Working Papers

The End of the American Dream? Inequality and Segregation in US cities (with Alessandra Fogli and Veronica Guerrieri)

HFH Slides (June 2021)

Since the ’80s the US has experienced both an increase in income inequality and an increase in residential segregation by income. After documenting this fact, we develop a general equilibrium model where parents choose the neighborhood where to raise their children. Segregation and inequality amplify each other because of local spillovers that affect the education returns. We calibrate the model using 1980 US data and the estimates for neighborhood exposure effects in Chetty and Hendrent (2018). We then show that segregation contributes to 28% of the increase in inequality between 1980 and 2010 after an unexpected permanent skill premium shock.

Pandemic Control in ECON-EPI Networks (with Marina Azzimonti, Alessandra Fogli, and Fabrizio Perri)

SED Slides (July 2021)

We develop an ECON-EPI network model to evaluate policies designed to improve health and economic outcomes during a pandemic. Relative to the standard epidemiological SIR set-up, we explicitly model social contacts among individuals and allow for heterogeneity in their number and stability. In addition, we embed the network in a structural economic model describing how contacts generate economic activity. We calibrate it to the New York metro area during the 2020 COVID-19 crisis and show three main results. First, the ECON-EPI network implies patterns of infections that better match the data compared to the standard SIR. The switching during the early phase of the pandemic from unstable to stable contacts is crucial for this result. Second, the model suggests the design of smart policies that reduce infections and at the same time boost economic activity. Third, the model shows that re-opening sectors characterized by numerous and unstable contacts (such as large events or schools) too early leads to fast growth of infections.

Identification and Estimation of Discrete Choice Demand Models when Observed and Unobserved Characteristics are Correlated (with Amil Petrin and Boyoung Seo)

The standard  Berry, Levinsohn, and Pakes (1995) (BLP) approach to estimation of demand and supply parameters assumes that the product characteristic unobserved to the researcher but observed by consumers and producers is conditionally mean independent of all characteristics observed by the researcher. We extend BLP to allow all product characteristics to be endogenous, so the unobserved characteristic can be correlated with the other observed characteristics. We derive moment conditions based on the assumption that firms – when choosing product characteristics – are maximizing expected profits given their beliefs at that time about preferences, costs, and competitors’ actions with respect to the product characteristics they choose. Following Hansen and Singleton (1982) we assume that the “mistake” in the choice of  the amount of the characteristic that is revealed once all products are on the market is conditionally mean independent of anything the firm knows when it chooses its product characteristics. We develop an approximation to the optimal instruments and we also show how to use the standard BLP instruments. Using the original BLP automobile data we find all parameters to be of the correct sign and to be much more precisely estimated. Our estimates imply observed and unobserved product characteristics are highly positively correlated, biasing demand elasticities upward significantly, as our average estimated price elasticities double in absolute value and average markups fall by 50%.

Accessibility or Amenities? Estimating the Value of Light Rail Transit (with Veronica Postal)

This paper examines consumer marginal willingness-to-pay for the introduction of light rail transit in Minneapolis. We estimate the resulting change in local property prices to assess what share is attributable to the direct effect of improved access to public transit and what share is attributable to the increase in local amenities. After assembling a rich spatial dataset encompassing every residential property in Minneapolis and hundreds of thousands of businesses and neighborhood amenities, we use machine learning techniques to estimate a hedonic pricing surface. We extend the method of Boosted Smooth Trees introduced by Fonseca et al. (2018) to a high-dimensional dataset and to incorporate instrumental variables, allowing us to control for endogeneity in amenity changes. Our results indicate that the price of properties located within a half mile of a light rail station increased by around 11.3%. The direct impact of access to the light rail itself is estimated to increase local housing prices by 5.5%, while the estimated spillover due to changes in amenities is quantifiable at 5.8%.