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Stratification by regulation: Are bootleggers and Baptists biased?

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Abstract

This paper investigates whether and to what extent regulation may be associated with wage inequality. Using regulation measures created by Al-Ubaydli and McLaughlin (Regul Govern 11:109–123, 2017), I find that regulation is associated with larger within-occupation wage inequality. Specifically, I show that a worker at the 90th wage percentile realizes a raise of $1.19 per hour relative to the 10th percentile earner for each standard deviation increase in regulation. That represents a 3.5% raise for a worker at the 90th percentile. Overall, increases in the regulatory burden are associated with 42–45% of the change in the 90th–10th percentile wage ratio from 2002 through 2014.

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Fig. 1

Source: Al-Ubaydli and McLaughlin (2017)

Fig. 2

Source: US Census Bureau, Current Population Survey, Annual Social and Economic Supplements, Table H-2

Fig. 3

Source: Piketty and Saez (2003, updated to 2012) and Atkinson and Piketty (2007)

Fig. 4

Source: Alvaredo et al. (2014)

Fig. 5

Source: United States Bureau of Labor Statistics. Occupational Employment Statistics (OES)

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Notes

  1. Although measured wage inequality in the United States appears to have grown faster and remained larger than wage inequality in most other developed economies (Blau and Kahn 1996), recent work suggests that the income inequality reported by Piketty and Saez (2003) may overstate the decline in income inequality witnessed in the 1950 s and overstate the increase in income inequality reported in the 1980 s (Geloso et al. 2017; Mechling et al. 2017).

  2. Although the Al-Ubaydli and McLaughlin data are available before 2002, the occupation wage data by NAICS code are available only from 2002 onwards.

  3. Discussants have suggested taking the logs of both sides so that the coefficients are elasticities. However, logging imposes a functional form and assumes that each additional regulatory restriction has a smaller effect on income inequality. However, that does not appear to be the case, particularly for the first-difference estimations. If I group the regulatory restrictions and create an independent variable representing the first 2000 regulatory restrictions, an independent variable representing the next 2000, from 2000 to 4000, and so on, and then re-estimate Eqs. (1) and (2) with each of those groups of regulatory restrictions, I do not observe a consistent positive, yet decreasing pattern. The results are shown in the Appendix. In the absence of such a pattern, I believe that it is safer to use the additive functional form assuming that the magnitude of the regulatory effects is independent of the stock of restrictions on the books when new rules are issued.

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Acknowledgements

I received valuable comments and suggestions from seminar participants at the 2017 Public Choice Society Meetings and the Institute for Economic Inquiry’s Mini Conference on the Regressive Effects of Regulation at Creighton University. I wish to extend my gratitude to Dustin Chambers, Angela K. Dills, Jeremy Horpedahl, Patrick McLaughlin, Nathan R. Murphy, Colin O’Reilly, William F. Shughart II, Michael Thomas, and Diana Weinert Thomas for their valuable insights and recommendations. Errors or deficiencies that have to this point survived this counsel are most assuredly mine alone.

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  1. Department of Economics, College of Business, Western Carolina University, 224D Forsyth, 1 University Drive, Cullowhee, NC, 28723, USA

    Sean E. Mulholland

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Correspondence to Sean E. Mulholland.

Appendix: Inequality and number of restrictions

Appendix: Inequality and number of restrictions

See Tables 8 and 9.

Table 8 Inequality and regulation by number of restrictions
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Table 9 Inequality and regulation by number of restrictions: first-difference
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Mulholland, S.E. Stratification by regulation: Are bootleggers and Baptists biased?. Public Choice 180, 105–130 (2019). https://doi.org/10.1007/s11127-018-0597-2

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