The Cost of Labor Adjustment: Inferences from the Gap

Abstract

We study labor adjustment costs. We specify a dynamic optimization problem at the plant-level, allowing for both convex and non-convex adjustment costs. We estimate the parameters of the adjustment process using an indirect inference procedure in which simulated moments are matched with data moments. For this study we use estimates of reduced-form adjustment functions obtained by the gap methodology' reported in Caballero-Engel as data moments. Contrary to evidence at the micro level in support of non-convex adjustment costs, our findings indicate that piecewise quadratic adjustment costs are sufficient to match these aggregate moments.