Markov Forecasting Methods for Welfare Caseloads

26 Pages Posted: 14 Dec 2005 Last revised: 15 Dec 2005

See all articles by Jeffrey Grogger

Jeffrey Grogger

University of Chicago - Irving B. Harris Graduate School of Public Policy Studies; National Bureau of Economic Research (NBER)

Date Written: October 2005

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.

Suggested Citation

Grogger, Jeffrey T., Markov Forecasting Methods for Welfare Caseloads (October 2005). NBER Working Paper No. w11682. Available at SSRN: https://ssrn.com/abstract=823185

Jeffrey T. Grogger (Contact Author)

University of Chicago - Irving B. Harris Graduate School of Public Policy Studies ( email )

1155 East 60th Street
Chicago, IL 60637
United States

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

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