Occupational wage inequality in the United States

Andrey Kamenov

Andrey Kamenov, Ph.D. Probability and Statistics

There are a lot of ways to measure wage inequality (or any measure of income in general). Arguably, the most well-known and widely used is the Gini index.

It is most commonly used to compare income inequality in different countries, as well as in the same country over time. For example, in the U.S. it has been steadily rising for the last half century, increasing from 38.6 percent in 1968 to 47.7 percent in 2012, according to the U.S. Census Bureau. That means a pretty significant increase in income inequality – we’ll focus on the meaning of these numbers later.

Of course, the income data shows some recognizable patterns. Some states exhibit higher Gini index values than the others, and there are also differences between different occupations. We’ll now focus on the latter.

The Gini index is calculated from the area below the Lorenz curve, which shows the proportion of total income shared by the people with the lowest wages. Unfortunately, we don’t have the complete wage dataset, so we have to get creative.

First, we interpolate the annual wage quantiles provided by the Occupational Employment data (using monotone cubic splines), then extrapolate beyond the 10 percent and 90 percent quantiles using techniques outlined in [1].

This allows us to plot Lorenz curves. Here are some examples for large occupation categories (as well as for the entire workforce):

As you can see, the Lorenz curves are quite similar for the entire workforce and Sales (and related) occupations. Other categories show significantly less pronounced wage inequality.

Finally, here’s the chart showing Gini index values for all major categories.

Gini index values for major occupation categories

Unsurprisingly, almost all occupations exhibit smaller Gini index values than the one calculated using the data for all employed Americans.

References:

[1] Estimation Of Summary Measures Of Income Size Distribution From Grouped Data, Emmet Spiers

Source(s):

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About Andrey Kamenov

Andrey Kamenov

Andrey Kamenov, Ph.D. Probability and Statistics

Andrey Kamenov is a data scientist working for Advameg Inc. His background includes teaching statistics, stochastic processes and financial mathematics in Moscow State University and working for a hedge fund. His academic interests range from statistical data analysis to optimal stopping theory. Andrey also enjoys his hobbies of photography, reading and powerlifting.

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