Determinants of AFDC Caseload Growth. E. CROSS-SECTIONAL AND PANEL STUDIES OF MICRO DATA

07/01/1997

Numerous studies have examined issues related to AFDC participation using cross-sectional data on individuals, usually obtained from large national surveys such as the Survey of Income and Program Participation (SIPP), the Panel Survey of Income Dynamics (PSID), and the Current Population Survey (CPS). The focus of many of the early studies has been the impact of AFDC benefit levels and the benefit reduction rate on the labor supply of female heads of households. These studies rely on the cross-state variation in AFDC benefits to estimate the impact on labor supply and find that the AFDC program does generate work disincentives; however, the magnitudes of the effects vary considerably across studies. For example, a review by Danziger et al. (1981) found the reduction in work effort to range from one to ten hours per week (10 to 50 percent of non-transfer labor supply levels).

Other studies have involved static models of AFDC participation, that is, the researchers estimate the likelihood of AFDC participation at a point in time as a function of demographic, economic, and AFDC program variables. These studies also find that the level of AFDC benefits and the benefit reduction rate significantly affect AFDC participation. The impact of wages on participation is also found to be important in some studies. Other factors significantly and positively associated with participation include age, having less education, poor health, and having greater numbers of children.(14)

Recent cross-sectional studies of AFDC participation have examined the importance of Medicaid, private health insurance, and medical need on AFDC participation (Blank, 1988; Moffitt and Wolfe, 1992). The findings from these studies have been mixed, mainly due to the manner in which the Medicaid benefit variable is specified. When the Medicaid variable (the value of Medicaid benefits) is specified at the individual level, based on the individual family's health status and medical care utilization, the results indicate that Medicaid availability has a significant positive effect on AFDC program participation. When a cruder measure of the value of Medicaid benefits is used, specified using a state-level average cost estimate, the relationship between Medicaid availability and AFDC participation is not significant.

Other recent literature on AFDC program participation has focused on the estimation of dynamic models of welfare participation using panel data on individuals, typically from the PSID or the National Longitudinal Survey of Youth (NLSY). These studies examine the determinants of program entry and exit, and time spent on the AFDC rolls. Using data from the PSID, Bane and Ellwood (1994) find race, education, marital status, work experience, and disability status to be important determinants of first-spell duration and recidivism. In examining the reasons for the first-spell of AFDC receipt, Bane and Ellwood find that changes in family structure account for about 80 percent of first-spells: over 40 percent begin when a wife becomes a female head, and 39 percent begin when an unmarried woman without a child becomes a female head with child. Only 7 percent begin due to a fall in the female head's own earnings, and about 5 percent due to a fall in other sources of income. The study does not address the factors that influence family structure. While their findings suggest that economic factors are unimportant, the effect of economic factors on family structure is not considered.

In studying welfare exits, Bane and Ellwood found that only about 25 percent of exits are due to an increase in the female head's earnings, while about 30 percent are due to marriage. The low rate of exits due to earnings in the Bane and Ellwood study is partially due to their hierarchical classification scheme that attributes an exit to marriage rather than earnings if the woman both married and increased her earnings. Other studies have found the proportion of welfare exits due to increased earnings to be in the range of 30 to 50 percent (Blank, 1988; Gritz and McCurdy, 1991; Pavetti, 1993). When Bane and Ellwood examine earnings in the first year off welfare regardless of marital status, they find that 41 percent of former recipients earned over $6,000 (1992 dollars) in that year.

Not surprisingly, the factors found to be associated with welfare exits differ between exits due to earnings and exits due to marriage or other factors. Greater education and previous work experience significantly increase the likelihood of earnings exits, but are not strongly associated with other types of exits. Race and marital status (never married versus widowed or divorced) have a much stronger influence on exits due to other reasons than on earnings exits. Never-married women and blacks are significantly less likely to leave welfare for marriage or other reasons (Bane and Ellwood, 1994).

The cross-sectional and panel studies of the determinants of AFDC participation provide important information about specific factors found to affect the likelihood of welfare entries and exits, and the duration of time spent on the rolls. The dynamic studies of welfare participation indicate that changes in family structure play a more important role than changes in a female head's earnings. This suggests that the direct effect of changes in the economy (i.e., the effect on the female head's earnings) on AFDC caseloads may be minimal. What one cannot determine from these studies is the indirect effect of changes in the economy on the AFDC caseload, as it impacts and causes changes in family structure. If poor labor market conditions for males affect the probability of marriage and/or divorce, then AFDC participation among females is also likely to be affected.

Fitzgerald (1991) touches on this issue in his study of determinants of AFDC exits by including a variable representing the quality of the "marriage market", that is, the ratio of single employed males to single males. This variable has a significant positive effect on the likelihood of welfare exits. His estimates indicates that a ten percent increase in the ratio of single employed males to single males decreases the likelihood of being on AFDC after 24 months by 8 percent (from 0.50 to 0.46).(15) In models where the effects were estimated separately for blacks and whites, however, the availability of single employed males had a significant positive effect on exits for whites only (a 10 percent increase reduces the likelihood of AFDC participation from 0.46 to 0.35 at 24 months). Another interesting finding from this study is that the unemployment rate had a significant negative effect on exits for blacks but was insignificant for whites. Taken together, these results indicate that the marriage market is more important for whites, and the labor market is more important for blacks in contributing to welfare exits.

While cross-sectional and panel studies are useful in identifying the important determinants of AFDC participation, they are much less useful in estimating the impacts of specific factors on caseload growth over time. The primary reason is that the marginal effects estimated in a cross-section (which rely on the variation across individuals at a point in time) are likely to differ from those estimated using aggregate changes over time. This is partly because the relationships among factors may change over time, and partly because the idiosyncratic behavior of individuals may introduce sufficient "noise" in the data to mask the effects of aggregate variables, unless very large sample sizes are available.

A few panel studies have a feature that makes them especially useful for examining the impact of labor market conditions and other area factors on AFDC participation of individuals. These studies link area labor market and other variables to individual observations, permitting the researcher to examine the relationship over time between changes in participation and changes in the area variables. Some studies have used major longitudinal survey databases and state-level area variables, but have found little evidence that changes in these variables have an impact on participation measures.(16) There are several possible reasons for the lack of findings, in addition to the possibility of no real effects. First, the number of observations in these surveys is small given the level of idiosyncratic variation in participation measures. Second, the studies typically control for demographic variables such as marital status, so don't recognize the potential impacts of area variables on household structure. Third, it may be that area variables for smaller areas than states are more relevant to participation than state variables, and changes in the variables for smaller areas may not be highly correlated with state-level changes.

Hoynes (1995) is the only study we know of to estimate a panel model of AFDC participation with area factors that uses a very large micro database and sub-state area variables. Her data were constructed from administrative records for AFDC recipients who are California Medicaid (Medi-Cal) enrollees -- nearly all AFDC recipients in the state -- for the period from 1987 to 1992. She estimates models for duration on AFDC using data for households that entered AFDC during the period. Her explanatory variables include household demographic variables, several county labor market variables, and community (Census tract or zip code area) demographic and economic variables based on the 1990 Census.

Hoynes found that the county market variables had substantial, statistically significant effects on duration in a variety of specifications. Her estimates imply that a three percentage point increase in the unemployment rate -- comparable to the average state-wide increase observed in California during the 1990-91 recession -- increases the chance that AFDC household that has been on the roles for less than 6 months will continue on the roles for at least one more month by 10 percent. Smaller effects were found for longer stayers. Similar results were found for other labor market variables. The findings are especially strong given that Hoynes controls for household characteristics that could themselves be influenced by labor market conditions.