Running head: INSTITUTIONAL FINANCIAL AID AND FIRST-YEAR GPA 1

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1 Running head: INSTITUTIONAL FINANCIAL AID AND FIRST-YEAR GPA 1 The Effects of Institutional Financial Aid on First-Year Collegiate Grade Point Average: A Regression-Discontinuity Approach Bradley R. Curs * and Casandra E. Harper ** Department of Educational Leadership and Policy Analysis University of Missouri December, 2009 Under review at The Review of Higher Education * Bradley R. Curs: Phone: , cursb@missouri.edu, Mailing Address: Educational Leadership and Policy Analysis, 202 Hill Hall, University of Missouri, Columbia, MO, ** Casandra E. Harper: Phone: , harpercas@missouri.edu, Mailing Address: Educational Leadership and Policy Analysis, 202 Hill Hall, University of Missouri, Columbia, MO,

2 INSTITUTIONAL FINANCIAL AID AND FIRST-YEAR GPA 2 Abstract Using a regression-discontinuity design, we investigate whether a merit-based financial aid program has a causal effect on the first-year grade point average of first-time out-of-state freshman at the University of Oregon. While further research is needed to understand how financial aid changes the behaviors of students, our results indicate that merit-based financial aid has a positive and significant causal effect on first-year collegiate grade point average. Further, we find that the relationship between financial aid and grade point average is larger for the subsamples of low-income students and students of color. Keywords: financial aid, merit aid, GPA, academic success, regression-discontinuity

3 INSTITUTIONAL FINANCIAL AID AND FIRST-YEAR GPA 3 The Effects of Institutional Financial Aid on First-Year Collegiate Grade Point Average: A Regression-Discontinuity Approach The increasing prominence of merit-based grant programs at public institutions of higher education make understanding the behavioral effects of financial aid on student success an important and timely inquiry. While research into the effects of financial aid on college access, retention, and graduation is fairly deep, less is known about the affects financial aid has on academic success within the classroom. Using a causal estimation technique, regressiondiscontinuity design, this paper attempts to answer the following research question: Does institutional financial aid have a causal effect on collegiate success as measured by a student s first-year grade point average? By exploiting a unique natural experiment at the University of Oregon (UO) during the early 2000 s we investigate whether a merit-based financial aid program has a causal effect on the first-year grade point average (GPA) of first-time freshman. Our results indicate that meritbased financial aid has a positive and practically significant effect on first-year collegiate GPA. Further, we find that the relationship between financial aid and GPA is larger for the subsamples of low-income students and students of color. The findings provide evidence that financial aid may encourage academic success, and thus retention, by ways beyond simply helping a student afford to attend a particular institution. Literature Review Evidence of the effect of financial aid on retention has been widely studied though, as noted by Alon (2005), the results of such research have been mixed, revealing positive, negative, and non-significant effects. Alon notes that the relationship between aid and academic success would intuitively be positive, given that financial support would hopefully provide students more

4 INSTITUTIONAL FINANCIAL AID AND FIRST-YEAR GPA 4 time to devote to academics rather than having to work. One limitation of current research on this topic is that differences in various forms of financial aid are not always examined separately, particularly need vs. merit-based aid (Alon; Hossler, Ziskin, Kim, Cekic, & Gross, 2008). The research that does examine the unique effects of merit aid tends to be institutional, rather than national, in scope due to the more precise measures of aid available at the local level (Hossler et al., 2008). Given the marked increase in merit aid programs over need-based support (Heller, 2002; Marin, 2002), particularly within the past decade (Cornwell, Lee, & Mustard, 2005; Doyle, 2006; Dynarski, 2004), a clearer understanding of the unique effect of merit aid on students academic success is warranted. The most recent figures on financial aid indicate that exclusively needbased aid constituted 48 percent of all aid to undergraduates, exclusively merit-based aid accounted for 19 percent, with the rest, 33 percent, accounted for by other programs and by programs with both need and merit components (National Association of State Student Grant and Aid Programs, 2009, p. 2). The number of merit based programs by state has increased in recent years; in 1993, only two states had merit aid programs but by 2002 this number increased to 13 states (Dynarski, 2004), and current figures reveal 27 states with such programs (National Association of State Student Grant and Aid Programs). In terms of specific dollar amounts awarded, $2.76 billion was allocated to undergraduates in the academic year in the form of merit aid and $4.07 billion was distributed as need-based aid (National Association of State Student Grant and Aid Programs). Merit aid programs have been associated with a number of outcomes, some of which are likely unintended, such as high school (Heller & Rogers, 2003) and college (Dynarski, 2004) grade inflation, or students decision to take less rigorous courses, while other outcomes match

