Analyzing occupational licensing among the states

Abstract

The study provides new evidence of the influence of occupational regulations on the U.S. economy. Our analysis, unlike previous studies, was able to obtain a representative sample of the population at the state level, which allowed us to estimate the cross-sectional effects of occupational licensing for each state. The state-level analysis demonstrates considerable variation in percentage of the workforce that has attained a license, and unlike minimum wages or unionization, licensing shows no regional patterns in the distribution of occupational licensing. The analysis also shows considerable variation in the influence of licensing on earnings across the states. The national estimates suggest that occupational licensing raises wages by about 11% after controlling for human capital and other observable characteristics. Finally, our analysis shows the influence of occupational regulation on wage inequality across the income distribution.

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Notes

  1. 1.

    The potential rents generated by restricted entry into an occupation have long been recognized by economists. Adam Smith, in his 1776 work The Wealth of Nations (Smith 1776), notes that trades conspired to reduce the availability of “skilled craftsmen” in order to raise wages. Friedman and Kuznets and Friedman recognized that members of an occupation worked in their own self-interest to restrict supply, increase demand, and maximize “profits” for members of their occupation (Friedman and Kuznets 1945; Friedman 1962). Empirical estimates for the price effects are summarized in Kleiner (2006, pp. 60–61).

  2. 2.

    In the Table 11 of “Appendix 1”, we show the occupational distribution of individuals in the sample, and it is largely similar to other national surveys such as the American Community Survey.

  3. 3.

    The logistic model estimates and corresponding average marginal effects are not shown since both the linear probability and the logistic models produce substantively identical results.

  4. 4.

    Additional details of the analysis can be found in “Occupational Licensing: A Framework for Policymakers”.

  5. 5.

    Tables 6, 7, and 9 report the unadjusted coefficients. Because the dependent variables were in logs, we make the appropriate adjustments in the text when we discuss the magnitude of the economic impact of the dummy variables: \(100\times (\exp ({\hat{\beta }})-1)\).

  6. 6.

    Occupational licensing could raise wages if the right set of regulations were chosen to restrict supply and limit the tasks of unlicensed workers. Moreover, licensed workers could enhance demand by signaling that they are providing a higher-quality service or greater human capital to consumers (Friedman 1962; Spence 1973).

  7. 7.

    We use the 2010 Standard Occupational Classification (SOC) system.

  8. 8.

    These coefficients are estimated without controlling for occupation fixed effects because the relatively small number of state-level observations does not provide enough degrees of freedom to estimate these parameters.

  9. 9.

    The last group, with wages ranging from $60,000 to $65,000, has a higher than expected average effect; however, this group is represented by only one state which could be a reason for higher than expected effect.

  10. 10.

    Occupational licensing transfers income from consumers (who pay more in the form of higher prices) to licensed workers (who receive more income in the form of higher wages). In addition, evidence suggests that there can be a loss in overall output of about 0.1% of annual consumption expenditures that stems from the output that is lost as a consequence of occupational licensing (Kleiner 2006).

  11. 11.

    This approach was developed and popularized by economist Arnold Harberger (Harberger 1954). Further, using the discussion in Schmidt (2012) we show estimates of both deadweight loss and the misallocation effects.

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Correspondence to Morris M. Kleiner.

Additional information

We especially thank Dick M. Carpenter for the development of the data used in this study, and the editor and reviewer for their comments on earlier versions of the paper. We also thank discussants at the Southern Economic Association and the American Economic Association annual meetings for their comments and suggestions.

Appendices

Appendices

Appendix 1

See Table 11.

Table 11 Occupational distribution in the Harris survey
Table 12 Employment losses, deadweight losses, and misallocation of resources due to occupational licensing
Table 13 Three scenarios of potential annual costs of occupational regulations for the U.S

Appendix 2: Simulated losses as a consequence of occupational regulation

In order to generate simulated effects of occupational licensing on the labor market, we use the Kleiner (2011) example to illustrate this approach. Suppose that the entire 15% wage premium for licensing found in the K&K analysis (2013) was due to monopoly effects (as opposed to productivity gains), labor supply is perfectly elastic, and the labor demand elasticity was 0.5 (Hammermesh 1993). Kleiner estimated that the United States has approximately 38 million licensed workers with average annual earnings of $41,000. Under these assumptions, licensing resulted in 2.8 million fewer jobs with an annual cost to consumers of $203 billion.Footnote 10 Using the same approach and a newer and larger sample, we estimate the influence of licensing at the state and national level.

Using estimated licensing coefficients, we simulate the employment losses, deadweight losses,Footnote 11 and misallocation of resources as a consequence of occupational licensing at the national level and for the 16 states where regulations were significant. These estimates are shown in Table 12 of “Appendix 2”.

In Table 13 of “Appendix 3” we present three scenarios of potential annual costs of occupational regulations for the U.S. economy. The lower-bound results were calculated by using the effects of occupational licensing that were estimated based on the parameters generated from the Harris Survey data, and they are smaller than those found in the K&K study. The middle-level results were totals of the effects that were calculated for the 16 states where occupational regulations were statistically significant. We used the same approach for simulating the parameters of interest at the state level as the one that we used for national level. Finally, the upper-bound simulations were calculated by using the Harris data, but with the average licensing effect parameter of 15% that was estimated in the K&K study.

The middle-level simulations from Table 13 of “Appendix 2” show that the job losses due to occupational regulations are about 1.6 million jobs or approximately 1.3% of total nonfarm employment in 2013, and the lost output due to occupational regulation exceeds $13 billion or approximately 1.5% of total household consumption in 2013. The latter simulations can be viewed as a lower bound for the potential economic effects of occupational licensing. We further estimate the Schmidt trapezoid (Schmidt 2012), which takes into account the misallocation of both labor and capital due to the losses that these regulations create beyond the much smaller Harberger triangle. As expected, the simulated economic consequences of regulations are much larger. The calculated misallocation of economic resources due to occupational licensing is more than $170 billion. We consider the estimate of the misallocation of economic resources to be more accurate assessment of the effect that licensing regulations have on the U.S. economy.

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Kleiner, M.M., Vorotnikov, E. Analyzing occupational licensing among the states. J Regul Econ 52, 132–158 (2017). https://doi.org/10.1007/s11149-017-9333-y

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Keywords

  • Occupational licensing
  • Wage determination with occupational licensing
  • Income inequality with occupational licensing