1. The Effect of State Funded Merit Scholarships for Higher Education on Pre-College Academic Performance Christopher C. Klein And Elizabeth A. Perry-Sizemore

2. Objectives of State Merit Aid Promote higher achievement in school (K-12) – awards conditional on performance Encourage pursuit of postsecondary education – by awarding scholarships Encourage achievement in higher education – standards to retain scholarships in college Provide incentives for the state’s brightest students to pursue postsecondary education in state – Scholarships useable at in-state institutions only

3. Incentives for Pre-College Performance Students otherwise discouraged by financial barriers to college may persist to gain awards Parents may push students for higher achievement K- 12 to gain awards for college Teachers may put more effort toward marginal students to increase numbers qualifying for awards at their schools Teachers’ attentions may encourage otherwise marginal students to persist and/or raise their performance

4. Measures of Pre-College Performance GPA – subject to grade inflation State NCLB test scores – not comparable across states SAT/ACT scores – selection problem Dropout Rates – NCLB reporting comparable across states – Could be influenced by grade inflation – Reflects student/parent/family choice behavior NAEP Test Scores – No selection problem (random sample) – Comparable across states – Only 8 th grade math test widely available

5. Literature Distributional effects – an income redistribution program from non-white, low-income, uneducated households to white, rich, well-educated households (Campbell and Finney; Rubenstein and Scafidi; Cornwell and Mustard; Borg and Stranahan) College enrollment – positive effects of merit aid, but may merely provide an entitlement to already- qualifying individuals (Dynarski; Cornwell, Mustard, and Sridhar; Heller and Rasmussen; Binder, Ganderton, and Hutchens) Performance of merit aid recipients – marginal qualifiers for HOPE perform better in college than non- qualifying students – students in (science, engineering, and computing) majors with more challenging grading standards are more likely to lose their HOPE scholarships. – HOPE has led Georgia residents attending the University of Georgia to enroll in fewer credit hours and withdraw from more courses than non-Georgia residents. (Henry, Rubenstein, and Bugler; Dee and Jackson; Cornwell, Lee and Mustard)

6. History: Merit Aid and Graduation Rates Klein and Perry (2007) – 1996, 2000 state level data, imputed grad rates – Merit aid negative, significant => selection problem – Merit aid positive, sometimes significant in difference equations Klein and Perry (2008) – 2000 district level data, imputed grad rates – Merit aid negative, sometimes significant – Some data available on over 15,000 districts, but less than 6,000 usable observations

7. Merit Aid States StateCriteriaAwardYear ArkansasGPA & ACT$2500 increasing1991 GeorgiaGPATuition+1993 FloridaGPA & ACT/SAT< Tuition1997 KentuckyGPA (ACT bonus)Varies1999 LouisianaSAT or ACTTuition and fees1997 MississippiGPA & ACT< $25001995 MissouriSAT or ACT$20001987 New MexicoGPATuition1998 South CarolinaGPA & SAT< $4700 plus books1998 TennesseeGPA or SAT/ACT< $55002004 West VirginiaGPA or SAT/ACTTuition and fees1999 – funded 2001

8. Methodology Conventional education production models – Dropouts: D = F(A, X, Σ, U) – Math Scores: M = G(A, Σ, U) A is merit aid, X and Σ are vectors of contemporaneous and historical school and student characteristics, U is a vector of unobserved factors (ability, etc.) By substitution, Σ can be eliminated D = F(A, M, X, U)

9. Empirical Models Estimate by OLS D g,s,t = a + bA s,t + cM g,s,t + H’X g,s,t + e g,s,t + u g,s Where e g,s,t is a classical error term u g,s is the error associated with U g indexes grade, s indexes states, t indexes time M is also a function of U, correlated with u g,s Coefficients are biased and inconsistent Estimate by IV-2SLS and differences

10. Empirical Models Two Stage Least Squares – First Stage M g,s,t = α+ βA s,t + Γ’X g,s,t + ε g,s,t + μ g,s – Second Stage D g,s,t = a + bA s,t + c + H’X g,s,t + e g,s,t + u g,s Differences ∆D g,s,t = d + f(A s,t )+ g(∆M g,s,t )+ Π’(∆X g,s,t )+ v g,s,t ∆M g,s,t = γ + κA s,t + θ’∆X g,s,t + ξ g,s,t These do not represent “Value Added”

11. Data Collected from the National Center for Education Statistics (NCES), State level Follow cohorts that took the eighth grade NAEP math test in 2000, 2003 – 9th grade 2000-01, 10th grade 2001-02, 11th grade 2002-03, 12th grade 2003-04; – 9th grade 2003-04 and 10th grade 2004-05 Includes dropout rates by grade-level required by No Child Left Behind

12. Pooled Sample Statistics 2001-2005 States by High School Grade Level VariableObs.MeanStd. devDefinition Merit3060.20590.4050Dummy for Merit Aid Math262273.3610.059NAEP 8 th grade math score Dropout2804.06251.9058Dropout rate (%) FandR2790.36920.1328Free & reduced price lunch students TotStud306944,2071,106,728Total students Black3060.15760.1634Proportion Black students Hisp3060.10330.1175Proportion Hispanic students White3060.66750.1987Proportion White students RExPerS3064550.951003.13Real Expenditures/student, K-12

13. Empirical Results OLS Dropout OLS Math 2SLS Dropout OLS DDrop OLS DMath Merit0.021-0.898**0.1772.54*** Math-0.038-4.759***-0.181*** XFullRestrictedFullRestricted R-sq0.28570.73820.23680.13580.4352 Adj. R-sq0.24830.73150.20440.07900.4031 F7.63***109.96***7.31***2.39**13.54*** N22224122282131 * sig. at 10%**sig. at 5%***sig at 1%

14. Discussion Dropout rates are 0.9 percentage points lower in Merit Aid states on average, all else equal Mean of Dropout is approx. 4.0% Merit aid may reduce dropout rates by 20%, or 3.6 percentage points over four years This is substantial, but not enough to solve the drop out problem in states such as Tennessee with graduation rates around 70%

15. Discussion Changes in drop out rates are not well explained by any of the available variables Changes in NAEP 8 th grade math scores are 2.5 points higher in merit aid states on average, all else equal Mean change in math scores is 3.34 with a range from -3 to +16 Effect of Merit Aid is 83% of the mean change in math score

16. Conclusion The results support the hypothesis that merit aid induces students to invest more in pre-college academic pursuits and to persist in high school. Radical alteration of merit aid due to state budget shortfalls may unintentionally harm K-12 academic performance. Further investigation – Does merit aid alone improve K-8 performance? – Are there other programs in merit aid states contributing to these effects, such as pre-K through grade 4 funding from lotteries?