Richard Florida is a co-founder and editor at large of CityLab and a senior editor at The Atlantic. He is a University Professor and Director of Cities at the University of Toronto’s Martin Prosperity Institute, and a Distinguished Fellow at New York University’s Schack Institute of Real Estate.
Why a small city like McAllen, Texas, has less total debt than Silicon Valley, but is still in worse shape.
There are few things more American than debt. Nationwide in the U.S., the average adult with a credit file now carries more than $50,000 worth of total debt, and more than one-third of American adults—77 million in total—had debt in collections last year, according to two new reports released Tuesday by the Urban Institute.
The reports focus on the geographic concentrations of debt across U.S. states and metro areas. The research, conducted in cooperation with Encore Capital Group's Consumer Credit Research Institute (CCRI), covered a random sample of 7 million adults based on 2013 credit information from TransUnion. (The roughly nine percent of adults with no credit file, generally low-income consumers, were not included). They collected data on total debt, mortgage date, debt past due, and debt in collections, among other measures, for the 50 states and 100 largest metro areas.
Debt is distributed quite unevenly across the United States, as the map above from the Urban Institute shows. In 2013, average total debt ranged from a high of nearly $100,000 in San Jose, California, the heart of Silicon Valley ($97,150), to less than $25,000 in McAllen, Texas. The report notes that "the top 20 percent of tracts account for 42 percent of all debt holdings in America. Meanwhile, the bottom 20 percent of tracts account for just 6 percent of U.S. debt."
My Martin Prosperity Institute colleague Charlotta Mellander ran a simple correlations analysis of the economic and demographic factors that might be associated with higher or lower levels of debt in a given metro area. (As usual, I caution that correlation does not equal causation and points only to associations between variables).
Overall, total debt is driven by mortgage debt, with a correlation of .96 between the two. Mortgage debt is also more geographically concentrated than total debt. As the report notes: “The top 20 percent of census tracts account for 48 percent of U.S. mortgage debt, while the bottom 20 percent represent just 3 percent.”
Average total debt is highest in knowledge-based metros on the West and East Coasts, where incomes and housing costs are higher. As the report notes, those living in these regions often have higher debt because their high incomes and/or assets give them access to greater lines of credit. The report finds a close correlation (of roughly .75) between total debt and income.
Our MPI analysis backs this up. Mellander found that debt is higher in denser and more knowledge-based metros. Total debt is closely associated with population density (.56), the share of adults with college educations (.77), and the share of workers in the creative class (.69). All of these factors are also closely linked to higher incomes. Conversely, total debt is negatively associated with the share of workers doing blue-collar working class jobs (-.53) and even more so with smaller, more sprawling metros where a larger share of commuters drive to work alone (-.60).
Debt to Income
The ratio of debt to income provides another, and perhaps better, way to look at the geographic distribution of debt, because it shows not just the total amount of debt but the share of income that Americans are spending on their debt.
Debt-to-income ratios also vary substantially across metros, from a high of 1.01 in Boise, Idaho, to a low of .47 in McAllen, Texas. Again, debt to income is highest in the costly housing metros of the West Coast, but it is also high in Denver (.92) and Colorado Springs (.99), Provo and Ogden, Utah (.94 and .89), Minneapolis (.90), Albuquerque (.89) and Virginia Beach (.88).
The next two maps show the ratio of debt to income for mortgage debt and non-mortgage debt, which includes debt on items like credit card purchases, vehicle loans and unpaid medical bills.
Mellander's analysis picks up an interesting pattern in the relationship between income and mortgage versus non-mortgage debt. The ratio of mortgage debt to income goes up as income goes up (with a correlation of .51 between the two). But the ratio of non-mortgage debt to income goes down as incomes rise (with a correlation of -.77). The same basic pattern holds for density, which is positively correlated with mortgage debt to income ratios (.40) and negatively with non-mortgage debt to income ratios (-.62). In other words, denser, higher income metros have more mortgage debt, while non-mortgage debt is higher in less affluent, more sprawling metros.
Good Debt vs. Bad Debt
There is “good debt” and then there is “bad debt.”
Taking on debt to pay for more education or even to buy a home is different than running up a credit card for clothing purchases, travel or big phone or Internet bills. Debt accrued to fuel consumption or pay for emergencies can burden Americans "far into the future," according to the second Urban Institute report, and lead to "financial stress, associated health risks, and insolvency if [they] cannot be repaid."
While one in three Americans have debt in collections, that figure also varies substantially by metro, as the map below shows.
Note the "debt collection belt" running across the Southern half of the United States (it's marked in the darker blue), which indicates tracts where more than 61 percent of consumers had debt in collection in 2013. More than 40 percent of the population had debt in collections in 26 of the 100 largest metros, including McAllen, Texas (with a whopping 51.7 percent), Las Vegas (49.2 percent), Lakeland, Florida (47.3 percent), Columbia, South Carolina (45.2 percent), and Jacksonville, Florida (45 percent). Conversely, just six of the country's 100 largest metro areas have less than a quarter of their population with debt in collections. Those cities include Minneapolis-St. Paul (20.1 percent), Honolulu (21 percent), Boston (22.4 percent), Madison, Wisconsin (22.6 percent), San Jose (23 percent) and Bridgeport, Connecticut (25 percent).
While overall debt levels are higher in larger, denser, more knowledge-based metros, according to our MPI analysis of the Urban Institute data, the reverse is true for bad debt. Mellander's analysis finds that debt in collections is lower in larger, denser and more economically advanced metros. She finds negative correlations between the share of population with debt in collections and density (-.42), the share of adults that are college grads (-.60) and the share of workers in the creative class (-.54). Conversely, debt in collection is positively associated with metros with a large share of workers in the working class (.30) and in smaller but more sprawling metros where a larger share of workers drive to work alone (.36). Furthermore, total debt is negatively correlated with the share of adults with debt in collection (.-.60).
The bottom line: Total debt is trending higher in denser, larger, more knowledge-based metros where incomes are greater and housing costs are among the priciest in the nation. But it's smaller, less affluent, more blue-collar metros that face the highest levels of financial distress.