Denser housing construction can alleviate rising housing costs, but opponents frequently cite car traffic as a primary concern. We quantify these heterogeneous traffic costs from new residents across the Boston Combined Statistical Area. Using data on households’ intra-metro-area travel, the road network, road speeds, and routing decisions, we estimate monthly traffic counts on every street. We find that moving a house from the 25th percentile of the distribution of nearby street traffic to the 75th percentile decreases its value by 20%, while adding the same number of monthly trips to the street of a similar house at the 75th percentile only decreases its value by 2.7%. We estimate a structural, hedonic model of households’ residential choices and visits to points of interest and find that households are willing to pay to avoid both car traffic on their street and travel time, but that these preferences vary widely across the population. Using the model and estimates of how traffic volumes affect road speeds, we simulate the traffic externalities caused by adding new residents in different locations. We find that a Massachusetts state law targeting a 10% housing stock increase to land near public transit stops causes $3.3 billion in traffic externalities from these new residents, an $820 million reduction relative to spreading those homes uniformly across space. Building those units on thoroughfares instead would decrease welfare costs by an additional $520 million.

We develop a new, spectral approach for identifying and estimating average counterfactual outcomes under a low-rank factor model with short panel data and general outcome missingness patterns. Applications include event studies and studies of outcomes of “matches” between agents of two types, e.g. workers and firms, typically conducted under less-flexible Two-Way-Fixed-Effects (TWFE) models of outcomes. Given an infinite population of units and a finite number of outcomes, we show our approach identifies all counterfactual outcome means, including those not estimable by existing methods, if a particular graph constructed based on overlaps in observed outcomes between subpopulations is connected. Our analogous, computationally efficient estimation procedure yields consistent, asymptotically normal estimates of counterfactual outcome means under fixed-\(T\) (number of outcomes), large-\(N\) (sample size) asymptotics. In a semi-synthetic simulation study based on matched employer-employee data, our estimator has lower bias and only slightly higher variance than a TWFE-model-based estimator when estimating average log-wages.

This paper analyzes the effects of the largest field experiment to date that incentivizes drivers to limit driving during peak hours and congested areas via usage-based congestion pricing. The experiment monitored the driving behavior of 10,000 Israeli drivers who were recruited over the course of 2020. During the first six months of a driver’s participation in the experiment, their driving behavior is monitored and recorded; afterwards, drivers receive an annual budget and are charged for each kilometer driven during historically congested times in congested areas. Whatever remains in each driver’s budget is paid to them a year later. We use comprehensive data on driving behavior of participating drivers over the course of 2020 and 2021 to evaluate how usage-based congestion pricing affects driving behavior. The staggering of driver recruitment facilitates identifying treatment effects via a difference-in-differences approach. We find that drivers decrease their congested driving behavior by 10-20% across a myriad of outcomes designed to detect both extensive margin (i.e. whether to take a trip) and intensive margin (i.e. when to take a trip) responses. We also find that there is significant treatment effect heterogeneity across drivers that can be predicted by pre-treatment driving behavior. The most affected drivers tend to be those who contributed more to congestion and who appear to have more flexibility in their driving choices and easier access to public transit, but they are not disproportionately socioeconomically advantaged or disadvantaged. We then estimate a model of demand for taking “habitual” trips by car at different times given different travel durations and costs. Using this model and an estimated relationship between traffic densities and speeds on major highways, we consider the equilibrium impacts of congestion pricing on network-wide speeds and welfare.

We propose a sensitivity analysis for Synthetic Control (SC) treatment effect estimates to interrogate the assumption that the SC method is well-specified, namely that choosing weights to minimize pre-treatment prediction error yields accurate predictions of counterfactual post-treatment outcomes. Our data-driven procedure recovers the set of treatment effects consistent with the assumption that the misspecification error incurred by the SC method is at most the observable misspecification error incurred when using the SC estimator to predict the outcomes of some control unit. We show that under one definition of misspecification error, our procedure provides a simple, geometric motivation for comparing the estimated treatment effect to the distribution of placebo residuals to assess estimate credibility. When we apply our procedure to several canonical studies that report SC estimates, we broadly confirm the conclusions drawn by the source papers.

In Federalist systems, the most local tier of government typically has jurisdiction over land use decisions. The decentralization theorem discussed in Oates (1972) and Oates (1999) suggests that these local governments are best suited to cater to their constituents’ preferences. We propose an empirical test of this theory. Chapter 40B, a Massachusetts state law, provides a source of quasi-experimental variation in the level of government with jurisdiction over siting of new housing developments. When less than 10% of a jurisdiction’s permanent housing stock is considered affordable, that jurisdiction loses its discretion over whether new, partially affordable housing developments can be built in its borders. Using the tools developed in Kashner and Ross (2024), we estimate the effect of local control over the siting of new housing developments on the traffic externalities generated by the residents of those developments. We find that new housing developments built when local governments have control over siting decisions cause 35% smaller welfare costs on existing metropolitan area residents due to increased car traffic. This effect is driven by a 55% decrease in welfare costs on households within the siting jurisdiction’s borders, with a statistically insignificant and economically small decrease in traffic externalities on households in other jurisdictions.