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Barriers to prosperity: the harmful impact of entry regulations on income inequality

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Abstract

Entry regulations, including fees, permits and licenses, can make it prohibitively difficult for low-income individuals to establish footholds in many industries, even at the entry-level. As such, these regulations increase income inequality by either preventing access to higher paying professions or imposing costs on individuals choosing to enter illegally and provide unlicensed services. To estimate this relationship empirically, we combine entry regulations data from the World Bank’s Doing Business Index with various measures of income inequality, including Gini coefficients and income shares to form a panel of 115 countries. We find that countries with more stringent entry regulations tend to experience more income inequality. In countries with average inequality, increasing the number of procedures required to start a new business by one standard deviation is associated with a 7.2% increase in the share of income accruing to the top decile of earners, and a 12.9% increase in the overall Gini coefficient. This result is robust to the measure of inequality, startup regulations, and potential endogeneity. We conclude by offering several policy recommendations designed to minimize the adverse effects of entry regulations.

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Notes

  1. For example, to obtain a license to offer hair-braiding services legally in Pennsylvania, a person must train for 300 h at a licensed school, have a minimum of a tenth-grade education, and pass both a theory and a practical exam (McLaughlin 2013).

  2. While the impact of government regulations on the size of the shadow economy and overall income inequality (including income earned in both the formal and informal sectors) is a very interesting topic, data limitations prevent further exploration. The best available data (see Dreher et al. 2014; Schneider 2005) covers the very limited time span of 1999–2003.

  3. For a literature review of the early empirical research surrounding the relationship between occupational licensing and quality, see Gross (1986).

  4. McLaughlin et al. (2014) review three studies that observe a positive relationship between licensing and service quality. Feldman and Begun (1985) find that occupational restrictions in optometry improve the quality of eye examinations, Martin (1982) discovers a positive correlation between reciprocal licensing and quality, and Holen (1978) finds that entry requirements for dentists are associated with a lower rate of dental-health neglect. The authors reviewed 13 studies that observe a neutral or negative relationship between licensing and quality, including Carroll and Gaston’s (1981) study of electricians.

  5. Stigler (1970) provides several examples of regressive public policy, including the provision of higher education, fire and police services, farm policy, minimum wage rules, Social Security, public housing and tax exemptions for charitable contributions.

  6. The database can be accessed at https://www.wider.unu.edu/project/wiid-world-income-inequality-database.

  7. The reports and datasets are available from the Fraser Institute at http://www.freetheworld.com/reports.html.

  8. The KOF Index of Globalization measures globalization along three dimensions (economic, social, and political). The data and a description of the methodology used in its construction are available at http://globalization.kof.ethz.ch/.

  9. The WDI income share data are available at http://data.worldbank.org/indicator/SI.DST.10TH.10.

  10. Despite the care taken to produce a comparable income share series, the data are not perfectly compatible across countries, as nations use different survey designs and measure different welfare concepts (i.e., income versus expenditures). As there is no systemic relationship between income and expenditure shares, we follow Lakner and Milanovic (2013) and make no adjustment for this difference.

  11. The Gini coefficient is equivalent to twice the area between the Lorenz curve and the line of perfect equality.

  12. The ATG dataset is available at http://go.worldbank.org/9VCQW66LA0.

  13. The Doing Business database is available at http://www.doingbusiness.org/data.

  14. The Barro-Lee dataset is available at: http://www.barrolee.com.

  15. The data are from the World Development Indicators database at http://data.worldbank.org/data-catalog/world-development-indicators.

  16. The Penn World 9.0 dataset is available at: http://www.ggdc.net/pwt.

  17. The observations on ethnic fractionalization are reported for the early 1900 s only, so we employ those measures under the assumption that ethnic fractionalization is slow to change.

  18. Because fewer than three observations are available on so many nations in our unbalanced panel, we cannot estimate model Eq. (1) with country-level (cross-sectional) fixed effects (\(\alpha_{i} )\), as doing so would effectively dummy-out most of the variation in the dependent variable.

