Abstract
An explicit goal of policymakers in drafting welfare reform policies was to reduce incentives for nonmarital childbearing. This paper estimates the extent to which state welfare reforms have lowered age and race-specific nonmarital fertility. Using state-level data from 1984 to 1999—a time period that includes the passage and implementation of national welfare reform—we estimate fixed effects models corrected for heteroscedasticity and autocorrelation. We find evidence that the family cap, a policy that decreases or eliminates the incremental increase in benefits for mothers who have an additional child while on welfare, is associated with a decline in nonmarital birth ratios. However, we also find that the family cap is associated with higher marital birth rates. Taken together with other research, our findings suggest evidence of policy endogeneity.
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Notes
States that either did not apply for approval or did not receive approval on their application were: Alaska, Kentucky, Nevada, New Mexico, and Rhode Island.
States that did not implement the waivers prior to August, 1996 were: D.C., Idaho, Kansas, Louisiana, Maine, South Carolina, and Tennessee. These states then either implemented them under the new Temporary Assistance to Needy Families (TANF) laws or rewrote them.
We also used a third definition of the dependent variable—the proportion of single women in the population with a nonmarital birth, where the proportion of single women is estimated using state-specific marriage rates from the Current Population Survey (CPS). However, the denominator is endogenous and difficult to measure. Regression results using this definition of the dependent variable are not reported here, but are similar to the findings in the regression using the “nonmarital birth rate” as defined above.
Values of the ratio and rate that are 0 or 1 for at least one age and race group are excluded from the entire analysis for consistency. Twenty-two observations are excluded: Maine, 1992, 1997, 1999; Vermont, 1985, 1987, 1989–1991, 1993, 1994, 1996–1999; New Hampshire, 1994, 1999; North Dakota, 1989; South Dakota, 1997; Idaho, 1990; Wyoming, 1989, 1993; and Montana 1999.
We included both lagged and unlagged values for several variables in order to capture the 9-month lag associated with childbearing as well as decisions made after conception such as pregnancy resolution and marriage decisions. Lagged values were not included when the correlation between lagged and contemporaneous values was greater than 0.8 nor for variables calculated as a 3-year moving average. An F-test shows that the included lagged variables contribute to the model in the post-teen regressions but not in the teen regressions.
The welfare policy data in this paper differ from data used by the Council of Economic Advisers [CEA] (1997) in several regards. First, while in its most recent report the CEA does take into account the implementation date of policies it does not account for their scope within each state. The CEA data only include welfare reform policies that were implemented statewide. We interviewed officials from many states in order to collect accurate data about the statewide scope of implementation. Therefore we consider a more complete set of welfare reform policies because we include policies that were not implemented statewide. Second, the CEA classifies the welfare reform policies into six categories: termination of time limits, work requirement time limits, family caps, JOBS exemptions, JOBS sanctions and the earnings disregard. Our classification differs somewhat because we explicitly include more welfare reform policies hypothesized to have an impact on nonmarital fertility decisions. Alternate specifications that have used the CEA definitions of waivers produce qualitatively similar results to those presented here (see Sabia 2006)
To make the measurement of each welfare reform policy precise, the scope and time frame of request and implementation are taken into account in the coding. Thus the welfare reform variables are not constrained to be either 0 or 1; rather, they represent a proportion between 0 and 1 (inclusive of the end points). The equation for the welfare reform policy variables is: dt,s = pt,s*at,s, where d is an indicator of the presence of the policy in year t and state s; and pt,s is the proportion of the year that the policy is in place, and at,s is the proportion of the population in the state in the area in which the policy is implemented. If the economic constraints imposed by the program are what matter for behavior, we would expect only implementation dates to have a significant effect on nonmarital fertility. However, the announcement of and discussion about the intended policy changes heighten public awareness and may result in response to the proposed policies before actual implementation (e.g., an advertising effect). In this case one could expect the request or approval dates to be correlated with the dependent variable. In order to account for this possibility, our models include two separate variables for each of the seven welfare policies. The request date is the date that the state sent the proposed demonstration project to DHHS for approval. This variable is turned on only for the years in which the waiver to implement the policy had been requested and prior to the year it was actually implemented. The implementation date is, to the best knowledge of DHHS, the date when the policies were instituted statewide or in the entire area intended by the demonstration project. This variable is set to zero in all years prior to the year in which the particular policy was implemented.
The magnitudes we report in this section are based on the marginal effects presented in Appendix A1.
In a previous paper we decompose the change in nonmarital birth ratios between 1987 and 1996 into the effects attributable to changes in: (1) demographic and economic variables, (2) welfare reform policies, and (3) the time effect (Horvath-Rose and Peters 2001). The decomposition showed that the actual increase in predicted nonmarital birth ratios is attributable primarily to unobserved time effects captured by time dummies. We found that, relative to unexplained time trends, policy variables have a small overall effect on changes in nonmarital birth ratios. However, it is important to note that policy measures could have an indirect impact on nonmarital childbearing by affecting the socioeconomic characteristics of the population, for example, through education policies and by affecting attitudes that are reflected in the time trend.
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Horvath-Rose, A.E., Peters, H.E. & Sabia, J.J. Capping Kids: The Family Cap and Nonmarital Childbearing. Popul Res Policy Rev 27, 119–138 (2008). https://doi.org/10.1007/s11113-008-9076-7
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DOI: https://doi.org/10.1007/s11113-008-9076-7