Conrad Palor
The February 2023 Public forum resolution asks debaters to consider the harms and benefits of right-to-work laws. Right-to-work (RTW) laws are laws that allow employees to choose whether to join a labor union or pay union dues. As of the date of this writing, 27 states have right-to-work laws with the topic garnering much attention in academic circles with several studies concluding both the pro side and the con side. Some studies have found that right-to-work laws lead to economic growth and job creation, while others have found that they lead to lower wages and weaker unions. For debaters to have success on this topic, they must be able to engage in rigorous evidence comparison and understand the nuances of the studies they’re reading in the debate. One of the most important aspects of evidence comparison and debating the nuances of studies is having a firm understanding of statistics and statistical concepts.
The first statistical concept to discuss is that of correlation versus causation. Correlation refers to a relationship between two variables, either positively or negatively. Causation indicates that one variable is the result (cause of) of the other variable. For two variables to have a causal relationship they must be correlated, however, just because two variables are correlated does not mean one caused the other. For example, Con teams will likely claim on this topic states that enact right-to-work laws experience higher economic growth than states that do not enact these laws. To establish causation, Con teams must prove that the positive economic growth is a result of right-to-work laws and not because of some other factor or variable. Conversely, Pro teams it should counter that states that are more likely to pass right-to-work laws are also more likely to pass other pro-business policies. Debaters should then argue that these other policies are what caused the economic growth in the states that passed RTW laws and not the RTW laws themselves.
The second statistical concept relevant to the RTW laws debate is that of endogenous variables. An endogenous variable is defined by the Corporate Finance Institute as “a variable that depends on other variables in a statistical and/or economic model. If the value changes for an endogenous variable, it is because there are changes to its relationships with other variables in the same model.” For example, in a study on the impact of right-to-work laws on wages, the wages of workers in right-to-work states may be influenced by factors such as the overall health of the economy, the level of unionization in the state, or demographic variables such as women and minority participation in the state’s labor market. To measure the direct impact of the passage of right-to-work laws on wages, it is necessary to control for these endogenous variables.
The last important statistical concept is that of statistical significance. Statistical significance refers to the likelihood that a relationship between two variables is not due to chance. Statistical significance is a mathematical calculation that statisticians and economists use to determine that the relationship between two variables is not due to random chance. On a study the impact of right-to-work laws and wages, the authors would need to show that the relationship is statistically significant in order to draw conclusions.
Overall, evidence comparison is an important skill to develop in debate akin to impact weighing. This is especially true on the February 2023 topic as there are multiple studies that conclude seemingly opposite things. For debaters to find success, they must be able to navigate and justify why the studies they cite are preferable than those of their opponent’s. Statistical concepts are a great way for debaters to engage in those comparisons.