5 INSTITUTIONAL FINANCIAL AID AND FIRST-YEAR GPA 5 the original intent, such as college enrollment decisions (van der Klaauw, 2002) specifically, choosing four-year over two-year institutions and remaining in-state (Dynarski). One recent study looking specifically at the effects of merit aid found that merit aid recipients were more likely to persist in college, which the authors hypothesized could be because students felt more allegiance toward their chosen institution as a result of receiving such recognition (Kuh, Cruce, Shoup, Kinzie, & Gonyea, 2007). Other research suggests that merit-aid recipients are more likely to persist simply because of their individual level characteristics that would have been predictive of success regardless of this form of financial support (Hossler et al., 2008). A major critique of merit aid programs is that the financial support tends to benefit middle- and upper-income students more so than lower-income students (Doyle, 2008; Dynarski, 2004). As a result, merit aid programs can exacerbate disparities by class and race, since race and class are correlated (Dynarski). Further research is needed to examine the effects among lowincome students and students of color who do receive this type of financial support. Although the relationship between aid and retention has been readily explored, the impact of aid on academic success is less clear. Cabrera, Nora, and Castaneda (1992, 1993) have established that students perceptions of their ability to pay can influence their academic performance and the extent and nature of their academic integration (as cited in St. John, Cabrera, Nora, & Asker, 2000, p. 43). Stater (2009) found a positive relationship between both need and merit-based aid on college GPA, with merit aid having a larger effect. Georgia s HOPE Scholarship, which is merit-based, has been associated with a 0.13 increase in freshman GPA among in-state students, and a reduction in students likelihood of taking math and science courses (Cornwell et al., 2006). Our need to better understand the relationship between merit aid and student outcomes, such as GPA, is critical since an increasing proportion of financial aid is

6 INSTITUTIONAL FINANCIAL AID AND FIRST-YEAR GPA 6 merit, rather than need-based (Doyle, 2006), and we know relatively little about effect of merit aid on learning (Henry, Rubenstein, & Bugler, 2004). Undergraduate academic success, as defined by GPA, has been associated with or predicted by a number of pre-college and college factors. College GPA has been positively correlated with gender, race, and family income (Betts & Morell, 1998), as well as standardized test scores, merit aid, and parents education (Kuh et al., 2007). The college experiences associated with college GPA include participation in academically engaging practices, such as making connections with faculty (Fischer, 2007), which leads students to perform better academically, to be more satisfied, and to persist and graduate (Kuh et al., p. 24). Indeed, Titus (2004) found that a student s probability of persistence increases by 8% points with a one standard deviation increase in the student s college academic performance, measured by college GPA (p. 688). Academic performance, therefore, is a key outcome of interest when considering students experiences in college and their likelihood of graduating. Conceptual Framework Previous research in this area indicates interconnected relationships between financial aid, integration into the college environment, and academic achievement and/or persistence. Tinto s theory of student departure (1975, 1993) has been widely used in higher education research to help frame these associations. This theory takes into account students pre-college characteristics, goals and commitments (both to the institution as well as outside the institution, upon entry and over time), institutional experiences (social and academic, formal and informal), and academic and social integration. As noted above, students commitment to their institution, and performance while there, are both influenced by the students financial situation (Alon, 2005; Cabrera et al., 1992; Kuh et al., 2007). Empirical research has indicated that academic

7 INSTITUTIONAL FINANCIAL AID AND FIRST-YEAR GPA 7 performance leads to persistence (Titus, 2004). By examining the influences on students GPA, we are addressing one piece of the larger retention puzzle while recognizing the interrelated nature of financial support, grades, and retention. Tinto s model is useful in framing a broad understanding of the nature of the relationship between financial aid and academic achievement, the particular focus of this study; however, the quasi-experimental design of this study allows for an investigation of this relationship without needing to control for each construct posited by Tinto s model. An explanation of why our quasiexperimental design allows us to estimate causal relationships between financial aid and GPA without the need to control for all constructs within Tinto s theory is detailed in the next section. Thus, we are using Tinto s model to help frame our conceptual understanding of why financial aid is related to academic achievement (in this case GPA) rather than as a guide for appropriate variable selection. Empirical Framework and Causal Estimation Strategy Despite a theoretical rationale linking financial aid to academic success, there is little causal evidence that links the receipt of financial aid awards to increases in a student s GPA. This study uses a causal research design to estimate the relationship between institutional financial aid and first-year collegiate GPA. Estimating the causal effect of financial aid on academic success is not as straightforward of a task as one would expect. As institutional financial aid award decisions are made by financial aid administrators there is a strong likelihood that the financial aid packages are correlated with characteristics of the applicant, such as the motivation to succeed, which are often unobserved to the researcher. This potential correlation of financial aid and the error term (i.e. financial aid is said to be endogenous) causes concern that

