Abstract
Given the current economic climate, the need to utilize every dollar spent to its fullest potential is critical. Especially, in hard economic times, it is important for resource allocation to be cost efficient. In education, it is well known that socioeconomic variables are large determinants of student performance. The need for fiscal responsibility must simultaneously be met with necessary funding to compensate for environmental harshness. In this chapter, we analyze costs of providing education in Illinois public elementary school districts. Cost efficiency and environmental costs are estimated in a three-stage data envelopment analysis model.
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Notes
- 1.
- 2.
Färe, Grosskopf, and Lovell (1994) is an excellent source for production theory and various DEA models.
- 3.
This extends Ruggiero (1999). As correctly pointed out by an anonymous referee, the two-stage model has been criticized by Simar and Wilson (2007). However, Banker and Natarajan (2008) provide a statistical foundation and derive the conditions under which parameter estimates are consistent. McDonald (2009) proves that OLS is a consistent estimator while tobit is inappropriate. See Johnson and Kuosmanen (2009) for an alternative one-stage approach.
- 4.
- 5.
We use superscript C to indicate that the measured index is composed.
- 6.
Data are available online at http://www.isbe.state.il.us/research/htmls/report_card.htm.
- 7.
In 2009, there were 379 elementary districts.
- 8.
Because we focus on elementary school districts, we exclude Chicago School District 299, a district composed of 606 schools. Note that 40% of our sample is located in Cook and DuPage counties.
- 9.
As pointed out by an anonymous reviewer, a higher teacher price index could result from lower teacher turnover. However, a district with lower turnover faces higher costs, ceteris paribus; these costs should be controlled in the second-stage analysis. Also, a higher value of our teacher price index could be indicative of better quality teachers. As such, our index provides a control for teacher quality. The reviewer recommended an alternative fixed-effects model to control for teacher prices. We recognize the importance of this alternative specification; unfortunately, time constraints prevent us from performing this additional analysis.
- 10.
An anonymous reviewer suggests an alternative interpretation for the negative coefficient on the teacher price index: more experienced and/or better-educated teachers increase operating costs but not productivity.
- 11.
An anonymous reviewer requested an alternative second-stage regression using the percentage of households from low income instead of the percent minority. The correlation between the two environmental cost indices was 0.974, providing a measure of robustness.
- 12.
As pointed out by an anonymous reviewer, it might be the case that wealthier districts offer other services and/or activities that may enhance education but not necessarily the outcomes chosen in our analysis.
References
Afriat, S. N. (1972). Efficiency estimation of production functions. International Economic Review, 13, 568–598.
Bessent, A., Bessent, W., Kennington, J., & Reagan, B. (1982). An application of mathematical programming to assess productivity in the Houston Independent School District. Management Science, 28, 1355–1367.
Banker, R., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30, 1078–1092.
Banker, R., & Morey, R. (1986). Efficiency analysis for exogenous fixed inputs and outputs. Operations Research, 34, 513–521.
Banker, R., & Natarajan, R. (2008). Evaluating contextual variables affecting productivity using data envelopment analysis. Operations Research, 56, 48–58.
Boles, J. (1966). Efficiency squared – efficient computation of efficiency indexes, proceedings (pp. 129–136). Pullman: Western Farm Economic Association.
Boles, J. (1971). The 1130 Farrell efficiency system – multiple products, multiple factors. Berkeley: Giannini Foundation of Agricultural Economics.
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2, 429–444.
Duncombe, W., Miner, J., & Ruggiero, J. (1997). Empirical evaluation of bureaucratic models of inefficiency. Public Choice, 93, 1–18.
Färe, R., Grosskopf, S., & Logan, J. (1983). The relative efficiency of Illinois electric utilities. Resources and Energy, 5, 349–367.
Färe, R., Grosskopf, S., & Lovell, C. A. K. (1994). Production frontiers. New York: Cambridge University Press.
Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society A, 120, 253–281.
Hanushek, E. (1979). Conceptual and empirical issues in the estimation of educational production functions. The Journal of Human Resources, 14(3), 351–388.
Hanushek, E. (1986). The economics of schooling: production and efficiency in public schools. Journal of Economic Literature, 24(3), 1141–1177.
Harris, A., & Goodall, J. (2008). Do parents know they matter? Engaging all parents in learning. Educational Research, 50(3), 277–289.
Hochschild, J. L. (2003). Social class in public schools. Journal of Social Issues, 59(4), 821–840.
Jackson, A., & Gaudet, L. (2010). Factories: getting rid of learning. American Journal of Business Education, 3(1), 61–63.
Jesson, D., Mayston, D., & Smith, P. (1987). Performance assessment in the education sector: Educational and economic perspectives. Oxford Review of Education, 13(3), 249–266.
Johnson, A., & Kuosmanen, T. (2009). How operational conditions and practices affect productive performance? Efficient semi-parametric one-stage estimators. SSRN Working Paper. http://ssrn.com/abstract=1485733.
Mancebón, M., & Muñiz, M. (2008). Private versus public high schools in Spain: disentangling managerial and programme efficiencies. The Journal of the Operational Research Society, 59(7), 892–901.
McDonald, J. (2009). Using least squares and tobit in second stage DEA efficiency analyses. European Journal of Operational Research, 197, 792–798.
Primont, D., & Domazlicky, B. (2006). Student achievement and efficiency in Missouri schools and the No Child Left Behind Act. Economics of Education Review, 25(1), 77–90.
Ray, S. (1991). Resource use efficiency in public schools: a study of Connecticut data. Management Science, 37, 1620–1628.
Ruggiero, J. (1996). On the measurement of technical efficiency in the public sector. European Journal of Operational Research, 90, 553–565.
Ruggiero, J. (1998). Non-discretionary inputs in data envelopment analysis. European Journal of Operational Research, 111, 461–469.
Ruggiero, J. (1999). Nonparametric analysis of educational costs. European Journal of Operational Research, 119, 605–612.
Ruggiero, J. (2004). Performance evaluation in education. In W. W. Cooper, L. Seiford, & J. Zhu (Eds.), Handbook on data envelopment analysis (pp. 323–348). New York: Springer.
Simar, L., & Wilson, P. (2007). Estimation and inference in two-stage, semi-parametric models of production processes. Journal of Econometrics, 136, 31–64.
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Flavin, J.S., Murphy, R., Ruggiero, J. (2012). A DEA Application Measuring Educational Costs and Efficiency of Illinois Elementary Schools. In: Johnson, M. (eds) Community-Based Operations Research. International Series in Operations Research & Management Science, vol 167. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-0806-2_13
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