Working Paper: NBER ID: w10006
Authors: Russell Cooper; Jonathan Willis
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.
Keywords: No keywords provided
JEL Codes: J23; E24
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Adjustment costs at the plant level can be modeled through a dynamic optimization problem (D24) | Structure of adjustment costs (L11) |
| Piecewise quadratic adjustment costs are sufficient to match aggregate moments derived from empirical data (C51) | Ability to explain observed labor adjustments (J29) |
| Micro-level evidence supporting nonconvex costs (D24) | Aggregate analysis aligns better with a quadratic cost structure (C51) |