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Replication data for: A Comment on the Economics of Labor Adjustment: Mind the Gap: Evidence from a Monte Carlo Experiment

Version
V0
Resource Type
Dataset
Creator
  • Bayer, Christian
Publication Date
2009-12-01
Description
  • Abstract

    This comment addresses a point raised in Russell Cooper and Jonathan Willis (2003, 2004), which discusses whether the "gap approach" is appropriate to describe the adjustment of production factors. They show that this approach to labor adjustment as applied in Ricardo J. Caballero, Eduardo Engel, and John C. Haltiwanger (1997) and Caballero and Engel (1993) can falsely generate evidence in favor of nonconvex adjustment costs, even if costs are quadratic. Simulating a dynamic model of firm-level employment decisions with quadratic adjustment costs and estimating a gap model from the simulated data, they identify two factors producing this spurious evidence: approximating dynamic adjustment targets by static ones, and estimating the static targets themselves. This comment reassesses whether the first factor indeed leads to spurious evidence in favor of fixed adjustment costs. We show that the numerical approximation of the productivity process is pivotal for Cooper and Willis's finding. With more precise approximations of the productivity process, it becomes rare to falsely reject the quadratic adjustment cost model due to the approximation of dynamic targets by static ones. (JEL E24, J3)
Availability
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Relations
  • Is supplement to
    DOI: 10.1257/aer.99.5.2258 (Text)
Publications
  • Bayer, Christian. “A Comment on the Economics of Labor Adjustment: Mind the Gap: Evidence from a Monte Carlo Experiment.” American Economic Review 99, no. 5 (December 2009): 2258–66. https://doi.org/10.1257/aer.99.5.2258.
    • ID: 10.1257/aer.99.5.2258 (DOI)

Update Metadata: 2020-05-18 | Issue Number: 2 | Registration Date: 2019-10-12

Bayer, Christian (2009): Replication data for: A Comment on the Economics of Labor Adjustment: Mind the Gap: Evidence from a Monte Carlo Experiment. Version: V0. ICPSR - Interuniversity Consortium for Political and Social Research. Dataset. https://doi.org/10.3886/E113352