State Merit Aid Program and Student Persistence in College: Evaluating the Effect of Florida s Bright Futures Program

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1 State Merit Aid Program and Student Persistence in College: Evaluating the Effect of Florida s Bright Futures Program Shouping Hu, Ph.D. Department of Educational Leadership and Policy Studies Florida State University 1210H Stone Building Tallahassee, FL Mark A. Partridge, Ph.D. Candidate Department of Economics Florida State University 113 Collegiate Loop Tallahassee, FL Liang Zhang, Ph.D. Center for the Study of Higher Education Penn State University 310E Rackley Building University Park, PA Prepared for presentation at the annual meeting of the Association for Educational Finance and Policy (AEFP), New Orleans, LA. This manuscript was prepared with financial support from the Institute of Education Sciences, U.S. Department of Education, through Grant # R305A The opinions expressed are those of the authors and do not represent views of the Institute or the U.S. Department of Education, or anyone else. The findings reported in this paper are preliminary. Please do not cite or quote without permission. Address queries to Shouping Hu at March 1,

2 Abstract Since Georgia s initiation of the HOPE scholarship program in 1993, numerous studies have examined the impact of merit-aid programs on college enrollment and educational attainment in higher education. In this paper, we present preliminary findings from an evaluation study on the effects of Florida s Bright Futures program, a major merit based scholarship program, on student persistence in the first year in college in Florida s state university system (SUS). Using data of the entering cohort of first-time in college students in and from Florida s K-20 Data Warehouse, we examine the impacts of Bright Futures program by estimating models such as difference-in-differences (DD), regression discontinuity (RD), and other methods. Results from different methods offer different insights on the impacts of Bright Futures program on student persistence within the first year in college in Florida s state university system (SUS). Although there is no significant increase in the gap of persistence rates for the Bright Futures qualifiers over non-qualifiers after the implementation of Bright Futures program, the results show that Bright Futures qualifiers are more likely to persist, and the Florida Academic Scholarship (FAS) in the Bright Futures program has significant causal effect on student persistence through the first year in college in the state university system. Implications for research and policy are discussed in light of research findings. 2

3 State Merit Aid Program and Student Persistence in College: Evaluating the Effect of Florida s Bright Futures Program I. Introduction Financial assistance to students has been considered as one of the major policy tools for the federal government and states to finance higher education in the United States. At the federal level, grants, loans, work-study programs, and tax incentives are key components to assist students to go to and succeed in college. A priority in federal involvement in financing students is the effort to promote equal opportunities for students in higher education, even though the emphasis on this priority could shift somewhat over time (St. John, 2003). At the state level, need-based state financial aid programs have been in existence for many states. However, recent changes in policy landscape at the state level in particular is the ushering of a slew of merit based financial aid programs, signifying by the establishment of Georgia s HOPE scholarship program in 1993 (Cohen-Vogel, Ingle, Albee, & Spence, 2008; Heller and Marin, 2002; St. John, 2003). In the last two decades, approximately 15 states have adopted merit-based financial aid programs as part of a dramatic financial aid policy shift, although this number varies in the literature depending on how merit-based programs are defined (Doyle, 2006). The policy goals of state merit aid programs include expanding college access for state residents, retaining the highest achieving students in the state, and giving students additional incentives for academic achievement both in high school and in college (Heller, 2004). State merit-based financial aid programs have strong appeal to voters because of their broad bases of eligibility and their meritocratic rationales. Many of the recent state merit aid programs have expanded their base of recipients to students with solid but not stellar academic records, and in some cases allow 30 percent of high school seniors to qualify (Dynarski, 2004). Critics of these programs have pointed out that financial aid is funneled to students who would 3

4 likely attend college anyway and have the least need for aid, especially when merit-based aid directly competes with need-based aid for increasingly scarce funds (Heller & Marin, 2002). About the same time when state merit aid programs ignite contentious debates in the policy arena and attract wide interests from scholars in various disciplines such as economics, education, and public policy, the United States has seen its historical lead in postsecondary education slipping from a global perspective (National Commission on the Future of Higher Education, 2006; OECD, 2005). For the U.S. to regain the lead in postsecondary attainment for its citizens, many argue that policy consideration in higher education may need to shift from the traditional emphasis on access to student success, particularly, to student persistence and degree attainment (Adelman, 2007). In fact, college persistence has been one of the weakest links in student educational attainment process in the United States. College graduation rate has been just around 50% for decades (Kuh, Kinzie, Schuh, Whitt, & Associates, 2005). The statistics from the Organisation for Economic Cooperation and Development (OECD) show the United States is trailing several other countries in the percentage of population ages having attained tertiary education (2005). Furthermore, empirical evidence has consistently shown the critical juncture of the first year in college on student persistence and eventual degree completion (Tinto, 1993, 2011). In this study, we examine the impact of Florida s Bright Futures program on student persistence from the fall semester to the spring semester of the first year in college in the state university system (SUS), a critical transition juncture in student college life. This decision is based on the importance of persistence within the first year in student educational progress, as well as the data elements available at the time of this study. Further, the renewal criteria for Bright Futures program where continuation depending on student college performance make the 4

