City Research Online

Abstract

Estimating labor adjustment costs is plagued by a variety of errors, many arising from data limitations. Most researchers have assumed that adjustment decisions are made at the firm level, that adjustment
happens at the frequency at which a firm is observed (typically annually or quarterly), and that adjustment costs are incurred on net changes in employment. In this paper, I estimate a dynamic optimization model of labor adjustment of establishments based on data that permit 1) specifying any desired adjustment frequency, 2) estimating the model based on net and on gross employment flows and 3) allowing for simultaneous hirings and separations. The unit of observation is an establishment. Results for adjustment costs depend crucially on the model specification. Only a monthly adjustment model yields cost parameters in a reasonable range, while estimates from quarterly and annual adjustment models imply negative (adjustment implies a gain rather than a loss) or excessive adjustment costs. Estimating the model on net employment changes implies hiring and separation costs of around four annual median salaries, while the model on gross changes implies costs on the order of 1.7 annual median salaries. Adjustment costs differ significantly between small and large establishments. However, a dynamic model performs only marginally better than a static model with respect to out-of-sample predictions.

Publication Type:	Article
Additional Information:	This is the peer reviewed version of the following article: Yaman, F. (2019). Structural estimation of labor adjustment costs with temporally disaggregated data. Economic Inquiry, which has been published in final form at https://onlinelibrary.wiley.com/doi/abs/10.1111/ecin.12804
Publisher Keywords:	Dynamic discrete choice; Adjustment costs; Labor demand; Temporal aggregation
Subjects:	H Social Sciences > HB Economic Theory H Social Sciences > HD Industries. Land use. Labor
Departments:	School of Policy & Global Affairs > Economics
SWORD Depositor:	Symplectic Administrator

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