Examining the State-Level Minimum Wage-Income Inequality Relationship

Examining the State-Level Minimum Wages-Income Inequality Relationship

Introduction: Throughout the social and economic policy analysis community, the effects of the minimum wage on various socio-economic indicators are extensively studied and debated. One of these significant socio-economic indicators is income inequality. One argument suggests that income inequality is reduced by increasing the minimum wage, as doing so raises the income of low-income workers, thus reducing inequality with those whose incomes fall above the income line (Thompson). On the other hand, a 2014 Congressional Budget Report suggested that the positive effects of a higher minimum wage on income inequality is balanced by higher unemployment for some low-income workers as a result of losing jobs to the higher minimum wage (i.e. higher cost to retain) (Alsalam et al., 1). This in turn could lead to an increase in inequality through the decline of former workers' wages, thus balancing out against the rise in wages of still-employed workers. Based on this debate, today's will take a close look at state-level minimum wages and evaluate whether higher minimum wages are associated with a significant change in income inequality at the state level. 

Methodology: State-level minimum wage data from 2018 (to match up with the most recent Gini coefficient data, below) was extracted from the Department of Labor's website. Any states that did not have a state-level minimum wage were given the Federal minimum wage. Minnesota, Nevada and Oklahoma were excluded because their state-level minimum wages are applied differently to firms depending on firm size ("Changes to"). 

The explanatory variable (income inequality) will be operationalized using the Gini coefficient, which ranges from 0 to 1 and measures the distribution of income by percentiles within a jurisdiction (Duffin). In other words, in an ideal world, if the bottom 20% of a population received 20% of the jurisdiction's income, the next 20% received 20%, and so on, the Gini coefficient would be 0, representing a perfect distribution. On the other hand, if one person held all of the jurisdiction's income and everybody else made no income, then the Gini coefficient would be 1, representing perfect inequality. State-level Gini coefficient data came from Statista, and was most recently available from 2018 (Duffin). 

Aside from the independent and explanatory variables, I will also look at whether additional third variables are associated with income inequality. Variables that are related to state-level legislation and their sources are listed below:

• Right-to-Work Legislation: "Right to work" laws prohibit unions from charging non-unionized employees dues that fund union initiatives, or to even force employees to join a union ("Right to work frequently"). The 2010s witnessed a major surge of right-to-work laws being passed in many states, and even nationally, which President Trump proposed in 2017 (Bruno and Manzo IV, 1). In addition to contributing to a decline in union membership among workers (explored more in another variable, below), such laws have been argued to be (indirectly, and somewhat directly) responsible for the increase in income inequality through decreasing unionization, which leads to lower wages among workers ("The Impact"). This connection is due to the fact that when unions are stronger and have more bargaining power, they are able to be more assertive on employers to pay higher wages, thus reducing inequality (Hiltzik). Right-to-work laws are argued to increase income inequality through weakening unions as a result of reduced union membership, thus giving unions less bargaining power (Hiltzik). Data on right-to-work legislation status by state (as of 2018) was taken from the National Right to Work Committee's website, and each state was coded "1" if they had right-to-work legislation in force, and "0" if not. 
• Highest Income Bracket Tax Rates: One of the most basic socio-economic rationales of the income tax is that it redistributes wealth from higher-income individuals to lower-income individuals, thus helping improve the lives of those with little income (McLure). In the United States, individuals are levied the income tax that varies on what income bracket they fall into, with the highest bracket receiving the highest tax rate (Scarboro). Data on state-level income tax bracket rates in 2018 came from The Tax Foundation (Scarboro). Note that in 2018, 7 states did not have an income tax (Alaska, Florida, Nevada, South Dakota, Texas, Washington, and Wyoming) (Scarboro), and were coded as "0.0%" for that variable.