5 persistence within the first year more suitable for the evaluation on the impact of the financial aspect of the program on student persistence. Most studies on the effect of merit-aid programs have used Georgia as the context because the HOPE Scholarship program is one of the earliest and probably the most well-known one in the nation; however, since eligibility criteria and award generosity vary across programs, studies of different state programs are helpful in understanding how the differences in programs are related to their effects on student college outcomes. In addition, the availability of detailed student data from Florida s K-20 Educational Data Warehouse provides a unique opportunity to student a well developed state merit aid program in a state known for the diversity of its population to reflect the changing demographics in the country. Thus, the guiding question is: Does Bright Futures program have any impacts on student persistence in the first two years in college in Florida s state university system? II. Florida s Bright Futures Program The state of Florida was one of the earliest states to adopt a state sponsored merit aid program. The state established a merit-based aid program in 1981 known as the Florida Undergraduate Scholars Fund (which later became the Florida Academic Scholars Award). In 1991, Florida introduced its second statewide merit-based program when it initiated the Vocational Gold Seal Scholarship (which later became the Florida Gold Seal Vocational Scholars award) specifically for vocational students. The current merit aid program, the Bright Futures program, was established in 1997 after the widely known HOPE scholarship program established in neighboring state of Georgia. The Bright Futures program somewhat mirrors Georgia s HOPE scholarship program with an emphasis on student academic achievement in high school and college in scholarship eligibility consideration. The previously existing two programs in the state, Florida Academic Scholars Award (FAS) for students on academic tracks 5

6 and Florida Gold Seal Vocational Scholars Award (GSV) for students on vocational tracks, were integrated into the newly created Bright Futures program with the addition of the Florida Medallion Scholars Award (FMS), also for students on academic tracks. The major changes in 1997, when the Bright Futures program was established and FMS was added, broadened participation of students in merit aid program in the state of Florida and substantially increased total funding to the program. The FAS awards carry 100% of tuition and some allowance for fees and college-related expenses while requiring 3.5 GPA on 15 college preparatory credits in high school and SAT at 1,270 or ACT at 28 for initial qualification and 3.0 cumulative GPA on all postsecondary work attempted for renewal. The FMS awards cover 75% of tuition and required fees while requiring 3.0 GPA on 15 college preparatory credits and SAT at 970 or ACT at 20 for initial qualification and 2.75 GPA on all postsecondary work for renewal. The GSV awards are similar to the FMS award but are directed toward students in vocational tracks. According to Florida s legislation concerning the Bright Futures program, Bright Futures scholarships are available for Florida residents who are enrolled in an eligible Florida public or private postsecondary institution. For private institutions, the award amount is determined by the tuition rates at equivalent public institutions. Part-time students are also eligible for the program as long as they are enrolled for at least six semester credit hours. Students need to apply for the scholarship by April 1 of the last semester before high school graduation. A student is eligible to accept an initial award for three years following high school graduation and to accept a renewal award for seven years following high school graduation. A student can maintain eligibility by reapplying during subsequent application periods up to three years after high school graduation. 6

7 Florida s Bright Futures Scholarship program, like the HOPE scholarship program in Georgia, has the trait of being simple and straightforward. That is, as long as students have met the qualification of academic performance and file an application for the scholarship program, they are qualified for the generous financial assistance for their college tuition and related expenses. Between the nine years of and , the number of students who are eligible for the Bright Futures program steadily increased, as did the number of students who renewed their scholarships. The total disbursement for the Bright Futures programs in that time period was about two billion dollars. Merit aid programs vary in the generosity of funding and by the rigor of eligibility criteria. These differences will likely provide different incentives to students who are considering college and other decisions. The Bright Futures program in Florida provides a typical case of a large-scale tuition-subsidy program, with similar programs in Georgia, Louisiana, Nevada, Tennessee, and West Virginia (Delaney & Ness, 2010). One minor difference for Bright Futures from some other state merit aid programs is its tiered structure that awards 75% tuition to students with 3.0 GPA in high school and 20 ACT and 100% tuition to those with 3.5 high school GPA and 28 ACT. The tiered structure remains in college where 3.0 and 2.75 college GPAs are used. In addition, unlike George s HOPE Scholarship program, Bright Futures allows part-time college attendance as long as students meet a certain credits requirement. III. Literature Review It has been a long tradition for economists and educational researchers to study the impacts of student financial aid programs on students (St. John, 2003). Efforts have been put forth mostly on the impacts of need-based financial aid programs on student educational decisions. Much of the literature relating to student financial aid (in the form of loans, grants, 7

