Probably the most rehearsed explanation of the gender pay gap is discrimination. After accounting for traditional labor market factors, a large residual gap remains. This residual gap is also called the unexplained gap. Researchers often commit the fallacy of equating unexplained effect to discrimination effect instead of omitted variable bias. In fact, most wage decomposition models are probably contaminated by bias. This article will explain that much of the residual gap is likely due to other causes. In particular, time flexibility. The evidence for the discrimination effect is often ambiguous.
Category: Income
Back in 2014 I wrote an extensive review of studies on the income mobility rate over time and across countries and discussed whether it truly fits the Great Gatsby Curve, a term based on the observation of the negative relationship between mobility and inequality, that is considered by many as unfair because it implies that higher inequality causes lower mobility. However I did not consider Black-White difference in mobility. Because mobility and inequality are interrelated, I will cover both topics here. Three findings are worth noting: 1) IQ explains a very large share of the BW mobility gap, 2), the Civil Right did not improve the Blacks’ outcomes 3) the Black Migration aggravated the Blacks’ social outcomes.
Regression to the mean, RTM for short, is a statistical phenomenon which occurs when a variable that is in some sense unreliable or unstable is measured on two different occasions. Another way to put it is that RTM is to be expected whenever there is a less than perfect correlation between two measurements of the same thing. The most conspicuous consequence of RTM is that individuals who are far from the mean value of the distribution on first measurement tend to be noticeably closer to the mean on second measurement. As most variables aren’t perfectly stable over time, RTM is a more or less universal phenomenon.
In this post, I will attempt to explain why regression to the mean happens. I will also try to clarify certain common misconceptions about it, such as why RTM does not make people more average over time. Much of the post is devoted to demonstrating how RTM complicates group comparisons, and what can be done about it. My approach is didactic and I will repeat myself a lot, but I think that’s warranted given how often people are misled by this phenomenon.
Continue reading
In his recent book Hive Mind economist Garett Jones argues that the direct effect of IQ on personal income is modest, and that most of the benefits of higher IQ flow from various spillover effects that make societies more productive, boosting everyone’s income. This, he says, explains the “IQ paradox” whereby IQ differences appear to explain a lot more of the economic differences between nations than within them.
Jones does not say in his book what he thinks the exact effect of IQ on personal income is, but on Twitter he has asserted that “Fans of g would do well to look at the labor lit: 1 IQ point predicts just 0.5% to 1.2% higher wages.” He has also said that, in terms of standardized effect sizes, IQ accounts for only about 10% of variance in personal income (a correlation of ~0.32).
While I don’t doubt Jones’s overall thesis that the effect of IQ on productivity is broader than its effect on personal productivity or income, I think he understates the importance of IQ in explaining income differences between individuals. I analyzed a large American population sample and found a substantially larger effect of IQ on permanent income than previous investigations. It appears that the literature Jones refers to has failed to pay sufficient attention to various measurement issues. Continue reading