  19. To ensure that this result is not idiosyncratic to Freedom House’s measure of democracy, we also re-estimate columns 7 and 8 from Table 3 using an alternative measure of democracy, the Polity IV (2016) panel, which reflects six institutional/governmental qualities, such as political competition and executive authority, for up to 167 countries between 1800 and 2016. The index ranges in value from − 10 (monarchy) to + 10 (consolidated democracy). The estimation results are virtually identical across all of the models’ covariates, with positive and statistically significant coefficients on the polity measure of democracy. Like the Freedom House measure, these results suggest that more authoritarian nations exhibit less income inequality, all else equal. The Polity-based estimates are not reported, but are available from the authors upon request.

  20. The average coefficient on Log GDP is 32.3841 and the average coefficient on Log GDP squared is − 1.8246. The turning point of the parabola thus is − 32.3841/(2 × (− 1.8246)) = 8.8743 or $7146 (Exp[8.8743]).

  21. Under the null hypothesis that the instruments are valid, the test for over-identifying restrictions regresses the residuals from the TSLS regression on the full set of instruments. Multiplying the goodness of fit (R2) by the sample size (N) yields a test statistic that is asymptotically χ2 distributed under the null hypothesis, where the degrees of freedom equal the instrument rank less the number of endogenous variables.

  22. Including all of the regulatory instruments as control variables would violate the rank condition, so we must exclude at least one regulatory instrument.

  23. The over-identification test statistic (with 24 degrees of freedom) equals 12.49, well below the 10% critical value of 33.20.

  24. The over-identification test statistic (with 27 degrees of freedom) equals 21.19, well below the 10% critical value of 36.70.

  25. The over-identification test statistic (with 24 degrees of freedom) equals 22.94, well below the 10% critical value of 33.20.

  26. In fact, this is supposed to be the first step undertaken by a regulatory agency when performing an economic analysis of a proposed rule. See Ellig and McLaughlin (2012) and Ellig et al. (2013).

References

  • Addai, I., Opoku-Agyeman, C., & Ghartey, H. T. (2013). An exploratory study of religion and trust in Ghana. Social Indicators Research, 110, 993–1012.

    Article  Google Scholar 

  • Algan, Y., & Cahuc, P. (2006). Job protection: The macho hypothesis. Oxford Review of Economic Policy, 22, 390–410.

    Article  Google Scholar 

  • Andersen, T. B., Dalgaard, C. J., & Selaya, P. (2016). Climate and the emergence of global income differences. Review of Economic Studies, 83, 1334–1363.

    Article  Google Scholar 

  • Barro, R. (2000). Inequality and growth in a panel of countries. Journal of Economic Growth, 5, 5–32.

    Article  Google Scholar 

  • Barro, R., & Lee, J. W. (2013). A new data set of educational attainment in the world, 1950-2010. Journal of Development Economics, 104, 184–198.

    Article  Google Scholar 

  • Calderón, C., Chong, A., & Valdés, R. O. (2004). Labor market regulations and income inequality: Evidence for a panel of countries. IDB Working Paper 514, Inter-American Development Bank, Washington, DC.

  • Campos, N. F., Saleh, A., & Kuzeyev, V. (2011). Dynamic ethnic fractionalization and economic growth. Journal of International Trade and Economic Development, 20, 129–152.

    Article  Google Scholar 

  • Carpenter, D. M. (2012). Testing the utility of licensing: Evidence from a field experiment on occupational regulation. Journal of Applied Business and Economics, 13, 27–41.

    Google Scholar 

  • Carroll, S. L., & Gaston, R. J. (1981). Occupational restrictions and the quality of service received: Some evidence. Southern Economic Journal, 47, 959–976.

    Article  Google Scholar 

  • Carter, J. (2007). An empirical note on economic freedom and income inequality. Public Choice, 130, 163–177.

    Article  Google Scholar 

  • de Rugy, V. (2014). Occupational licensing: Bad for competition, bad for low-income workers. Mercatus Center Charts, Mercatus Center at George Mason University, Arlington, VA. http://mercatus.org/sites/default/files/derugy-occupation-licensing-final-pdf.pdf.

