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Abstract

Evaluating the impacts of public school funding on student achievement has been an important objective for informing education policymaking but fraught with data and methodological limitations. Findings from prior research have been mixed at best, leaving policymakers with little advice about the benefits of allocating public resources to schools or how it might best be done. In this study, the authors take advantage of a pilot supplemental funding program in North Carolina that used a quantitative index of educational advantage to select the most educationally disadvantaged districts in the state to receive funding. The targeted districts received supplemental funds of $250 per pupil or $840 per academically disadvantaged pupil for the 2 years of the pilot. Using a regression discontinuity design and multilevel models with extensive controls, the authors estimate that the marginal average treatment effect of the supplemental funding was 0.133 standard deviation units and that the effect on educationally disadvantaged students was 0.098 standard deviation units. The treatment effect represents approximately one third of the difference between the average score in top performing and low performing high schools.

Keywords

disadvantaged students education funding high schools regression discontinuity

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References

Barrow

Rouse

C. E.

(2005). Causality, causality, causality: The view of education inputs and outputs from economics (Working Paper 2005-15). Chicago, IL: Federal Reserve Bank of Chicago. Retrieved from http://ssrn.com/abstract=869440

Boozer

Rouse

(2001). Intraschool variation in class size: Patterns and implications. Journal of Urban Economics, 50(1), 163–189.

Cook

(2008). Waiting for life to arrive: A history of the regression-discontinuity design in psychology, statistics, and economics. Journal of Econometrics, 142, 636–654.

Cook

T. D.

Shadish

W. R.

Wong

V. C.

(2008). Three conditions under which experiments and observational studies produce comparable causal estimates: New findings from within-study comparisons. Journal of Policy Analysis and Management, 27(4), 724–750.

Dee

T. S.

Levine

(2004). The fate of new funding: Evidence from Massachusetts’ education finance reform. Educational Evaluation and Policy Analysis, 26, 199–215.

Diaz

J. J.

Handa

(2005). An assessment of propensity score matching as a nonexperimental impact estimator: Evidence from Mexico’s PROGRESA program. Journal of Human Resources, 41(2), 319–345.

Glazerman

Levy

D. M.

Myers

(2003). Nonexperimental versus experimental estimates of earnings impacts. Annals of the American Academy of Political and Social Science, 589(1), 63–93.

Goldberger

A. S.

(1972). Selection bias in evaluating treatment effects: some formal illustrations. Unpublished manuscript.

Gormley

W. T.

Jr. Gayer

(2005).Promoting school readiness in Oklahoma: An evaluation of Tulsa’s pre-K program. Journal of Human Resources, 40(3), 533–558.

10.

Gormley

W. T.

Jr. Phillips

Gayer

(2008). Preschool programs can boost school readiness. Science, 320, 1723–1724.

11.

Greenwald

Hedges

L. V.

Laine

R. D.

(1996). The effect of school resources on student achievement. Review of Educational Research, 66, 361–396.

12.

Guryan

(2003). Does money matter? Estimates from education finance reform in Massachusetts. Chicago, IL: University of Chicago.

13.

Hanushek

E. A.

(1997). Assessing the effects of school resources on student performance: An update. Educational Evaluation and Policy Analysis, 19, 141–164.

14.

Hanushek

E. A.

(2003). The failure of input-based schooling policies. Economic Journal, 113(485), F64–F98.

15.

Hanushek

E. A.

Kain

J. F.

Markman

J. M.

Rivkin

S. G.

(2003). Does peer ability affect student achievement? Journal of Applied Econometrics, 18(5), 527–544.

16.

Hanushek

E. A.

Rivkin

S. G.

Taylor

L. L.

(1996). Aggregation and the estimated effects of school resources. Review of Economics and Statistics, 78(4), 611–627.

17.

Henry

G. T.

Gordon

C. C.

Rickman

D. K.

(2006). Early education policy alternatives: Comparing the quality and outcomes of Head Start and state pre-kindergarten. Educational Evaluation and Policy Analysis, 28, 77–99.

