Geographical Patterns of Income Inequality in the U.S. at the State and County Levels

I have long been intrigued by the geography of income inequality in the United States. As maps of the GINI coefficient show, income inequality is highest some of the country’s richest states (New York, Connecticut) and in some of its poorest (Louisiana, Mississippi). Similarly, some of the country’s most Democratic-voting states and some of its most Republican-voting ones are characterized by pronounced income inequality. Relatively low levels of income inequality are concentrated in an area that might crudely be described as the center-north-west, with four contiguous states occupying the lowest category on the map (Utah, Idaho, Wyoming, South Dakota). Low population density characterizes states with low income inequality. All of the states in the bottom two categories on this map except Hawaii have a lower-than-average population density. Politically, these states show the same mixed pattern that characterized the most economically polarized states. Although all the states in the lowest GINI category are bright red on electoral maps, two that fall into the next lowest category (Vermont, Hawaii) are bright blue.

A county-level GINI map clarifies the geography of U.S. income inequality and reveals some interesting patterns (unfortunately, the best map that I could find on this topic is somewhat dated). As can be seen, the elevated levels of income inequality found in northeastern states is largely an urban phenomenon. In the southeast, in contrast, some counties with high GINI coefficients are metropolitan (in southeastern Florida, for example), but others are markedly rural. In Western and Great Plains states characterized by relatively low income inequality, quite a few of their rural counties have high GINI scores.

In North Dakota, South Dakota, and Montana, some rural counties characterized by high income inequality also have a high percentage of Native American residents. To illustrate this correlation, I have placed a GINI map of the Dakotas next to one of indigenous population percentage. But there are a few striking exceptions to this pattern, two of which are noted on the map. As can be seen, Divide County, North Dakota has a small Native American population and a high GINI coefficient. Pete Morris’s agricultural explanation of income inequality, outlined in his comment on yesterday’s post, is probably relevant here as well. In contrast, Buffalo County, South Dakota has a large Native American population and a low GINI coefficient. Both of these counties have very small populations. Buffalo County is noteworthy for having the least populous county seat in the United States (Gann Valley, with a population of 14).

In the south-central region of the country, most counties with high levels of income inequality have large black populations. But again, interesting exceptions can be found. As can be seen, Jefferson County, Arkansas has a high percentage of Black residents and a mid-level GINI ranking. In contrast, Marshall County, Alabama has a very low percentage of Black residents and a high level of inequality. Jefferson County, intriguingly, is known for its concentration of “correctional facilities,” mostly located in and around Pine Bluff. Marshall County, Alabama, in contrast, is part of the Huntsville-Decatur Combined Statistical Area, a region noted for its many well-paid technical workers, owing largely to that presence of NASA’s Marshall Space Flight Center, the United States Army Aviation and Missile Command, and the FBI ‘s Operational Support Headquarters. Marshall’s largest city, Albertville, is mostly noted, however, as the home of the fire-hydrant-manufacturing Mueller Company. As noted by the Wikipedia article on the city, “Albertville holds the title of “Fire Hydrant Capital of the World.” To commemorate the one millionth fire hydrant, a chrome fire hydrant was placed outside the Albertville Chamber of Commerce.”

The next GeoCurrents post will examine the geography of income  inequality in the country’s largest metropolitan areas.