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Going, going, gone: the effects of aid policies on graduation at three large public institutions

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Abstract

This paper exploits uniquely detailed data and cross-institution variation in aid for three large public universities to identify the effects of aid on the probability of college graduation. The results indicate that need-based and merit-based aid both increase graduation rates at large public institutions, but primarily through the types of students that ‘select’ these institutions. Merit-based aid facilitates an institution attracting students who have higher observed academic ability that raises the probability of graduation. Need-based aid enables an institution to attract students with non-academic attributes such as social and cultural networks that, while often unobserved, improve graduation success. Broadly, our results suggest that recent aid policy that has moved away from need-based aid for low-income students (reducing their ability to find the best institutional match) and toward merit-based aid (that alters the distribution of high ability students across colleges) could foster stagnant graduation rates even with rising enrollment rates that have been observed over the last three decades.

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Notes

  1. There may be a variety of reasons why students fail to fill out a FAFSA that are related to eligibility for aid (e.g., lack of knowledge about aid availability or filing procedures and deadlines). However, our evidence suggests these cases are unlikely to represent a significant portion of non-FAFSA applicants, as students in our sample who do not fill out a FAFSA are similar to FAFSA filers in terms of pre-enrollment academic credentials, having slightly lower high school GPAs and high school class ranks, but slightly higher SAT scores. As expected, non-filers originate from areas with significantly higher median incomes than filers and receive essentially no need-based financial aid.

  2. The 3,206 individuals excluded from the sample are qualitatively similar to those retained in the sample in important attributes such as high school class rank, high school GPA, median household income and total first-year financial aid. There were 195 of 3,206 excluded observations that were dropped due to missing values of first year college GPA. Presumably, these students dropped out too early to compile a first semester GPA. Although in general, excluding premature dropouts may overstate the true effect of aid, they constitute only 1.7% of all enrollees. Also, these students descriptively tend to be relatively bright, well-to-do students majoring in difficult technical subjects—observations inconsistent with the notion they have limited chances for success and little ability to respond positively to aid.

  3. While the means of average eligibility and average need-based aid are numerically similar for the sample of enrollees ($5,377 vs. $5,101), the distributions of these variables differ substantially (e.g. the standard deviation of eligibility is nearly $1,000 greater than that of need-based aid), illustrating the role of need-based aid in equalizing income differences across students. Although eligibility and need-based aid are highly correlated (i.e., ρ = 0.75), instrumental variables techniques exploit the exogenous (independent) variation in these need measures reflected in their distributional differences to identify their separate graduation effects. Moreover, high collinearity between predicted aid and eligibility inflates the standard errors, which can yield insignificant (but not wrong-signed) coefficients. The significance of these aid variables in the graduation models (presented subsequently) suggests that multicollinearity is not an issue.

  4. Graduation rates are nearly identical at UO and IU (55.4% and 55.6%, respectively) but somewhat higher at CU (63.2%). Similarly, average first year need-based aid is similar among enrollees at UO and IU ($6572 and $6511, respectively) but higher at CU ($8986). Average first year merit aid is also similar among UO and IU enrollees ($181 and $171, respectively) but significantly higher at CU ($480). The percentage variation in both types of aid between CU and the other two schools is large even relative to the percentage variation in graduation rates. This, along with the fact that high school class ranks are very similar across institutions (78th percentile at UO, 80th percentile at IU, and 81st percentile at CU) lends support to the assertion that similar students at different schools receive different amounts of aid.

  5. Even if SAT score is correlated with latent graduation-enhancing attributes, the inclusion of the inverse Mills ratio in the graduation model acts as a filter on this correlation if the latent attributes in question are time-invariant and also affect enrollment. Time-varying shocks that occur during college and affect graduation (for example, a parent passing away or losing a job) are clearly uncorrelated with SAT scores.

  6. We explicitly incorporate time variation in aid (for a given student) into our model by estimating second through fifth year aid as a function of year in school. This accounts for systematic or unsystematic changes in aid that may occur over the course of a college career, due to changes in federal guidelines, institutional policies and practices, and so on. Furthermore, the instrumental variables construction predicts time variation in aid that is exogenous with respect to unobserved time-varying characteristics of the student. We average the predicted aid values over five years of college in order to obtain a single measure of aid with which to explain the single dichotomous outcome of graduation. Averaging also increases the efficiency of the estimates by “washing out” measurement error occurring at particular points in time.

  7. There are other potential sources of selection. For example, enrollment results may be driven by selection bias in the decision to apply (e.g., the marketing and recruiting activities of colleges that give students an idea of their potential financial aid packages prior to application), which cannot be tested due to a lack of non-applicant data. Likewise, geographic differences in large public institutions (e.g., heavy competition between the private and public sectors in the northeast that could amplify the marginal effect of aid and a history of discrimination in the south that could lead to significant racial differences in the effects of aid) may limit external validity. It follows that we can best generalize to the population of financially needy applicants to “similar” institutions.

  8. The usual OLS formulae for the standard errors in the graduation equation are biased because of the presence of predicted values of the aid variables and the heteroskedasticity naturally arising from a dichotomous dependent variable. Thus, we bootstrap the standard errors, a non-parametric technique well-suited to cases in which the mean and variance of the error term are unknown. By repeatedly sampling randomly from the available data, the bootstrapping approach obtains the “empirical distribution” for the standard error of each coefficient. We report the mean of this distribution as the estimated standard error.

  9. This result may also imply administrators can increase graduation rates by “funneling” students to professional majors that, while benefiting particular institutions and departments, is of dubious social value. In this case we are unable to directly test whether administrators actually behave in this fashion.

  10. Several studies find non-financial factors affect retention, including the student’s family structure and socioeconomic background (Ver Ploeg, 2002), the gender composition and teaching ability of the institution’s faculty (Langbein & Snider, 1999; Robst, Keil, & Russo, 1998), and to what extent there is a match between the ability of students and the quality of the college (Light & Strayer, 2000).

  11. Average yearly in-state tuitions at these institutions over the sample period (1994–2000) are as follows: Colorado: $2941; Indiana: $3905; Oregon: $3605. Average yearly out-of-state tuitions are: Colorado: $14,919; Indiana: $11,827; Oregon: $12,189. Thus, $1000 of aid in any given year is more than a quarter of average in-state tuition and between 6.7% and 8.2% of out-of-state tuition at each of these institutions.

  12. Following Tinto’s (1993) attrition model, Cabrera (1990 and 1992) uses direct student integration measures to show that need-based aid helps integrate the student into the social and academic fabric of the institution.

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Acknowledgements

The authors would like to thank Doug Anderson of Indiana University at Bloomington, Lou McClelland of the University of Colorado at Boulder, and Martha Pitts of the University of Oregon for generously providing the data. We are grateful for the comments and suggestions offered by Van Kolpin, Jean Stockard, Joe Stone, an anonymous referee, and seminar participants at the University of Georgia, the Southern Economic Association and the NBER. Any remaining errors are, of course, our own.

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  1. Economics, University of Oregon, Eugene, OA, 97403-1285, USA

    Larry Singell

  2. Public Administration and Policy, University of Georgia, 204 Baldwin Hall, Athens, GA, 30602, USA

    Mark Stater

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Correspondence to Mark Stater.

Appendix

Appendix

Table A1 Tobit models for 1st year financial aid and eligibility

   
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Table A2 Tobit models for 2nd–5th year aid and eligibility

   
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Singell, L., Stater, M. Going, going, gone: the effects of aid policies on graduation at three large public institutions. Policy Sci 39, 379–403 (2006). https://doi.org/10.1007/s11077-006-9030-7

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