Abstract
The cost of attending college has risen steadily over the past 30 years, making financial aid an important determinant of college choice for many students and a subject of concern for colleges and state governments. In this paper, we estimate the effect of rule-based merit aid assignment on students’ enrollment decisions at the University of Iowa. Iowa evaluates many students using an admissions score comprised of high school grade point average and class rank, core high school courses, and one’s ACT test score. Students from out-of-state who meet a specific threshold score qualify for the National Scholars Award (NSA), presently worth nearly $20,000 over 4 years. We employ a regression discontinuity model to take advantage of award assignment criteria, finding that the award does increase the probability of enrollment at Iowa. This result is robust for several applicant subsamples and passes falsification checks using Iowa residents, who are ineligible for the award. Preliminary analysis of an earlier, tiered version of the currently single-valued award suggests that the NSA may have strong effects on very high-achieving candidates.
This is a preview of subscription content, access via your institution.
Notes
- 1.
Price increases computed using 2013 dollars.
- 2.
Published prices typically include direct charges (e.g., tuition and fees, room and board) and indirect costs (books, supplies, transportation, and personal expenses).
- 3.
Students’ admissions scores may be updated after they submit their applications, but we view their scores at the time that they apply. As a result, some students who do not appear eligible are offered the NSA. Despite this, the probability of a student being offered the NSA rises by over 70 percentage points at the cutoff for NSA receipt.
- 4.
Because the majority of University of Iowa test-takers take the ACT, applicants with SAT scores have these scores converted to ACT scores using equi-percentile method concordance tables.
- 5.
The website of the Board of Regents of Iowa refers to these as “English, mathematics, natural science, social science, and foreign language” courses, with single-semester courses counting as half a course for RAI purposes. A full list of the NCES codes of qualifying courses may be found at http://www.regents.iowa.gov/RAI/NCES.pdf.
- 6.
An RAI value of 410 is exceptional—a student ranked first in her high school class with a perfect GPA and ACT score would have to complete 32 core courses to obtain this score. A score of 93 is very poor, corresponding to an ACT score of 12, a last-place class rank, a GPA of 1.0, and only 10 core courses completed.
- 7.
This corresponds to a 3.0 GPA, an ACT score of 18, 50th percentile class rank, and 20 completed core courses.
- 8.
The Business School has only ACT and GPA requirements. The Nursing school requires minimum ACT scores of 28 overall and 25 in science, a GPA of 3.8 out of 4.0, and no deficiencies in high school course requirements.
- 9.
Many high schools choose not to disclose class ranks, for instance.
- 10.
Students who are eligible for the NSA are automatically granted the award; no action is required on their part.
- 11.
For an example of how this might work, see Hershbein (2013).
- 12.
- 13.
Students within 5 RAI points of the cutoff for NSA eligibility have a mean ACT score of 24.4 with a standard deviation of 3.1 below the cutoff and a mean ACT value of 24.8 with a standard deviation of 2.9 above the cutoff. Retaking the ACT with a score of 24 or 25 produces mean gains of 0.6 points if the applicant has tested only once previously, 0.5 if she has tested twice, and 0.4 if she has tested three times (Andrews and Ziomek, 1998).
- 14.
Corresponding GPA values have a mean of 3.49 below the cutoff and a mean of 3.55 above the cutoff; both groups have a standard deviation of approximately 0.19.
- 15.
Altering one’s class rank is made all the more difficult by the fact that class rank is an ordinal measure, not a cardinal one. If one’s peers are similarly motivated and all improve their GPAs by the same amount, their rank ordering will remain unchanged.
- 16.
This improvement will be muted if she has taken more than five courses per school year. If she is already getting A’s in some courses, she will have to improve her other courses correspondingly, which may be even harder.
- 17.
Students’ ACT scores, transfer credits, and AP credits are available and may be used in future research.
- 18.
If we count this applicant only once, we also have to determine which application to consider her primary application, or whether to somehow combine multiple application profiles.
- 19.
If we restrict our analysis to the 2008 cohort and later, just over 3 % apply to Business, 8 % to Engineering, and 0.5 % to Nursing.
- 20.
Only international students did not have states of residence listed. As these students were already captured in the ethnicity variables, we do not include variables for students who did not list a state of residence.
- 21.
The t-statistic in column (2) of Table 3 has an associated p value of approximately 0.1061.
- 22.
Race in this case may be serving as a partial proxy for socioeconomic status, which we do not observe.
- 23.
The estimates closest to statistical significance have large negative values; while estimates of the impact of NSA eligibility on out-of-state student enrollment are not significantly different from zero, Wald tests suggest that these estimates are statistically different from the predicted impact on in-state students.
- 24.
In 2010, over 47 % of nonresident applicants were offered the NSA.
- 25.
Alternatively, it could be used purely as need-based aid. Ethnicity is almost certainly correlated with financial need, and it may be that differential need, rather than ethnicity per se, is causing differential responses to NSA receipt across ethnic groups. Some research on this topic is possible using self-reported family income values from SAT and ACT questionnaires, but FAFSA or similar data would provide more reliable estimates.
References
Andrews, K., & Ziomek, R. (1998). Score gains on retesting with the ACT assessment. ACT Research Report Series, 98(7), 1–29.