8 scholarships and work study programs) and persistence indicates a positive relationship between aid and enrollment, persistence, and attainment (St. John, 2003). Studies on state programs are on the rise as states become increasingly more active in student financial aid beyond traditionally need-based programs (Hu, Trengove, & Zhang, 2012). For example, there is a large amount of literature relating to Georgia s HOPE scholarship, which was the seminal large-scale, lottery funded collegiate scholarship program in the United States. In this section, we outline the relevant research in this area to set a framework for performing the first rigorous and longitudinal analysis of the Florida s Bright Futures scholarship with respect to its impact on student persistence. A. Student Aid and College Enrollment and Attainment Most recent research suggests a positive relationship between student aid and educational enrollment. One key study performed by Dynarski (1999) demonstrated that while capturing the causal effects of student aid on persistence is difficult, capturing the impact of a shift in aid policy that impacts some students, but not all, can facilitate a causal study design. In her paper she found that an increase of $1,000 in federal financial aid contributed to an increase in educational attainment of.16 years. In another study using Current Population Survey (CPS), Dynarski (2002) found that merit aid programs across seven states were related to the increase of the likelihood of traditional college student enrollment. Even though the findings may not be considered causal, they nevertheless show that the HOPE scholarship program was related to the increase of student college attendance. She also found that merit aid contributes to increased college enrollment and college choice, shifting enrollment from two year to four-year public colleges. In other studies, Dynarski (2004) and Singell (2006) found that merit aid programs increase the probability of attendance and increase the likelihood of attending a four-year versus 8

9 two-year institution. This shift is likely the goal of aid programs, since educational attainment can be much higher for four year institutions, but these results are focused on HOPE scholarship recipients and may have limited generalizability outside of the state of Georgia. Interestingly, Dynarski and Scott-Clayton (2008) reviewed evidence of financial aid programs efficacy in increasing enrollment and argued that the complexity of the federal aid system can hinder overall efficacy. In a more recent study examining data from the Performance-Based Scholarship (PBS) demonstration, Cha and Patel (2010) found that low-income students who received merit based scholarships were more likely to be enrolled full time, to enroll in and earn more credit hours and to accrue less debt than a matched group of non-scholarship recipients. Conversely, Sjoquist and Winters (2011) found no overall impact of state merit based financial aid programs on college attainment when examining census data with over two million observations. Since this analysis includes a larger range of universities, the inherent heterogeneity of schools likely influenced these results. B. Student Aid and Persistence A critical aspect of student success in college is persistence, and much effort has been on whether financial aid could affect student persistence. Moline s (1987) early work on persistence used a then unique dependent variable, which was the continuous measure of credits completed as opposed to the dichotomous measure of enrollment. Though this is considered a useful dependent variable measuring persistence, student aid demonstrated no significant impacts on persistence when examining University of Minnesota data on enrollment and financial aid. More recently, DesJardins, Ahlburg, and McCall (2000) estimated a hazard model where the dependent variable was the duration of continuous enrollment in college until a student first "stops out" or discontinues regular enrollment for the first time. They found that the impacts of 9

10 financial aid are time variant throughout a student s collegiate career and that all forms of aid (loans, scholarships, work study) except grants facilitate increased persistence in early years of college. Grants showed no significant impact on persistence. For later years, work-study programs had the highest statistically significant positive impact on persistence. Similarly, Bettinger (2004) found that Pell Grants reduce stop out of students. He examined individuals who failed to qualify for aid prior to entry to college, but subsequently qualify and receive aid and found that grants can increase persistence, but not across all model specifications. When an expanded model was estimated with student characteristics included, no significant impacts of student aid on persistence were measured. Perhaps most notably, in their rigorous, random assignment evaluation, researchers at MDRC (Richburg-Hayes et. al., 2009) found that students who received the New Orleans Open Doors financial aid program targeted at low income college students with children were more likely to register for college, earn credits, and persist, among other positive benefits. Thus, the literature so far seems to suggest there is a positive relationship between financial aid and persistence for some populations, but these impacts may be difficult to observe and/or measure due to limitations in data available. Still, there is not much research on the impact of merit aid programs on student persistence in college, specifically in Florida (Hu, Trengove, & Zhang, 2012). C. The Impacts of HOPE Scholarship Due to its similarity to the Bright Futures scholarship, special attention is paid to research relating to Georgia s HOPE scholarship. Henry, Rubenstein, & Bugler (2004) found that HOPE scholarship recipients earned more college credits and higher GPAs and were more likely to graduate within four years as compared to similar students who did not receive the scholarship. Recipients were only more likely to persist if they maintained the scholarship. Similarly 10