8 INSTITUTIONAL FINANCIAL AID AND FIRST-YEAR GPA 8 the estimated coefficients for financial aid on collegiate GPA are biased when estimated through standard regression techniques (Becker, 2004; Dynarski, 2003; Heller, 2004; Riegg, 2008). One technique to control for the potential of unobserved individual heterogeneity being correlated the measure of financial aid is regression-discontinuity design (Angrist & Levy, 2009; Schneider, Carnoy, Kilpatrick, Schmidt, & Shalveson, 2007; Riegg, 2008). Regressiondiscontinuity design has been utilized to estimate the causal effects of various higher education policy interventions including behaviors associated with financial aid (van der Klaauw, 2002) and remedial course-taking (Lesik, 2006). Regression-discontinuity design is a quasiexperimental empirical technique that separates subjects with otherwise similar attributes into treatment and comparison groups based upon a decision rule that is outside of the subject s control. In other words, the subjects do not make the choice to participate in the treatment group and they can not alter their behavior to adjust to the treatment s criteria. The University of Oregon Dean s Scholarship The Dean s Scholarship at the UO provides a unique natural experiment to apply regression-discontinuity design to investigate the causal impacts of financial aid on college success. From the to the academic years the determination of the Dean s Scholarship for out-of-state students was based upon the applicant s high school GPA. Specifically, students with GPAs in the ranges , , and 4.0+ received $2,000, $3,000, $4,000, and $5,000 in merit aid, respectively. The Dean s Scholarship is also awarded to instate students; however, the rules that govern who receive an award do not follow as strict of rules and thus are not suitable for a regression-discontinuity empirical strategy. While the Dean s scholarship is the primary form of institutional aid at the UO, the assignment rule does not perfectly explain financial aid offers. The UO also offers a number of

9 INSTITUTIONAL FINANCIAL AID AND FIRST-YEAR GPA 9 other financial aid programs, including diversity scholarships and need-based grants. However, for 82% of students in our sample the Dean s Scholarship was the sole institutional financial aid program a student received and accounted for nearly 70% of total financial aid dollars awarded by the UO. Table 1 presents information by high school GPA regarding the Dean s scholarship levels, the percentage of students in each Dean s category that receive only the Dean s scholarship, and the average institutional financial aid award by Dean s category. Figure 1 plots the average institutional financial aid award, as indicated by the solid line, for out-of-state students by high school GPA. Notice the jumps in the average institutional financial aid award between each of the GPA groupings discussed above. The jumps in the institutional financial aid award are primarily due to the discontinuities in the award formula of the Dean s scholarship, as shown by the dashed line. These discontinuities in the financial aid award can be exploited using regression-discontinuity to estimate the causal effect of financial aid. A Fuzzy Regression-Discontinuity Causal Estimation Strategy A fuzzy regression-discontinuity design can be employed when an exogenous assignment rule is highly correlated with the actual treatment status but does not fully explain the treatment (Lesik, 2006; van der Klaauw, 2002). The Dean s scholarship at the UO is a natural application of regression-discontinuity design, as the determination of who receives, and how much is received, is based upon an assignment rule related to a student s high school GPA. Fuzzy regression-discontinuity design employs a two-stage procedure to estimate the causal impact of a policy treatment which is equivalent to an instrumental variables strategy (Angrist & Levy, 2009). In the first stage, the predicted treatment level is estimated through a regression of the actual treatment status on the assignment rule variable and indicator variables based upon the

10 INSTITUTIONAL FINANCIAL AID AND FIRST-YEAR GPA 10 exogenous decision rule to account for the discontinuity. The causal impact of the treatment is identified in the second stage as the coefficient on the predicted treatment status variable from the first stage when regressed upon the outcome variable controlling for the continuous assignment rule. Specific to this analysis, equation [1] represents the first stage analysis which estimates the level of financial aid (AID i ) an applicant is offered as a function of the discontinuities in the Dean s Scholarship formula and student s high school GPA. [1] AID = α + δ36hsgpa36 + δ37hsgpa37 + δ38hsgpa38 + δ40hsgpa40 + λf ( HSGPA ) + ε i i i Specifically, HSGPA i represents the student s high school and HSGPA 36, HSGPA 37, HSGPA 38, and HSGPA 40 are indicator variables which equal one if the student s high school GPA is larger than 3.6, 3.7, 3.8, and 4.0, respectively. To account for potential nonlinearities in the relationship between high school GPA and institutional financial aid awards at the UO, beyond those due to the discontinuities, a polynomial function of HSGPA i is modeled. Results of the first stage estimation of equation [1] (second stage results are presented in Table 3) for the total sample of out of state students can be found in Appendix A, however due to the ancillary nature of these results discussion is minimized. In general, the first stage results model institutional financial aid quite well, with R 2 and F statistics well above rule of thumb minimums to indicate quality instruments (Kennedy, 2003). This is primarily due to the decision rule for the Dean s scholarship explaining a large portion of the variance in institutional financial aid awards. Equation [2] represents the second stage regression which estimates the student s first year collegiate GPA (CGPA i ) based upon the predicted financial aid level ( polynomial function of the student s high school GPA. AID ˆ i ) and a

