GINI Coefficient

Patterns of Income Inequality in Major U.S. Metropolitan Areas and Population Change, 2020-2022

Article-Grid, Economic Geography / By

The four U.S. states with the highest levels of income inequality are, in order, New York, Connecticut, Louisiana, and Mississippi. When mapped at the county level, however, New York and Connecticut appear to have lower levels of inequality than Louisiana and Mississippi. The seeming discrepancy is easily explained by population density. In New York and Connecticut, high GINI coefficients are found in densely populated counties that are part of the greater New York metropolitan area; counties with smaller cities, in contrast, tend to have average levels of income inequality, whereas most rural counties in these states have relatively low levels. (Unfortunately, the scale of resolution on the maps that I have used does not adequately reveal this phenomenon; most of New York City, for example, is obscured by the heavy black line that is used for state boundaries.) In Louisiana and especially Mississippi, in contrast, many rural and semi-rural counties are characterized by pronounced income inequality.

But how do the high levels of income inequality in the New York area compare to those found in and around other major U.C. cities? To address this question, I extracted details from the county-level GINI map of the United States to show the situation in the vicinity of 16 major metro areas found across the country. As can be seen, in each case the central county or counties, those with the highest population densities, have higher levels of income inequality than the more suburban and peripheral counties.

Such comparisons are made difficult, however, by the incommensurable nature of the units. In some cases, inner counties are extremely small; San Francisco County, for example, is coterminous with the city of San Francisco, whereas New York City is itself divided into multiple counties. In contrast, Phoenix tends to vanish in the vast expanse of Maricopa County.

But even with these limitations in mind, there are still some intriguing lessons to be drawn from these maps. At the high end of the inequality spectrum is Miami, followed by New York and San Francisco, where almost all counties in the greater metro areas have average to high GINI coeffiecient. Seattle, Denver, Minneapolis, and Washington DC/Baltimore, in contrast, are surrounded by suburban and peripheral counties with relatively low levels of income inequality. I was surprised to see this pattern in the Washington D.C. area, which is by some measures the wealthiest part of the country. As can be seen on the small map, Baltimore and the District of Columbia are, not surprisingly, characterized by high inequality, as is, more surprisingly, rural Talbot County in eastern Maryland. Affluent Montgomery County, in contrast, falls in the middle category.

Many of the country’s major metropolitan areas saw population decreases between 2020 and 2022. Such declines tended to be steepest in areas of pronounced inequality. The New York metro area, for example, lost 2.6 percent of its population and the San Francisco metro area 3.6 percent, the steepest drop in the country. The less unequal Seattle, Denver, Minneapolis, and Phoenix metro areas, in contrast, all gained population. But exceptions are certainly found. The Washington, D.C. area, with its relatively income-equal suburban counties, lost population, although just barely (0.21 percent), while the highly unequal Miami metro area gained population, although again just barely (0.02 percent).

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

Article-Grid, Economic Geography, North America / By

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.

Explaining Seeming Discrepancies on County-Level Income Maps of the United States

Article-Grid, Economic Geography, North America / By

When working on a recent GeoCurrents post that involved maps of income in the United States, I noticed a few unusual patterns. A number of counties, for example, are mapped as having relatively high per capita personal income and relatively low median household income, whereas in others the opposite pattern obtains. In part this is a matter of household size, an explanation that works particularly well for Utah. Consider, for example Utah County, Utah which is characterized by relatively low per capita personal income, relatively high median household income, and a large number of people per household. In contrast, Grand County is characterized by relatively high per capita personal income, relatively low median household income, and a small number of people per household.

In Utah, the number of people per household correlates closely with religion. Members of the LDS church (Mormons) often have high fertility rates, leading to large households. Utah County, Utah, home of Brigham Young University, is usually considered the cultural center of the LDS faith. As can be seen on the second map below, Utah County has one of the highest fertility rates in the country. In contrast, Grand County has a relatively low fertility level (which is not shown in the map due to its small population) and the lowest LDS percentage in the state. Whereas Utah as a whole is roughly 62% Mormon, in Grand County the figure is only 26%.

These easy correlations, however, collapse when one examines North Dakota. As can be seen on the map below, Cavalier County has the highest per capita personal income in the state, which is why it is outlined with a heavy white line on the map posted here. But Cavalier County is also characterized by relatively low median household income and relatively few people per household. This seeming anomaly can be explained by taking into account the different way that the two income measurements are determined. Median household income is calculated by taking the income of all households in a county and finding the middle point; per capita personal income, on the other hand, is calculated by dividing the total income of all persons in the county by the population. If a county has a small population with a few very high-income individuals, the per capita personal income figure is inflated, whereas the median household income figure will remain low if most households have lower incomes.

If this explanation is correct, one would expect Cavalier County to have a relatively high Gini Coefficient, which measures the degree of inequality. The most recent GINI map of all U.S. counties that I was able to find (posted below) indicates that this is indeed the case. Overall, North Dakota is characterized by very wide range in GINI figures, which is probably largely an attribute of the small populations of most of its counties.

 Regardless of its income level, Cavalier has not exactly been a thriving county over the past century. It had more than 15,000 people in 1920 and fewer than 4,000 in 2020.