11 Cornwell, Mustard, and Sridhar (2006) found that HOPE increased freshmen enrollment in Georgia. Interestingly, this gain in overall attendance only accounted for 15% of scholarship recipients. This indicates that 85% of the HOPE recipients would have likely entered college without the scholarship and that the HOPE funds crowd out other methods of higher education finance. In another study, Cornwell (2003) found that recipients of the HOPE scholarship lowered the amount of students who signed up for fully loaded semesters and encouraged withdrawal from courses. Ultimately, he showed that Georgia residents completed fewer courses than nonresidents as freshmen. At the time of this study, a semester minimum of credit hours was not set for retention. This result demonstrates that the HOPE scholarship program may have unintended consequences due to the loose regulations that permit students to take light course loads. D. The Impacts of Florida s Bright Futures Program There have not been many rigorous evaluations of the Bright Futures scholarship program. One study on the impacts of this program found that the program contributes to students in high school taking more rigorous coursework and to increased enrollment at Florida colleges (Harkreader, Hughes, Tozzi, and Vanlandingham, 2008). These impacts are strongest for needy and minority students. However, due to the nature of methods used, the results from that study are mainly correlational. In a recent study using different in differences method analyzing IPDES data to examine the effects of Bright Futures on student college enrollment and degree production, Zhang, Hu, and Sensenig (in press) found that after the implementation of the Bright Futures program in 1997, there were significant increases in student college enrollment and some increase in college degree production in Florida. Given the large differences in enrollment increase and degree production increase, they suggested that further exploration on the effects of Bright Futures on student persistence may help explain the phenomenon of the 11

12 relatively less impacts of Bright Futures on degree production than enrollment, which is the focus of the current study. IV. Data and Variables Florida maintains one of the most extensive and integrated educational databases in the country the Florida Education Data Warehouse (EDW), which includes all education-related data collected by the state from public colleges and universities (e.g. student course work, grades, degree program), public schools (e.g. student course work, grades, SAT or ACT scores), as well as student background information (e.g., gender, race/ethnicity, in-state vs. out-of-state residency, etc.) (Ewell, Schild, & Paulsen, 2003). For our paper we utilize data for entering first year students in both (cohort 1) and (cohort 2) in 10 of the 11 Florida State University System institutions (FSUS) 1. These two cohorts are selected for analysis since (1) cohort 1 is the last freshmen entering class in the FSUS that was not eligible to receive Bright Futures scholarships as they exist today and (2) cohort 2 represents an entering class in the FSUS that is able to take advantage of the Bright Futures program after it has been operating for several (7) years. Other than presumable maturation of the program, there were no major changes in eligibility requirements in the Bright Futures program. Each of these two cohorts has six years of transcript data, which includes course enrollment and course grades and student progression in college. GPAs were computed using publicly available university handbooks retrieved in December Dependent variables are created based on student course enrollment information from the transcript data. Data on 1 The FSUS includes Florida Agricultural and Mechanical University, Florida Atlantic University, Florida Gulf Coast University, Florida International University, Florida Polytechnic University, Florida State University, New College of Florida, University of Central Florida, University of Florida, University of North Florida, University of South Florida, and University of West Florida. Note that New College of Florida was excluded from analysis as that university does not offer traditional grades to students upon course completion. 12

13 student academic performance include high school transcript data, high school grades, SAT and/or ACT scores, college transcript data including grade for each course, and credit hours enrolled each semester. There are 18,272 students in cohort and 31,214 students in cohort , all in the state university system. We created a variable indicating student persistence from their first fall to first spring semester. This dichotomous measure takes on a value of 1 when a student started in the fall of the first semester continued to take course credits in subsequent spring semester. Table 1 presents summary statistics of the cross sectional data by cohort 2. The persistence rate from the first semester to the second semester in college was 75% for the cohort and 78% for the cohort. The average cumulative GPAs by the end of the first year were 2.68 and 2.81 respectively. The independent variables of interest for these analyses are student eligibility for FAS and FMS based on student academic records in high school. A comprehensive record of actual disbursement of these scholarships is not available from the EDW dataset, thus a proxy was computed using high school GPA, SAT and ACT records. In addition, we used eligibility to make the method of difference-in-differences viable since Bright Futures awards were not available for the first cohort as the program did not yet exist as it did for the second cohort. A student was coded as eligible for FAS if they achieve a high school GPA of 3.5 or higher and a SAT of 1270 or higher or an ACT of 27 or higher. Similarly, a student is coded as eligible for FMS if they achieve a high school GPA greater than or equal to 3.0 but less than 3.5 and a SAT 2 Table 1 provides summary statistics using students records for the fall semester they enter college. Persistence and cumulative GPA variables were calculated using the longitudinal data and were converted to cross section variables prior to analysis and reporting. 13

14 score greater than or equal to 970 but less than 1,270 or an ACT score greater than or equal to 20 but less than 27. V. Empirical Strategies In order to fully understand the role of Bright Futures program in student educational progression, we used a variety of research methods to analyze the data, with awareness that different methods may answer different research questions. First, we examined mean differences in persistence over Bright Future eligibility criteria. Then we estimated conventional OLS and Logit regression models to examine how students qualifying for different types of awards (in comparison to those whose qualifications do not meet Bright Futures eligibility) persisted in the two cohorts. This type of analysis could shed light on how students whose qualifications met the scholarship eligibility persist in comparison to others in two different policy environments, when one cohort ( ) was exposed to the scholarship program while the other ( ) was not. We then used difference-in-differences (DD) methods to compare how the gaps in student persistence rates changed for the two cohorts, which could provide insights on the overall impacts of the implementation of the Bright Futures program on the general student population. Finally, we used regression discontinuity (RD) designs to examine how the Bright Futures eligibility affects student persistence behaviors, which could provide more rigorous evidence on the causal effects of the program on students. However, this estimated effects are applicable for students whose qualifications were just above the cut-off points for scholarship eligibility to those whose qualifications were just below, an estimate of local average treatment effect (LATE). Below is more detailed explanation of our estimation methodologies. A. Mean Differences, OLS and Logit 14

