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This research was motivated by the confluence of three events: the surge in importance of alternative work arrangements in the overall workforce; the increased need to place individuals "at risk of welfare recipiency" in jobs, some of which are likely to be alternative in nature; and the likelihood of an economic downturn in the near future. This juxtaposition led very naturally to the core research question: how important are alternative work arrangements to the at risk work force; how do these jobs compare to conventional work; and what will be the impact of a downturn on this sector of the workforce? While we have been able to provide some answers to these questions, data restrictions have limited the scope of the analysis.
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The analysis of the CPS data uncovered a series of useful preliminary facts about at-risk temporary help workers. In particular, we found some evidence that workers at risk of public assistance receipt fare worse in alternative work arrangements than do other workers in such arrangements. This held true across a variety of dimensions: wages, incidence of part-time work, job duration and employer-provided benefits. The CPS analysis also demonstrated that at-risk workers are also less happy with their work, and more likely to be in the job for reasons of necessity than are other temporary help workers.
The CPS analysis also verified the result that has often been cited in the literature--that businesses use temporary work as a response to short-term demand fluctuations, rather than as a long-term production decision. This has clear implications for the sector when the macro economy experiences a downturn.
In addition, the CPS analysis found that at-risk temporary help workers, by and large, had much lower levels of education than did other workers--suggesting that the alternative to temporary help employment for this group might well be nonemployment rather than employment. (60) This finding led us to use the SIPP data to make comparisons between individuals who were in temporary work and those who were not employed as well as between individuals who were in temporary work and regular employment.
The results of the SIPP analysis were quite striking. As expected, we found that work histories were an important contributor to whether or not individuals were employed by temporary agencies. Although we were unable to fully control for work histories, it is likely that our efforts improved the match by much more than would be possible using cross-sectional data--suggesting that simple tabulations of outcomes for different groups of workers are likely to be misleading. In addition, we found that while individuals who had a spell in temporary work definitely had worse earnings and employment outcomes than did those who worked in the "nontemporary" sector, they did much better than similar individuals who had a spell in nonemployment. The incidence of welfare receipt and income below twice the poverty line was also reduced as compared with individuals.
These results raise important questions about the appropriate counterfactual to use in making comparisons. In other words, the answer to the research question "Does temporary help employment improve outcomes for at-risk workers?" depends critically on whether the comparison group is those who were not employed during the initial observation period, or those who were in regular employment.
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A major constraint that was faced in this research was data inadequacies. Small sample sizes and inadequate work history information in the CPS meant that the dataset could only be used for tabular purposes. While the SIPP provided better work history information, the definition of temporary work was not nearly as rich as the one provided by the CPS, and, again, insufficient sample size meant that only one definition of at-risk workers could be used, rather than the plethora of possible measures. Furthermore, although the work history data in the SIPP improve our ability to obtain good matched comparison groups, the match remains problematic for some of the comparisons. In addition, the differences between temporary help employment estimates derived from household surveys, such as the CPS, and establishment surveys, such as the CES, are troublingly large.
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One question that could not be answered from the CPS and SIPP analysis is the impact of an economic downturn on the temporary help industry in general, and at-risk workers within that industry in particular. However, other data sources, particularly the CES, provide some clue.
A crude analysis of quarterly CES data from 1982:1 to 2001:2 suggests that temporary help employment is extremely responsive to GDP changes--a simple regression of the log of temporary help employment against the log of GDP suggests that the elasticity exceeds three. This is supported by a graphical analysis: the plateauing or downturns in temporary help employment exactly match downturns in economic activity. In addition, somewhat ominously, employment in this industry has taken a very clear downturn in the latest two quarters for which data are available.
These results are not unexpected. A study by Mangum, Mayall and Nelson (1985) noted that the temporary help industry was quite procyclical long ago, and this has been confirmed by Abraham (1990) and Houseman (2001).
What does this imply about the impact of an economic downturn on at-risk workers in temporary work? Several factors work together to suggest that they will be the first to be laid off. First, the low education levels of this group make them much more vulnerable than other workers. Second, the very nature of temporary work means that there are likely to be very low rates of job-specific skill acquisition, and hence that there are minimal firing costs to employers. Third, since all temporary workers are outside the standard employment relationship (Benner, 1997), there are few penalties associated with layoff. Finally, employers explicitly use non standard work arrangements to buffer against changes in the economy, so it should be expected that this sector will be disproportionately affected by an economic downturn.
All of this taken together suggests that there are likely to be substantial adverse consequences to at-risk temporary help workers in an economic downturn, although the data do not permit this to be quantified.
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Just as the main constraints that have been faced in this study have been due to data problems, the main suggestions for future work center around exploiting new and different data sources to analyze the research question. The ideal data source would consist of CPS-quality measures of alternative work arrangements in a relatively large survey dataset that could be linked with long and detailed work histories. This would enable the complex definitions of alternative work arrangements to be examined together with adequate work history controls and varied outcome measures. Such a dataset is currently being developed at the Census Bureau, and if further research were warranted, the next steps could include the following:
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60. This is confirmed by the distribution of temporary workers in our sample (see Table A.1). Roughly, two-thirds of at-risk temporary work spells were preceded by nonemployment and one third by employment.
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