 (less than likely to attend). In both cases, shades are darker when statistically significant. Because Census data used to create modal students does not contain SAT scores, each synthetic student was given multiple scores across the distribution of possible scores. Each map shows results for synthetic students given SAT scores at the stated point in the distribution: 30th, 50th, 70th, and 90th

When considering only those census tracts with probabilities of enrollment that are statistically significant, I find statistically significant differences in population characteristics between the bottom and top quartiles of predicted probability—those in which the synthetic student is least and most likely to enroll, respectively. Tracts in the bottom quartile of probable enrollment tend to have more persons of color, lower average educational attainment, and lower median income than those in the top quartile. Computing the distance to the nearest public, two-year, and public two-year institution for each census tract centroid, I find that tracts that produced non-enrollees in the simulations tend to be farther removed from the nearest institution. Figure 2 shows the differences in median distance to nearest college between tracts with non-enrollees and those with enrollees. Looking at the upper right-hand facet in which synthetic students were given the 50th percentile of SAT scores, the median distance to the nearest public two-year institution for non-enrollee tracts is around 12 miles. For enrollee tracts, the median distance is less than five miles. Results are similar across the range of SAT percentiles.

Fig. 2

Median distances in miles to nearest public, two-year, and public two-year for census tracts that produced modal synthetic students likely (Prob > 0.5) and unlikely (Prob < 0.5) to enroll within two years of high school graduation across distribution of SAT percentiles. Only those tracts with statistically significant positive or negative predictions ($p < 0.05$) are included