15 As an initial step, we produced average persistence rates for the persistence measure across the Bright Futures award criteria, FAS, FMS, and no award. These results are presented for the overall sample and by race/ethnicity and gender. We compare the differences in persistence of cohort 1 ( ) to cohort 2 ( ) to see if there was an overall change in average persistence after the Bright Futures program began. These results are not causal, but can give good insight into whether student persistence rates have changed over time. We then estimated the relationship between Bright Futures scholarship eligibility and persistence using simple ordinary least squares (OLS) and logit models separately for the two cohorts. That analysis cannot demonstrate how Bright Futures has impacted student persistence over time, but offers a comparison of the impacts of eligibility of Bright Futures on persistence of students in cohorts 1 and 2. The basic model for this estimation is: Y! = α! + βs! + βp! + e!" (1) Where Y is the probability of persisting from the first fall to first spring, α is the intercept, e is the error term, S is a vector of student characteristics, and P is a vector of program impact variables. S includes SAT score, high school grade point average (GPA), race/ethnicity, gender, Florida residency, and an indicator of whether or not a student qualified for Bright Futures only using an ACT score. P includes a proxy for FAS and FMS eligibility. Student ACT scores were converted into SAT scores in analyses, but we also controlled for the potential lack of compatibility of ACT and SAT scores by including a dichotomous variables for students who took ACT only. B. Difference in Differences The identification strategy using a difference-in-differences model is straightforward. To start with, we estimated our DD models on two samples: (1) only Florida resident students who 15

16 attended Florida institutions and (2) all students who attend Florida institutions who were qualified for either of the two Bright Futures program before and after the Bright Futures program was enacted (before and after 1997). The two models were estimated using the following model: Y!" = α post! + Z!" β + θ! + e!" (2) where Y!" is our dependent variable of individual student i of cohort t. post is a dummy variable which indicates the implementation of merit-based aid in FL. θ! is cohort fixed effects. Z!" includes a set of covariates in for student i in cohort t that are the same for the respective models in the OLS and logit models above. The estimate of α reflects the difference in outcomes before and after policy implementation in Florida. Since our dependent variables that are categorical, OLS regression was used in modeling because differencing strategies are not appropriate for non-linear modeling. For the first sample s specification, the sample of Florida students, the α are represented by dummy variables that include (1) an indicator for those individuals who qualified for FAS and were in cohort 2, (2) an indicator for those individuals who qualified for FMS and were in cohort 2, (3) an indicator for those individuals who qualified for FAS, and (4) an indicator for those who qualified for FMS. Thus the omitted variable for indicators 1 and 2 is represented by those individuals who did not qualify for FMS or FAS and were in cohort 2. For 3 and 4, the omitted variable is represented by those individuals who did not qualify for FMS or FAS in either cohort. The estimates of these program variables will demonstrate how Florida residents were impacted by the introduction of the current form of the Bright Futures program, and will show level effects of the full FAS scholarship and the less comprehensive FMS scholarship. 16

17 Summary statistics for the combined cohort and individual cohort samples are provided in table 4. Estimation of the second sample s mode (the sample of cohort 1 and 2 individuals who would be qualified for either FAS or FMS based on academic credentials only), the α are represented by dummy variables that include (1) an indicator for Florida Residents in the second cohort, (2) an indicator for Florida residents, and (3) an indicator for cohort 2 students. Estimates of these program variables will shed light onto how different qualified Florida residents persisted as compared to non-residents who would have likely qualified for Bright Futures. Summary statistics for the combined cohort and individual cohort samples are provided in table 6. C. Regression Discontinuity Design A substantial quantity of literature has developed that centers on the use of RD to examine postsecondary access and educational attainment (Bettinger, 2004; Lesik, 2006; Trochim, 1984; Van der Klaauw, 2002). RD is a useful technique for situations in which there are specific, measurable criteria for eligibility into a program (Van der Klaauw, 2002). This is the case for the Bright Futures program. It is worthwhile to note that the RD design assumes that the students whose eligibility scores are close to the cutoff threshold are very similar, akin to being randomly assigned around this threshold. Take college persistence as an example, the RD design answers whether those students who are slightly above the eligibility threshold (thus receiving merit aid) are more likely to persist into the next year than those who are slightly below the threshold, holding all other factors constant. Formally, the RD method estimates a local linear regression (Imbens & Lemieux 2008) around a specific cutoff point ( S 0 ) as follows: 17

