The goal of the SIPP analysis is an improved understanding of how temporary work affects subsequent labor market outcomes. Broadly put, we want to estimate how outcomes for persons who begin temporary agency jobs would differ if they had not taken such jobs. The basic approach is to compare the subsequent outcomes for those employed in temporary work with persons who have similar demographic and human capital characteristics, but who did not work for temporary agencies.
To undertake this analysis, we need to define employment in temporary work and identify plausible comparison groups to serve as counterfactuals. Our sample of temporary agency workers includes all instances in which persons begin work for an agency (as either their primary or secondary job) as measured by the SIC code. The decision to focus on the start of spells of temporary work simplifies the modeling of comparison groups and fits naturally with our interest in the effect of decisions to take temporary work rather than other options.
We measure the effect of temporary work relative to both regular employment and nonemployment.(66) To operationalize this, we compare our sample of temporary agency workers to two comparison groups: one matched sample of persons employed in nontemporary work and another of persons not employed. By using two comparison groups, we hope to learn the subsequent effects of obtaining a temporary agency job as compared with being employed at a "regular" job and with not being employed.
The samples are matched based on propensity scores. Roughly put, we estimate a regression model that describes the probability of starting a job with a temporary agency. The predicted probability from such a model is known as a propensity score. We follow recent research (Dehejia & Wahba (1998), Berk and Newton (1985), and Rosenbaum & Rubin (1984)) in choosing comparison group members who match members of our sample of temporary agency workers in their likelihood of becoming a temporary worker, as measured by their propensity score. An alternative would be to match on many characteristics of the individuals (e.g., those included in the regression model). Rosenbaum and Rubin (1984), however, argue that matching on the propensity score, which is a single variable, is nearly as effective as matching on all of the many variables used in the regression model to predict propensity score.
Using the matched comparison groups, we estimate the effect of entering temporary work on several outcomes measured a year later. The outcome measures include employment status, wages, hours worked, health insurance coverage, and receipt of public assistance. These subsequent outcomes are then compared to those for each of the comparison groups, with the differences in outcomes interpreted as the effects of entering temporary work relative to the counterfactual; that is, working at a nontemporary job or not working.