Welcome

I am a PhD student in Economics at the University of Mannheim.
My main research interests are environmental economics, applied microeconomics, innovation economics and transportation.

I am on the 2024 - 2025 job market.

My job market paper studies how environmental regulation affects innovation by upstream supplier companies in the context of the car industry. In other projects, I study policies and interventions aimed at reducing environmental externalities associated with corporate benefit schemes subsidizing passenger transportation.

Job Market Paper

Shifting Gears: Environmental Regulation in the Car Industry and Technological Change Among Suppliers

Abstract

Decarbonizing industries to mitigate climate change requires technological change. Innovation by suppliers can play a crucial role for the technological transition, particularly when suppliers have expertise on zero-emission technologies. In this paper, I study the effect of environmental regulation in a downstream industry on the innovation outcomes of suppliers in the context of the European CO2 emission standard for passenger cars. I construct a novel data set that links administrative data on car manufacturer compliance to supplier patent data using information on automotive supply chains. To identify causal effects of changes in the stringency of the emission standard, I leverage the heterogeneous exposure of automotive suppliers to changes in the composition of the European car market in the aftermath of the 2015 Volkswagen diesel scandal. I find that exposure to more stringent environmental regulation increases innovation for zero-emission vehicle technologies among existing suppliers. In addition, the likelihood that car manufacturers form new supply chain links to firms with prior knowledge on technologies to reduce vehicle emissions increases in response to more stringent environmental regulation. These results suggest that environmental regulation induces economically significant technology spillovers to the regulated industry.

CV

Working Papers

No Place Like Home: Charging Infrastructure and the Environmental Advantage of Plug-in Hybrid Electric Vehicles
(joint with Wolfgang Habla, Benjamin Rübenacker and Ulrich Wagner)

Abstract

Many European companies operating company car fleets face the challenge of lowering CO2 emissions from their fleets. A particularly large lever in this regard exists for Plug-in Hybrid Electric Vehicles (PHEVs), as those can be run on fuel or electricity and often exhibit low electric driving shares. This paper examines the effects of a large German company installing charging stations at their employees' homes. Leveraging quasi-experimental variation in the delivery and installation of home chargers, we quantify the impact of this technology on energy use and CO2 emissions of 856 PHEV company cars. As fuel and electricity expenditures for these cars are covered by the employer, home charging mainly changes the non-monetary costs to an employee. We find that access to home charging increases electricity consumption by 317.9 kWh per quarter and decreases fuel consumption by 97.97 liters, reducing CO2 emissions by 38%. Moreover, access to home charging increases the employee's propensity to choose a Battery Electric Vehicle (BEV) upon renewal of the lease by 30%-points. We use these estimates to compute private levelized abatement costs of home chargers for a range of scenarios characterizing the diffusion of BEVs. With current tax-inclusive energy prices, home chargers break even for the company within eight to 16 years.

Publications

Can social comparisons and moral appeals encourage low-emission transport use?
Transportation Research Part D: Transport and the Environment
(joint with Wolfgang Habla and Ulrich Wagner)

Abstract

Because company cars add to corporate CO2 footprints, companies are beginning to replace cars with mobility budgets that employees can use for leisure and commuting trips. This study examines whether nudges can encourage sustainable travel in such a subsidized setting. We conduct a field experiment with 341 employees of a large German company. Observing expenditure items charged to the mobility budget, we test if social comparisons and a climate-related moral appeal induce a shift towards low-emissions transport modes. We find that simultaneous application of both nudges causes a reduction in car use, particularly taxi and ride sharing, as well as substitution towards micromobility, but not public transport. The social comparison alone is not effective, and the treatment effects of the combined nudge vanish in the second half of the treatment period. Survey evidence suggests that these results are driven by a minority that complies with the communicated social norm.

Work in Progress

The Alignment Effect of Auditing: Evidence from Energy Efficiency Retrofits
(joint with Andreas Gerster and Michael Kramm)

Dormant Papers

Are there Rebound Effects from Electric Vehicle Adoption? Evidence from German Household Data
(joint with Vera Huwe, ZEW Discussion Paper No. 20-048, this version October 2020)

Abstract

We analyze rebound effects of electric vehicle adoption on both the extensive (vehicle ownership) and the intensive (vehicle mileage) margin using cross-sectional household level data on vehilce ownership and use from Germany. For the identification of changes in the number of cars owned after electric vehicle adoption, we predict counterfactual car ownership using a supervised learning approach. We then investigate the effect of electric vehicle adoption on household mileage based on a matching of households owning electric vehicles to similar owners of conventional cars. We cannot verify a significant increase in the number of cars owned for households with one electric and one conventional vehicle. However, electric vehicle ownership is associated with a significant reduction in annual mileage of -23 % of the sample mean. For the selection of covariates for matching, we contrast an ad hoc variable selection with a data-driven variable selection method (double LASSO). Here, we find that the data-driven variable selection changes the magnitude of the estimation results substantially.