TY  - JOUR
AU  - Grogger,Jeffrey
TI  - Markov Forecasting Methods for Welfare Caseloads
JF  - National Bureau of Economic Research Working Paper Series
VL  - No. 11682
PY  - 2005
Y2  - October 2005
UR  - http://www.nber.org/papers/w11682
L1  - http://www.nber.org/papers/w11682.pdf
N1  - Author contact info:
Jeffrey Grogger
Irving B. Harris Professor of Urban Policy
Harris School of Public Policy
University of Chicago
1155 E. 60th Street
Chicago, IL   60637
Tel: 773/542-3533
Fax: 773/702-0926
E-Mail: jgrogger@uchicago.edu
AB  - 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.
ER  -