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.
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).
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
WORK IN PROGRESS
THE EFFECTS OF URBAN RENEWAL PROGRAMS ON 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.
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.
ARBITRAGE AND FIRM HETEROGENEITY IN A COMPETITIVE MARKET: EVIDENCE FROM THE NYC TAXI INDUSTRY
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 where 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 over 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, Universidad de Vigo.
PATIENT-SPECIFIC INFORMATION AND NEW DRUG ADOPTION: EVIDENCE FROM DIGITAL HEALTH
Joint with Jonathan Elliott.
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.