
Research
Working Papers
Big Push Policies and Network Externalities: Insights from Quebec's EV Fast-Charging Market,
2026
2nd place, Thesis in 5 Minutes, Climate Positive Energy
Industries with strong complementarities and network externalities may exhibit multiple equilibria. I examine whether government provision of network infrastructure can remedy the resulting inefficiency. Using vehicle registration and charging transaction data from Québec, I estimate a structural model of EV demand in which consumers value the charging network through the origin--destination trips it supports. I decompose the effect of a large government network deployment---a Big Push---into a coordination component, shifting the market from the low- to the high-adoption equilibrium, and an externality component, capturing stations that remain privately unprofitable even under full coordination. The Big Push raises EV adoption by 67%, with coordination contributing 31% and externalities a further 27%. It expands the network from 249 to 921 chargers, crowding out private investment in the core while crowding it in at the periphery, and generates $484 million in consumer value and $88 million in climate benefits for $22 million of government investment. This paper provides empirical evidence that direct government provision of network infrastructure can resolve coordination failures.
The Causal Impact of On-Street Charging Stations on EV Adoption, with Katalin Springel and
Eric Nussbaum. 2026
Best student short presentation, CREEA
The transition to electric vehicles (EVs) is central to decarbonization plans around the world. However, in many large cities, millions of people live in multi-unit buildings without a dedicated parking space where they can install a private EV charger. In these contexts, expanding access to on-street charging near residents’ homes is an important policy objective. However, these investments are costly and their benefits remain uncertain. To shed light on this question, we study the deployment of Montreal’s public on-street charging network, the largest such network in North America. We use data on EV ownership and charging-station locations, together with transaction-level charging usage. Our analysis is highly granular, allowing us to compare changes in EV adoption across street blocks. To address endogenous charger placement, we construct an instrument using pre-existing underground transformers, proximity to which substantially reduces station installation costs. Our preliminary results show an economically meaningful and statistically significant effect of the local charging network on EV adoption. In our preferred specification adding a charging station in a neighborhood with a low prevalence of detached housing, where residents are therefore likely to have limited access to private parking, increases EV adoption by 52%. This estimate implies a back-of-the-envelope abatement cost of approximately $68 per ton of avoided CO2. An additional charger may also reduce congestion at nearby stations, while the EVs it induces can create additional congestion. The reduced-form adoption estimate captures the net equilibrium effect of these responses but does not separate the direct effect of additional charging capacity from the congestion response. To quantify these channels, we are developing a structural model of EV choice as a function of charging-station availability.
The Impact of Violent Crimes on Commuting: Evidence from Toronto, with William Arbour and Émile Gendreau Côté. 2025
Violence on public transit can reduce ridership through two channels: an incident can disrupt service immediately, and news coverage of the crime can lead riders to avoid the system. We study first reports of crimes in the Toronto subway from 2022 to 2025, linking each report to the date and station of the crime, daily station entries and exits, service delays, and ride-hailing trips. A travel survey identifies riders whose usual route passes through the reported station, even when they enter and leave elsewhere. On the day of the crime, entries at the affected station fall by 3.2 percent, delays rise fivefold, and entries at adjacent stations increase. Coverage of the crime is not followed by a comparable decline: during the seven days beginning with the first report, entries at exposed stations change by -0.03 percent and exits by -0.36 percent; neither estimate is statistically significant. We find no detectable avoidance at the station, line, or network level and no broad substitution toward Uber or Lyft. Transit crime briefly displaces trips around the affected station, while coverage of the crime is not associated with a measurable reduction in aggregate travel.
Safer, Faster, Farther: Evidence from Montreal’s Bike Lane Network, 2026
The installation of bike lanes at the cost of traffic lanes and parking has become common practice in major American cities. Proponents argue that these measures raise cycling ridership, but evidence remains scarce. I address this question using the rollout of Montreal's Bike Network as a natural experiment. I combine data on deployed bike corridors with origin-destination data from the city-owned bike-sharing system. I first exploit the phased deployment of the city’s bike lane network and find that protected bike lanes reduce collisions. I then estimate a difference-in-differences model using a control corridor on the other side of Mount Royal. I find that cycling speed increases by 7% and trip distance by 8%, contributing to a 32% increase in ridership. I find no evidence that it reflects business stealing from nearby bike lanes. I also find no significant effect on subway ridership, suggesting that the increase in cycling trips is primarily additional rather than a substitution away from public transit.
Work in Progress
Who Bears the Risk? Data Center Entry, Utility Pricing, and Large Load Tariffs, (with Jacquelyn Pless and Zhenxuan Wang)
Carbon Taxes Are Not Enough: Network Externalities and the Case for Industrial Policy
Publication
Reproducibility and robustness of economics and political science research, (with Abel Brodeur et al., see full list of authors on pages 2-3), Nature, 652:151–156, 2026
Science aspires to be cumulative. Reproducibility efforts strengthen science by testing the reliability of published findings, promoting self-correction, and informing policy-making. Computational reproductions, whereby independent researchers reproduce the results of published studies, are an essential diagnostic tool. Such efforts should have greater visibility. However, little social science reproduction and robustness has been conducted at scale Here we reproduced original analyses and conducted robustness checks of 110 articles that were published in leading economics and political science journals with mandatory data and code sharing policies. We found that more than 85% of published claims were computationally reproducible. In robustness checks, our reanalyses showed that 72% of statistically significant estimates remain significant and in the same direction, and the median reproduced effect size is nearly the same as the originally published effect size (that is, 99% of the published effect size). Additionally, 6 independent research teams examined 12 pre-specified hypotheses about determinants of robustness. Research teams with more experience found lower levels of robustness, and robustness did not correlate with author characteristics or data availability.
The Economic Effects of Long-Term Climate Change: Evidence from the Little Ice Age: Replication, (with Nikolai Cook, Tongzhe Li, Taylor Wright), Economic Inquiry, 64:212–220, 2026
Waldinger (2022) finds a positive relationship between temperature and city size during the climate change of 1600-1850. We show the main result differs by city size. Cities with less than 1000 inhabitants (which make up 23.5% of observations and are 49.6% of cities at some point) exhibit a strong and positive relationship between temperature and city size, whereas cities with always more than 1000 inhabitants exhibit a negative relationship. Further examination of the underlying city size data, which bins populations into coarse thousand-wide population intervals, finds the original analysis to be robust to a number of reasonable alternative researcher choices.