Research

This paper studies the welfare and distributional effects of dynamically priced highway toll lanes. We develop and estimate a model with heterogeneous drivers who choose departure times under imperfect information, then decide between priced (faster) and unpriced (slower) lanes once uncertainty resolves. Using toll transactions and traffic data from Washington’s I-405, we find that tolling raises welfare, especially when converting from carpool-only. Welfare gains arise partly from the “option value” of paying for speed during worse-than-expected traffic. Lower-income drivers benefit most, driven largely by where and when they drive. Further adjustments to the pricing algorithm can improve efficiency without sacrificing equity.

How does the location of affordable housing affect tenant welfare, the distribution of assistance, and broader social objectives such as racial and economic integration? Using administrative data on households living in units funded by the Low-Income Housing Tax Credit (LIHTC), we first show that tenant characteristics such as income, race, education, and family structure vary widely across neighborhoods, despite common eligibility thresholds. To quantify the welfare implications, we develop and estimate a residential choice model in which households choose from both market-rate and affordable housing options, where the latter are priced below-market and must be rationed. Moving a new development to a neighborhood with less poverty and better access to good schools and jobs increases aggregate tenant welfare and reduces both racial and economic segregation. However, it is also more costly to provide and disproportionately benefits more moderate-need, non-Black/Hispanic households. This change in the distribution of assistance arises in part because of the rationing process: households that only apply for assistance in opportunity-rich neighborhoods crowd out other households willing to apply anywhere. Relative to the choice of where to build, policy levers available post-construction—such as lowering the eligibility thresholds—have only limited effects on outcomes.

Revealed preference arguments are commonly used when identifying models of both single-agent decisions and non-cooperative games. We develop general identification results for a large class of models that have a linearly separable payoff structure. Our model allows for both discrete and continuous choice sets. It incorporates widely studied models such as discrete and hedonic choice models, auctions, school choice mechanisms, oligopoly pricing and trading games. We characterize the identified set and show that point identification can be achieved either if the choice set is sufficiently rich or if a variable that shifts preferences is available. Our identification results also suggest an estimation approach. Finally, we implement this approach to estimate values in a combinatorial procurement auction for school lunches in Chile.

The years following the Great Recession were challenging for forecasters. Unlike other deep downturns, this recession was not followed by a swift recovery, but generated a sizable and persistent output gap that was not accompanied by deflation as a traditional Phillips curve relationship would have predicted. Moreover, the zero lower bound and unconventional monetary policy generated a policy environment without precedents. We document the actual real-time forecasting performance of the New York Fed dynamic stochastic general equilibrium (DSGE) model during this period and explain the results using the pseudo real-time forecasting performance results from a battery of DSGE models. We find the New York Fed DSGE model’s forecasting accuracy to be comparable to that of private forecasters—and notably better, for output growth, than the median forecasts from the FOMC’s Summary of Economic Projections. The model’s financial frictions were key in obtaining these results, as they implied a slow recovery following the financial crisis.

The transition to electric vehicles (EVs) is critical to decarbonizing the transportation sector, yet inadequate charging infrastructure remains a barrier to adoption. In this paper, we take the perspective of a social planner deciding where to install new EV charging stations and where to upgrade existing ones. The geographic distribution of charging stations affects the planner’s objective through two channels. First, it affects EV adoption: we model heterogeneous consumers in the market for new (electric and gas-powered) vehicles who care about vehicle characteristics and charging station density. Second, it affects the travel and charging behavior of EV drivers: we model forward-looking drivers who choose where to travel next and whether to charge there, subject to battery constraints and charging station availability. We estimate the model using data on vehicle registrations, cell phone pings, and charging station locations, characteristics, and usage. Finally, we solve the social planner’s optimal siting problem, taking into account installation and upgrade costs.

Unlike many other goods allocated through centralized mechanisms, affordable housing is durable: who receives a unit today affects the supply of units available to reallocate in the future. We build a dynamic model of the allocation mechanism that endogenizes the arrival rate of vacant units. Households in the model make decisions on both whether to apply and, if allocated a unit, whether to move out each period. Policy changes that affect the move-out rate (e.g., giving households more choice when applying or allowing tenants to swap units) lead to a tradeoff between providing longer stays in subsidized housing to fewer households or shorter stays to more households. Optimal policy depends on dynamic considerations such as how match quality, need, and any treatment effects on households evolve over time.

We investigate the causes and consequences of social capital using a new measure of neighbor visits inferred from smartphone location data. Our goals in this project are threefold. First, we aim to document how factors including distance, income, homeownership, and the presence of “third places” affect the probability that a pair of households is connected in the visitation network, as well as how these patterns have changed over time. Second, we will investigate how social capital shapes households’ financial and migration responses to negative shocks. Finally, we will estimate a model of residential choice and endogenous social network formation and use it to explore how policies affect long-run social connectedness and migration.