
1. Overview

Most states have developed their own AFDC caseload forecasting models, which are typically used for budget and staffing projections. A 1990 study conducted by the state of Oregon documents the variety of techniques used by the fifty states in forecasting their AFDC caseloads (Oregon State Department of Human Resources, 1990). According to this study, most states use a simple trend analysis (fitting a linear relationship to past series of caseload values) to predict future values for the AFDC caseload. Several states, however, use a multivariate regression framework, incorporating demographic, economic, and programmatic factors, to forecast their AFDC caseloads.
We reviewed state models for two reasons. First, individual state models may reveal important determinants of the AFDC caseload that have not been examined in other studies. Second, our plan called for using the model we developed to simulate caseload series in a few selected states. For this analysis, we wanted to select states that have welldeveloped AFDC models so that we could compare the results of our model to findings from the state models. In addition, we expect that the developers of the state models would be helpful in interpreting our findings.
The state models discussed here provide additional examples of how particular factors may be measured and incorporated in our model of caseload growth. The models also identify important statespecific policy changes that occurred during the time period we will examine. The MinnesotaCare program in Minnesota and the FIP program in Washington are examples. To the extent that such policy changes are identified, they can be included in our model of caseload growth. Given the fact that we will not be able to identify all such policy changes for all states, the state models that do provide information on policy changes will allow us to test the sensitivity of our results to the omission of statespecific policy variables.
Below, we describe selected state models that go beyond trend analysis or ARIMA models^{(7)}^{,} incorporating factors believed to influence AFDC caseload growth in a multivariate regression framework. The models we describe are those used by Florida, Maryland, Minnesota, Oregon, Texas, and Washington. In addition, we discuss a report by Barnow (1988) that presents a guide for states to use in developing their own AFDC caseload models, using New Jersey as an example.