11 INSTITUTIONAL FINANCIAL AID AND FIRST-YEAR GPA 11 [2] CGPA = α + βaid ˆ + γg( HSGPA ) + ε i i i i Equations [1] and [2] are estimated jointly using a two-stage least squares estimator (i.e. instrumental variables technique) to account for the fact that AID ˆ i is a generated regressor estimated based on the results of the first stage. A failure to account for the generated nature of AID ˆ i would yield biased estimates of the standard errors of β (Angrist & Levy, 2009; Greene, 2000). β, the coefficient on the predicted financial aid award, can be interpreted as the causal effect of the financial aid program on first year collegiate GPA. While equation [2] can estimate the unbiased and causal effect of the financial aid program in question, adding covariates can increase the efficiency of the regression-discontinuity estimation procedure and help control for the noise within the collegiate GPA measure (Angrist & Levy, 2009). [3] CGPA = α + βaid ˆ + γg( HSGPA ) + ϕ X + ε i i i i i Therefore, equation [3] is equivalent to equation [2] with a vector (X i ) of observed individual attributes identified by Tinto (1993) to affect formal collegiate academic performance including pre-entry attributes (sex, race, SAT scores, high school GPA, high school English and Mathematics credits, high school type) and goals and commitments (interest in the UO, major choice at UO). Also included in the vector of control variables are year dummy variables to account for annual variation in students experiences based upon year of entry. The choice of the functional form of high school GPA within equations [1] and [2] is an important modeling assumption within a regression-discontinuity design. Improperly modeling the functional form of the decision variable may lead to biased estimates of the true effect of the polity treatment in question (Angrist & Levy, 2009).

12 INSTITUTIONAL FINANCIAL AID AND FIRST-YEAR GPA 12 To test robustness of our estimates across various functional forms we have estimated equations [2] and [3] with several alternative functional forms of high school GPA in both the first and second stages of the two-stage least squares procedure. Appendix B provides the results of these robustness tests for the total sample of out of state students using alternative polynomial functions, ranging from a linear to quartic representation, of the decision variable HSGPA i. The specifications presented in Appendix B are equivalent to those presented in Table 3, with the sole difference being the alternative functional form of HSGPA i. Based upon the results for alternative functional forms, we have decided to present our findings with the use of the quadratic form for HSGPA i. This decision was made due to the fact the quadratic term HSGPA i for was significant in some of our alternative models, while the cubic and quartic terms never reached significance levels greater than Further, the differences in the estimated effects of institutional financial aid were similar for both the quadratic and the cubic functional forms, thus for parsimony we choose to report our findings using the quadratic functional form. Thus, all subsequent findings will be based upon estimations with a quadratic functional form of HSGPA i included. Sample Data The empirical analysis uses data from the UO admissions office for Fall-term freshman out-of-state enrollees for the academic years through Specifically, detailed individual level characteristics are contained in the dataset including the student s financial aid package, academic performance measures, declaration of major and detailed background information. The dataset contains complete information on 2,138 out-of-state enrollees. Descriptive statistics for the sample used in this analysis can be found in Table 2.

13 INSTITUTIONAL FINANCIAL AID AND FIRST-YEAR GPA 13 Limitations While we can make a causal statement about the relationship between financial aid and GPA, we are unable to specifically determine the nature of that relationship. Merit aid programs in other states have noticed a marked increase in college grades, but it is not known whether students are trying harder academically, pursuing less rigorous or fewer courses, or are the beneficiaries of grade inflation (Dynarski, 2004). The sample for this study is unique in its focus on out-of-state students, which is a contribution in itself but might limit the applicability of the findings to more broad samples of college students. Also, although our sample size was large enough to conduct all of the analyses performed in this study, our subgroup analyses particularly for the students of color was more limited. The fact that only marginal significance of merit aid was found for students of color is likely due to the limited sample size rather than a lack of effect. Further research is needed to test the effect of merit aid for students of color. Empirical Results Table 3 presents results from the second stage of fuzzy regression-discontinuity estimates of the relationship between institutional financial aid and their first-year GPA for first-time freshman enrollees at the UO using a two-stage least squares estimator. Column 1 presents the results of the estimation of equation [2] which estimates the effect of intuitional financial aid solely based upon the regression-discontinuity design, while column 2 presents the results of the estimation of equation [3] with control variables. First stage results can be found in Appendix A. In general, the findings support the idea that increased financial aid leads to increased academic performance as measured through first-year collegiate GPA. Specifically, a $1000

