
A. INTRODUCTION

In this chapter we describe the data set we have constructed for this project. Variable definitions and sources are listed in the Appendix. We discuss the dependent variables in Section B and explanatory variables in Section C.


B. DEPENDENT VARIABLES

For both the Basic and UP programs, we estimate three participation models: caseload, total recipients, and child recipients. In addition, we estimate an average monthly benefit per family (AMB) for the combined programs. We have defined the dependent variables for each model as follows:
 Caseload  the natural logarithm of the average monthly caseload (i.e., families receiving a payment of at least $10 during the month) in the program during the quarter.^{(1)}
 Total Recipients  the natural logarithm of the average monthly number of recipients (i.e., individuals in families receiving a payment of at least $10 during the month) in the program during the quarter.
 Child Recipients  the natural logarithm of the average monthly number of child recipients ( i.e., children in families receiving a payment of at least $10 during the month) in the program during the quarter.
 Average Monthly Benefits (AMB)  the natural log of the combined average monthly payment per family for the Basic and UP programs during the quarter divided by the appropriate regional CPIU, for each program.^{(2)}
We acquired electronic caseload data from the Administration for Children and Families (ACF) for both the Basic and UP programs, by month, for the period from October 1978 through March 1995. ACF likewise provided us with total recipient data for both the Basic and UP programs for the period from October 1982 through March 1995. ASPE supplemented the ACF data with total recipient data extracted from the database assembled by Grossman (1985) for the period from January 1978 through September 1982. ACF also supplied us with child recipient data from January 1978 through March 1995. We received these data in a combination of electronic and hard copy formats. All participation data used in the model match those published in the ACF publication Quarterly Public Assistance Statistics and its monthly predecessor, Public Assistance Statistics.
The statelevel AMB data are for the period from 1980.1 through 1993.4 only, and were obtained from numerous editions of Quarterly Public Assistance Statistics.
We obtained the regional CPIs electronically from the Bureau of Labor Statistics. There are indices for four regions: North East, North Central, Southern, and Western. The regional CPIs appear bimonthly from 1980 through 1986 and monthly from 1986 to the present. We calculate quarterly values from the bimonthly/monthly indices.^{(3)}


C. EXPLANATORY VARIABLES

Four types of explanatory variables appear in the models:
1. Demographic variables;
2. Labor market variables;
3. AFDC program variables; and
4. Variables for other programs and state laws.
We discuss the construction and use of these groups of variables below.


Notes

1. Each month's figure receives a weight equal to the ratio of the number of days in the month to the total number of days in the quarter in computing the average monthly caseload.
2. Initially, we had hoped to obtain separate average monthly benefit data for the Basic and UP programs, but ACF does not collect the data separately. For most quarters in the time series, quarterly data were available from ACF. When only monthly variables were available, quarterly data were computed by taking a weighted average of the AMB in each month of the quarter.
3. From 1980 through 1986, the second and fourth quarter values of the regional CPI are the average of the two bimonthly values appearing in those quarters. For the first and third quarters, however, the value equals the one bimonthly estimate in the quarter, February and August, respectively. From 1987 through 1995, is the average of the three monthly values in the given quarter.
4. We identify AFDC Basic units as those families or subfamilies reporting having received AFDC benefits with a single reference person who has never been married, who is widowed, divorced, or separated, or whose spouse is absent. Based on conversations with individuals at ASPE, we also flag families headed by a married couples as AFDC Basic units if either parent has a disability or has held a job during the month in question. We classify all remaining families or subfamilies receiving AFDC benefits as AFDC UP units.
5. Depending on the source for these initial rates, adjustments may also be made in order to obtain estimates that reflect average monthly caseloads.
6. We unsuccessfully attempted to produce statelevel estimates of female headed households (FHHs). The plan required: 1) estimation of a nineregion pooled regression model for FHH using regional FHH estimates from the Current Population Survey (CPS) and vital statistics data aggregated to the regional level; 2) prediction of state values from the estimated model using state vital statistics data; and 3) controlling predicted state series to estimates from the 1980 and 1990 Censuses. However, these efforts failed to produce reliable estimates. The vital statistics variables proved to be very poor predictors of FHHs at the regional level. We attribute this to the erratic behavior of the FHH estimates over time within region. We expected that CPSbased FHH estimates at the regional level would be sufficiently reliable for our purpose, but this is apparently not so.
7. Monthly total unemployment by state were readily available from the BLS back to 1978. However, using this series would have limited the number of lags available for use in model. To obtain the maximum number of lags possible, we calculated and used the alternate total unemployment series described above. The correlation coefficient for the series available from the BLS and the calculated series was .93.
8. Although the value of MTBBR varies within a state over time, changes generally correspond to changes in federal requirements (OBRA81 and DEFRA84) and changes in the EITC. Thus, changes usually occur in the same quarter for most, if not all, states and are approximately equal across states. Overall, MTBBR follows a downward trend throughout the period.
9. A given family's maximum AFDC benefit may differ from the state's "typical" benefit as calculated by the ACF due to factors such as: locality, housing arrangements, family composition, or special needs.
10. More specifically, for each state we observed the quarter in which the nominal benefit changed after 1982. For most states, the quarter was the same every year, so we assumed that earlier changes were made in the same quarter in previous years. For states that did not follow a consistent pattern, with Florida being the most notable of these states, we assumed that changes before October 1982 occurred in the same quarter as the first change after October 1982. In addition, we crosschecked ACF data with semiannual maximum monthly payment data from the Congressional Research Service. When a discrepancy appeared between the two series, an effort was made to explain the discrepancy and include the appropriate data.
11. We explored the use of effective benefit reduction rates estimated by Fraker, et al (1985) and McKinnish, et al (1995). Unfortunately, a series including all 50 states and the District of Columbia began in 1984 and only extended through 1991.
12. Technically, AFDC monthly benefits paid fall to zero as soon as the calculated benefit falls below $10; i.e., there is a notch in the disposable income schedule a few dollars below the earnings cutoff. Our measure smoothes over that notch.
13. The monthly standard allowance has varied over time as a result of federal legislation, in particular OBRA81, DEFRA84, and FSA88.
14. A review of waivers implemented since 1991 appears in GAO (1996).
15. Merz, et al (1995) uses the term "therapeutic" for "medically necessary" and/or "elective" abortions.
16. The Appendix provides a more detailed explanation for the interpolation process used to create each quarterly series.