2. State Models

Barnow (1988), in a study conducted for ASPE, developed a guide for states to use in constructing models to predict AFDCBasic and UP caseloads. Models were developed using the state of New Jersey as an example, spanning the period 1978 to 1985. In these models, the quarterly AFDC caseload is regressed on: the number of divorces, the real average weekly wage in the retail trade industry, the number of persons unemployed in the state, outofwedlock births, the total state population, a set of dummy variables representing permanent and phasein effects of OBRA81, a dummy variable representing the implementation of the 1984 Deficit Reduction Act (DEFRA), a set of seasonal dummy variables, and a set of variables interacting OBRA with the divorce, unemployment, wage, and outofwedlock birth variables. Many other variables were tested but not included in the final models. These include the need standard, the number of marriages, employment in the retail industry, earnings in the personal services industry, and the number of persons who have exhausted their Unemployment Insurance benefits.
Florida
Several governmental entities in the state of Florida employ statistical models to forecast the state's AFDC caseload. The Governor's office utilizes an OLS regression framework combined with ARIMA modeling techniques to forecast AFDC caseload and costs. Explanatory variables used in the models include the female population ages 1545 and the unemployment rate. The Legislature bases its forecasts on a multivariate regression model using quarterly data from 1981 to the present. The dependent variable is the average monthly AFDC caseload, seasonally adjusted. Explanatory variables include the state unemployment rate (seasonally adjusted), the state female population ages 18 to 44, and a dummy variable accounting for policy changes occurring after 1987. In the past, the regression equation has included a variable representing movement in and out of unskilled labor (e.g., labor in the retail trade).
Maryland
The Maryland AFDC Net Flow Model is one component of a larger, macro model of the Maryland economy. Models are specified separately for the AFDCBasic and AFDCUP caseloads. The AFDCBasic model is specified as a log linear model where the number of paid cases is a function of the unemployment rate for the atrisk population, a measure of real net income gain from work, the size of the atrisk population, an index of help wanted ads, and the rate of AFDC case closings. The atrisk population is defined as female headed households with children under age 18. The real net income gain from work is the real average monthly wages after taxes minus the real monthly combined value of AFDC, food stamp, and Medicaid benefits.^{(8)} Explanatory variables used in the AFDCUP model include the unemployment rate, an index of leading economic indicators developed by the state, the index of help wanted ads, and a lagged value of the UP caseload.
Minnesota
The Minnesota model uses monthly timeseries data to forecast AFDCBasic and AFDCUP caseloads, specifying a structural component and an ARMA component in the model. The AFDCBasic structural component includes variables for unemployment (including lags), outofwedlock births, the real payment standard for a family of three, and the number of families with children enrolled in Minnesota's publiclysubsidized health insurance program, MinnesotaCare. MinnesotaCare is believed to have an impact on AFDC caseloads because it offers an affordable alternative to Medicaid should an AFDC recipient have an opportunity to take a job that does not offer health insurance benefits. This variable has proven to be a statistically significant predictor of Minnesota's AFDC caseload. The AFDCUP model uses the same variables as those used in their AFDCBasic model, except that inwedlock births are used instead of outofwedlock births.
Oregon
Oregon uses a multivariate regression model to forecast caseload growth where the number of AFDC cases is a function of the fertility rate of unwed women, the female population ages 1544, the number of divorces, the real value of the average AFDC cash grant to a family of three, the federal poverty level for a family of three, and the total number of births. The data for this model are monthly, but the model is used to produce annual forecasts. Oregon projects four years into the future. For the nine years previous to 1994, the Oregon model consistently produced Rsquares of over 90 percent. In 1994, however, the State of Oregon's AFDC policy changed from one of eligibility determination to one of diversion. That is, the Department of Human Resources first seeks ways to keep individuals/families off AFDC by primarily helping parents keep their current jobs or find new adequate employment. As a result, the explanatory power of the model has dropped significantly because the model does not contain a variable that captures this important policy change.
Texas
One of several models used by the state of Texas is a multivariate regression model where the dependent variable is the number of AFDCBasic cases. The model's explanatory variables include: the number of separated/divorced femaleheaded households with children under age 18; the number of nevermarried femaleheaded households with children under 18; the combined (inflation adjusted) cash value of monthly AFDC, food stamp and Medicaid benefits for the typical threeperson AFDC family; the average (inflationadjusted) wage rate in the retail sector; the gap between 'full' and actual nonagricultural employment; and a dummy variable representing the implementation of the JOBS program in the fourth quarter of 1990. Increases in the caseload have been attributed to the JOBS program; however, staff at the Department of Human Services believe there is a crossover effect due to recent expansions in Medicaid eligibility, and that new Medicaid applicants are also being found to be eligible for AFDC.
Washington
Washington uses entry and exit models to project AFDC caseloads. The state uses separate models to forecast AFDCBasic and AFDCUP caseloads. In both models, a month's caseload is equal to the previous month's caseload plus projected entries minus projected exits. Apparently, the models were quite successful in forecasting caseload changes until 1994, when reforms began to reduce caseloads.
The AFDCBasic entry model regresses the entry rate (new entrants divided by the population of males less than age 19 and females less than age 49 not on AFDC) on the outofwedlock birth rate, seasonal dummy variables, and a reform status variable representing the impact of the Family Independence Program (FIP) initiated by Washington in 1988. The reform variable measures the weighted percentage of state community service offices operating under FIP or FSA.^{(9)} The AFDCBasic exit rate model regresses the exit rate (exits divided by the Basic caseload) on: the ratio of the threeperson grant to typical earnings in nonmanufacturing employment; the gap between the employment rate and its previous maximum, where the employment rate is the ratio of total nonagricultural employment to number of Washington residents between the ages of 18 and 64); seasonal dummies; and the reform status variable.
The AFDCUP entry rate equation regresses the entry rate (new UP cases divided by the population under age 49) on the gap between the employment rate and its previous maximum, seasonal dummies, and the reform status variable. The AFDCUP exit equation regresses the exit rate (UP exits divided by the UP caseload) on the gap between the employment rate and its previous maximum, seasonal dummies, and the reform status variable.
