Over a span of less than 15 years, the FSP caseload has exhibited three
prominent turning points. Caseload declined from 22.4 million participants
in 1981 to 18.6 million in 1988. Between 1989 and 1994, it rose to an
historical high of nearly 27.5 million people before plummeting to 17.1
million in 2000. Caseload then rebounded to 21.3 million people between
2000 and 2003.
Such extraordinary levels of volatility present a serious challenge to the
agencies charged with operating the program. Although little can be done to
reduce the volatility, the administrative challenges posed by that volatility
could be reduced if more accurate caseload forecasting were possible.
This study develops an approach to forecasting based on a leading indicator
suggested by Markov theory. The leading indicator is a function only
of current entry and exit rates. Unlike conventional econometric forecasting,
the approach developed does not require forecasts of future environmental
conditions. The technique is applied to caseload data from California.
The results highlight both the advantages and limitations of the
approach. On the plus side, the forecasts are fairly accurate and perform
reasonably well in predicting a recent turning point. On the minus side, the
horizon over which the approach produces forecasts is determined by the
data, and in the case of the California data, they vary greatly between
segments of the caseload. For the public aid segment, which includes households
that receive cash welfare payments as well as Food Stamps, the
approach yields forecasts 16 months ahead. However, for the non-aided
segment, the forecast horizon is only 4 months. Thus, the approach yields
relatively long-term forecasts for the aided segment but relatively short-term
forecasts for the non-aided segment. The study finds that the forecast
horizon appears to be related to the extent of turnover in the caseload.