14 INSTITUTIONAL FINANCIAL AID AND FIRST-YEAR GPA 14 increase in institutional financial aid is found to increase college GPA by roughly 0.08 (without controlling for individual attributes) or 0.06 (when controlling for individual attributes) GPA points, as indicated in Table 3, columns 1 and 2, respectively. Thus, the typical student who has a 3.6 high school GPA and would receive a $2000 Dean s scholarship would be expected to have a first year GPA between 0.12 and 0.16 higher than the comparable student with a 3.59 high school GPA. This is comparable to previous research that found that merit-based aid led to a 0.13 increase in GPA (Cornwell et al., 2006). The relationships between control variables and first-year GPA are relatively consistent with theoretical expectations. High school GPA is found to have an increasing, although at a slightly decreasing rate, effect on college GPA throughout the plausible values of high school GPA. Students with higher values of their SAT verbal and math scores are more likely to have higher college GPA. Females have an expected college GPA about 0.1 GPA points higher than comparable males. Students who choose science majors have lower GPAs and those that choose music and architecture majors have higher GPAs when compared to those that do not declare majors as freshman. The relationships between control variables and first-year collegiate GPA remain fairly robust across specifications, and thus discussion will be held to a minimum in subsequent discussion. Two interesting empirical patterns emerge when the model is estimated on alternative subsamples. Table 4 presents the results when we estimate equation [3] (the model is identical to the results presented in column 2 of Table 3) restricting our analysis to various subsamples within the overall population of out of state students. The first empirical pattern indicates that out of state students who do not file a FAFSA form are less responsive, with respect to first-year collegiate GPA, to increases in financial aid when compared to those students filed a FAFSA.

15 INSTITUTIONAL FINANCIAL AID AND FIRST-YEAR GPA 15 Specifically, FAFSA filers experience an increase in their first-year GPA of roughly 0.9 per $1000 of institutional financial aid (Table 4, column 2), while the effect of non-filers much smaller and insignificantly different than zero (Table 4, column 3). This may indicate that FAFSA filers are more money motivated with respect to college behaviors, or more likely to alter other behaviors, such as employment, when presented with an increase in financial aid. A second empirical pattern emerges when we estimate the model for specific subgroups, specifically low-income students and students of color. Low-income students, defined as those with positive Federal financial aid eligibility, are expected to increase their first-year GPA by roughly 0.10 (Table 4, column 4). Similarly, the increase in first-year GPA with respect to a $1000 increase in financial aid for students of color is 0.11 (Table 4, column 5). This GPA change for low-income students and students of color is significant and noteworthy, as the difference in first-year GPA for a student with a 3.6 high school GPA who received $2000 from the Dean s scholarship is likely to be roughly 0.2 GPA points higher than a similar student with a 3.59 high school GPA who did not receive a scholarship. Our results would indicate that the institutional financial aid program at the UO is successful at not just encouraging students to attend UO, but also at increasing their success as measured by first-year GPA. Further, the effect of financial aid appears to be strongest when given to certain underrepresented populations. Discussion Despite a robust literature that estimates the relationship between financial aid and college access, retention, and graduation, the effect of financial aid on grade point averages has been relatively understudied. This manuscript presents some of the first causal evidence as to the effect of financial aid on a student s collegiate GPA. Consistent with previous literature, we find

16 INSTITUTIONAL FINANCIAL AID AND FIRST-YEAR GPA 16 positive effects of institutional financial aid on first year GPA (Cornwell et al., 2006; Titus, 2004). Further, we find that the financial aid effects on first-year GPA are larger for both lowincome students and students of color when compared to the total population of students. Implications for Research While this study provides causal evidence linking financial aid and GPA, further research is needed to understand how financial aid changes the behaviors of students. Our results would indicate that the positive effect of financial aid on retention and graduation, as measured in the literature, may come through not simply through the increased ability of a student to afford tuition and remain in school, but also through an increased incentive to receive better grades or a substitution away from outside employment and into academic endeavors. The results of this study focus on the first half of Tinto s (1993) theory in that students pre-entry attributes are linked to students goals, commitments and intentions, which are then associated with students academic success. Future inquiry should examine how financial aid alters students collegiate experience, in terms of their academic and social integration, which then ultimately influences students persistence to graduation. In particular, the issues surrounding how the receipt of financial aid affects student behavior in areas such as: on and off-campus employment, integration to campus activities, time spent studying, student-faculty interactions, and students course and major choices. Further, research examining the institutional context might examine the preponderance of grade inflation, optimal design of financial aid programs based on alternative award criteria, and the effectiveness of financial aid across various subgroups of students.

