A Latent Variable Approach to Examining the Effects of HR Policies on the Inter- and Intra-Establishment Wage and Employment Structure: A Study of Two Precision Manufacturing Industries
Studies over the last two decades make clear that firms’ attempts to remain competitive in a global economy have taken them in many different directions. The proliferation of strategies has thrown into relief patterns by which differences in the wage structure of establishments appear to be associated with the ways they are organizing work and implementing new technologies. Lazear and Shaw (2008) have found that inter-firm differences in pay levels have both continued to grow over time, and are clearly correlated with measures of within-firm wage variation, signaling the importance of differences among firms’ internal pay strategies. The current study examines two detailed industries in the precision manufacturing sector and asks – What are the differences in the wage and employment structures of establishments that appear to be using HR policies in different ways? Are groups of workers affected equally or differently? What types of skills are targeted? The study addresses these questions using latent variable techniques and a variety of detailed measures of the wage structure of establishments, termed wagestrucp, that include measures designed to co-vary with establishments’ usage of particular types of HR wage policies aimed at increasing qualitative flexibility. A mixed continuous /discrete latent variable model is used to examine a latent construct defined by the inter-correlation between 1) those occupational wages that are most highly correlated with measures of the establishment wage structure, 2) the employment intensities of one or more detailed occupations previously found to ‘discriminate’ between high and low wage establishments, 3) the establishment wage differential, and 4) the measures wagestrucp. In both studies, the occupational wages most highly correlated with measures of the wage structure of the establishment include occupations directly involved in ensuring quality, including Inspectors, and in both studies, the ‘discriminator’ occupations include occupations most directly involved in the most technically complex activities. In both studies, the latent variable explains a substantial portion of the variance of these variables. Among large Medical Device manufacturers, the latent variable also explains most of the inter-establishment wage variation of the industry /establishment-size cell.
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- Brent A. Coull & Alan Agresti, 2000. "Random Effects Modeling of Multiple Binomial Responses Using the Multivariate Binomial Logit-Normal Distribution," Biometrics, The International Biometric Society, vol. 56(1), pages 73-80, 03.
- B. D. McCullough & H. D. Vinod, 2004.
"Verifying the Solution from a Nonlinear Solver: A Case Study: Reply,"
American Economic Review,
American Economic Association, vol. 94(1), pages 400-406, March.
- B. D. McCullough & H. D. Vinod, 2004. "Verifying the Solution from a Nonlinear Solver: A Case Study: Reply," American Economic Review, American Economic Association, vol. 94(1), pages 391-396, March.
- Kremer, M & Maskin, E, 1996.
"Wage Inequality and Segregation by Skill,"
96-23, Massachusetts Institute of Technology (MIT), Department of Economics.
- S. Rabe-Hesketh & A. Skrondal, 2001. "Parameterization of Multivariate Random Effects Models for Categorical Data," Biometrics, The International Biometric Society, vol. 57(4), pages 1256-1263, December.
- Patrick J. Heagerty, 1999. "Marginally Specified Logistic-Normal Models for Longitudinal Binary Data," Biometrics, The International Biometric Society, vol. 55(3), pages 688-698, 09.
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