Markov Forecasting Methods for Welfare Caseloads / Jeffrey Grogger.

By:

Grogger, Jeffrey

Contributor(s):

National Bureau of Economic Research

Material type: Text

TextSeries: Working Paper Series (National Bureau of Economic Research) ; no. w11682.Publication details: Cambridge, Mass. National Bureau of Economic Research 2005.Description: 1 online resource: illustrations (black and white)Subject(s):

I3 - Welfare, Well-Being, and Poverty

Online resources:

Available additional physical forms:

Hardcopy version available to institutional subscribers

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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October 2005.

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.