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Markov Forecasting Methods for Welfare Caseloads

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  • Jeffrey Grogger

Abstract

Forecasting welfare caseloads, particularly turning points, has become more important than ever. Since welfare reform, welfare has been funded via a block grant, which means that unforeseen changes in caseloads can have important fiscal implications for states. In this paper I develop forecasts based on the theory of Markov chains. Since today's caseload is a function of the past caseload, the caseload exhibits inertia. The method exploits that inertia, basing forecasts of the future caseload on past functions of entry and exit rates. In an application to California welfare data, the method accurately predicted the late-2003 turning point roughly one year in advance.

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Bibliographic Info

Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 11682.

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Date of creation: Oct 2005
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Publication status: published as Grogger, Jeffrey, 2007. "Markov forecasting methods for welfare caseloads," Children and Youth Services Review, Elsevier, vol. 29(7), pages 900-911, July.
Handle: RePEc:nbr:nberwo:11682

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  1. Pagan,Adrian & Ullah,Aman, 1999. "Nonparametric Econometrics," Cambridge Books, Cambridge University Press, Cambridge University Press, number 9780521355643.
  2. Jeffrey Grogger & Steven J. Haider & Jacob Klerman, 2003. "Why Did the Welfare Rolls Fall During the 1990's? The Importance of Entry," American Economic Review, American Economic Association, American Economic Association, vol. 93(2), pages 288-292, May.
  3. Jacob Alex Klerman & Steven J. Haider, 2004. "A Stock-Flow Analysis of the Welfare Caseload," Journal of Human Resources, University of Wisconsin Press, vol. 39(4).
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Cited by:
  1. Hayashi, Masayoshi, 2014. "Forecasting welfare caseloads: The case of the Japanese public assistance program," Socio-Economic Planning Sciences, Elsevier, Elsevier, vol. 48(2), pages 105-114.

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