The big divides between rich and poor in the US are drawing increased attention, which is a good thing. Income inequality has been steadily growing in the US, and it’s a big problem.

As we’ve pointed out, this problem has an important spatial dimension as well. The concentration of poverty, in particular, amplifies all of the negative effects of poverty—and unfortunately, over the past four decades, the number of high poverty neighborhoods has been increasing. Poor people are now considerably more likely to live in neighborhoods where a large fraction of their neighbors are also poor.

But some of what’s being written about inequality at the city level is misleading, meaningless, or simply wrong.

There’s a kind of conundrum that confronts us when we talk about income inequality. Judged at a national level, a wide diversity of income levels is a bad thing. But in any particular neighborhood, having a diversity of incomes is pretty much the opposite: an indicator of economic integration. Conversely, lower levels of variation in income at the national level could be taken as a sign of a more equal society. But if there are very low levels of variation in income in a particular neighborhood, that’s pretty much a sure sign of strong economic segregation (whether that’s a neighborhood composed largely of the well-to-do or of the poor). The key point is this: while greater equality is generally a good thing at a national level, it can be a bad thing at a highly local level.

The reason of course is that at the neighborhood level, the distribution of income is shaped not by the overall distribution of income in the economy, but by the price of housing and the desirability of neighborhoods.

The confusion generated by this conundrum is very much in evidence in an article that appeared in Next City last week. Entitled “Five Charts that Detail Wealth and Inequality in U.S. Cities,” the article summarizes a new report by the Washington, DC-based Economic Innovation Group using a range of zip code level Census data to assess levels of economic distress among and within metropolitan areas.

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The featured table in this report lists the “ten most prosperous cities.”

This is less a list of “most prosperous cities” than it is a list of “most exclusive suburbs.” In each case, these are suburban cities on the periphery of one of the nation’s larger and more successful metropolitan areas. The reason they score so low on the distress indicator is not because they’ve created lots of jobs, but because their land use planning systems and high priced housing effectively exclude poorer residents from locating there.

For example, consider Flower Mound, Texas. According to the Census Bureau’s “On the Map” data service, of the 32,152 workers who lived in the city in 2013, 28,482 (88 percent) worked outside the city limits. Flower Mound’s story is not about its localized economic success, but rather about being a bedroom community for relatively high income people who work somewhere else—and not being a place that many low income people can afford.

Flower Mound, TX. Credit: Google Maps
Flower Mound, TX. Credit: Google Maps


Describing such places as “the country’s most prosperous cities” isn’t so much wrong as it is incomplete and misleading.

And it diverts our attention from the fact that the creation of such exclusive enclaves is one of the factors that is amplifying the spatial economic segregation of metropolitan areas. Within a single metropolitan area—Phoenix—some suburban cities are classified as being prosperous and equal (Gilbert and Scottsdale) while another suburb a few miles away (Glendale) is among the most distressed.

This is clear when you look at the data in the EIG report: the two cities with the highest levels of “equality” are Cleveland and Detroit—essentially because poverty is so severe and widespread.

Just because we can compile data on income levels and economic inequality at the city level doesn’t mean that these are the useful units to use to assess or diagnose these problems.

In an important sense, municipalities are simply the wrong units for measuring economic performance—they don’t correspond to entire functioning economies, and they vary so widely in how their defined from region to region that comparisons simply aren’t meaningful.