Regulation and poverty: an empirical examination of the relationship between the incidence of federal regulation and the occurrence of poverty across the US states


We estimate the impact of federal regulations on poverty rates in the 50 US states using the recently created Federal Regulation and State Enterprise (FRASE) index, which is an industry-weighted measure of the burden of federal regulations at the state level. Controlling for many other factors known to influence poverty rates, we find a robust, positive and statistically significant relationship between the FRASE index and poverty rates across states. Specifically, we find that a 10% increase in the effective federal regulatory burden on a state is associated with an approximate 2.5% increase in the poverty rate. This paper fills an important gap in both the poverty and the regulation literatures because it is the first one to estimate the relationship between the two variables. Moreover, our results have practical implications for federal policymakers and regulators because the greater poverty that results from additional regulations should be considered when weighing the costs and benefits of additional regulations.

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  1. 1.

    For complete details on how the FRASE index is calculated, see the appendix to McLaughlin and Sherouse (2016, pp. 29–31).

  2. 2.

    The Mercatus Center estimates that the average reader (reading at a rate of 300 words per minute) would take nearly 3 years to read the current CFR if reading it were a full-time job:

  3. 3.

    For more information on RegData, see

  4. 4.

    To put that number into perspective, note that nominal GDP in 2011 equaled $15.8 trillion (see Therefore, the cumulative impact of regulations from 1949 to 2011 was roughly 2.5 times the size of the US economy in 2011.

  5. 5.

    The decomposition of changes in poverty into changes in income distributions (inequality) and changes in mean income (growth) has a long history in development economics. It was first pioneered by Datt and Ravallion (1992) and was later used by many subsequent scholars (see, for example, Bourguignon 2003).

  6. 6.

    Following common practice, we retain the period fixed effect in Eq. (6) despite its first-differenced specification.

  7. 7.

    To calculate the povety rate, the Census Bureau estimates the proportion of Americans living in households with income below the poverty threshold. Based on the assumption that a poor household spends one-third of its income on food, the poverty income threshold is equal to triple the inflation-adjusted cost of a minimally nutritious diet in 1963, with adjustments for household size and the age of the respective members.

  8. 8.

    Poverty rates and threshold values can be obtained from the Census Bureau website:

  9. 9.

    Data on real per capita GDP can be accessed at the BEA’s website:

  10. 10.

    The Gini panel can be downloaded from Frank’s website:

  11. 11.

    The FRASE index can be downloaded from the Mercatus Center’s RegData website:

  12. 12.

    RegData 2.2 includes a variable called “regulatory restrictions” that contains quantifications of words like “shall” or “must” in federal regulation that are likely to obligate or prohibit a specific action and another variable called “industry relevance” that contains estimates of the probability that a given regulatory restriction pertains to a particular industry. See McLaughlin and Sherouse (2018) for a detailed description of RegData 2.2.

  13. 13.

    Going forward, we will treat the District of Columbia as a state: instead of referring to the “50 US States plus the District of Columbia,” we will simply refer to the group as “the states”.

  14. 14.

    Regulatory restrictions—as explained supra 12 and in McLaughlin and Sherouse (2018)—are words in federal regulation that are likely to create an obligation to perform some specific action, or a prohibition from doing so.

  15. 15.

    Any exogenous trend variables become constants.

  16. 16.

    Our results after excluding DC are not reported here, but are available from the authors upon request.

  17. 17.

    Government expenditures and state GDP data are obtained from the US BEA.

  18. 18.

    High school completion rate data are from the US Census Bureau and can be accessed at

  19. 19.

    Agricultural output (North American Industry Classification System sector 11) and state GDP data are obtained from the US BEA.


  1. Adams, R. H. (2004). Economic growth, inequality and poverty: Estimating the growth elasticity of poverty. World Development, 32(12), 1989–2014.

    Article  Google Scholar 

  2. Al-Ubaydli, O., & McLaughlin, P. A. (2015). RegData: A numerical database on industry-specific regulations for all United States industries and federal regulations, 1997–2012. Regulation and Governance, 11(1), 109–123.

    Article  Google Scholar 

  3. Apergis, N., Dincer, O., & Payne, J. E. (2011). On the dynamics of poverty and income inequality in US states. Journal of Economic Studies, 38(2), 132–143.

    Article  Google Scholar 

  4. Bailey, J. B., Thomas, D. W., & Anderson, J. R. (2018). Regressive effects of regulation on wages. Public Choice.

    Google Scholar 

  5. Bourguignon, F. (2003). The growth elasticity of poverty reduction: Explaining heterogeneity across countries and time periods. In T. S. Eicher & S. J. Turnovsky (Eds.), Inequality and growth: Theory and policy implications (pp. 3–26). Cambridge, MA: MIT Press.

