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School District Enrollment Projections and Budget Forecasting

Part of the Palgrave Studies in Public Debt, Spending, and Revenue book series (PDSR)

Abstract

School districts use enrollment projections to inform budgetary forecasts and strategic planning. In practice, enrollment projections should consider demographic and socio-economic factors, which can then be used to generate forecasts for personnel salary and benefits and other expenditures. Revenue forecasts should be conducted for different streams of local, state, and federal revenues and then considered in relation to expenditure forecasts when formulating the budget. Despite the typical approach to projections and forecasts that school districts use during the budget process, there is a limited but growing literature evaluating their effectiveness. This chapter reviews the limited literature on enrollment projections and budgetary forecasts in school districts and then descriptively analyzes forecast error with a panel of Kentucky school districts from 2001 to 2013. Results show that forecast error varies over the business cycle, with revenue underestimations increasing in magnitude during the Great Recession. Given these results and a discussion of limitations in the literature, the chapter concludes that additional research on school district projection and forecast methods is necessary.

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Fig. 15.1
Fig. 15.2

Notes

  1. 1.

    Data were obtained from the Kentucky DoE which maintains a validated longitudinal database.

References

  • Arapis, T., & Reitano, V. (2018). A glimmer of optimism in government savings accumulation? An empirical examination of municipal unassigned fund balance in Florida. Public Finance Review, 46(3), 389–420.

    CrossRef  Google Scholar 

  • Arapis, T., Reitano, V., & Bruck, E. (2017). The fiscal savings behavior of Pennsylvania school districts through the great recession. Public Budgeting & Finance, 37(3), 47–70.

    CrossRef  Google Scholar 

  • Arsen, D., & Ni, Y. (2012). The effects of charter school competition on school district resource allocation. Educational Administration Quarterly, 48(1), 3–38.

    CrossRef  Google Scholar 

  • Baker, B. D., & Richards, C. E. (1999). A comparison of conventional linear regression methods and neural networks for forecasting educational spending. Economics of Education Review, 18(4), 405–415.

    CrossRef  Google Scholar 

  • Barrett, N., Fowles, J., Jones, P., & Reitano, V. (2018). Forecast bias and fiscal slack accumulation in school districts. The American Review of Public Administration, 1–13.

    Google Scholar 

  • Bartle, J. R., Ebdon, C., & Krane, D. (2003). Beyond the property tax: Local government revenue diversification. Journal of Public Budgeting, Accounting & Financial Management, 15, 622–648.

    CrossRef  Google Scholar 

  • Bifulco, R., & Reback, R. (2014). Fiscal impacts of charter schools: Lessons from New York. Education Finance and Policy, 9(1), 86–107.

    CrossRef  Google Scholar 

  • Bishop, L. (1979). Dealing with declining school enrollments. Education and Urban Society, 11(3), 285–295.

    CrossRef  Google Scholar 

  • Casey, J. P., & Mucha, M. J. (2007). Capital project planning and evaluation. Chicago: Government Finance Officers Association.

    Google Scholar 

  • Deng, J.-L. (1982). Control problems of grey systems. Systems & Control Letters, 1(5), 288–294.

    CrossRef  Google Scholar 

  • Deng, J.-L. (1989). Introduction to grey systems theory. The Journal of Grey System, 1(1), 1–24.

    Google Scholar 

  • Dougherty, M. J., Klase, K. A., & Song, S. G. (2003). Managerial necessity and the art of creating surpluses: The budget-execution process in West Virginia cities. Public Administration Review, 63(4), 484–497.

    CrossRef  Google Scholar 

  • Duncombe, W., & Hou, Y. (2014). The savings behavior of special purpose governments: A panel study of New York school districts. Public Budgeting & Finance, 34(3), 1–23.

    CrossRef  Google Scholar 

  • Dye, R. F., & Reschovsky, A. (2008). Property tax responses to state aid cuts in the recent fiscal crisis. Public Budgeting & Finance, 28(2), 87–111.

    CrossRef  Google Scholar 

  • Fishbein, J., & Vehaun, D. (2009). Managing the personnel budgeting process. Government Finance Review, 67–75. Retrieved from http://www.gfoa.org/sites/default/files/GFR_AUG_09_67.pdf.

  • Florida Office of Economic and Demographic Research. (2018). Education estimating conference public schools pre-K - 12 enrollment. Tallahassee, FL: Florida Office of Economic and Demographic Research. Retrieved from http://edr.state.fl.us/Content/index.cfm.

    Google Scholar 

  • Frank, H. A., & Wang, X. (1994). Judgmental vs. time series vs. deterministic models in local revenue forecasting: A Florida case study. Public Budgeting and Financial Management, 6(4), 493–517.

