There is likely to be much debate about welfare reform in the 1996 presidential election campaign. The cornerstone of this debate should be how to address the “mismatch” between the education and job skill content needs of occupations in the local labor market and the education and skills characteristics of low-skill, public assistance recipients (i.e the AFDC recipient). This mismatch consists of two components. One component, the “skills mismatch”, is grounded in the education and skills characteristics of labor supply and demand, while the other component, the “spatial mismatch”, is underpinned by the geographic relationship between labor supply and demand.
Specifically, the skills mismatch suggests that the education and skills required for the majority of today’s labor market occupations are different from the job skills and education attainment of low-skilled labor market participants, or potential participants. The spatial mismatch is based on the notion that jobs which pay “liveable” wages for which low-skilled workers qualify, or can be trained, are locating to suburban areas outside of the central city, while at the same time, many residents (mainly black residents) of the central city have had a difficult time following these jobs to the suburbs because of housing and labor market discrimination (Kain, 1968). If these two mismatches are placed in the context of welfare reform, the skills mismatch then involves the education and skills of public assistance recipients and the jobs for which they might qualify. The spatial mismatch deals with where public assistance recipients reside (e.g. in central city public housing neighborhoods) and the location of the jobs analyzing commute times.
In theory, if there are not enough entry level job openings in occupations which require little or no education and skills training in order to provide employment to any low-skilled individual who wants to be employed, then a skills mismatch does likely exist. Likewise, if there are not enough job openings in entry level occupations, that pay a liveable wage, and are located near to where low-skilled, low-income individuals live, then it is likely that a spatial mismatch exists. Again, in terms of welfare reform, if there are not enough entry level job openings to employ every able-bodied, public assistance recipient who wants a job, or that are located near to where low-skilled public assistance recipients reside, then it is likely that the skills and spatial mismatches exist.
The works by Leete and Bania (1995a; 1995b) found evidence of the “skills” and “spatial” mismatches by showing that there are too few job openings to meet the potential demand of AFDC recipients in the Cleveland-Akron metropolitan area. Leete and Bania accomplished this by developing a “labor market information system” which they used to rank the local labor market occupations (and corresponding job openings) into categories according to the education and job skill content of various occupations. Furthermore, the system is used to determine whether there are enough job openings in the local labor market, given: (1) the population of, and the education attainment and job skills characteristics of public assistance recipients in a given geographical region; and (2) the spatial relationship between major employment centers where the majority of job openings will occur and where the given public assistance recipient population resides.
Some of the data required by Leete and Bania’s model were developed by the U.S. Department of Labor (DOL) over twenty-five years ago, and arguably might not be representative of labor market conditions in the 1990’s (Capelli, 1993; Spenner, 1983). The authors use recent census data to compensate for the age of the DOL data. However, it is unclear whether the census data is needed. Labor market research has not conclusively determined the effects of technology advancements on the education and skills requirements of occupations in the current labor market. Furthermore, since the census data classifies occupation categories rather than individual occupations (i.e. cashier vs. department store cashier, etc.) it is questionable whether the census data can show the change in the education and job skills content of occupations in the labor market. The author of this study feels that the census data is necessary in the Leete and Bania model because it provides valuable information about the effects of technology on occupational education and skills requirements of the broad occupation categories.