    Google Scholar 

  6. Chambers, D., Collins, C. A., & Krause, A. (2017). How do federal regulations affect consumer prices? An analysis of the regressive effects of regulation. Public Choice.

    Google Scholar 

  7. Chambers, D., & Dhongde, S. (2011). A non-parametric measure of poverty elasticity. Review of Income and Wealth, 57(4), 683–703.

    Article  Google Scholar 

  8. Chambers, D., McLaughlin, P. A., & Stanley, L. (2018). Barriers to prosperity: The harmful impact of entry regulations on income inequality. Public Choice.

    Google Scholar 

  9. Chambers, D., & Munemo, J. (2017). The impact of regulations and institutional quality on entrepreneurship. Mercatus Working Paper, Mercatus Center at George Mason University, Arlington, VA.

  10. Chambers, D., Wu, Y., & Yao, H. (2008). The impact of past growth on poverty in Chinese provinces. Journal of Asian Economics, 19(4), 348–357.

    Article  Google Scholar 

  11. Coffey, B., McLaughlin, P. A., & Peretto, P. (2016). The cumulative cost of regulations. Mercatus Working Paper, Mercatus Center at George Mason University, Arlington, VA.

  12. Crain, N. V., & Crain, W. M. (2010). The impact of regulatory costs on small firms. Washington, DC: Small Business Administration, Office of Advocacy.

    Google Scholar 

  13. Crain, W. M., & Crain, N. V. (2014). The cost of federal regulation to the U.S. economy, manufacturing and small business. Washington, DC: National Association of Manufacturers.

    Google Scholar 

  14. Datt, G., & Ravallion, M. (1992). Growth and redistribution components of changes in poverty measures: A decomposition with application to Brazil and India in the 1980s. Journal of Development Economics, 38(2), 275–295.

    Article  Google Scholar 

  15. Dawson, J. W., & Seater, J. J. (2013). Federal regulation and aggregate economic growth. Journal of Economic Growth, 18(2), 137–177.

    Article  Google Scholar 

  16. Dhongde, S. (2006). Decomposing spatial differences in poverty in India. In R. Kanbur, A. J. Venables, & G. Wan (Eds.), Spatial disparities in human development: Perspectives from Asia (pp. 273–288). Tokyo: United Nations University Press.

    Google Scholar 

  17. Frank, M. W. (2009). Inequality and growth in the United States: Evidence from a new state-level panel. Economic Inquiry, 47(1), 55–68.

    Article  Google Scholar 

  18. Friedman, M. (1962). Capitalism and freedom. Chicago: University of Chicago Press.

    Google Scholar 

  19. Higgs, R. (1987). Crisis and leviathan. New York: Oxford University Press.

    Google Scholar 

  20. Johnson, C., Formby, J. P., & Kim, H. (2011). Economic growth and poverty: A tale of two decades. Applied Economics, 43(28), 4277–4288.

    Article  Google Scholar 

  21. Kleiner, M. M., & Krueger, A. B. (2013). Analyzing the extent and influence of occupational licensing on the labor market. Journal of Labor Economics, 31(2), 173–202.

    Article  Google Scholar 

  22. McLaughlin, P. A., Ellig, J., & Shamoun, D. Y. (2014). Regulatory reform in Florida: An opportunity for greater competitiveness and economic efficiency. Florida State University Business Review, 13(1), 96–127.

    Google Scholar 

  23. McLaughlin, P. A., & Sherouse, O. (2016). The impact of federal regulation on the 50 states. Arlington, VA: Mercatus Center at George Mason University.

    Google Scholar 

  24. McLaughlin, P. A., & Sherouse, O. (2018). RegData 2.2: A panel dataset on US federal regulations. Public Choice.

    Google Scholar 

  25. Meng, X., Gregory, R., & Wang, Y. (2005). Poverty, inequality, and growth in urban China, 1986–2000. Journal of Comparative Economics, 33(4), 710–729.

    Article  Google Scholar 

  26. Ram, R. (2007). Roles of income and equality in poverty reduction: Recent cross-country evidence. Journal of International Development, 19(7), 919–926.

    Article  Google Scholar 

  27. Thomas, D. W. (2012). Regressive effects of regulation. Mercatus Working Paper, Mercatus Center at George Mason University, Arlington, VA.

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Chambers, D., McLaughlin, P.A. & Stanley, L. Regulation and poverty: an empirical examination of the relationship between the incidence of federal regulation and the occurrence of poverty across the US states. Public Choice 180, 131–144 (2019).

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  • Regulation
  • Poverty
  • States
  • Regressive effects
  • RegData

JEL Classification

  • D31
  • I32
  • J38
  • K20
  • R10