Aud, S., Wilkinson-Flicker, S., Kristapovich, P., Rathbun, A., Wang, X., & Zhang, J. (2013). The condition of education 2013 (NCES 2013-037). Washington, DC: U.S. Department of Education, National Center for Education Statistics. Retrieved March 25, 2014 from http://nces.ed.gov/pubsearch.
Avery, C., & Hoxby, C. M. (2004). Do and should financial aid packages affect students’ college choices? In C. M. Hoxby (Ed.), College choices: The economics of where to go, when to go, and how to pay for it. Chicago: University of Chicago Press.
Baum, S., & Ma, J. (2013). Trends in college pricing—2013. Washington, DC: The College Board.
Braunstein, A., McGrath, M., & Pescatrice, D. (1999). Measuring the impact of income and financial aid offers on college enrollment decisions. Research in Higher Education, 40(3), 247–259.
Carruthers, C., & Ozek, U. (2013). Losing HOPE: Financial aid and the line between college and work. Center for Analysis of Longitudinal Data in Education Research Working Paper 91.
Cornwell, C. M., Mustard, D. B., & Sridhar, D. J. (2006). The enrollment effects of merit-based financial aid: Evidence from Georgia’s HOPE Program. Journal of Labor Economics, 24(4), 761–786.
DesJardins, S. L. (2001a). Assessing the effects of changing institutional aid policy. Research in Higher Education, 42(6), 653–678.
DesJardins, S. L. (2001b). Assessing the impact of a change in institutional aid policy: A simulation tool. Journal of Student Financial Aid, 30(3), 7–16.
DesJardins, S. L., Ahlburg, D. A., & McCall, B. P. (2006). An integrated model of application, admission, enrollment, and financial aid. Journal of Higher Education, 77(3), 381–429.
Dynarski, S. (2000). Hope for whom? Financial aid for the middle class and its impact on college attendance. National Tax Journal, 53(3), 629–662.
Dynarski, S. (2003). Does aid matter? Measuring the effect of student aid on college attendance and completion. American Economic Review, 93(1), 279–288.
Heller, D. E. (1997). Student price response in higher education: An update to Leslie and Brinkman. Journal of Higher Education, 68(6), 624–657.
Heller, D. E. (1999). The effects of tuition and state financial aid on public college enrollment. Review of Higher Education, 23(1), 65–89.
Hershbein, B. J. (2013). Worker signals among new college graduates: The role of selectivity and GPA. Upjohn Institute Working Paper 13-190.
Imbens, G. W., & Lemieux, T. (2008). Regression discontinuity designs: A guide to practice. Journal of Econometrics, 142, 615–635.
Kim, D. (2004). The effect of financial aid on students’ college choice: Differences by racial groups. Research in Higher Education, 45(1), 43–70.
Kim, J., DesJardins, S. L., & McCall, B. P. (2009). Exploring the effects of student expectations about financial aid on postsecondary choice: A focus on income and racial/ethnic differences. Research in Higher Education, 50, 741–774.
Lee, D. (2008). Randomized experiments from non-random selection in U.S. house elections. Journal of Econometrics, 142, 675–697.
Leslie, L. L., & Brinkman, P. T. (1987). Student price response in higher education: The student demand studies. Journal of Higher Education, 58(2), 181–204.
Manski, C. F. (1977). The structure of random utility models. Theory and Decision, 8(3), 229–254.
McCrary, J. (2008). Manipulation of the running variable in the regression discontinuity design: A density test. Journal of Econometrics, 142, 698–714.
McFadden, D. (1976). Aggregate travel demand forecasting from disaggregated behavioral models. Transportation Research Record, 534, 24–37.
Moore, R. L., Studenmund, A. H., & Slobko, T. (1991). The effect of the financial aid package on the choice of a selective college. Economics of Education Review, 10(4), 311–321.
Parsons, G. R. (2000). Narrow choice sets in a random utility model of recreation demand fishing in maine. Land Economics, 76(1), 86–99.
Phillips, M. (2011). Cost of college on the rise (again). http://freakonomics.com/2011/10/27/cost-of-college-on-the-rise-again/. Accessed March 8, 2014.
Savoka, E. (1991). The effect of changes in the composition of financial aid or college enrollment. Eastern Economic Journal, 17, 109–121.
Scott-Clayton, J. (2011). On money and motivation: A quasi-experimental analysis of financial incentives for college achievement. Journal of Human Resources, 46(3), 614–646.
van der Klaauw, W. (2002). Estimating the effect of financial aid offers on college enrollment: A regression discontinuity approach. International Economic Review, 43(4), 1249–1287.
Acknowledgments
We would like to thank administrators at the University of Iowa, particularly Don Szeszycki, Beth Ingram, Carol Evans, Thomas Kruckeberg, and Beth Cole, for providing us with access to admissions and financial aid data and assistance in understanding institutional admissions and financial aid policies and processes. Jeff Smith, Brian McCall, Charlie Brown, and Kevin Stange also provided invaluable feedback and comments. Any errors are our own.
Author information
Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Leeds, D.M., DesJardins, S.L. The Effect of Merit Aid on Enrollment: A Regression Discontinuity Analysis of Iowa’s National Scholars Award. Res High Educ 56, 471–495 (2015). https://doi.org/10.1007/s11162-014-9359-2
Received:
Published:
Issue Date:
Keywords
- Regression discontinuity
- Financial aid
- Merit aid
- College enrollment
- College choice