18 y! = α 1 S S! + β f S 1 S S! + X!! δ + e! (3) where y! is our dependent variables, college persistence, of individual student i. 1{} is an indicator function, taking the value one if the logical condition in brackets holds and the value zero if not. In this case, it takes value of one if a student SAT score or high school GPA is above the cutoff point (1270 and 3.5, respectively). f (S) is an unknown smooth function of student score or GPA. Finally, Xi is a vector of observed student characteristics, including for example gender and race/ethnicity. Note that these covariates are to be included in the estimation, but they are unlikely to affect the estimates as they are unlikely to exhibit any discontinuity around the cutoff (Imbens & Kalyanaraman, 2009). The parameter α gives the difference, or discontinuity, in outcomes at the cutoff point, i.e., the effect of the scholarship program on student outcomes. Equation (3) assumes sharp RD design, i.e., the prediction of treated vs. untreated is perfect based on test scores. Results from this sharp design can be interpreted as intent-to-treat (ITT) effects, which estimates the effect of providing someone the treatment, regardless whether they choose to participate or not. This certainly might not be the case with a large program such as the Bright Futures program. However, given that we only have data on student SAT/ACT, high school GPAs, and college GPAs, but not other award information yet, we must estimate a sharp design in this paper. We recognize that sharp designs assume strict program adherence and that it may be the case that some qualified students do not apply for and receive Bright Futures awards. We argue that this will cause a negligible bias in results as there is no penalty for applying for Bright Futures and Florida high schools likely encourage all students to apply for the program s scholarships. Choosing optimal bandwidth, or sample of students just above and below the selection criteria, of estimation for RD design is important because it involves balancing between 18

19 precision and bias. Using a larger bandwidth yields more observations, thus more precise estimates. However, the local linear functional form becomes less accurate with a large bandwidth, leading to biased estimates. The optimal bandwidth depends on the distribution of variables in our analysis. Imbens and Kalyanaraman (2009) show that an optimal bandwidth can be determined in the case of local linear regression as follows: h!"# = C!!!!! /!(!)!!!!!!!!!!!!!!!!!!!! N!!! (4) Where C(K) is a number specific to the assumed kernel function (e.g., for rectangular kernel, for Epanechnikov kernel); σ! c is the variance of regression at the cutoff point; m!! c is the second derivative of estimated regression on either side of the cutoff point; f c is the density of c at the cutoff point, and r represents the regularization term developed by Imbens and Kalyanaraman (2009). In empirical analysis, this can be achieved through a three-step procedure, with the first step estimating the density of f c and conditional variance of σ! c, a second step of estimating the second derivatives of m c, and a third step of estimating the regularization terms. This procedure is estimated using STATA s regression discontinuity add-in, rd (Nichols, 2007). We model both cohorts to compare discontinuities over time to see if there was any change pre- and post- program implementation. We first estimate the discontinuity in persistence of those individuals just above and just below the high school GPA cutoff of 3.5 for FAS and of 3.0 for FMS. For the models that use GPA as the selection criteria, we use the Imbens and Kalyanaraman (2009) method of bandwidth selection and report results on the computed optimal bandwidth. After estimating RD coefficients for the GPA selection model, we restricted the samples and re-estimated the models. We restricted the GPA model to a sample of individuals with 1270 and higher SAT score for FAS and with a 970 and higher SAT score for FMS. 19

20 We recognize that there may be some gaming or doctoring of running variables such as high school GPA and SAT score by students so that they are able to qualify for scholarships. This may be in the form of inflated GPAs or of retaking the SAT several times in order to receive a sufficient score for Bright Futures qualification. As an initial step, we estimated the continuity of the running variables of SAT score and GPA around the cutoff for FAS and FMS using techniques popularized by McCrary (2008). Specifically, we examined histograms of GPA and SAT score and focused on the density function at specific cutoff points. There does appear to be some discontinuity in GPA and SAT biased over the cutoff point, but we contend that these students still represent those that would be similar to untreated students since multiple criteria are required for selection and treatment should not dramatically affect students with doctored records versus those that are not. We can also not attribute this doctoring to any one factor, as students may simply work harder if they perceive they are near the cutoff of treatment, which would be a positive program side effect. VI. Results A. Mean Differences and OLS and Logit Models The summary statistics for cohort 1( ) and cohort 2 ( ) in Table 1 show that around three quarters of students persist from the fall to first spring semesters in the first year in Florida s State University System. There are increases in average GPA in high school and college, SAT and ACT from cohort 1 to cohort 2. Also, it is noteworthy to mention the average high school GPAs for cohort 1 (3.43) and cohort 2 (3.70) seem high. This is likely due to the fact that the samples are of students admitted to 10 of the 11 FSUS schools which are mostly selective, and the high school GPA scale has a maximum value of 5.0. We also see that FAS 20

