We model worker heterogeneity in the rents from being employed in a Diamond-Mortensen-Pissarides model of matching and unemployment. We show that heterogeneity, reflecting differences in match quality and worker assets, reduces the extent of fluctuations in separations and unemployment. We find...

We introduce worker differences in labor supply, reflecting differences in skills and assets, into a model of separations, matching, and unemployment over the business cycle. Separating from employment when unemployment duration is long is particularly costly for workers with high labor supply....

We decompose underlying disturbances in total hours into three kinds: disturbances that shift the steady-state level of hours, those that change the sectoral composition of employment in the long-run, and those that cause temporary movement of hours around the steady-state. Our identifying...

We investigate the steady decline in aggregate unemployment rates in Korea since the 1960's. We argue that a pronounced decrease in the intensity of reallocation shocks, which resulted in a downward trend in the natural rate of unemployment, has been an important factor in this decline. Our...

We investigate the role of labor-supply shifts in economic fluctuations. A new VAR identification scheme for labor supply shocks is proposed. According to our VAR analysis of post-war U.S. data, labor-supply shifts account for about half the variation in hours and one-fifth of variation in...

We investigate the mapping from individual to aggregate labor supply using a general equilibrium heterogeneous-agent model with an incomplete market. The nature of heterogeneity among workers is calibrated using wage data from the PSID. The gross worker flows between employment and nonemployment...

To generate persistence we augment the standard real business cycle (RBC) model with a ``learning by doing'' (LBD) mechanism, where current labor supply affects workers' future labor productivity. Our econometric analysis shows that the LBD model fits aggregate data much better than the standard...