17 INSTITUTIONAL FINANCIAL AID AND FIRST-YEAR GPA 17 Implications for Policy & Practice Previous research found strong relationships between institutional financial aid and enrollment, whereas this study demonstrates that financial aid has a secondary benefit to institutions in influencing students academic success, a key outcome in higher education. Given the link between academic success as measured by grades and college retention (Tinto, 1993; Titus, 2004), this research suggests that institutions may benefit from the use of financial aid to certainly increase students GPA, and hopefully increase their chances of graduating college. Further, the relationship between financial aid and GPA was estimated to be strongest among traditionally underrepresented populations. Thus, given these encouraging results among lowincome students and students of color, institutions should revisit their own financial aid policies to ensure that these groups of students are adequately represented within their pool of financial aid recipients. Conclusion Given the increased use of financial aid at public institutions of higher education, understanding the unique and direct effects of this aid on students academic success is imperative. This paper contributes to an understanding of how financial aid can positively affect college GPA, particularly among low-income students and students of color. Institutions should consider these results when allocating resources toward financial aid programs and creating criteria for the award of merit aid.

18 INSTITUTIONAL FINANCIAL AID AND FIRST-YEAR GPA 18 References Alon, S. (2005). Model mis-specification in assessing the impact of financial aid on academic outcomes. Research in Higher Education, 46(1), Angrist, J. D. & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist s Companion. Princeton, NJ: Princeton University Press. Becker, W. E., (2004).Omitted variables and sample selection in studies of college-going decisions. In C. Teddlie & E. A. Kemper (Series Eds.) & E. P. St. John, (Vol. Ed.), Readings on Equal Education: Vol. 19, Public Policy and College Access: Investigating the Federal and State Roles in Equalizing Postsecondary Opportunity (pp ). New York: AMS Press. Cabrera, A. F., Nora, A., & Castaneda, M. B. (1992). The role of finances in the persistence process. Research in Higher Education, 33, Cornwell, C., Lee, K.H., & Mustard, D.B. (2006). The effects of state-sponsored merit scholarships on course selection and major choice in college. Institute for the Study of Labor (IZA) discussion papers, 1953, Retrieved from ftp://repec.iza.org/repec/discussionpaper/dp1953.pdf Doyle, W.R. (2006). Adoption of merit-based student grant programs: An event history analysis. Educational Evaluation and Policy Analysis, 28(3), Doyle, W.R., (2008). Access, choice, and excellence: The competing goals of state student financial aid programs. In S. Baum, M. McPherson, & P. Steele (Eds.), The effectiveness of student aid policies: What the research tells us (pp ). NY: The College Board.

19 INSTITUTIONAL FINANCIAL AID AND FIRST-YEAR GPA 19 Dynarski, S. M., (2002). Does aid matter? Measuring the effect of student aid on college attendance and completion. American Economic Review, 93(1), Dynarski, S. (2004). The new merit aid. In C.M. Hoxby (Ed.), College choices: The economics of where to go, when to go, and how to pay for it (pp ). Chicago, IL: University of Chicago. Greene, W. H. (2000). Econometric Analysis (4 th. Edition). Upper Saddle River, NJ: Prentice Hall. Heller, D. E., (2002). State merit scholarship programs: An introduction. In Heller, D. E., & Marin, P. (Eds.). Who should we help? The negative social consequences of merit scholarships (pp ). Cambridge, MA: The Civil Rights Project at Harvard University. Heller, D. E., (2004). NCES research on college participation: A critical analysis. In C. Teddlie & E. A. Kemper (Series Eds.) & E. P. St. John, (Vol. Ed.), Readings on Equal Education: Vol. 19, Public Policy and College Access: Investigating the Federal and State Roles in Equalizing Postsecondary Opportunity (pp ). New York: AMS Press. Heller, D.E., & Rogers, K.R. (2003). Merit scholarships and incentives for academic performance. Paper presented at the Association for the Study of Higher Education Annual Meeting, Portland, OR. Henry, G.T., Rubenstein, & Bugler, D.T. (2004). Is HOPE enough? Impacts of receiving and losing merit-based financial aid. Educational Policy, 18(5), Hossler, D., Ziskin, M., Kim, S., Cekic, O., & Gross, J.P.K. (2008). Student aid and its role in encouraging persistence. In S. Baum, M. McPherson, & P. Steele (Eds.), The

