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Monopsony Power in Occupational Labor Markets

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We collect data from the 1979 National Longitudinal Survey of Youth and create comparable measures of monopsonistic power for up to 46 occupational labor markets in the USA, starting in 1979 and ending in 2000. Our results suggest most occupational labor markets during that period were characterized by substantial amounts of monopsonistic, wage-setting power. Furthermore, after controlling for individual, time, and industry fixed effects, our results show a negative and significant correlation between the extent of monopsony power that characterizes a market and both, the wages and fringe benefits received by workers.


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  1. 1.

    Mendez (2019) uses a similar approach to the one used in this paper, but looks at the effects of monopsony on the training of workers instead.

  2. 2.

    In the limit, as \(\frac {\lambda }{\delta }\) approaches infinity, the distribution of wages F(w) collapses to the perfectly competitive case and all workers get paid their marginal product.

  3. 3.

    Some data sets do contain the necessary information to compute the λ and δ parameters, but are not available to the public free of charge. The US Census Longitudinal Employer-Household Dynamics, for example, collects detailed data from both the employer and the employee, simultaneously. This data set, however, lacks the worker occupation information that would be necessary to replicate this study.

  4. 4.

    We preferred to use the NLSY79 over the CPS because the CPS does not contain continuous records of employment which are key for generating our approximation.

  5. 5.

    Results available upon request


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Correspondence to Fabio Méndez.

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Appendix A: Theoretical Appendix

We derive the expected value of the labor sypply elasticity faced by firms (Eq. 2 in the text). As usual, the labor supply elasticity can be expressed as the mark up on the wage \(\varepsilon _{N,w}=\frac {w}{p-w}\). Furthermore, as shown by Manning (2003), the equilbrium solution of the model yields a wage distribution \(F(w)=\frac {\delta +\lambda }{\lambda }\left [1-\sqrt {\frac {p-w}{p-b}} \right ]\); with wages ranging from wmin = b to \(w_{max}= p-\left (\frac {\delta }{\delta +\lambda }\right )^{2}(p-b)\). To obtain the value in the text, the expected of the elasticity faced by firms is obtained simply by calculating the corresponding integral: \({\int }_{{w}_{min}}^{{w}_{max}} {\varepsilon }_{N,w} f(w) dw \).

Taking the derivative of F(w) and substituting for εN,w, this expression can be written as \({\int }_{{w}_{min}}^{{w}_{max}} \frac {w}{p-w} \frac {\lambda +\delta }{2\lambda }\frac {(p-w)^{\frac {-1}{2}}}{(p-b)^{\frac {1}{2}}} dw\); and further simplifying it can be expressed as follows:

$$ \frac{\lambda+\delta}{2\lambda}\frac{1}{(p-b)^{\frac{1}{2}}} {\int}_{{w}_{min}}^{{w}_{max}} w (p-w)^{\frac{-3}{2}}. $$

The solution of this integral can be verified to be

$$ \frac{\lambda+\delta}{2\lambda}\frac{1}{(p-b)^{\frac{1}{2}}}\left[2(p-w)^{\frac{1}{2}}+ 2p(p-w)^{\frac{-1}{2}}\right] \left|{~}_{{w}_{min}}^{{w}_{max}}\right. $$

which leads to the result reported in the paper when evaluated.

Appendix B: Empirical Appendix

Table 12 present the results obtained when a logistic regression is used to estimate the effects of monopsony on the availability of life and health insurance availability and of interest. More specifically, Table 12 show the results obtained after logit regressions are conducted while controlling for time and individual fixed effects in all cases, and for industry fixed effects in some of the specifications, as before.

Table 12 Health and life insurance logit regressions

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Méndez, F., Sepúlveda, F. Monopsony Power in Occupational Labor Markets. J Labor Res 40, 387–411 (2019).

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  • Monopsony
  • Wages
  • Fringe benefits

JEL Classification

  • J42
  • J31
  • J51