Location Sorting and Endogenous Amenities: Evidence from Amsterdam

NEW DRAFT! Joint with Tomás Domínguez-Iino. Revision requested at Econometrica.

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.

Online Appendix. Supplementary Material.

Awarded Best Student Paper Prize 2019 by the Urban Economics Association and Best Job Market Paper Prize 2019 by the European Economic Association.

The Welfare and Distributional Consequences of Neighborhood Change: Evidence from Chicago's Public Housing Demolitions

Draft. Joint with Eric Chyn and Bryan Stuart

This paper studies one of the largest spatially targeted redevelopment efforts in the United States to date: public housing demolitions sponsored by the HOPE VI program. Focusing on Chicago, we estimate welfare impacts using a structural model that features a rich set of equilibrium responses motivated by descriptive analysis. Our results indicate that demolitions reduced welfare for Black and Hispanic households, especially those with low-income levels. In contrast, higher-income white households benefited. Counterfactual simulations explore how housing policy can mitigate negative impacts of demolitions and suggest increased public housing site redevelopment is the most effective policy for reducing racial inequality.

Featured: BFI Insights, Chicago Booth Review


Almagro, M., and Andrés-Cerezo, D. (2020)

The Construction of National Identities,  Theoretical Economics, 15 (2), 763-810

Supplementary Appendix.

Almagro, Milena and Orane-Hutchinson, Angelo (2020)

Supplementary Appendix.

Featured: Marginal Revolution. Covid Economics: Vetted and Real-Time Papers, Issue 13. VoxTalks, Episode 28.

Almagro, M., Coven, J., Gupta A., and Orane-Hutchinson, A. (2023)

Disparities in COVID-19 Risk Exposure: Evidence from Geolocation Data, Regional Sciencie and Urban Economics103933


Optimal Urban Transportation Policy: Evidence from Chicago

Joint with Felipe Barbieri, Juan Camilo Castillo, Nathaniel Hickok, and Tobias Salz

Urban transportation policies have become a focal point in cities' efforts to curb congestion and address environmental and distributional concerns. This paper characterizes the optimal mix of policies and evaluates their welfare and distributional effects. To that end, we present a framework of a municipal government aiming to maximize welfare. The government chooses the prices and frequencies of different modes of transportation, subject to a budget constraint that introduces monopoly-like distortions. We move on to an empirical application of this framework to the city of Chicago. We first construct a novel dataset of all relevant transportation modes. On the demand side, our empirical model captures the rich heterogeneity in travel choices. On the supply side we account for differential congestion and costs of different road-based modes. Our counterfactual results suggest that if the city only intervenes on public transit, it should lower transit prices even further but also lower frequency to meet its budget constraint. On the other hand, introducing a per-kilometer tax on drivers leads to higher welfare gains. 

Awarded NBER Transportation Economics in the 21st Century Grant

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

Joint with Aradhya Sood

Racially-restrictive covenants, which prevented the sale and rental of housing to several racial and ethnic minorities, were a common phenomenon in the first half of the 20th century in many northern cities in the U.S. In this paper, we study how these racially-restrictive covenants affected the socio-economic and geographic structure of urban areas and how their effects have persisted over time. In the first part of the paper, we leverage plausible exogenous variation in the changes of water bodies and soil quality to predict the presence of covenants. We find that racial covenants are negatively correlated with natural amenities, suggesting that they were used as substitutes of location characteristics that were potentially highly valued by city residents.  In the second part of the paper, we will employ a location choice model to disentangle the various channels through which racial covenants shaped the geography of northern cities and measure the welfare of counterfactual housing policies.

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

Joint with Elena ManresaSlides.

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

Joint with Olivia Bordeu and Gregorio Caetano.  

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