In addition, the following socio-demographic and -economic variables that are independent of state-level legislation will be studied to understand similar effects:

• Post-Secondary Degree Attainment: In the discussion of American poverty and income inequality, a topic that has been extensively discussed is the "college premium," in which college-educated individuals have seen significant increases in income and employment opportunities in recent years than non-college educated individuals (Barscher et al., 21). This variable will be operationalized using the college degree attainment rate by state in 2018, whose data I retrieved from The Chronicle of Higher Education. The data was broken down by attainment levels; I manually calculated the percent of each state's population by combining bachelor's, professional, and doctorate degree attainment together ("Compare the States"). 
• Unionization Rate: In connection with right-to-work laws noted above, declining (private sector) unionization and union membership regardless of "right-to-work" laws has been argued to be a significant cause behind recent increases in income inequality (Hiltzik). Data on each state's union membership rate out of the total labor force came from the Bureau of Labor Statistics ("Hawaii and New York"). 

Statistical analysis will consist of summary statistics and correlation While doing multi-OLS regression would likely reveal some critical relationships between the variables being studied, I currently don't have the means to conduct such analysis at the moment, but if I do get access, I will follow-up on this post and conduct further analysis. 

Results:

Table 1: Summary Statistics

Notes: There are 48 total observations. All standard deviations were found using the Excel function "STDEV.P". Values in red under the "quartiles" column indicate values within the middle 50% of each variable's distribution.

Table 2: Correlation Matrix:

Summary statistics for each variable is included in Table 1. Per the correlational results in Table 2, neither the independent or the control variables have a strong correlation (either direction) with the explanatory, although some of the correlational directions seem interesting based on research presented above. Particularly, the main IV-DV relationship is positively-correlated, meaning that an increase in state-level minimum wages is associated with an increase in the gini coefficient (representing income inequality). Even more surprising is that states with higher top marginal tax rates have an even stronger positive relationship with their respective Gini coefficients. In addition, the relationship between each state's right-to-work laws and unionization %, with respective Gini coefficients is unusual as the former is negatively associated with the Gini coefficient while the latter is positively associated. This indicates that at the macro-level, higher unionization is associated with higher Gini coefficient values, and that "right-to-work" laws are associated with lower Gini coefficients. Finally, higher levels of post-secondary educational attainment ("PSAttain") is positively correlated with higher Gini coefficient values. 

Conclusion and Implications: While the results of this study seem to show significant results, there are several important limitations to keep in mind. First, this study is a macro-level data survey, and does not take into account a variety of micro-level factors that could influence income inequality, as well as income inequality within states. Second, I was unable to study several critical factors that have been widely-cited as contributing to recent increases in income inequality, particularly globalization, automation, political factors, and Federal-level policies (such as the 2017 Tax Cuts and Jobs Act). Finally, when talking about income inequality in this context, I only looked at the absolute level and extent of income inequality, and not the actual relative situation at a macro- or micro-level. In other words, a jurisdiction could have high income inequality per se, but where even low-income residents have a reasonable standard of living and sufficient opportunities to improve their socio-economic status above a level that is considered "reasonable." 

Despite these factors, there are two critical implications of this study's results. The first is that conducting an econometric analysis is critical to further understanding the interaction between the variables used in this study. Second, the results of this study also show that in connection to what was said above, more than just state-level policies likely have an impact on income inequality, something that I did not study for this post. Regardless, the results of this study contribute to the ever-growing body of discussion and research about income inequality, one of the most pressing policy issues of the 21st century. 

Works Cited: 

Alsama, Nabeel, William Carrington, Molly Dahl, and Justin Falk. "The Effects of a Minimum Wage Increase on Employment and Family Income." Congressional Budget Office, Feb. 2014, www.cbo.gov/sites/default/files/113th-congress-2013-2014/reports/44995-MinimumWage.pdf. 

Bartscher, Alina, Moritz Kuhn, and Moritz Schularick. "The College Wealth Divide:

Education and Inequality in America, 1956-2016." St. Louis Federal Reserve Bank, 13 Dec. 2018, www.stlouisfed.org/~/media/files/pdfs/hfs/is-college-worth it/bartscher_kuhn_schularick_college_wealth_divide.pdf?la=en. Accessed 19 Jul. 2020. 

