Does Occupational Licensing Reduce Income Inequality in the United States?

The primary research question of the study is whether occupational licensing regulations influence income inequality across U.S. states. The researchers examined state-level data from 2000–2016, including licensing coverage, wages, and income distributions. They found that occupational licensing produces larger wage premiums for low- and medium-wage occupations compared to high-wage occupations. This wage effect leads to a modest reduction in overall income inequality, as licensing disproportionately benefits lower-income groups by raising their income share. The effect size is smaller than unionization's impact but statistically significant, with a 1% increase in licensing coverage reducing the top-to-bottom income ratio by 0.027.

Full Citation and Link to Article

Wendy Chen, William W. Franko, Robert J. McGrath (2024). "Occupational licensing and income inequality in the states." Journal of Policy Analysis and Management. DOI: 10.1002/pam.22660. https://onlinelibrary.wiley.com/doi/10.1002/pam.22660.

Extended Summary

Central Research Question

The study investigates how occupational licensing laws affect income inequality within U.S. states. While licensing regulations are often discussed in terms of their impact on wages and market conditions, their broader influence on statewide income inequality remains underexplored. Specifically, the authors ask: Do occupational licensing regulations exacerbate or reduce income inequality at the state level? The central hypothesis posits that the impact of licensing depends on the wage levels of the occupations affected by these regulations, potentially mitigating inequality if the largest wage premiums are found among low- and medium-wage jobs.

Previous Literature

The decades-long increase in income inequality in the U.S. has spurred extensive research on the role of public policy in shaping income distributions. Previous studies have identified several drivers of inequality, including skill-biased technological changes, globalization, and deregulatory policies. Additionally, fiscal policies like tax changes and social welfare programs have been shown to affect top and bottom income shares.

Occupational licensing has received growing attention, as it directly influences labor markets by imposing standards such as education, exams, and fees for specific professions. Earlier research (e.g., Kleiner & Krueger, 2010) demonstrates that licensing creates wage premiums, often estimated between 5% and 15%. However, it has been debated whether such premiums increase inequality by benefiting workers in high-wage professions (e.g., lawyers, doctors) or reduce it by disproportionately raising wages in low- and medium-wage occupations.

This study builds on prior literature by emphasizing the heterogeneity in licensing effects across wage classes and focusing on state-level income inequality rather than individual or occupation-specific outcomes.

Data

The study draws on several sources to compile a comprehensive dataset spanning 2000–2016:

Occupational Licensing Data: The authors use the licensing database developed by Redbird (2016, 2017), which tracks state-level licensing laws across occupations. This dataset includes measures of licensing coverage at the 6-digit Standard Occupational Classification (SOC) code level.
Wage Data: Wage information comes from the Bureau of Labor Statistics’ Occupational Employment and Wage Statistics (OEWS) program. The wages are adjusted for inflation and regional cost-of-living variations using state-year price indices.
Income Inequality Measures: State-level income distribution data is sourced from the Census Bureau’s Current Population Survey (CPS). The primary measure of inequality is the ratio of income earned by the top 20% to that earned by the bottom 20%.
Control Variables: Additional state-level variables, including union membership rates, real GDP per capita, government ideology, and demographic characteristics, are included to account for other potential influences on income inequality.

The unit of analysis differs across sections, focusing on state-occupation codes when assessing wage effects and aggregating to the state level for income inequality models.

Methods

The study employs a multi-step analytical approach:

Wage Premium Analysis: Using fixed-effects regression models, the authors estimate the wage effects of licensing within state-occupation pairs. Occupations are classified as high-wage, medium-wage, or low-wage based on their average wages over time, ensuring that wage classifications are not endogenous to licensing effects.
Income Inequality Models: The relationship between licensing coverage and state-level inequality is analyzed using state fixed-effects models. The dependent variable is the top 20%/bottom 20% income ratio, with supplementary models examining changes in the top and bottom income shares separately.
Heterogeneity Analysis: To assess whether licensing affects different wage classes unequally, interaction terms are included in the wage models. The authors also explore whether licensing disproportionately affects employment levels across wage groups.

The models control for time trends and state characteristics, allowing the authors to isolate the impact of licensing from broader economic and political factors.

Findings/Size Effects

The study’s findings provide robust evidence that occupational licensing laws have heterogeneous effects across wage classes and influence state-level income inequality in nuanced ways:

Wage Effects: Licensing increases wages in low- and medium-wage occupations but has little to no effect on high-wage professions.
- For low- and medium-wage occupations, licensing generates wage premiums of 1.1% to 1.9% on average.
- In contrast, high-wage occupations experience smaller or even negative wage premiums, likely due to the saturation of licensing requirements in these fields over time.
Income Inequality: Consistent with the Decreasing Inequality Hypothesis, the analysis reveals that licensing reduces state income inequality overall.
- A 1% increase in the proportion of the workforce covered by licensing laws reduces the top 20%/bottom 20% income ratio by 0.027.
- This effect is modest compared to union membership, which reduces the same ratio by 0.127 for a 1% increase. Nonetheless, licensing remains one of the few policy variables with a measurable impact on inequality.
Top and Bottom Income Shares: Licensing reduces the income share of the top 20% while modestly increasing the share of the bottom 20%.
- A 1% increase in licensing coverage decreases top income shares by 0.03% and increases bottom income shares by 0.006%. These effects align with the observed wage premiums for low- and medium-wage jobs.
Employment Effects: Licensing slightly reduces employment within licensed occupations, with the largest reductions observed in high-wage professions. However, these declines are too small to offset the wage effects, allowing licensing to exert a net positive impact on income equality.

Conclusion

This study challenges the conventional narrative that occupational licensing inherently exacerbates income inequality. Instead, it demonstrates that licensing’s effects depend on where wage premiums are concentrated across the income distribution. By disproportionately benefiting low- and medium-wage workers, licensing helps compress state income distributions, reducing inequality in states with greater licensing coverage.

The authors caution that these findings likely represent a lower bound for licensing’s effects on inequality due to limitations in measuring the stringency of licensing laws. Additionally, while licensing reduces inequality, its costs—such as barriers to entry and higher consumer prices—must also be considered in evaluating its overall societal impact.

In a broader context, the findings underscore the role of state-level labor market regulations in shaping economic inequality. Licensing joins unionization and minimum wage laws as a key mechanism through which states can influence income distributions, even if such effects are indirect or unintended.

Future research could refine these results by incorporating more granular measures of licensing restrictiveness and examining industry-specific impacts. Nonetheless, this study contributes to a growing understanding of how public policy can mitigate economic disparities in a period of widening inequality.