18.

Hines

J. R.

Thaler

R. H.

(1995). Anomalies: The flypaper effect. Journal of Economic Perspectives, 9, 217–226.

19.

Holland

(1986). Statistics and causal inference. Journal of the American Statistical Association, 81, 945–960.

20.

Imbens

G. W.

Lemieux

(2008). Regression discontinuity designs: A guide to practice. Journal of Econometrics, 142, 615–635. doi:10.1016/j.jeconom.2007.05.001

21.

Jacob

B. A.

Lefgren

(2004). Remedial education and student achievement: A regression-discontinuity analysis. Review of Economics and Statistics, 86(1), 226–244.

22.

Killeen

K. M.

Monk

D. H.

Plecki

M. L.

(2002). School district spending on professional development: Insights available from national data (1992-1998). Journal of Education Finance, 28, 25–50.

23.

Mark

M. M.

Shotland

R. L.

(1987). Alternative models for the use of multiple methods. New Directions for Program Evaluation, 1987(35), 95–100.

24.

McCrary

(2007). Testing for manipulation of the running variable in the regression discontinuity design. Journal of Econometrics, 142, 698–714.

25.

National Research Council. (1999). Making money matter: Financing America’s schools. Washington, DC: National Academy Press.

26.

No Child Left Behind Act of 2001, Pub. L. No. 107-110, 115 Stat. 1425 (2002).

27.

Reichardt

C. S.

Henry

G. T.

(in press). The regression-discontinuity design in Cooper

(ed.). The handbook of research methods in psychology. Washington, DC: American Psychological Association.

28.

Rosenbaum

P. R.

Rubin

D. B.

(1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70, 41–55.

29.

Rubin

D. B.

(1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66, 688–701.

30.

Rubin

D. B.

(2005). Causal inference using potential outcomes: Design, modeling, decisions. Journal of the American Statistical Association, 100, 322–331.

31.

Schochet

P. Z.

(2008). Technical methods report: Statistical power for regression discontinuity designs in education evaluations (NCEE 2008-4026). Washington, DC: National Center for Education Evaluation and Regional Assistance. http://ies.ed.gov/ncee/pubs/

32.

Shadish

Cook

Campbell

(2001). Experimental and quasi-experimental designs for generalized causal inference: Boston, MA: Houghton Mifflin.

33.

Taylor

C. H.

(2001). The relationship between student performance and school expenditures: A review of the literature and new evidence using better data. In Monk

D. H.

Walberg

H. J.

Wang

M. C.

(Eds.), Improving educational productivity (pp. 167–184). Greenwich, CT: Information Age.

34.

Thompson

C. L.

Brown

Cunningham

Montrosse

B. M.

(2008). Leadership, organizational characteristics and performance in North Carolina high schools. Carolina Institute for Public Policy, Chapel Hill, North Carolina.

35.

Todd

P. E.

Wolpin

K. I.

(2003). On the specification and estimation of the production function for cognitive achievement. Economic Journal, 113(485), F3–F33.

36.

Trochim

W. M. K.

Cappelleri

J. C.

Reichardt

C. S.

(1991). Random measurement error does not bias the treatment effect estimate in the regression-discontinuity design: II. When an interaction effect is present. Evaluation Review, 15(5), 571–604.

37.

van der Klaauw

(2002). Estimating the effect of financial aid offers on college enrollment: A regression discontinuity approach. International Economic Review, 43(4), 1249–1287.

38.

van der Klaauw

(2008). Regression discontinuity analysis: A survey of recent developments in economics. Labour, 22(2), 219–245.

39.

Wilde

E. T.

Hollister

(2007). How close is close enough? Evaluating propensity score matching using data from a class size reduction experiment. Journal of Policy Analysis and Management, 26(3), 455–477.

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Targeted Funding for Educationally Disadvantaged Students

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