21 eligibility is a much smaller proportion of the sample than FMS, because, for example, over half of the sample was eligible for FMS in cohort 2. When examining mean differences among potential qualifiers for Bright Futures program, we see that across all subsamples, those who qualify for the scholarship program are, on average, more likely to persist than those who do not qualify (Table 2). This result does not suggest causality, but demonstrate that those who are eligible for Bright Futures awards tend to have higher persistence rates in the first year of college. Those descriptive statistics in Table 2 also show very similar patterns in the gaps of persistence rates between FAS and FMS qualifiers compared to non-qualifiers in the two cohorts under study. Table 2 also presents the statistics on the changes in the gaps of persistence rates between cohorts 1 ( ) and 2 ( ), which shows negligible changes for the overall student population. Results from OLS and logit estimates in table 3 show that there still appears to be a positive relationship between Bright Futures qualifiers and persistence. Specifically, students in cohort 1 who would be qualify for FAS and FMS had higher probability of persisting than nonqualifiers. Similarly for students in cohort 2, those who would qualify for FAS were more likely to persist within the first year, even though those who would qualify for FMS did not show higher probability of persisting than non-qualifiers. B. Difference in Differences As described before, we conducted two types of DD analyses. For the first DD model, only used Florida residents in the analytical sample to compare the change in persistence patterns between those whose qualification met the Bright Futures eligibility to non-qualifiers (first difference) between the two cohorts (second difference), controlling for measures on student background characteristics. Table 4 presents the descriptive statistics of this sample, which are not notably different from the previously discussed summary statistics, and table 5 includes 21

22 estimates from the first type of DD analysis. The results show that even though FAS and FMS eligibility were positively related to persistence, the implementation of the Bright Futures program (where cohort 2 students would be affected) did not significantly increase the persistence gaps between the Bright Futures qualifiers and non-qualifiers across the two cohorts, suggesting that there was no significant net impacts of the implementation of the Bright Futures program on persistence within the first year in the FSUS. Tables 6 and 7 report results from analysis of the second DD model where both Florida residents and non-residents were included as long as their high school academic qualifications would qualify for the Bright Futures program. As indicated in Table 7, even though Florida residents were more likely to persist than non-residents, the gaps in persistence rates between residents and non-residents actually decreased due to the implementation of the Bright Futures program, possibly suggesting the lack of net positive effects of Bright Futures program on the probability of persisting for resident students in comparison to non-residents. It is worth noting that the validity of DD in providing causal effects of the program on students heavily depends on the suitability of the comparison groups. In this study, the assumption would be that the Bright Futures program would have little, if any, impacts on the students who are not eligible for the Bright Futures scholarship, either because of their lack of academic qualifications as specified by the program, or due to their residency status. We will discuss those issues further later in the paper. C. Regression Discontinuity Design Results from various RD analyses using the full sample and the restricted samples for both cohort (cohort 1) and (cohort 2) are presented in Table 9 and 10. For students in cohort , there are no significant effects detected for both the FAS and FMS scholarships in the Bright Futures program throughout the samples. This is not surprising 22

23 because that cohort was in college before the full implementation of the program known as the Bright Futures since This is important baseline though for the interpretation of results for the cohort of Students in the cohort of were subject to the effect of the Bright Futures program. Results in Table 10 for the full sample indicate that FAS scholarship has significant and positive causal effect on student persistence within the first year in the state university system, even though the FMS scholarship does not show significant causal effect. This pattern holds for the analysis of the restricted sample as well. FAS scholarship significantly and positively enhances student persistence within the first year in college, even though FMS does not show significant effect. In fact, we can compare the results from analyses on the two cohorts and combine the features of RD and DD together and make some interpretations of the effects of Bright Futures on student persistence. There do not seem to have a discontinuity of student persistence measure in the cohort that was not subject to the full implementation of Bright Futures program to the significant positive jumps in persistence measures in the later cohort exposed to the effects of Bright Futures program. It appears that at least the FAS scholarship has significantly caused students to persist through the first year in college in Florida s state university system. VII. Conclusion and Discussion We use a wide range of analytical methods on a range of samples using students in Florida s state university system to evaluate the effects of the Bright Futures program on student persistence through the first year in college. The evidence from this study at least points to the following conclusions. First, students whose qualifications meet the Bright Futures award 23

24 eligibilities persist at higher rates than those whose credentials do not qualify. This should not be surprising because students with higher academic qualifications tend to persist at higher rates. This pattern holds for students both before and after the full implementation of the Bright Futures program, from analyses with mean comparisons, OLS, and Logit analyses. Second, the findings from our two types of DD analysis do not show any increases in persistence rates enjoyed by the students who are eligible for Bright Futures program over the counterparts who are not, after the implementation of the Bright Futures program in comparison to the situation before the program implementation. At the first blush, these findings may indicate that the Bright Futures may not have net causal effects on student persistence through the first year. However, as mentioned before, the validity of such an interpretation has to rely on the assumption that student composition remain somewhat static, with the only change is the implementation of the Bright Futures program. This is an assumption not feasible. In fact, the implementation of the program affects student mobility across state border where more resident students decided to attend in-state institutions, make admissions to state university systems for resident and non-resident students more competitive (Zhang, Hu, & Sensenig, in press). That is, the academic quality of the comparison groups in the late cohort ( ) would be higher too due to the dynamics of student college choice and migration related to the Bright Futures program. The increased quality could lead to higher persistence rates as the literature has shown (Tinto, 1993, 2011). An alternative interpretation then could be that the Bright Futures program may have helped maintain even higher persistence rates for those who are affected by the program. Finally, the results from RD analyses clearly demonstrated the positive causal effects of one of the scholarship (FAS) of the Bright Futures program on student persistence through the 24

