People wait in line looking for jobs during a Job Fair at the Miami Dade College in Miami, Florida in 2009. Miami-Dade county went into recession in 2007. Carlos Barria/Reuters

Urban counties in the United States were more likely to enter the Great Recession earlier when they had a larger gap between the rich and the poor.

Not only has income inequality surged in the United States and other advanced nations over the past couple of decades, it is worse in America’s largest, densest, most innovative and knowledge based cities and metro areas. High levels of inequality carve deep divides in our nation and its cities, lead to fraying social ties, rising crime and worsening health outcomes, and can also damage the prospects for innovation, job creation and economic growth in the long run.

A new study published in the Journal of Regional Studies by Paul Lewin, Philip Watson, and Anna Brown of the University of Idaho takes a close look at the connection between income inequality and the resilience of U.S. counties in the wake of the economic crisis.

The study brings together two subjects of considerable interest to urbanists—inequality and resilience. When urbanists and city leaders think about resilience, they mainly think in terms of resilience to natural disasters like hurricanes and floods or other adverse environmental impacts that climate change continues to exacerbate. But resilience has economic connotations as well, capturing the extent to which places are able to withstand and bounce back from adverse economic shocks, like deindustrialization or a significant economic downturn.

The Great Recession of 2007 was the most devastating economic crisis since the Great Depression. As the study notes, it thus provides a “natural experiment” of sorts to ferret out the different kinds of locations that can withstand and bounce back from this severe economic shock.

To get at this, the study uses a series of advanced statistical models to examine the effect of the Great Recession on the economic status of US counties. In particular, it takes a close look at the effects of inequality on the resilience of these counties while controlling for other factors such as size, density, age, race, income levels and the mix of industries.

The study covers 639 urban counties in the United States and when they went into recession between 2006 and 2010. It defined a county as entering the recession if the county experienced a decrease in per-capita personal income after it reached a business cycle pick. In 2007, 126 urban counties entered a recession, another 266 counties followed in 2008 and 180 more did in 2009. By 2010, only 50 urban counties had not fallen into a recession.

Here’s an interactive map from the mapping firm Esri that shows these urban counties during the years they entered the recession, according to the study’s data.

While the increase in inequality in the United States is well known, the chart below from the study paints the picture in stark relief, showing the dramatic rise in the Gini coefficient—the basic measure of income inequality from 1945 to 2015.

The Gini coefficient actually declines from 1945 to about 1975— largely as a result of policies to create a social safety net and bolster the middle class—and then grows in a straight line upward from 1975 to the present. No matter how well we think we understand the recent growth in inequality, the steep slope of the line on this graph indicating the dramatic rise in income inequality is staggering.

Economic inequality has widened for families since the mid-1970’s. (Lewin, Watson, and Brown/Journal of Regional Studies)

Income inequality also varies substantially across places. The next chart, also from the study, shows the level of inequality across US states. The District of Columbia tops the list followed by three other affluent knowledge based states, New York, Connecticut, and Massachusetts; Louisiana, Florida, and Alabama in the South are next, followed by California and Texas. At the other end of the spectrum, Utah, Alaska, Wyoming, and New Hampshire have much lower levels of inequality.

Variation across states for the Gini coefficient (Lewin, Watson, and Brown/Journal of Regional Studies)

But the variation of inequality is even greater across counties. While the Gini coefficient ranges from a high of 0.53 in D.C. to a low of 0.42 in Utah, it spans from a high of 0.67 to a low of 0.20 for the highest and lowest US counties. See the map from ESRI above.

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What exactly enables a county to be resilient, to withstand an economic crisis more quickly than others?

To get at this, the study develops so-called “hazard ratios,” which identify the factors that affected the ability of counties to be resilient in the face of the Great Recession. (These hazard ratios are based on a Cox model—a model which is typically used in medical research to identify the factors that contribute to patient survival rates).

Hazard ratios greater than 1 mean that a variable increases the probability that a county will enter into recession. A 1.10 ratio means a one-unit increase in that variable increases the hazard ratio by 10 percent. A hazard ratio of less than 1 means the variable lessens the probability a county will enter into recession. The table below shows the hazard ratios for income inequality based on the Gini coefficient and other factors in the study.

The Gini coefficient was one of the most effective way of predicting how likely a county was to go into recession. (Lewin, Watson, and Brown/Journal of Regional Studies)

Income inequality has a hazard ratio of 1.066, one of the highest in the study—in other words, a one-unit increase in the Gini coefficient increased a county’s risk of entering the recession earlier by 6.6 percent.

Other factors that increase the risk of recession include: having greater dependence on proprietor income (inventory and capital consumption based businesses) (1.126 or 12.6 percent), an older population (1.074 or 7.4 percent), a larger retired population (1.013 and 1.3 percent), greater dependence on dividends (1.054, 5.4 percent), more service employment (1.015, 1.5 percent), and more government employment (1.015, 1.5 percent). Private service sector employment and government employment were equally predictive of the risk of recession (both 1.015 and 1.5 percent).

Interestingly, two factors which are typically thought to be associated with local economic performance, density, and the share of racial groups in a county are basically a statistical wash. The biggest factor in mitigating the risk of recession was government transfers (welfare services like food stamps, Social Security, or government subsidies).

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Ultimately, the study shows that income inequality has a negative effect on the resilience of urban counties, reducing their ability to withstand a recession and its shocks, for several reasons.

For one, more unequal places with greater concentrations of wealth among the wealthy leave the rest of the population more vulnerable to economic shocks. More unequal places tend to have higher levels of geographic segregation which limit the ability of less advantaged neighborhoods to cope with ad bounce back from economic downturns. More unequal places also have a smaller middle class which also leaves them more exposed to economic crisis.

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