Conference Agenda

In Germany and several other European countries, taxpayers can deduct commuting expenses between their home and workplace from taxable income. The design of such commuter tax allowances has important implications for redistribution, individual incentives, and environmental outcomes, and remains the subject of ongoing policy debate. Using an experiment with more than 3,000 respondents from a representative sample of the German population, we study how information about the distributive and environmental aspects of the commuter tax allowance affects beliefs and support for alternative reform designs. Participants report beliefs about the current regulation as well as their support for multiple reform options, and are randomly exposed to information on either income-based distributional effects or potential environmental consequences of the current regulation. Providing information on distributional effects substantially reduces perceptions of social fairness of the current system by 22 percentage points and increases support for an income-independent mobility allowance by around 10 percentage points. In contrast, information about environmental implications does not affect policy support.

Old diesel cars without modern emissions control technology substantially contribute to air pollution by emitting high amounts of fine particulate matter, which is known to be detrimental to human health. Periodic vehicle registration fees offer a potentially powerful lever to speed up the retirement of old and polluting vehicles, yet little empirical evidence exists on the matter. This paper analyzes how higher registration fees on old and polluting diesel vehicles in the Netherlands accelerate their outflow from the vehicle fleet. It leverages the staggered rollout of diesel particulate filters as factory-fitted equipment to create quasi-random variation in pollution levels across otherwise comparable diesel car models. By applying Synthetic Difference-in-Differences complemented with a hazard model, this paper establishes that the tax increase on old and polluting cars is effective at reducing their numbers, albeit at the cost of being a very regressive policy.

We study anticipatory behavior in response to Sweden’s feebate vehicle policy. Using administrative data on vehicle registrations linked to individual and firm characteristics, we document pronounced anticipation effects that are largely driven by intertemporal substitution but also lead to excess vehicle adoption of roughly 3 percent. Vehicles adopted ahead of implementation are systematically dirtier and subsequently driven more intensively, amplifying the environmental damages of the policy announcement. We find that anticipatory adoption is driven primarily by dealer stock-management strategies, household liquidity, and information salience, whereas vehicle prices and charging infrastructure play little role in explaining these behaviors. We then quantify the environmental implications of anticipatory behavior, accounting for excess adoption, the higher emissions intensity of vehicles purchased in anticipation, and their more intensive subsequent usage. Ultimately, the environmental costs of the anticipatory behavior amount to approximately $62 million, or around half of the budget allocated to the policy in 2019.

We provide field evidence that prosocial behavior can persist even under intense competition, a possibility suggested by recent theory and experiments. California almond producers---atomized firms in a homogeneous-goods market---can mitigate pesticide externalities near schools by shifting applications to evenings or weekends at a private cost. Institutional features of this setting allow us to address important confounders (market power, reputational incentives, misreporting, and liability risk) and isolate prosocial behavior using plausibly exogenous within-firm variation in decision-level externality intensity. We find mitigation rises with externality severity despite the competitive environment, with patterns that reflect an altruistic component in firms' behavior.

Cities around the world have adopted micro-mobility programs to enhance public transportation networks and accessibility, and many continue to explore strategies to improve the programs’ socio-economic impacts. We provide evidence on the effect of offering free access to bicycle sharing on travel behavior, road safety, and air quality. Leveraging a unique policy in Taipei, Taiwan—home to the world’s largest bicycle sharing programs of its kind—where a per-ride subsidy of $0.20 USD makes rides under 30 minutes free of charge. Using spatial and temporal difference-in-differences (DiD) designs and multiple administrative datasets, we find that the policy increases shared bicycle ridership by more than 30% and reduces urban traffic volumes by 6%. Consistent with these changes, traffic accidents involving casualties declined by 8% following the policy implementation. We do not find statistically significant changes in air quality. Combining these estimates with the social costs of traffic accidents yields a benefit-cost ratio exceeding 10. The increase in shared bicycle ridership suggests that individuals respond disproportionately to zero prices or exhibit a low value of travel time when it comes to short trips.

We study whether bureaucrats preemptively reflect the executive politician's preferences in their decisions. Combining novel administrative data from the Federal Emergency Management Agency (FEMA) with hydrological models, we find that a standard deviation decrease in a county's alignment with the president leads to a 4 percentage point drop in the probability of bureaucrats flagging a county as requiring federal aid following an average-sized flooding event. This bias disappears in the most severe floods. We find evidence suggesting that such biases are significantly reduced when a career civil servant is overseeing the bureaucratic process rather than a political appointee.

This study examines the effect of air pollution on solar photovoltaic (PV) power generation in South Korea, utilizing hourly provincial data from 2017 to 2023. We apply a double/debiased machine learning (DDML) framework to estimate the impact of PM2.5 on solar PV output, addressing potential endogeneity by using wind direction as an instrumental variable. Our results reveal that increased PM2.5 concentrations significantly reduce solar PV generation, with the DDML model showing a larger marginal impact compared to conventional ordinary least squares (OLS) and instrumental variable (IV) methods. Specifically, a 10% increase in PM2.5 leads to a 4.4% decline in solar PV output, far exceeding the 0.4% reduction estimated with OLS and the 3.2% decline with IV. These findings highlight the limitations of OLS and IV methods in capturing the complex, potentially non-linear relationship between air pollution and PV performance. To address this critical methodological gap, the DDML approach leverages the flexibility of machine learning techniques to offer more robust causal estimates, mitigating biases from functional form misspecification and high-dimensional confounding. Our results indicate that a 1μg/m3 increase in hourly average PM2.5 leads to substantial economic losses in the solar sector, estimated at 0.53 million USD per year. These findings highlight the significant economic benefits of policies aimed at reducing air pollution, as cleaner air can enhance the efficiency of renewable energy systems and support the transition to a sustainable energy future.