Congestion pricing policies are widely debated and critics often argue that toll costs outweigh congestion relief. We show theoretically that welfare gains depend on two forces: driver heterogeneity, which allows tolls to improve travel times for drivers with high value of time by pushing drivers with low value of time off of congested roads during in-demand times, and the extent of revenue recycling. We quantify the effect of both forces using data from a large-scale experiment that charged 10,000 Israeli drivers per-kilometer driving fees over two years. Causal estimates show that pricing reduced congested driving by roughly 10%, mainly through fewer cross-city trips and shifted departure times, with the strongest responses among drivers with greater schedule flexibility and higher baseline pricing exposure. We then develop and estimate a model of time-varying traffic equilibrium with heterogeneous drivers and a granular road network, combining experimentally identified demand with nonparametrically estimated congestion technology. We use the model to simulate the equilibrium impact of the experiment’s per-kilometer fee schedule on traffic patterns in the Tel Aviv commuting zone and compare it with optimized per-kilometer fees and cordon prices. Finally, we assess how preference and traffic exposure heterogeneity and the degree of revenue recycling affect the value of congestion pricing policies.

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 adding 50 typical households’ monthly car trips to the streets of housing units in the bottom 20% of baseline monthly car trips through their street decreases the value of that home by almost 7% on average, while adding that same quantity of monthly trips to the streets of housing units in the top 20% of nearby trips would decrease the values of those homes by an average of 0.04%. 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 quantify 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 additional residents, a 20% reduction relative to spreading those homes uniformly across space. Building those units on thoroughfares instead would decrease traffic externalities by 34% relative to the uniform allocation.

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 30% smaller welfare costs on existing metropolitan area residents due to increased car traffic. This effect is driven by a 42% 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.

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 adding 50 typical households’ monthly car trips to the streets of housing units in the bottom 20% of baseline monthly car trips through their street decreases the value of that home by almost 7% on average, while adding that same quantity of monthly trips to the streets of housing units in the top 20% of nearby trips would decrease the values of those homes by an average of 0.04%. 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 quantify 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 additional residents, a 20% reduction relative to spreading those homes uniformly across space. Building those units on thoroughfares instead would decrease traffic externalities by 34% relative to the uniform allocation.

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 30% smaller welfare costs on existing metropolitan area residents due to increased car traffic. This effect is driven by a 42% 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.

We develop a 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. people and places, typically conducted using less-flexible Two-Way Fixed Effects (TWFE) models of outcomes. Given finite observed outcomes per unit, we show our approach identifies all counterfactual outcome means, including those not identified by existing methods, if a particular graph algorithm determines that units’ sets of observed outcomes have sufficient overlap. 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. When estimating province-level averages of held-out wages from an Italian matched employer-employee dataset, our estimator outperforms a TWFE model-based estimator.

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.