Right-to-Work laws and income inequality

Aparna Mathur
March 23, 2016 2:51 pm | AEI

In a new paper, my co-authors and I study whether Right-to-Work (RTW) laws are possible contributors to increasing income inequality in the US. RTW statutes remove union membership as a prerequisite for employment by making it illegal for labor unions and employers to enter into contracts that require employees to be fee-paying members of a union.

In general, there is a widespread belief that RTW laws have contributed to widening income inequality in the US. However, remarkably few papers have studied the direct link between RTW laws and income inequality, and none have done so using the Synthetic Control Method (SCM) approach, which offers a distinct advantage over traditional models.

The existing literature (detailed in the paper) presents some evidence of economically significant impacts of unionization on wages. These studies take the negative association between unionization and lower wages as evidence that RTW laws have constrained organized labor and worsened income inequality. At the same time, employment growth has been shown to be higher in RTW states relative to non-RTW states over the period 2001-2011 which, in principle, is an inequality mitigating factor.

Studies of the net impact of RTW laws on inequality, meanwhile, are surprisingly few. Our paper is an attempt to address the following question: does adopting a RTW law result in greater income inequality in a state?

We find no significant impact of RTW on a comprehensive set of measures of income inequality.

Our data covers nearly a 50-year period (1964-2013). This is important since, by most measures, inequality in the United States started to rise in the 1980s. Seventeen of the early adopter states instated their RTW laws in the 1940s and the 1950s and these states offered little pre-intervention information. Meanwhile, Indiana, Michigan and Wisconsin passed their laws in 2011 or later and offered little post-intervention information. The four states that we examine – Idaho, Louisiana, Oklahoma and Texas – are the only states that enacted RTW laws over a period of five decades between the 1960s and the 2000s, thus offering a reasonable number of both pre- and post-intervention periods for us to study.

We conduct a comparative case study of each of the four exposed states using the Synthetic Control Method (SCM), which is increasingly being used to evaluate the impacts of state-level policies. (See end note on methodology.) We find no significant impact of RTW on a comprehensive set of measures of income inequality.

As more states adopt or consider Right-to-Work laws, there is an ongoing debate on whether these laws are contributing to rising income inequality in the US. While our findings are specific to these four states, they do have somewhat broader implications. It is important to reiterate that these four states, where we do not find any impact of RTW on inequality, are the only states that converted to RTW between 1964 and 2010. Most of the RTW states implemented RTW laws in the 1940s or the 1950s. However, inequality in the US started to exacerbate in the mid-1980s. If RTW statutes had an impact on inequality, it would have to be that RTW started to have a causal effect on inequality in the states that enacted the law in the 1940s and the 1950s with a lag of more than 30 years.

Therefore, while worsening inequality in the US merits extensive exploration, RTW laws do not seem to be the answer. The suppression of income growth in the middle and the lower part of the income distribution is well documented and can originate from many different sources in an economy like the US’s. Perhaps more attention needs to be paid to disparities in aspects of the labor market beyond collective bargaining.

A note on methodology: Why is SCM our preferred approach?

In program evaluation, researchers often select comparisons on the basis of subjective measures of similarity between the affected and the unaffected regions or states. SCM provides a synthetic state that is a combination of the control states. A data-driven procedure calculates “optimal” weights to be assigned to each state in the control group based on pre-intervention characteristics, thus making explicit the relative contribution of each control unit to the counterfactual of interest (Abadie et al., 2010). Secondly, when aggregate data are employed (as the case is in this paper), uncertainty remains around the ability of the control group to reproduce the counterfactual outcome that the affected unit would have exhibited in the absence of the intervention. This type of uncertainty is not reflected by standard errors constructed with traditional inferential techniques for comparative case studies.

In terms of the timing of adoption of the laws, while almost half the states in the US currently have RTW laws, within the 50 year period between the 1960s and the 2000s, only 4 states (the ones we study) “switched” to RTW status. As a result, even though one can have a 50-year long panel for all US states, the fact that only 4 states switched to RTW underscores the choice of SCM as the preferred method for assessing the impacts of the RTW laws.

With so few treatment units, accurate inference is difficult. SCM, on the other hand, is devised to address precisely these kinds of situations. If instead, a state-level difference-in-difference (DID) regression analysis were chosen, it would almost tantamount to a cross-section analysis since very few units would have treatment variation over time.

In a program evaluation context, one of the more serious issues is finding appropriate comparison or control states that can provide a reliable counterfactual for the treatment (or RTW) states. Not every non-RTW state would be a suitable candidate for a comparison unit for a treatment state. For instance, RTW states are often lower income states (Reed 2003). It is also unlikely that we can find a single non-RTW state that would have characteristics such as the size of labor force, industry makeup, taxation policies, and numerous other state-specific factors similar to those of a treatment state.

One of the important contributions of our paper is that by estimating RTW’s impacts in each state individually, we accommodate for possible treatment heterogeneities. Keele et al. (2013) argue that treatment heterogeneity in state policies needs to be taken seriously. The assumption of a uniform effect across states that essentially differ in history, population, and a host of observed and unobservable characteristics can be restrictive. For example, as RTW laws were enacted at different times, the affected cohorts varied across states: the law was adopted in Louisiana almost two decades before the passage of the North American Free Trade Agreement (NAFTA); Texas passed the law at about the same time as NAFTA was enacted; and Oklahoma introduced a RTW law a little less than a decade after NAFTA. Given the different timings for the implementation of the RTW law across states, the pre-intervention period differs and this model accommodates the variance.

Reflecting on another source of heterogeneity across states, Canak and Miller (1990) show that the composition of business support for RTW laws varied across states and over time. The variation in business support is important from the perspective of how businesses react to RTW in terms of bringing in investment and generating employment.

This article was found online at:
http://www.aei.org/publication/right-to-work-laws-and-income-inequality/