Labor Market Competition and Employment Adjustment over the Business Cycle

Notes: These labor supply elasticities were obtained by estimating Equations 9–11, on a pooled sample of all (dominant) employment spells. Each model contains age, age-squared, and indicator variables for female, nonwhite, Hispanic, high school diploma, some college, college degree or greater, state-by-year, and each of 20 NAICS sectors. The second column restricts the sample to only those firms for which afirm-specific elasticity can be estimated (described in detail in Section IV). The third and fourth columns display the results when firm and individual heterogeneity are accounted for in each stage of the estimation process (for example, stratified proportional hazard models and conditional logits). For computational reasons (due mainly to the nonlinear nature of these models), a specification that controls for both firm and individual heterogeneity could not be estimated.

Notes: The first row represents estimates from Equations 9–11 where the only regressor in each model is log earnings. The second row estimates the same equations and includes age, age-squared, and indicator variables for female, nonwhite, Hispanic, education category controls, and year effects. Employer controls include number of employees working at the firm and industry indicator variables. The first four columns report the average firm-level elasticities of recruitment from employment and nonemployment and the separation elasticities to employment and nonemployment, respectively. The final column combines these elasticities, along with the calculated shares of separations/recruits to/from employment, separation rates, and growth rates to obtain the labor supply elasticity. The first two rows report only the long-run elasticities, while the third row describes the elasticities when a steady state is not assumed, and they are allowed to vary over time.

Notes: Three separate regressions, corresponding to Equations 9–11, were estimated separately for each firm in the data that met the conditions described in the data section. The coefficients on log earnings in each regression were combined, weighted by the share of recruits and separations to employment, separation rates, and growth rates according to Equation 6 to obtain the estimate of the labor supply elasticity to the firm. Demographic and human capital controls include age, age-squared, and indicator variables for gender, ethnicity, racial status, and education level. Employer controls include number of employees working at the firm and industry indicator variables. Year effects are included in all models.

Notes: A pooled national sample of all dominant employment spells subject to the sample restriction described in the data section is used in this set of regressions. The dependent variable is the natural log of quarterly earnings. Demographic controls include age, age-squared, and indicator variables for gender, ethnicity, racial status, and education level. Employer controls include the number of employees working at the firm and industry indicator variables. Tenure controls include the length (in quarters) of the employment spell, as well as its squared term. Year effects are included in all models. These results are unweighted, but all models were also estimated with demographic weights constructed by the author. There were no significant differences between the weighted and unweighted models. Standard errors are not reported because the t-statistics range from 500 to 1000, but they are available upon request along with all other estimated coefficients. There are 267,310,000 observations in each specification.