    CrossRef  Google Scholar 

  • Frank, H. A., & Zhao, Y. (2009). Determinants of local government revenue forecasting practice: Empirical evidence from Florida. Journal of Public Budgeting, Accounting & Financial Management, 21(1), 17–35.

    CrossRef  Google Scholar 

  • Government Finance Officers Association. (2014). Financial forecasting in the budget preparation process. Best practice. Retrieved from http://www.gfoa.org/financial-forecasting-budget-preparation-process.

  • Government Finance Officers Association. (2017). Best practices in school district budgeting. Best practice. Retrieved from http://www.gfoa.org/best-practices-school-district-budgeting.

  • Grip, R. S. (2009). Does projecting enrollments by race produce more accurate results in New Jersey school districts? Population Research and Policy Review, 28(6), 747.

    CrossRef  Google Scholar 

  • Hartman, W. T. (1988). School district budgeting. Lanham: Scarecrow Press.

    Google Scholar 

  • Hulpke, J. F., Watne, D. A., & Waldo, D. (1976). Budgeting behavior: If, when, and how selected school districts hide money. Public Administration Review, 667–674.

    Google Scholar 

  • Hussar, W. J., & Bailey, T. M. (2018). Projections of education statistics to 2026. (NCES 2018-019). National Center for Education Statistics. Retrieved from https://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2018019.

  • Jones, P. A. (2018). The influence of charter school competition on public school district revenues across the US. Journal of Education Finance, 43(4), 327–359.

    Google Scholar 

  • Kavanagh, S. C., & Williams, D. W. (2016). Informed decision-making through forecasting. Chicago: Government Finance Officers Association.

    Google Scholar 

  • Kong, D. (2007). Local government revenue forecasting: The California county experience. Journal of Public Budgeting, Accounting & Financial Management, 19(2), 178–199.

    CrossRef  Google Scholar 

  • McCluskey, W. J., Cornia, G. C., & Walters, L. C. (2013). A primer on property tax: Administration and policy. Hoboken, NJ: Wiley.

    Google Scholar 

  • McKibben, J. N. (1996). The impact of policy changes on forecasting for school districts. Population Research and Policy Review, 15(5–6), 527–536.

    Google Scholar 

  • McKibben, J. N. (2006). School district planning and the 2010 decennial census: Data uses and needs. Journal of Economic and Social Measurement, 31(3–4), 221–232.

    CrossRef  Google Scholar 

  • Morrison, P. A. (1996). Forecasting enrollments during court-ordered desegregation. Population Research and Policy Review, 15(2), 131–146.

    CrossRef  Google Scholar 

  • Rodgers, R., & Joyce, P. G. (1996). The effect of underforecasting on the accuracy of revenue forecasts by state governments. Public Administration Review, 56(1), 48–56.

    CrossRef  Google Scholar 

  • Rose, S., & Smith, D. L. (2012). Budget slack, institutions, and transparency. Public Administration Review, 72(2), 187–195.

    CrossRef  Google Scholar 

  • Rubin, I. S. (2015). Past and future budget classics: A research agenda. Public Administration Review, 75(1), 25–35.

    CrossRef  Google Scholar 

  • Smith, S. K., Tayman, J., & Swanson, D. A. (2001). State and local population projections: Methodology and analysis. New York: Kluwer Academic/Plenum Publishing Company.

    Google Scholar 

  • Tang, H.-W. V., & Yin, M.-S. (2012). Forecasting performance of grey prediction for education expenditure and school enrollment. Economics of Education Review, 31(4), 452–462.

    CrossRef  Google Scholar 

  • Ványolós, I. (2011). The impact of state-imposed fund balance limits on school districts: Evidence from New York state. Journal of Public Budgeting, Accounting & Financial Management, 23(2), 166–187.

    CrossRef  Google Scholar 

  • Welsch, D. M. (2011). Charter school competition and its impact on employment spending in Michigan’s public schools. Contemporary Economic Policy, 29(3), 323–336.

    CrossRef  Google Scholar 

  • Williams, D. W., & Onochie, J. (2013). The Rube Goldberg machine of budget implementation, or is there a structural deficit in the New York City budget? Public Budgeting & Finance, 33(4), 1–21. https://doi.org/10.1111/j.1540-5850.2013.12021.x.

    CrossRef  Google Scholar 

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Jones, P., Rakow, C., Reitano, V. (2019). School District Enrollment Projections and Budget Forecasting. In: Williams, D., Calabrese, T. (eds) The Palgrave Handbook of Government Budget Forecasting. Palgrave Studies in Public Debt, Spending, and Revenue. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-18195-6_15

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  • DOI: https://doi.org/10.1007/978-3-030-18195-6_15

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