Location Sorting and Endogenous Amenities: Evidence from Amsterdam
This paper argues that the endogeneity of amenities plays a crucial role in determining the welfare distribution across a city's residents. We quantify this mechanism by constructing a dynamic model of residential choice with heterogeneous households, where urban consumption amenities are the equilibrium outcome of a market for non-tradables. We estimate our model using Dutch administrative microdata and leverage spatial variation in tourism flows and the entry of home-sharing platforms, namely Airbnb, as shifters of location characteristics in Amsterdam. Our results reveal significant heterogeneity across local residents in their valuation of different amenities, as well as in the response of amenities to demographic composition. We then show that the distributional effects of the tourist boom hinge on this heterogeneity: after initial rent increases due to a reduction in the housing supply available to locals, younger groups---the most similar to tourists---are compensated by having amenities tilt in line with their preferences, while older families end up being additionally hurt by this shift in amenities. We show that taxes on undesirable amenities are more welfare-enhancing relative to tourism taxes.
The Welfare and Distributional Consequences of Neighborhood Change: Evidence from Chicago's Public Housing Demolitions
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
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, NBER Market Design, LACEA-LAMES, Philadelphia Fed, UCLA Spatial Conference, Berkeley, GEA, ASSA Meetings 2023, Toronto, Georgetown, Johns Hopkins, Maryland, Wharton Real Estate, University of Illinois Urbana-Champaign, Universidad de la Plata, Wash U + St Louis Fed, LSE, Oxford, Warwick, UCL/LSE/IFS IO Workshop, Chicago Fed, ERWIT/CURE, EnergyEcoLab UC3M, CEMFI Trade Conference, SED Meeting, Sciences Po, Chicago-Princeton Spatial Conference, Stanford Cities Workshop, Northwestern Interactions Conference, UEA North American Meeting 2023
De Jure versus De Facto Discrimination: Evidence from Racial Covenants
Awarded Russell Sage Foundation and Gates Foundation Pipeline Grant for Emerging Scholars.
Presented at (by coauthor or myself): Toronto Urban Brownbag, Toronto IO Brownbag, UEA Fall Meeting 2022, SEA 2022, AREUEA National Meeting 2023, Russell Sage and Gates Foundations Emerging Scholars Conference 2023
Featured: Chicago Booth Review
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, Caltech
Social Networks and Geographic Mobility
Although geographic mobility is a key driver of economic opportunity, lower-income households tend to move less frequently. This paper explores the role of local social networks as an additional friction to geographic mobility and its importance across income groups. We start by documenting how social connections affect household production and mobility across labor markets. We focus our empirical analysis on childcare because it is a critical component of household production. We find that lower-income families rely more on relatives for their childcare needs rather than using market providers. Further, we also find that households who use more their social networks for childcare are less likely to move. We propose a dynamic model of households' childcare production and location choice, and estimate the model by matching key moments in the data. We quantify how much local social ties can explain mobility frictions and how it varies across income groups.
Presented at (by coauthor or myself): European UEA 2023