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Relationship between types of school district expenditures and student performance

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  • Charles Jacques
  • B. Wade Brorsen

Abstract

An educational production function is estimated using achievement test scores to proxy school output, with socio-economic characteristics and expenditures in various categories as inputs. The data are school district level expenditures. Unlike most past research, a correction is made for the heteroscedasticity created by differences in school district size. Correcting for heteroscedasticity leads to statistical tests with greater power. Test scores were positively related to expenditures on instruction and instructional support, and are negatively related to expenditures on student support, such as counselling and school administration. The negative effect of counselling and administration could be due to counsellors taking up classroom time or administrators using classroom time with announcements or assemblies. Alternatively, the causality could go the other way. It could be that schools with problems hire more administrators and counsellors. The socioeconomic variables included may not fully capture the problems that a school faces. The results show that spending is useful when targeted towards instruction. The effect, although statistically significant, is not large. But, the research still finds that money matters if it is spent on instruction.

Suggested Citation

  • Charles Jacques & B. Wade Brorsen, 2002. "Relationship between types of school district expenditures and student performance," Applied Economics Letters, Taylor & Francis Journals, vol. 9(15), pages 997-1002.
    • Handle: RePEc:taf:apeclt:v:9:y:2002:i:15:p:997-1002
    DOI: 10.1080/13504850210148161
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    References listed on IDEAS

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    1. Borland, M V & Howsen, R M, 1996. "Competition, Expenditures and Student Performance in Mathematics: A Comment on Couch et al," Public Choice, Springer, vol. 87(3-4), pages 395-400, June.
    2. Brewer, Dominic J., 1996. "Does more school district administration lower educational productivity? Some evidence on the "Administrative Blob" in New York public schools," Economics of Education Review, Elsevier, vol. 15(2), pages 111-124, April.
    3. Douglas Staiger & James H. Stock, 1997. "Instrumental Variables Regression with Weak Instruments," Econometrica, Econometric Society, vol. 65(3), pages 557-586, May.
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    1. Devaraj, Srikant & Faulk, Dagney & Hicks, Michael, 2018. "School District Size and Student Performance," Journal of Regional Analysis and Policy, Mid-Continent Regional Science Association, vol. 48(4), August.
    2. Czyżewski Bazyli & Polcyn Jan, 2016. "Education Quality and its Drivers in Rural Areas of Poland," Eastern European Countryside, Sciendo, vol. 22(1), pages 197-227, December.
    3. Claus M. Hoerandner & Robert J. Lemke, 2006. "Can No Child Left Behind Close The Gaps In Pass Rates On Standardized Tests?," Contemporary Economic Policy, Western Economic Association International, vol. 24(1), pages 1-17, January.
    4. Mensah, Yaw M. & Schoderbek, Michael P. & Sahay, Savita P., 2013. "The effect of administrative pay and local property taxes on student achievement scores: Evidence from New Jersey public schools," Economics of Education Review, Elsevier, vol. 34(C), pages 1-16.
    5. Han, Kwideok & Whitacre, Brian E., 2018. "Student Performance and School Size: A Two-stage Spatial Quantile Regression Approach to Evaluate Oklahoma High Schools," 2018 Annual Meeting, February 2-6, 2018, Jacksonville, Florida 266597, Southern Agricultural Economics Association.
    6. Polcyn, Jan, 2017. "Edukacja jako dobro publiczne - próba kwantyfikacji [Education as a public good – an attempt at quantification]," MPRA Paper 76606, University Library of Munich, Germany, revised 2017.

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