Research

Does a carbon tax stimulate low-carbon innovation in the transport sector? We exploit an environmental tax reform in Sweden that introduced both a carbon and a value-added tax on transport fuels. Employing patent data and the synthetic control method, we consistently observe positive and economically significant effects of the tax reform. We disentangle the components and find that the carbon tax drives the effect while equivalent fuel price changes did not significantly affect low-carbon innovation. Our findings suggest that salient carbon prices may foster low-carbon innovation more effectively than fuel price elasticity estimates suggest.

Low Emission Zones (LEZs) reduce local air pollution by restricting emission-intensive vehicles from accessing designated areas and have been shown to improve population health. Little is known about the effects of driving restriction policies on other areas of life. This paper studies the effects of LEZs on the educational achievements of elementary school students in Germany, measured by secondary-school transition rates. Using school-level data from North-Rhine Westphalia (NRW), Germany’s largest federal state, we exploit the staggered adoption of LEZs since 2008 in a difference-indifferences framework. Our results imply that LEZs increased rates of transition to the academic track by 0.9-1.6 percentage points in NRW. Our findings on the district level for all of Germany confirm the external validity of these findings. Using geo-referenced data from the German Socio-Economic Panel, we provide suggestive evidence that a reduction in the prevalence of respiratory infections is a vital channel through which LEZs affect schooling outcomes. 

Do individuals learn from salient river floods and update their risk perceptions accordingly? We exploit the Western European flood in 2021 to estimate changes in property values across German flood risk regions. Flood-prone areas in proximity to the flood or with higher climate change beliefs experience housing price declines, while we observe no statistically significant effects in other flood risk zones. Our results imply that flood risk discounts in the housing market will not adjust uniformly, which may result in suboptimal capital allocation in areas at risk of future flooding.

Globally, current climate policy implementation is failing to deliver on the ambitions outlined in the Paris Agreement and the Nationally Determined Contributions. We argue that a more strategic and anticipatory approach to policy mix design and implementation can help contribute to realizing these ambitions. Policy mixes need to co-evolve as energy transitions progress, in order to effectively target multiple transition challenges as these change over time. Accordingly, policy assessments need to both consider interactions between instruments and adopt a dynamic perspective, in terms of design logics and evaluative criteria. We highlight the need for a construction and assessment methodology for designing policy mix pathways which explicitly considers durability risks and anticipatory design principles. Our paper addresses this gap by proposing and illustrating a novel assessment framework for policy mix pathway construction and ex-ante assessment. We identify generalizable durability challenges for effective anticipatory design: dynamic cost effectiveness, the distributive impacts of pathways, acceptance, along with fiscal and governance requirements to effectively implement. We illustrate the value of the approach by application to the German light duty vehicle sector transition. We construct and comparatively assess three alternative pathways promising to deliver the German LDV sector 2030 GHG targets. The pathways differ in their logics, selection and calibration of instruments, and which of the three main market dynamics driving a diffusion stage transition – BEV purchase, stock usage, and ICE scrappage – they target. We also assess the illustrative pathways and discuss trade-offs. We argue that this approach can help improve climate policy planning and implementation processes, thus reducing risks of backlash and increasing the likelihood of attaining ambitious mitigation targets.  

Climate movements have gained momentum in recent years, aiming to create public awareness of the consequences of climate change through salient climate protests. This paper investigates whether concerns about climate change increase following demonstrative protests and confrontational acts of civil disobedience. Leveraging individual-level survey panel data from Germany, we exploit exogenous variations in the timing of climate protests relative to survey interview dates to compare climate change concerns in the days before and after a protest (N=24,535). Following climate protests, we find increases in concerns about climate change by, on average, 1.2 percentage points. Further, we find no statistically significant evidence that concerns of any subpopulation decreased after climate protests. Lastly, the effectiveness of protests is highest when concern levels are low.

Taxes targeting fuel, road usage, or carbon emissions for environmental protection often face public opposition. Can widely accessible machine learning methods aid in predicting and understanding opposition to environmental taxes? This study uses the random forest algorithm to predict opposition to increased environmental taxes based on 41 theoretically relevant respondent characteristics. Drawing on nationally representative surveys, we predict individual tax opposition across 28 countries in 2010 and 2020 (N=70,710). Personal values and environmental evaluations tend to be more influential than demographics in predicting tax opposition, with key variables differing between countries and over time. A lack of commitment to pro-environmental behavior is the most important predictor in emerging economies. Conversely, concerns about environmental issues and prioritization of jobs and prices are influential in high-income countries, gaining prominence over the previous decade. Policymakers can leverage these insights to tailor communication of environmental tax increases in different contexts, emphasizing, for instance, job creation.

Does exposure to air pollution impact mental health? This paper uses administrative health insurance data to estimate the long-term cumulative effects of air pollution exposure on mental health outcomes. For identification, we exploit the staggered introduction of Low Emission Zones (LEZs) across German cities, which restrict access for emission-intensive vehicles. We find that LEZs reduce various air pollutants and improve the population's mental health measured by depression and anxiety diagnoses, prescriptions, and specialist visits. The health benefits emerge gradually, with younger individuals benefiting the most. Our findings suggest substantial mental health co-benefits and avoided health costs from improved air quality.

There is little knowledge about the shape, prominence and drivers of work-to-work transitions in low- and middle-income countries. This paper examines these elements in the context of South Africa and Indonesia – two middle-income countries with similar development levels yet different labour market characteristics. We employ a comparative cross-country methodology using long-term panel data. This enables us to examine work-to-work transitions across and within age cohorts and exploit the panel structure of the data through a fixed-effects model to identify the drivers of these transitions. We find that while the prominent transition types differ between the two countries, younger workers have higher transition rates. Moreover, we find that precarious forms of employment are persistent: individuals who start their careers at the bottom of the transition ladder (i.e., in informal work, the agriculture sector or a low-skill occupation) are less likely to transition out of this situation. Finally, we unveil suggestive evidence that computer and socioemotional skills play a role in encouraging certain transitions in South Africa and Indonesia, respectively.