Why the difference between correlation and causation matters

A blog post by a member of the Economic Policy Institute (a US think-tank) has claimed that the decline in Trade Union membership is the cause of the increase in (a single measure) of inequality in the USA.

The blog post looks at how membership of Trade Unions and the share of income that goes to the top 10% change during the period 1917-2012, notices that they appear to be negatively correlated, and therefore concludes that decline in trade union membership is responsible for the increase in inequality.

First, it is important to note that inequality has actually decreased over time, rather than increased as the article claims (see, for example, here and here). Moreover, the blog’s use of a very specific measure of inequality, focusing solely on the income share of the top 10%. It does not take into account any other factor that determines the level of inequality within a country – for example, whether the majority of income in the top 10% is distributed evenly across that 10%, or is concentrated in the top 1% or even the top 0.1%.

Indeed, a more comprehensive measure of inequality (such as the Gini coefficient) takes into account the distribution of income across the entire spectrum rather than merely focusing on a subset of that distribution. When looking at such measures over time, it becomes apparent that inequality across the entire distribution of incomes has barely increased since the 1960s, despite the measure used in the blog post having increased since that time.

(And that is not withstanding other ways in which inequality might arise, such as via the distribution of wealth, access to healthcare, and/or access to education.)

Second, the author’s evidence to support their argument that a decline in trade union membership is responsible for the increase in inequality  consists solely of the fact that the measure of US inequality they choose is negatively correlated with US Trade Union membership.

There are plenty of examples of two series being correlated over time, despite there not being any way in which a causal relationship can exist between them. For example, Tyler Vigen presents such examples as Arcade revenues being correlated with number of Computer Science PhDs being awarded, and there being a strong correlation between Maine’s divorce rate and per capita consumption of margarine.

Moreover, the article’s “analysis” fails to account for the countless other factors that could have affected inequality over the course of the almost 100 years covered. For example, demographic changes, changes in the industries, technological developments, new infrastructure, changing societal attitudes individually and together are likely to have contributed to the changes in inequality. Indeed, the correlation between the US Trade Union membership and the chosen measure of inequality appears to be the large increase in TU membership and the large decrease in inequality between 1936 and 1945. And it’s not as though there were other things going on during that period of time at all!  Despite this, the article attributes the changes in inequality solely to changes in Trade Union membership.

Finally, the article does not even try to come up with a mechanism by which Trade Union membership can affect inequality beyond a vague description of how trade unions increase bargaining power. There are no doubt plenty of other things that are negatively correlated with the measure of inequality used in the report and that the report’s author presumably also thinks is just as likely a reason for changes in inequality as is Trade Union membership (US military strength might well be one, as could the number of black and white television sets in use).

I look forward to the Economic Policy Institute writing about those in due course.




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