25 first year in the state university system, even though the other scholarship program (FMS) does not show clear significant effects. Since the nature of the RD design where students with very similar qualifications are compared, the causal interpretation of the effects of Bright Futures program on student first-year persistence could be considered very credible evidence. Taken together, it appears that the implementation of the Bright Futures program helped the increase of the probability of persisting through the first year in college for a small proportion of the student population. Some may wonder whether merit aid programs are the most effective way to promoting student persistence and worry about the costs associated with such programs. Those questions are legitimate and important questions and deserve further exploration, as indicated in Hu, Trengove, and Zhang s (2012) comprehensive work on multiple perspectives on the roles of state merit aid programs. The results from this study also have useful implications for future research on the effects of policies and programs. Both DD and RD have been considered viable research approaches in identifying the causal effects of programs and policies and have been widely used in program evaluation and policy analysis. However, researchers and policy analysts need to be keenly aware that the validity of the causal interpretation hinges on whether the assumptions hold. There are somewhat strict assumptions with the DD method. In the case of evaluating program effects of state merit aid programs, the dynamics of student choices and mobility, as well as possible institutional strategic choices, could make the assumptions hard to be attainable. However, such a method could still provide useful information in understanding program effects on students. The RD approach could be one of the most viable alternatives to true experimental designs in social sciences to provide credible evidence on the causal effects of merit aid programs on 25

26 student outcomes. Researchers and policy analysts could benefit from adopting multiple approaches in research design and data analysis, as demonstrated in this paper. This study could also lead to useful directions for future research on state merit aid programs. Since there are award "renewal" rules by which students need to meet to maintain the merit aid scholarship, it could be particularly useful to examine the patterns of award renewals and their effects on student continuous success in college beyond the first year. It would also be highly policy relevant to examine the effects of merit aid programs on students of different backgrounds such as gender, race and ethnicity, among others. Finally, given the complexity of award eligibility rules, it would be interesting to explore whether different ways of specifying the RD models (Reardon & Robinson, 2012) could generate new insights on the effects of merit aid programs on student educational progression. 26

27 References Adelman, C. (2007). Do we really have a college access problem? Change, 39 (4), Bettinger E. (2004). How financial aid affects persistence. In C. Hoxby (e.d.), College choices: The economics of where to go, when to go, and how to pay for it (pp ). Chicago: University of Chicago Press. Borg, M. O., & Stranahan, H. A. (2000). Lottery funded merit scholarships: Some lessons from the Florida Bright Futures Scholarship Program. University of North Florida, working paper. Retrieved from on January 13, Cha, P., & Patel, R. (2010). Rewarding progress, reducing debt: Early results from Ohio's performance-based scholarship demonstration for low-income parents. MDRC report accessed from on January 13, Cornwell, C. M., Lee, K. H., & Mustard, D. (2003) The effects of merit-based financial aid on course enrollment, withdrawal and completion in college. IZA Discussion Paper No Accessed from on January 13, Cornwell, C. M., Lee, K. H., & Sridhar, D. J. (2006). The enrollment effects of merit-based financial aid. Journal of Labor Economics, 24 (4), Delaney, J. A., & Ness, E. (2009, November). A state-level merit aid typology. Paper presented at the annual meeting of the Association for the Study of Higher Education (ASHE), Vancouver, Canada. DesJardins, S. L., Ahlburg, D.A., and Brian P. Mccall. (2002). Simulating the longitudinal effects of changes in financial aid on student departure from college. Journal of Human Resources, 37 (3), Dynarski S. M. (1999). Does Aid Matter? Measuring the Effect of Student Aid on College Attendance and Completion. NBER Working Paper Accessed from on January 13, Dynarski, S. M., (2002). The Consequences of Merit Aid. KSG Working Papers No. RWP Accessed from on January 13, Dynarski, S. M., (2004). The new merit aid. In C. Hoxby (e.d.), College choices: The economics of where to go, when to go, and how to pay for it (pp ). Chicago: University of Chicago Press. Dynarski, S. M. (2005). Building the stock of college-educated labor. NBER Working Paper Accessed from on January 13, Dynarski, S. M., & Scott-Clayton, J. (2008). Complexity and targeting in federal student aid: A quantitative analysis. NBER Working Paper No Accessed from on January 13, Harkreader,S., Hughes, J., Tozzi, M. H., &Vanlandingham, G. (2008). The impact of Florida's Bright Futures Scholarship Program on high school performance and college enrollment. Journal of Student Financial Aid, 38,

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