Brad Ross

• Itai Ater, Brad Ross, Adi Shany, Shoshana Vasserman (Working), Road Pricing Under Heterogeneity. Abstract

  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: the extent of driver heterogeneity—both in preferences and in exposure to highly demanded roads and times of day—and the extent of revenue recycling. We quantify the impact of both forces using data from a large-scale experiment that tracked driving behavior with and without usage-based congestion pricing incentives for nearly 10,000 drivers over two years. Using a model of time-varying traffic equilibrium with heterogeneous drivers and a granular road network, we show that a congestion pricing policy using the experiment’s price schedule would raise welfare by 94% of the value of adding a new lane to every highway and would raise enough revenue to fully compensate losers. Moreover, the extent of driver heterogeneity entails that optimal prices can improve net welfare with only a small fraction of toll revenue.

• Lihua Lei and Brad Ross (Under Revision), Estimating Counterfactual Matrix Means with Short Panel Data. Abstract

  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.

  Description

  Revise and resubmit at Econometrica.

• Zane Kashner and Brad Ross (Working), Measuring and Mitigating Traffic Externalities. Abstract

  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.

Past Courses

• FNCE2090 - Real Estate Investments

  This course provides an introduction to real estate investing with a focus on financial and economic analysis. It is intended both as a foundational class for students considering a career in real estate as well as a survey class for students interested in finance who want to learn about the real estate sector. Project evaluation, financing strategies, risk assessment, investment decision making, and real estate capital markets are covered. No prior knowledge of the industry is required, but students will rapidly acquire a working knowledge of real estate markets and will quickly develop the quantitative tools to help them make investment decisions. Classes are conducted in a standard lecture format with discussion required. The course contains cases that help students evaluate the impact of more complex financing and capital markets tools used in real estate. There are three case studies and two midterms. FNCE 1000 is required as the class assumes comfort with Corporate Finance concepts and terms.

• REAL2090 - Real Estate Investments

  This course provides an introduction to real estate investing with a focus on financial and economic analysis. It is intended both as a foundational class for students considering a career in real estate as well as a survey class for students interested in finance who want to learn about the real estate sector. Project evaluation, financing strategies, risk assessment, investment decision making, and real estate capital markets are covered. No prior knowledge of the industry is required, but students will rapidly acquire a working knowledge of real estate markets and will quickly develop the quantitative tools to help them make investment decisions. Classes are conducted in a standard lecture format with discussion required. The course contains cases that help students evaluate the impact of more complex financing and capital markets tools used in real estate. There are three case studies and two midterms. FNCE 1000 is required as the class assumes comfort with Corporate Finance concepts and terms.

Knowledge @ Wharton

• The Business of Signing a Superstar Athlete, Knowledge @ Wharton - 10/16/2026
• Why Gen Z Is Choosing Sports Betting Over Retirement Savings, Knowledge @ Wharton - 10/14/2026
• The Analytics Behind Baseball’s Small-Market Success, Knowledge @ Wharton - 10/14/2026
• When AI Makes Financial Decisions, Knowledge @ Wharton - 10/13/2026
• What Does It Mean to Be ‘Happy’ at Work?, Knowledge @ Wharton - 10/13/2026
• How AI Planning Could Create Financial Market Fragility, Knowledge @ Wharton - 10/13/2026
• 2026 AI Adoption Report, Knowledge @ Wharton - 10/13/2026
• Who Controls a Company’s Values After It Is Sold?, Knowledge @ Wharton - 10/09/2026
• How Lucky Strike Entertainment Is Betting on Screen-Free Experiences, Knowledge @ Wharton - 10/08/2026
• Why People Would Rather Give Nothing Than Too Little, Knowledge @ Wharton - 10/08/2026