PhD Candidate in Economics at NYU
19 W 4th Street, 10012, New York, NY
I am an Empirical IO economist with interests in Urban Economics and Applied Microeconomics. In my job market paper I study the implications of endogenous amenities in shaping welfare inequality of a city's residents. To do so, I build and estimate a structural dynamic model of a city's residential market using spatial variation from tourism flows and short-term rentals. I aIso have projects in Applied Theory and Microeconometrics.
I will be available for interviews at the ASSA meetings in San Diego as well as the European Job Market meeting in Rotterdam.
LOCATION SORTING AND ENDOGENOUS AMENITIES: EVIDENCE FROM AMSTERDAM
This paper argues 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): 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 CONSTRUCTION OF NATIONAL IDENTITIES
Joint with David Andrés.
Forthcoming at Theoretical Economics.
This paper explores the dynamics of nation-building policies and the conditions under which a state can promote a shared national identity on its territory. A forward-looking central government that internalizes identity dynamics shapes them by choosing the level of state centralization. Homogenization attempts are constrained by political unrest, electoral competition and the intergenerational transmission of identities within the family. We find nation-building efforts are generally characterized by fast interventions. We show that a zero-sum conflict over resources pushes long-run dynamics toward homogeneous steady states and extreme levels of (de)centralization. We also find the ability to foster a common identity is highly dependent on initial conditions, and that country-specific historical factors can have a lasting impact on the long-run distribution of identities.
ARBITRAGE AND FIRM HETEROGENEITY IN A COMPETITIVE MARKET: EVIDENCE FROM THE NYC TAXI INDUSTRY
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.
DATA-DRIVEN NESTS IN DISCRETE CHOICE MODELS
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.
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.