Working Paper: NBER ID: w11682
Authors: 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.
Keywords: welfare caseloads; forecasting; Markov chains
JEL Codes: I3
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| previous month's caseload (Ct-1) (C22) | current welfare caseload (Ct) (I38) |
| current entries (Et) (C51) | current welfare caseload (Ct) (I38) |
| current exit rate (Xt) (J63) | current welfare caseload (Ct) (I38) |
| previous month's caseload (Ct-1), current entries (Et), current exit rate (Xt) (C51) | current welfare caseload (Ct) (I38) |
| implied steady state (ISS) (C62) | current welfare caseload (Ct) (I38) |