Examining the Effect of Industry Trends and Structure on Welfare Caseloads
In: Economic Conditions and Welfare Reform
Previous studies of the macro-economic determinants of welfare caseloads have had difficulty in explaining changes in welfare caseloads during the last decade or so using the simple macroeconomic measure of unemployment. Because welfare recipients will typically get entry- level jobs, employment variables that are closely related to job vacancies, such as employment growth, are also important in determining welfare caseloads, as we show empirically in this study. Recognizing that welfare recipients face more substantial barriers to employment than those who typically have more education and skills, we constructed several macro-economic variables that reflect the education requirements of industries and the predominance of low-skilled workers hired by various two-digit sectors. Estimates based on a data set of annual time series observations aggregated to the state level suggest that these variables help in explaining welfare caseloads. More specifically, areas with higher concentrations of industries that hire welfare recipients and demand workers with higher education levels have higher caseloads. Based on a separate set of metropolitan-based estimates, we also found that gross job flows are positively correlated with welfare caseloads, with job destruction dominating the effects. While the two sets of results come from different types of estimation and for areas with different levels of aggregation, the results suggest that skill levels required of industries and the dynamics of the local labor market, which go beyond the typical measures of unemployment rate, help to explain the anomalies in changes in welfare caseloads during the past decade. The findings underscore that welfare recipients have barriers to employment that are different from the rest of the labor force and thus variables that more closely reflect their circumstances should be considered in explaining welfare caseloads. These findings are relevant to those attempting to predict caseloads at the na
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