While local air pollutants are well-known to have detrimental impacts on human health, we lack a complete understanding of the economic costs imposed by the negative health effects from exposure to maritime air pollution. In addition, because health effects are uncertain and likely to be heterogeneous due to the non-uniform population distribution and location of ships, it is difficult to design efficient policies to mitigate maritime emissions. Nevertheless, the transition to a net-zero emissions economy will require the implementation of policies to curb emissions in the maritime sector. By combining detailed Norwegian administrative data with highly granular geo-referenced data on shipping routes and air pollution, MARHEALTH aims to quantify the health effects of maritime air pollution and to assess the effectiveness of pollution mitigation policies in the shipping sector. These objectives will be addressed through three specific subgoals: 1) Generate high-quality data sets by combining administrative health and socio-economic data on individuals with highly granular geo-referenced data on shipping routes of all container and passenger vessels and satellite- and ground-based environmental data on air quality and weather. Further, I will collect and merge information on the transition from fossil fuel-operated to electrified passenger ships. 2) Empirical analysis of the health impacts of maritime air pollution by estimating causal impacts of pollution from ports. Linked data on health outcomes with socioeconomic characteristics of individuals allows me to uncover socio-demographic disparities in the health effects of exposed individuals. 3) Empirical quantification of the benefits of green transition policies in the maritime sector by merging the environmental and individual-level data with information on shipping routes of electrified vessels to econometrically recover causal estimates.

A key goal of environmental policy is to ensure sustainable choices and investment. For the individual, revising choices and behavior might be necessary to avoid costs or obtain benefits introduced by policies. Though a growing literature has investigated how different environmental policies produce winners and losers through differences in costs and benefits between individuals, less is known about the causes of varying adaption to new policies. In the context of environmental policy, there are no studies of how the "hand of the past" determines the ability to adapt through choices that are not easily reversed, such as location, and exposure to wealth effects from revaluation through existing endowments at the individual or firm level. The goal of DEEP is to provide new knowledge of how existing differences interact with environmental policies in shaping choices, with consequences for policy effectiveness and the evolution of disparities among households and firms. The key to achieve this is the construction of novel and exceptionally ambitious models of decisions that affect outcomes over time, combined with data of unparalleled detail on choices, outcomes, income and assets of individuals, households and firms in settings featuring rich and highly relevant policy interventions. The results will contribute both to our understanding of how inequality develops under policy choices in interaction with preexisting disparities, and our understanding of the trade-offs between equity and effectiveness when designing policies. The project will center on two strategically important areas: 1) electrification of transportation, where existing vehicle, location and wealth of households influence policy impact, with implications for wealth and welfare over time; and 2) renewable energy subsidies, where existing capacities of firms influence policy impact on investments and capacity decisions, with implications for the evolution of market structure and market power.

I3W aims at generating empirical knowledge and novel theories to explain the joint distribution of work, wealth, and welfare in the population using macroeconomic theory, structural quantitative models, and micro data. Specifically, I3W’s overall goal will be achieved through the following research objectives and their corresponding work packages (WP): I. Develop a macroeconomic framework of the drivers of welfare inequality. In WP1 I will develop a model based on expected utility—a function of lifetime consumption, lifetime leisure, and life expectancy and their joint distributions—for measuring inequality in economic well-being within a country using rich longitudinal datasets. This framework will allow me to measure and decompose the welfare inequality in a selection of European countries and compare to the US over time, allowing a deeper understanding of the observed heterogeneity in the population along various dimensions: life expectancy, hours worked, wealth, income, consumption, and ultimately also welfare. II. Analyze underlying factors affecting the drivers: the case of inequality of leisure in relation to access to insurance. In WP2 I will test the hypothesis that better insurance, interpreted in its most general form, leads to a more positive correlation between wages and hours worked. I will base my arguments on observations from micro data, show it theoretically using a theory of labor supply that is consistent with cross-country evidence, evidence over time, and evidence from micro data, and quantify the importance in practice with a carefully calibrated heterogenous-agent model. The outputs of WP1 and WP2 will be two working papers that will push the current boundaries of the welfare inequality research field while providing policymakers a tool box for the analysis and comparison across European countries of welfare inequality and its drivers.

Tax evasion represents a pervasive phenomenon and a substantial portion can be attributable to income held abroad. The general consensus at global level is that cross-border tax evasion can be fought effectively by further increasing information exchange between countries. Anecdotal evidence suggests that introducing a system for the automatic exchange of information (AEOI) is extremely costly. Yet, we lack a direct assessment of the related benefits. TAXFAIR aims to analyze the effectiveness of the AEOI system to mobilize tax revenues and to determine the characteristics of an AEOI system that maximizes tax revenue extraction. Specifically, 1. I will create a high-quality novel dataset based on administrative data on Norwegian residents and a dataset containing institutional information on locally implemented AEOI systems; 2. I will apply state-of-the-art regression models to the administrative dataset to quantify the tax revenue recovered from the introduction of a AEOI system; 3. By combining the institutional information dataset and the administrative dataset, I will empirically analyze the traits that maximize the tax revenues recovered from the local AEOI systems. Overall, TAXFAIR will provide a knowledge based framework for governments and policymakers which will allow them to increase the monetary resources highly needed for financing the recovery from the massive negative economic shock induced by the COVID19 pandemic. While working on the action, I will develop new methodological skills (especially on machine learning), cross-sectorial skills (through the research stay at the IMF), stronger communication skills (by presenting at international conferences and via video clips), and further teaching and organizational skills (especially by organizing a case study competition joint with the industry). Overall, the MSCA fellowship will undoubtedly jump-start my career allowing me to emerge as an independent researcher with multiple attractive career pathways.