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Understanding Workers' Financial Wellbeing in States with Right-to-Work Laws, Accessible Data
Figure 1. Union Membership Rate in the United States
In Figure 1, we use data from the Bureau of Labor Statistics and plot the overall union membership rate–the share of wage and salary workers who were members of unions–in the United States (US) from the year 1983 until 2022. There are three series on the graph. The first series plots overall union membership rate for only non-RTW states. The second series plots the overall union membership rate for entire US (the US average union membership rate). The third series plots the overall union membership rate for only RTW states. The figure shows that in the US, the average union membership rate has steadily dropped from 20 percent in 1983 to 10 percent in 2022. The graph also shows differences in the union membership rate between states that have implemented Right-to-Work (RTW) laws and states that have not. In states with RTW laws, the average membership rate for the annual periods from 2000 to 2022 is below 7 percent and in states that have not implemented RTW laws, the average union membership rate over the same period stands at 14 percent.
Note: Staff analysis using BLS data on union membership in all industries.
Figure 2. Trends in Labor Market Outcomes Before and After the Passage of RTW Laws
Figure 2 is composed of 4 individual line graphs. For each individual graph, we plot the estimated average treatment effects obtained from an event (or dynamic) analysis using linear regression specifications that control for year- and state- fixed effects along with state-level as well as individual-level demographic controls. Each graph includes 3 lines: one for the coefficient, one for the upper confidence interval, and one for the lower confidence interval. Each graph also follows how the coefficient and the confidence interval vary in the periods spanning from 6 years prior to the implementation of an RTW law to 6 years post-implementation. The top-left panel is labeled “Employment” and in this graph we look at the average treatment effects on the likelihood of being employed for prime-age (25-64) individuals who are in the labor force. The line tracking the coefficient generally appears to be rising in the years preceding and following the policy year. The average effect is .0090 and is statistically significant at the 1% level. The top-right panel is labeled “Job Opening Rate” and in this graph we repeat the dynamic analysis for job opening rate across states. The line tracking the coefficient generally appears to be rising in the years preceding and following the policy year. The average effect is .2328 and is statistically significant at the 5% level. The bottom-left panel is labeled “Inflation-Adjusted Wages” and here we look at inflation-adjusted annual earnings from wages and salaries. The line tracking the coefficient generally appears to be falling in the years preceding and following the policy year. The average effect is -1,893 and is statistically significant at the 5% level. Finally, the bottom-right panel is labeled “Union Membership Rate” and here we consider the dynamic effects of the RTW laws on union membership rate. The line tracking the coefficient generally appears to be falling in the years preceding and following the policy year. The average effect is -2.0823 and is statistically significant at the 1% level. All data on the four labor market outcomes considered in our analysis are obtained from the Bureau of Labor Statistics.
Notes: In the above figure, the employment-related outcome is a binary indicator that equals 1 if an individual in the labor force is employed, and 0 if unemployed. The analysis on employment and inflation adjusted wages is for prime-aged adults (between 25-64) only. For labor market earnings, we consider a continuous measure of annual wage deflated to 2010 dollars using the consumer price index (CPI-U-RS). The measures of job opening rate and union membership rate are expressed in percent terms. We drop the year of passage from our analysis as the reference category. In all regressions, we control for various individual-level characteristics such as age, race, ethnicity, sex, marital status, and educational attainment and for the political affiliation of the state’s governor as a proxy for state-level characteristics. We also control for state- and year-fixed effects.