Teaching
Environmental Economics: Models, Data, and Policy Design
Are you interested in environmental and energy policy? Do you want to improve your data science skills? If so, Environmental Economics is for you.
Other classes
Economics 250: PhD Environmental Economics (Fall 2023, Spring 2026)
This course, taught jointly with Allan Hsiao and Bard Harstad, provides an overview of modern empirical research in environmental and energy economics. As the world reduces emissions and adapts to climate change, governments face important policy design questions. How harmful is air pollution, and what is the social cost of carbon? What are the efficiency and distributional consequences of second-best policies such as automotive fuel economy standards, zero-emission vehicle mandates, and subsidies for energy efficiency and renewables? How can politically feasible policies be adjusted to improve economic efficiency? We discuss the latest evidence on these questions using tools from public economics and industrial organization.
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Economics 258: PhD Industrial Organization (2025)
This course, taught jointly with Matt Gentzkow and Ali Yurukoglu, trains PhD students in the field of industrial organization. We cover the central questions around imperfect competition, market structure, innovation, and regulation, as well as the models and empirical methods commonly used to tackle these questions. We study applications in industries such as digital platforms, media, autos, and electric power.
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MGTECON 340, Economics 185, GEP 135/235: Data Science for Environmental Business (2024)
This course, taught jointly with Guido Imbens, trains data scientists and data science managers for jobs in clean tech and sustainability. Each week, we have a guest speaker from a venture capital firm, clean tech startup, electric utility, renewable energy developer, or some other sustainability-related business. We do a quantitative case study of the speaker’s business problem, such as siting wind farms, measuring carbon footprints, rating ESG performance, managing EV charging, and predicting electric vehicle demand. In the next class, we discuss the analytical decisions students made on the case study and the business implications of the results.
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NBER Behavioral Public Economics Boot Camp (2022, 2023)
The NBER Behavioral Public Economics PhD Student Boot Camp is designed to help graduate students become experts in cutting-edge economics research and techniques at the intersection of behavioral and public economics, and its connections to related fields such as health, labor, household finance, development, and environmental economics. The camps are co-organized jointly with Doug Bernheim and Dmitry Taubinsky and have featured an outstanding lineup of guest lecturers: Nava Ashraf, Raj Chetty, Stefanie DeLuca, Manasi Deshpande, Ben Handel, David Laibson, Elizabeth Linos, Michael Grubb, Joana Naritomi, Ricardo Perez-Truglia, Mario Small, Johannes Spinnewijn, and Stefanie Stantcheva. The camps are funded by the Alfred P. Sloan Foundation.
Visit the course website for syllabus and lecture slides.
Economics 14.160 (MIT): PhD Behavioral Economics (2022)
This class, taught jointly with Frank Schilbach, covers recent topics in behavioral economics. Topics include deviations from the standard neoclassical model in terms of (i) preferences (time and risk preferences, reference dependence, and social preferences), (ii) beliefs and learning (overconfidence, projection bias, and attribution bias), and (iii) decision-making (cognition, attention, framing, and persuasion), as well as (iv) market reactions to such deviations. Applications cover a wide range of fields, including public economics, industrial organization, health economics, finance, environment/energy, and development economics.
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HLS 2589, HKS API-305, Economics 2050 (Harvard): Behavioral Economics, Law, and Public Policy (2021)
This class, taught jointly with Cass Sunstein, explores issues at the intersection of behavioral economics, law, and public policy. The first part of the class involves discussions of philosophical issues around paternalism and nudges, led by Professor Sunstein. Professor Allcott then gives a primer on behavioral economics and behavioral welfare analysis. The final part of the class involves in-depth discussions of specific policy issues, including energy efficiency, sin taxes, social welfare programs, consumer financial protection, and social media content moderation.
Download syllabus. Download lecture slides: behavioral economics and behavioral welfare analysis, economics review, energy efficiency, sin taxes, behavioral economics and poverty, consumer financial protection 1: payday lending, consumer financial protection 2: credit cards, political polarization and the media, social media and content moderation.
Economics 2450A (Harvard): PhD Public Economics (2020)
This is the first of two courses offered in the Public Economics sequence at Harvard in 2020-2021, taught jointly with Raj Chetty. This course covers basic issues in the optimal design of government policies. The goal of the course is to familiarize students with basic empirical methods and theoretical models in applied microeconomics, with a focus on connecting theory to data to inform economic policy. Topics include efficiency costs and incidence of taxation, social insurance, corporate taxation, externalities, education policy, and behavioral public economics.
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Download lecture slides: introduction, racial disparities and discrimination, education policy, public goods and externalities, optimal sin taxes, optimal nudges, and takeup and targeting.
Economics 316 (NYU): Undergraduate Industrial Organization (2011-2017)
This is a class on data science for business applications, designed for undergraduates in economics and computer science. We cover static monopoly pricing, dynamic pricing, price discrimination, oligopoly, differentiated products, entry models, behavioral industrial organization, and anti-trust. Students learn R and analyze data from business pricing experiments, product planning decisions, cartel enforcement cases, and other contexts. The course integrates active learning approaches and in-class quantitative case studies.
Download syllabus. Download lecture slides. Download problem sets, in-class case studies, and related data and R scripts.