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Incentives and the Work Decisions of Welfare Recipients
American Journal of Economics and Sociology, The, July, 2000 by Kevin Duncan
[ln.sub.e] ([P.sub.1i]/[P.sub.0i]) = [[beta].sub.0] + [[beta].sub.1] Real [Wage.sub.i] + [[beta].sub.2] [Z.sub.i] + [[beta].sub.3] [X.sub.i] (1)
[ln.sub.e] ([P.sub.2i]/[P.sub.0i]) = [[beta].sub.0] + [[beta].sub.1] Real [AFDC.sub.i] + [[beta].sub.3] [Z.sub.i] + [[beta].sub.4] [X.sub.i] (2)
where [P.sub.0i] refers to the probability that the ith respondent mixed welfare and work (received wage and AFDC income in the same year). [P.sub.1i] refers to the probability of working (without AFDC). [P.sub.2i] refers to the probability of receiving only AFDC (without work). Real Wage, from equation 1, is the inflation adjusted hourly wage earned by the individual(1982 as the base year). Similarly, Real AFDC, from Equation 2, is the inflation adjusted AFDC income received when the individual was on welfare. Z is a vector of respondent characteristics that affect labor market participation and X is a vector of factors, such as time, that measures policy changes. Employment conditions that influence welfare participation are also included in X. Hence, these specifications provide an opportunity to test the effect of wage and AFDC levels on work and AFDC decisions, holding all other factors constant.
The equations were estimated using a sub-sample of PSID respondents who received at least one dollar of AFDC income over the 1980-1987 period. The results from this sample are reported below. Equation 1 was also estimated using a sample of those PSID respondents who did and did not participate in AFDC. While these results are not reported, they are discussed. The equations were also estimated with dummy state of residence variables to control for differences in welfare policies between states. The results of these estimations, while not presented here, are also discussed below.
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Results
THE MEANS OF THE INDEPENDENT VARIABLES are reported in Table 3. [8] These means are for individuals in each of the categories (working only, mixing welfare and work and receiving AFDC benefits only). Results reported in Table 3 suggest that welfare recipients who work are older and are more likely to have been married but less likely to have young children, reside in an SMSA and to be black. These individuals are also more likely to reside in states with lower unemployment rates, to have high school degrees and to have experienced fewer years of assistance. The means for years of AFDC across the categories indicate movement between welfare and labor market states over the period.
Coefficient estimates for the logit model are reported in Table 4. A coefficient under the column [P.sub.1]/[P.sub.0] (Equation 1) can be interpreted as a change in the natural log of the odds ratio of working relative to mixing welfare and work, given a one unit change in the independent variable. The coefficient for the real wage is positive and significant at the .01 level indicating that an increase in the real wage increases the likelihood that a welfare recipient will work without a welfare subsidy. This finding did not change when the estimate of equation 1 included additional independent variables for the respondent's state of residence. The odds ratio was also estimated using a sample of PSID respondents who did and did not participate in AFDC over the period. Results of this estimation are similar to those reported here.
