LOCATION SORTING AND ENDOGENOUS AMENITIES: EVIDENCE FROM AMSTERDAM
This paper argues that the endogeneity of amenities plays a crucial role in the welfare distribution of a city's residents by reinforcing location sorting. We quantify this channel by leveraging spatial variation in tourism flows and the entry of home-sharing platforms, such as Airbnb, as shifters of location characteristics to estimate a dynamic model of residential choice. In our model, consumption amenities in each location are the equilibrium outcome of a market for services, which are supplied by firms and demanded by heterogeneous households. We estimate the model using detailed Dutch microdata, which allows us to track the universe of Amsterdam's residents over time and the evolution of a rich set of neighborhood amenities. Our results indicate significant heterogeneity across households in their valuation of different amenities, as well as in the response of amenities to demographic composition. We show that allowing for this endogenous response increases inequality between demographic groups whose preferences are closely aligned, but decreases it if substantially misaligned, suggesting heterogeneity in the two-way mapping between households and amenities plays a crucial distributive role. Finally, we highlight the distributional implications of our estimates by evaluating currently debated policies, such as zoning, as well as price and quantity regulations in housing markets.
Almagro, Milena and Orane-Hutchinson, Angelo (2020)
The Determinants of the Differential Exposure to COVID-19 and their Evolution over Time, Journal of Urban Economics: Insights, 1
SELECTED WORK IN PROGRESS
URBAN RENEWAL, GENTRIFICATION, AND INEQUALITY: EVIDENCE FROM CHICAGO PUBLIC HOUSING DEMOLITIONS
Presented at (by coauthor or myself): European UEA 2021, U Wisconsin-Madison, Notre Dame, OIGI Fall 2021, American UEA 2021, Atlanta Fed, Rochester, SMU, Chicago Booth, Central Bank of Colombia, Bureau of Economic Analysis, Barcelona Summer Forum Trade Workshop, IEB Urban Workshop, 2022 NBER SI Real Estate + Urban,
OPTIMAL URBAN TRANSPORTATION POLICY: EVIDENCE FROM CHICAGO
Presented at (by coauthor or myself): Harvard, Chicago Booth, MIT, Rice, Texas A&M, Yale, CEMFI, EIEF Applied Micro Junior Conference, Tinos IO Conference
DATA-DRIVEN NESTS IN DISCRETE CHOICE MODELS
Nested logit models represent consumers as agents that choose sequentially over product groups before choosing a final product, hence allowing for flexible substitution patterns across products. However, assuming knowledge of the nest structure has proven problematic in some applications. We propose a method that estimates both the nest structure as well as the structural parameters using product share data. We consider two different settings with price endogeneity: (1) longitudinal observations of products across a large number of markets, where conditional on a product fixed effect prices are exogenous and (2) single-market observations with a cost-shifter. In each setting, we develop estimators to recover the structure of the nest and the parameters and analyze its statistical properties. We propose two-step estimation strategies where in the first step we classify products and in the second step, we recover structural parameters. More specifically, in (1) we use the Bonhome and Manresa (2015) estimator to recover groups, and in the second step, we estimate the model conditional on the estimated nest structure. In (2) we make use of a control function approach to classify products using k-means clustering. We showcase the good performance of our method through a Monte Carlo experiment, and we apply it to the U.S. automobile market data first used in Berry, Levinsohn, and Pakes (1995).
Presented at (by coauthor or myself): NYU, Cornell, Minnesota, UCL.