Bruno, Robert, and Frank Manzo IV. "The Impact of “Right-to-Work” Laws on Labor Market Outcomes in Three Midwest States: Evidence from Indiana, Michigan, and Wisconsin (2010–2016)." Project for Middle Class Renewal, University of Illinois at Urbana-Champaign, 3 Apr. 2017, ler.illinois.edu/wp-content/uploads/2017/03/RTW-in-the-Midwest-2010-2016.pdf. Accessed 19 Jul. 2020. 

Hiltzik, Michael. "IMF agrees: Decline of union power has increased income inequality." Los Angeles Times, 25 Mar. 2015, www.latimes.com/business/hiltzik/la-fi-mh-imf-agrees-loss-of-union-power-20150325-column.html. Accessed 19 Jul. 2020. 

Jordan, Jeffrey, Aparna Mathur,Abdul Munasib, and Devesh Roy. "Did right-to-work laws impactincomeinequality?Evidence from U.S. states using the Synthetic Control Method." American Enterprise Institute, Mar. 2016, www.aei.org/wp-content/uploads/2016/03/rtw-laws-inequality.pdf." Accessed 19 Jul. 2020. 

McLure, Charles. "Income tax." Brittanica, last revised 20 Sept. 2017, www.britannica.com/topic/income-tax/Family-factors-and-personal-deductions. Accessed 19 Jul. 2020. 

"Right to work frequently asked questions." National Right to Work Legal Defense Foundation, www.nrtw.org/right-to-work-frequently-asked-questions/. Accessed 19 Jul. 2020. 

"The Impact of Right-to-Work laws and declining unionization on the economy." Service Employees International Union 49, 14 Nov. 2017, www.seiu49.org/wp-content/blogs.dir/60/files/2018/04/TWR_RTW-FactsSheet.pdf. Accessed 19 Jul. 2020. 

Thompson, Derek. "The Labor Department's New Report isn't So Gloomy." The Atlantic, 5 Oct. 2019, www.theatlantic.com/ideas/archive/2019/10/labor-departments-new-report-isnt-so-gloomy/599491/. Accessed 19 Jul. 2020. 

Data Sources:

“Changes in Basic Minimum Wages in non-farm employment under state law: Selected Years 

1968 to 2018.” United States Department of Labor, last revised Jan. 2020, www.dol.gov/whd/state/stateMinWageHis.htm. Accessed 19 Jul. 2020. 

"Compare the States Almanac 2018-Demographics." The Chronicle of Higher Education, 19 Aug. 2018, www.chronicle.com/interactives/almanac-2018. Accessed 19 Jul. 2020. 

Duffin, Erin. "Gini coefficient as a measure for household income distribution inequality for U.S. states in 2018." Statista, 11 Oct. 2019, www.statista.com/statistics/227249/greatest-gap-between-rich-and-poor-by-us-state/. Accessed 19 Jul. 2020. 

"Hawaii and New York had highest union membership rates, the Carolinas the lowest, in 2018." Bureau of Labor Statistics, United States Department of Labor, 22 Feb. 2019, www.bls.gov/opub/ted/2019/hawaii-and-new-york-had-highest-union-membership-rates-the-carolinas-the-lowest-in-2018.htm." Accessed 19 Jul. 2020.

"Right to work states timeline." National Right to Work Committee, nrtwc.org/facts/state-right-to-work-timeline-2016/. Accessed 19 Jul. 2020. 

Scarboro, Morgan. "State Individual Income Tax Rates and Brackets for 2018." The Tax Foundation, 5 Mar. 2018, taxfoundation.org/state-individual-income-tax-rates-brackets-2018/. Accessed 19 Jul. 2020. 

     Nathan Parmeter

     Author and Host, The Parmeter Politics and Policy Review