RESEARCH

WORKING PAPERS

LOCATION SORTING AND ENDOGENOUS AMENITIES: EVIDENCE FROM AMSTERDAM

Job Market Paper. Joint with Tomás Domínguez-Iino.

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

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.

​Presented at (by coauthor or myself): 2019 Econometric Society European Winter Meeting (Erasmus University), 8th Economic Advances (Universidad Torcuato Di Tella), 14th Economics Graduate Student Conference (Washington University in St. Louis), 14th Meeting of the Urban Economics Association (FRB of Philadelphia), 2019 Urban Economics Summer School (LSE), 2019 Young Economist Symposium (Columbia University), and NYU internal seminars (Applied Micro Lunch, Stern IO Workshop, Econometrics Lunch).

THE DETERMINANTS OF THE DIFFERENTIAL EXPOSURE TO COVID-19 IN NEW YORK CITY AND THEIR EVOLUTION OVER TIME

Draft. Joint with Angelo Orane-Hutchinson. Conditionally accepted at JUE: Insights.

In this paper we explore different channels to explain the disparities in COVID-19 incidence across New York City neighborhoods. To do so, we estimate several regression models to assess the statistical relevance of different variables such as neighborhood characteristics and occupations. Our results suggest that occupations are crucial for explaining the observed patterns, with those with a high degree of human interaction being more likely to be exposed to the virus. Moreover, after controlling for occupations, commuting patterns do not play a significant role. The relevance of occupations is robust to the inclusion of demographics, with some of them, such as income or the share of Asians, having no statistical significance. On the other hand, racial disparities still persist for Blacks and Hispanics compared to Whites albeit their effects are economically small. Additionally, we show that there is a selection effect with those residents in worse conditions being more likely to get tested. In our daily analysis, we show that this selection is considerably large at earlier dates but substantially decreases over time. While many occupations and demographics also become less important over time, we find effects consistent with higher intra-household contagion, in line with the progression of the pandemic as well as the health policies that have been set in place.

Online Appendix.

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

RACIAL DISPARITIES IN FRONTLINE WORKERS AND HOUSING CROWDING DURING COVID-19: EVIDENCE FROM GEOLOCATION DATA

Draft. Joint with Joshua Coven, Arpit Gupta, and Angelo Orane-Hutchinson.

We document that racial disparities in COVID-19 in New York City stem from patterns of commuting and housing crowding. During the initial wave of the pandemic, we find that out-of-home activity related to commuting is strongly associated with COVID-19 cases at the ZIP Code level and hospitalization at an individual level. After layoffs of essential workers decreased commuting, we find case growth continued through household crowding. A larger share of individuals in crowded housing or commuting to essential work are Black, Hispanic, and lower-income. As a result, structural inequalities, rather than population density, play a role in determining the cross-section of COVID-19 risk exposure in urban areas.

Federal Reserve Bank of Minneapolis, OIGI Working Paper 37 and Covid Economics: Vetted and Real-Time Papers, Issue 51.

PUBLICATIONS

Almagro, Milena and Andrés-Cerezo, David (2020)

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

Supplementary Appendix.

WORK IN PROGRESS

DATA-DRIVEN NESTS IN DISCRETE CHOICE MODELS

Joint with Elena Manresa. Slides.

Work in progress.

Discrete choice models are widely used to study many economic phenomena due to its tractability. Assumptions on idiosyncratic taste shocks are important determinants of the substitution patterns across choices and often yield unrealistic predictions on consumer behavior, such as the independence of irrelevant alternatives. Nested logit represents consumers as agents that choose sequentially over product groups, hence allowing for more flexible substitution patterns. Assuming knowledge of these nest has proven problematic in many applications. In this paper we make use of the panel structure of consumer choice data, where there are many consumers and relatively few products, to estimate both the nested structure as well as the structural parameters. We propose a two-step estimation strategy where in the first step we use clustering methods to classify products, and in the second step we estimate the model conditional on the estimated nest structure, as in Bonhomme, Lamadon, Manresa (2019). We show in Monte Carlo simulations the good performance of the estimator.

Presented at (by coauthor or myself): NYU.

ARBITRAGE AND FIRM HETEROGENEITY IN A COMPETITIVE MARKET: EVIDENCE FROM THE NYC TAXI INDUSTRY

Joint with Guillaume R. Fréchette, Alessandro Lizzeri, and Tobias Salz.

Work in progress.

This paper studies the role of imperfect spatial arbitrage in allocating resources in the New York City taxi market. We first provide evidence of substantial persistent heterogeneity in productivity among New York City taxi drivers and links this heterogeneity to differences in behavior by drivers. Given the access to identical capital (taxis) and opportunities, such heterogeneity could not exist in an equilibrium were full spatial arbitrage equates the chance to meet a passenger across locations. Moreover, the persistence in those differences implies that less efficient drivers do not necessarily exit the market over time. We present two distinct forms of evidence that this heterogeneity is in large part driven by differences in search behavior. First, the magnitude of this heterogeneity is larger in ``worse'' market conditions (when drivers take longer to find passengers and where ``ability'' becomes more important). Second, drivers display substantial heterogeneity in search patterns in a way that affects earnings. We then construct a model of drivers with heterogeneous knowledge that takes into account imperfect arbitrage in the profitability of locations. The model allows us to back out the underlying distribution of ability of drivers and the probability of exiting the market conditional on ability. Model simulations can be used to study the importance of the heterogeneity in ability, and the response of this market to different shocks, such as the entry of ridesharing apps, and to recover the long-run distribution of driver ability.

Presented at (by coauthor or myself): NYU, SAET 2017 (Faro), Universidad de Vigo.

PATIENT-SPECIFIC INFORMATION AND NEW DRUG ADOPTION: EVIDENCE FROM DIGITAL HEALTH

Joint with Jonathan Elliott.

Work in progress.

With the rise of digital health technologies, health care professionals increasingly have access to detailed real-time data on their patients. We evaluate to what extent access by physicians to this patient-specific information leads to more efficient patient-drug matches, especially in the context of the introduction of new drugs. To do so, we use data on hemophiliacs from a digital health app that allows patients to record treatments and symptoms (bleeding). A unique feature of our data is that we observe whether physicians access patient information and what information they observe. We leverage this aspect of the data to establish how patient information about drugs’ effects influences own prescriptions and the adoption of new drugs, which vary in effectiveness and in the rate of adoption. Additionally, we examine how the information diffuses across patients common to a physician and also within physicians’ social networks. We find that patient-specific information has a significant effect on the probability of adoption of new drugs and that there are large spillover effects across a physician’s patients as well as within physicians’ social networks.

Presented at (by coauthor or myself): NYU.