20 INSTITUTIONAL FINANCIAL AID AND FIRST-YEAR GPA 20 effectiveness of student aid policies: What the research tells us (pp ). NY: The College Board. Kennedy, P. (2003). A guide to econometrics (5 th edition). Cambridge, MA: MIT Press. Kuh, G.D., Cruce, T., Shoup, R., Kinzie, J., & Gonyea (2007). Unmasking the effects of student engagement on college grades and persistence. Paper presented at the annual meeting of the American Educational Research Association, Chicago, IL. Lesik, S. A. (2006). Applying the regression-discontinuity design to infer causality with nonrandom assignment. The Review of Higher Education, 30(1), Marin, P. (2002). Merit scholarships and the outlook for equal opportunity in higher education. In Heller, D. E., & Marin, P. (Eds.). Who should we help? The negative social consequences of merit scholarships (pp ). Cambridge, MA: The Civil Rights Project at Harvard University. National Association of State Student Grant & Aid Programs. (2005). NASSGAP 39th annual survey report on state-sponsored student financial aid academic year. Springfield: Illinois Student Assistance Commission. Riegg, S. K. (2008). Causal inference and omitted variable bias in financial aid research: Assessing solutions. The Review of Higher Education, 31(3), Schneider, B., Carnoy, M., Kilpatrick, J., Schmidt, W. H., & Shalveson, R. J. (2007). Estimating causal effects: Using experimental and observational designs. Washington, DC: American Educational Research Association. St. John, E.P., Cabrera, A.E., Nora, A., & Asker, E.H. (2000). Economic influences on persistence reconsidered: How can finance research inform the reconceptualization of

21 INSTITUTIONAL FINANCIAL AID AND FIRST-YEAR GPA 21 persistence models? In J.M. Braxton (Ed.), Reworking the student departure puzzle (pp ). Nashville, TN: Vanderbilt. Stater, M. (2009). The impact of financial aid on college GPA at three flagship public institutions. American Educational Research Journal, X(X), Tinto, V. (1975). Dropout from higher education: A theoretical synthesis of recent research. Review of Educational Research, 45, Tinto, V. (1993). Leaving college. Chicago: University of Chicago Press. Titus, M. A. (2004). An examination of the influence of institutional context on student persistence at 4-year colleges and universities: A multilevel approach. Research in Higher Education, 45(7), van der Klaauw, W. (2002). Estimating the effects of financial aid offers on college enrollment: A regression-discontinuity approach. International Economic Review, 43,

22 INSTITUTIONAL FINANCIAL AID AND FIRST-YEAR GPA 22 Table 1: The University of Oregon Dean s Scholarship Grade Point Average Less than Total Sample Dollar value of the Dean s Scholarship $0 $2000 $3000 $4000 $5000 N/A Percentage of students whose scholarship package is entirely composed of the Dean s scholarship Average total scholarship package across students $160 $2391 $3485 $4755 $5857 $851

23 INSTITUTIONAL FINANCIAL AID AND FIRST-YEAR GPA 23 Table 2: Sample Characteristics Out-of-State Students at the University of Oregon Standard Mean Minimum Deviation Maximum First year GPA at UO Institutional-based aid $1, $0.0 $16,125.6 Dean s Scholarship $ $0.0 $7,000.0 Institutional need-based grant $ $0.0 $1,000.0 Diversity Scholarship $ $0.0 $4,700.0 Other Scholarships $ $0.0 $12,700.0 Non-institutional-based aid $ $0.0 $5,596.2 Federal Pell Grant $ $0.0 $4,050.0 State-based grants $ $0.0 $1,401.0 Loans $5, $0.0 $31,110.2 Filed a FAFSA Female Students of color Age at application High school GPA Math SAT Verbal SAT English years in high school Math years in high school Attended private high school Notes: Characteristics reported are for the sample of 2,138 out-of-state first-time freshman students at the UO. This sample corresponds with the sample used to estimate the results in column 1 of Table 2.

24 INSTITUTIONAL FINANCIAL AID AND FIRST-YEAR GPA 24 Table 3: Fuzzy Regression Discontinuity Estimates of the Effect of Institutional Financial aid on First Year Collegiate GPA Dependent Variable: First Year UO GPA Independent Variable (1) (2) Institutional financial aid (1000 s) *** ** (0.0287) (0.0279) High school GPA 1.982* 2.352** (1.060) (1.007) High school GPA squared * (0.173) (0.164) Female 0.110*** (0.0313) Nonwhite (0.0345) Age at application (0.0299) Math SAT *** (0.0237) Verbal SAT 0.112*** (0.0229) Years of high school English (0.0504) Years of high school Mathematics (0.0237) Attended private high school (0.0353) Academic major indicator variables Yes Year indicator variables Yes Constant ** (1.628) (1.684) Observations R-squared Standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1 Notes: Coefficient estimates are the second stage results of an instrumental variables regression where institutional financial aid is treated as an endogenous variable. Predicted values of institutional financial aid are estimated based upon first stage regressions reported in Appendix A, which use indicators of the discontinuities in the financial aid formula as instruments. The dependent variable in all specifications is first year collegiate GPA, with a mean value of 2.9. The sample period is 1999 through 2004.