  • de Soto, H. (1989). The other path. New York: Harper & Row Publishers.

    Google Scholar 

  • Delavallade, C. (2006). Corruption and distribution of public spending in developing countries. Journal of Economics and Finance, 30, 222–239.

    Article  Google Scholar 

  • Djankov, S., McLiesh, C., & Ramalho, R. M. (2006). Regulation and growth. Economics Letters, 92, 395–401.

    Article  Google Scholar 

  • Domenech, J. (2011). Legal origin, ideology, coalition formation, or crisis? The emergence of labor law in a civil law country, Spain 1880–1936. Labor History, 52, 71–93.

    Article  Google Scholar 

  • Dreher, A., Meon, P. G., & Schneider, F. (2014). The devil is in the shadow. Do institutions affect income and productivity or only official income and official productivity? Public Choice, 158, 121–141.

    Article  Google Scholar 

  • Ellig, J., & McLaughlin, P. A. (2012). The quality and use of regulatory analysis in 2008. Risk Analysis, 32, 855–880.

    Article  Google Scholar 

  • Ellig, J., McLaughlin, P. A., & III Morrall, J. (2013). Continuity, change, and priorities: The quality and use of regulatory analysis across US administrations. Regulation and Governance, 7, 153–173.

    Article  Google Scholar 

  • Ertan, A., Fiszbein, M., & Putterman, L. (2016). Who was colonized and when? A cross-country analysis of determinants. European Economic Review, 83, 165–184.

    Article  Google Scholar 

  • Fearon, J. D. (2003). Ethnic and cultural diversity by country. Journal of Economic Growth, 8, 195–225.

    Article  Google Scholar 

  • Feenstra, R. C., Inklaar, R., & Timmer, M. P. (2015). The next generation of the Penn World Table. American Economic Review, 105, 3150–3182.

    Article  Google Scholar 

  • Feldman, R., & Begun, J. W. (1985). The welfare cost of quality changes due to professional regulation. Journal of Industrial Economics, 34, 17–32.

    Article  Google Scholar 

  • Freedom House. (2014). Freedom in the world 2014. Washington, DC: Freedom House. https://freedomhouse.org/report/freedom-world/freedom-world-2014.

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

    Google Scholar 

  • Gittleman, M., & Kleiner, M. M. (2016). Wage effects of unionization and occupational licensing coverage in the United States. Industrial and Labor Relations Review, 69, 142–172.

    Article  Google Scholar 

  • Gross, S. J. (1986). Professional licensure and quality: The evidence. Washington, DC: Policy Analysis 79, Cato Institute.

    Google Scholar 

  • Hernando, Z. (2012). Seasonal fluctuations and economic growth. Journal of Economic Development, 37, 1–27.

    Google Scholar 

  • Holen, A. (1978). The economics of dental licensing. Public Research Institute, Center for Naval Analyses, Alexandria, VA.

  • Hsiang, S. M., & Meng, K. C. (2015). Tropical economics. American Economic Review, 105, 257–261.

    Article  Google Scholar 

  • Kleiner, M. M., & Krueger, A. B. (2010). The prevalence and effects of occupational licensing. British Journal of Industrial Relations, 48, 676–687.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Kleiner, M. M., & Park, K. W. (2010). Battles among licensed occupations: Analyzing government regulations on labor market outcomes for dentists and hygienists. NBER Working Paper 16560.

  • Koenker, R., & Bassett, G., Jr. (1982). Robust tests for heteroscedasticity based on regression quantiles. Econometrica, 50, 43–62.

    Article  Google Scholar 

  • Kuznets, S. (1955). Economic growth and income inequality. American Economic Review, 45, 1–28.

    Google Scholar 

  • La Porta, R., Lopez-de-Silanes, F., Shleifer, A., & Vishny, R. (1998). Law and finance. Journal of Political Economy, 106, 1113–1155.

