Rust Belt Heuristics

A common demographic heuristic is loss aversion. Better to stop one resident from leaving than attract one newcomer. Rust Belt shame is all about loss aversion. If anyone leaves, whether for the suburbs or another state, then something is wrong with a place. A recent article in the Washington Post rekindles the dying cities geographic stereotype:

In America’s Rust Belt and parts of the Northeast, millennials and young professionals are leaving rather than moving in, and populations there are dwindling. Among those who remain, both the residents and the houses are aging.

The journalist (unwittingly) creates a new type of human, the net migrant. Negative migration indicates an exodus, not a lack of people moving into the city. The percentage (or absolute number) of the population actually leaving might be much lower than those exiting cool coastal cities. All that matters is whether or not there is a - or a +. Our loss aversion tendency compels us to fix the neighborhood and seduce the net migrant to stay.

Loss aversion at the regional or municipal scale turns a blind eye to strong neighborhoods in cities with lousy (i.e. Rust Belt) reputations. Quoting research from the St. Louis Fed, "Even in the most distressed older industrial cities some neighborhoods are doing quite well."

Rust Belt St. Louis is the case study. 35 tracts out of 218 total qualify as rebound neighborhoods (see Figure 1 above). With the 1970s (the nadir of urban America) as the baseline, Rust Belt cities have substantial areas of improvement as well as deepening distress. But the latter receives all the coverage and whitewashes a huge part of the United States with one broad brush stroke.

Indeed, in unpacking monolithic Rust Belt St. Louis, the Fed finds use for affordable housing efforts, "Subsidized housing also plays an important role in the continued economic diversity of rebound neighborhoods." Near anchor institutions reside young adults with enough earning power to drive up real estate prices beyond the means of the lowest wages. Regardless, the population drop defines the city. Loss aversion wins again, data be damned.