Research

Working Papers

“Gentrification and Neighborhood Change: Evidence from Yelp” (with Edward L. Glaeser and Michael Luca), NBER Working Paper 28271. [ssrn]

Abstract: How does gentrification transform neighborhoods? Gentrification can harm current residents by increasing rental costs and by eliminating old amenities, including distinctive local stores. Rising rents represent redistribution from tenants to landlords and can therefore be offset with targeted transfers, but the destruction of neighborhood character can – in principle – reduce overall social surplus. Using Census and Yelp data from five cities, we document that while gentrification is associated with an increase in the number of retail establishments overall, it is also associated with higher rates of business closure and higher rates of transition to higher price points. In Chicago and Los Angeles especially, non-gentrifying poorer communities have dramatically lower turnover than richer or gentrifying communities. However, the primary transitions appear to the replacement of stores that sell tradable goods with stores that sell non-tradable services. That transition just seems to be slower in poor communities that do not gentrify. Consequently, the business closures that come with gentrification seem to reflect the global impact of electronic commerce more than the replacement of idiosyncratic neighborhood services with generic luxury goods.

Publications

Abstract: The years following the Great Recession were challenging for forecasters. Unlike other deep downturns, this recession was not followed by a swift recovery, but instead generated a sizable and persistent output gap that was not accompanied by deflation as a traditional Phillips curve relationship would have predicted. Moreover, the zero lower bound and unconventional monetary policy generated an unprecedented policy environment. We document the actual real-time forecasting performance of the New York Fed dynamic stochastic general equilibrium (DSGE) model during this period and explain the results using the pseudo real-time forecasting performance results from a battery of DSGE models. We find the New York Fed DSGE model’s forecasting accuracy to be comparable to that of private forecasters, and notably better for output growth than the median forecasts from the FOMC’s Summary of Economic Projections. The model’s financial frictions were key in obtaining these results, as they implied a slow recovery following the financial crisis.