25 INSTITUTIONAL FINANCIAL AID AND FIRST-YEAR GPA 25 Table 4: Fuzzy Regression Discontinuity Estimates of the Effect of Institutional Financial Aid for Alternative Subsamples Dependent Variable: First Year UO GPA Independent variable Full Sample Did not File a FAFSA FAFSA Filers Needy Students Minority Students (1) (2) (3) (4) (5) Institutional financial aid (1000 s) ** ** 0.102** 0.109* (0.0279) (0.0435) (0.0378) (0.0437) (0.0616) High school GPA 2.352** ** 3.013** (1.007) (1.644) (1.301) (1.459) (2.400) High school GPA squared * ** * (0.164) (0.267) (0.211) (0.236) (0.390) Control Variables Yes Yes Yes Yes No Constant ** *** ** (1.684) (2.688) (2.222) (2.592) (4.019) Observations R-Squared Standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1 Notes: All specifications are identical to column 2 in Table 2. The dependent variable in all specifications is first year collegiate GPA, with a mean values for columns 1 through 5 of 2.90, 2.85, 2.94, 2.89, and 2.86, respectively. The sample period is 1999 through 2004.

26 INSTITUTIONAL FINANCIAL AID AND FIRST-YEAR GPA 26 Figure 1 Average Institutional Financial Aid by GPA Instiutional Financial Aid Awards High School GPA Total Institutional Aid Offer Dean's Scholarship

27 INSTITUTIONAL FINANCIAL AID AND FIRST-YEAR GPA 27 Appendix A: First Stage Results of the Estimation of Institutional Financial Aid Dependent Variable: Institutional Financial Aid (1000 s) Independent Variable (1) (2) High school GPA *** ** (1.996) (1.968) High school GPA squared 1.033*** 0.798** (0.320) (0.315) High school GPA is above *** 1.788*** (0.133) (0.131) High school GPA is above *** 0.916*** (0.145) (0.142) High school GPA is above *** 1.013*** (0.150) (0.147) High school GPA is above *** 0.720*** (0.203) (0.200) Female (0.0575) Nonwhite (0.0635) Age at application (0.0540) Math SAT (0.0434) Verbal SAT 0.107*** (0.0412) Years of high school English (0.0887) Years of high school Mathematics 0.114*** (0.0429) Attended private high school (0.0651) Academic major indicator variables Yes Year indicator variables Yes Constant 8.685*** 7.608** (3.102) (3.278) Observations R-squared F Statistic Standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1 Notes: Coefficient estimates are the first stage results of an instrumental variables regression reported in Table 2, where institutional financial aid is treated as an endogenous variable. The dependent variable in all specifications is the amount of institutional financial aid, with a mean value of $1,203. The sample period is 1999 through 2004.

28 INSTITUTIONAL FINANCIAL AID AND FIRST-YEAR GPA 28 Appendix B: Fuzzy Regression Discontinuity Estimates of the Effect of Institutional Financial Aid Estimated Across Alternative Functional Forms Dependent Variable: First Year UO GPA Independent variable Linear Quadratic Cubic Quartic (1) (2) (3) (4) (5) (6) (7) (8) Institutional financial aid (1000 s) *** *** ** *** ** (0.0147) (0.0150) (0.0287) (0.0279) (0.0288) (0.0281) (0.0385) (0.0374) High school GPA 0.632*** 0.640*** 1.982* 2.352** (0.0708) (0.0721) (1.060) (1.007) (5.477) (5.334) (51.14) (50.05) High school GPA squared * (0.173) (0.164) (1.654) (1.615) (23.78) (23.24) High school GPA cubed (0.167) (0.163) (4.853) (4.738) High school GPA to the fourth power (0.366) (0.357) Control Variables No Yes No Yes No Yes No Yes Constant 0.736*** ** (0.222) (0.644) (1.628) (1.684) (6.021) (5.916) (40.78) (39.97) Observations R-Squared Standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1 Notes: All specifications are identical to columns 1 and 2 in Table 2. The dependent variable in all specifications is first year collegiate GPA, with a mean value of 2.9. The sample period is 1999 through 2004.