    Article  Google Scholar 

  • Lakner, C., & Milanovic, B. (2013). Global income distribution: From the fall of the Berlin Wall to the Great Recession. World Bank Policy Research Working Paper 6719.

  • Leon, A. K., & Pfeifer, C. (2013). An empirical note on religiosity and social trust using german survey data. Economics Bulletin, 33, 753–763.

    Google Scholar 

  • Mahler, V. A., & McKeever, S. A. (2009). Income inequality in comparative perspective: A cross-national analysis. Prepared for the 21st International Political Science Association World Congress of Political Science, Santiago, July 12–16.

  • Martin, S. C. (1982). An examination of the economic side effects of the state licensing of pharmacists. Knoxville: University of Tennessee.

    Google Scholar 

  • McLaughlin, P. A. (2013). What information could help in analyzing regulation? Is there a problem to be fixed? Testimony before the Commonwealth of Pennsylvania House State Government Committee, Harrisburg, March 19. http://mercatus.org/sites/default/files/McLaughlin-testimony-31913.pdf.

  • 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, 96–127.

    Google Scholar 

  • Nicoletti, G., & Scarpetta, S. (2003). Regulation, productivity and growth: OECD evidence. The World Bank Policy Research Working Paper 2944.

  • Olson, M. (1965). The logic of collective action: Public goods and the theory of groups. Cambridge: Harvard University Press.

    Google Scholar 

  • Peltzman, S. (1976). Toward a more general theory of regulation. Journal of Law and Economics, 19, 211–240.

    Article  Google Scholar 

  • Polity IV. (2016). Polity IV annual time-series, 1800–2016. Center for Systemic Peace. http://www.systemicpeace.org/inscr/p4manualv2016.pdf.

  • Ram, R. (1997). Tropics and economic development: An empirical investigation. World Development, 25, 1443–1452.

    Article  Google Scholar 

  • Schneider, F. (2005). Shadow economies of 145 countries all over the world: Estimation results from the period 1999–2003. Discussion Paper, Department of Economics, University of Linz.

  • Shughart, W. F., II, Tollison, R. D., & Yan, Z. (2003). Rent seeking in the income distribution. Kyklos, 56, 441–456.

    Article  Google Scholar 

  • Stigler, G. J. (1970). Director’s law of public income redistribution. The Journal of Law and Economics, 13, 1–10.

    Article  Google Scholar 

  • Stigler, G. J. (1971). The theory of economic regulation. Bell Journal of Economics and Management Science, 2, 3–21.

    Article  Google Scholar 

  • Subrick, J. R. (2007). The legal origins of income inequality. Dissertations, Department of Economics, George Mason University, Fairfax, VA.

  • Theil, H., & Galvez, J. (1995). On latitude and affluence: The equatorial Grand Canyon. Empirical Economics, 20, 163–166.

    Article  Google Scholar 

  • Tollison, R. D., & Wagner, R. E. (1991). Romance, realism, and economic reform. Kyklos, 44, 57–70.

    Article  Google Scholar 

  • Tullock, G. (1975). The transitional gains trap. Bell Journal of Economics, 6, 671–678.

    Article  Google Scholar 

Download references

Acknowledgements

The authors thank Diana Thomas, Stefanie Haeffele-Balch, Ted Bolema, Bill Shughart, Richard Williams, and three anonymous reviewers for helpful comments on early drafts. All mistakes are the authors’ responsibility.

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  1. Department of Economics and Finance, Salisbury University, 1101 Camden Ave., Salisbury, MD, 21801, USA

    Dustin Chambers

  2. Mercatus Center at George Mason University, 3434 Washington Blvd., 4th Floor, Arlington, VA, 22201, USA

    Patrick A. McLaughlin & Laura Stanley

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Correspondence to Dustin Chambers.

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Chambers, D., McLaughlin, P.A. & Stanley, L. Barriers to prosperity: the harmful impact of entry regulations on income inequality. Public Choice 180, 165–190 (2019). https://doi.org/10.1007/s11127-018-0498-4

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