Labor Market Segmentation Theory: Reconsidering the Evidence

82 Pages Posted: 29 Jul 2000 Last revised: 3 Sep 2022

See all articles by William T. Dickens

William T. Dickens

Northeastern University - Department of Economics; Federal Reserve Banks - Federal Reserve Bank of Boston; Brookings Institution

Kevin Lang

Boston University - Department of Economics; National Bureau of Economic Research (NBER)

Date Written: June 1992

Abstract

We argue that Labor Market Segmentation theory is a good alternative to standard views of the labor market. Since it is sometimes argued that labor market segmentation theory is untestable, we first consider the uses of theory and the attributes of a good theory. We then argue that labor market segmentation has these attributes. It is internally consistent and is based on plausible assumptions about behavior and technology. More significantly, many of the predictions of the theory have been tested and confirmed. Further, from a dynamic view the theory has done quite well. When the theory has suggested new tests, far more often than not the predictions have been validated. Labor market segmentation theory has had to make little recourse to post-hoc explanations for unexpected empirical results. In contrast, human capital theory has required a series of post-hoc rationalizations to explain a large and growing body of empirical work motivated by the labor market segmentation perspective. Finally, we consider the implications of labor market segmentation theory for the practice of labor economics. We argue that further exploration of the implications of the theory for unemployment, trade. industrial policy and income distribution will provide useful insights and further tests of the theory.

Suggested Citation

Dickens, William T. and Lang, Kevin, Labor Market Segmentation Theory: Reconsidering the Evidence (June 1992). NBER Working Paper No. w4087, Available at SSRN: https://ssrn.com/abstract=232065

William T. Dickens (Contact Author)

Northeastern University - Department of Economics ( email )

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Kevin Lang

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