Labor Adjustment Patterns In The U.S. Steel Industry

Amaechi Nkemakolem Nwaokoro

doi:10.19030/jabr.v19i4.2185

Abstract

This study examines labor demand in the U.S. steel industry.&nbsp;&nbsp;During the period of 1963-1988, the industry witnessed a tremendous decline in its output and employment.&nbsp;&nbsp;This decline has been particularly severe in the 1980s.&nbsp;&nbsp;The overall goal of this study is to estimate models of labor demand for the industry.&nbsp;The study makes two main contributions.&nbsp;&nbsp;First, the study constructs a new high-frequency monthly data set on steel output and factor prices.&nbsp;&nbsp;Hamermesh (1993) notes that most studies of labor demand rely on annual data or quarterly data which is too intertemporally aggregated to model short-run labor adjustment.&nbsp;&nbsp;Second, labor demand in the industry is modeled as a function of the unfilled orders variable.&nbsp;&nbsp;This variable is included as a measure of future demand for the industry's output.&nbsp;&nbsp;The main challenge of this study is to deal with a number of econometric modeling issues.&nbsp;&nbsp;Recognizing that the industry's output demand is potentially an endogenous variable, the output is instrumented with a set of demand related variables including controls for various steel protection regimes.&nbsp;&nbsp;This study also corrects for serial correlation in these data through differencing and Hatanaka&rsquo;s autocorrelation procedures.&nbsp; The main results of the study are fourfold.&nbsp;&nbsp;First, as in previous studies, the Instrumental Variable (IV) estimates show that the real wage rate and output are the key variables for explaining the fluctuations in employment.&nbsp; Second, the IV results do suggest that endogeneity of output is a problem and that the OLS results are biased downward.&nbsp; Third, the unfilled orders variable increases the demand for labor.&nbsp;&nbsp;However, this result is somewhat sensitive across specifications.&nbsp;&nbsp;&nbsp;Lastly, the study finds a fast short run employment adjustment period of 1.6 months.&nbsp; A final note of caution concerns serial correlation.&nbsp; Serial correlation is relatively severe in the data.&nbsp;&nbsp;The study solves this problem through both differencing and autocorrelation corrections.&nbsp;&nbsp;However, the success of these procedures proved to be mixed.&nbsp;&nbsp;Thus, the estimates reported herein vary across different estimation approaches.

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