Publications

A rapid decarbonisation of the United States economy is expected to disproportionately impact regions historically embedded in domestic fossil fuel production. For decades, social scientists have documented the economic and human toll of deindustrialisation, foreshadowing the transitional risks that these regions may face amidst decarbonisation. However, econometric studies evaluating the magnitude, duration, and spatial distribution of unemployment impacts in declining mining regions remain scarce, despite their pertinence to policymaking. Therefore, using econometric estimation methods that control for unobserved heterogeneity via two-way fixed effects, spatial effects, heterogeneous time trends, and grouped fixed effects for a panel of 3,072 US counties covering 2002–2019, we demonstrate that coal mine closures induce a contemporaneous rise in county unemployment rate with spatial ripple effects. Furthermore, evidence of local-level resilience to such shocks over a two-year time horizon is weak. To further account for county-level heterogeneity, we construct a typology of coal counties based on qualities theorised to be resilient to industrial decline. Our findings suggest the significant potential of investing in alternative sectors in localities with promising levels of economic diversity, retraining job seekers, providing relocation support in rural areas, and subsidising childcare in places with low female labour force participation.

Meeting the Paris Agreement’s climate targets necessitates better knowledge about which climate policies work in reducing emissions at the necessary scale. We provide a global, systematic ex post evaluation to identify policy combinations that have led to large emission reductions out of 1500 climate policies implemented between 1998 and 2022 across 41 countries from six continents. Our approach integrates a comprehensive climate policy database with a machine learning–based extension of the common difference-in-differences approach. We identified 63 successful policy interventions with total emission reductions between 0.6 billion and 1.8 billion metric tonnes CO2. Our insights on effective but rarely studied policy combinations highlight the important role of price-based instruments in well-designed policy mixes and the policy efforts necessary for closing the emissions gap.

Whether driven by recessions, industrial policy, or structural shifts in labor demand, economic change is inevitably accompanied by labor market frictions. These frictions vary across occupations, increasing the risk of certain groups of workers being left behind in times of economic transformation. Such challenges to labor market stability are often studied as exogenous shocks, taking the preferences and actions of workers to be fixed or negligible. However, incorporating the adaptive behavior that workers employ to cope with uncertainty is critical to understanding both aggregate labor market fluctuations and adverse labor market outcomes. Therefore, we endow a labor market model built on an occupational mobility network with empirically grounded dynamics in application effort, reservation wages, and on-the-job search intensity, drawing on U.S. micro data. The model additionally accommodates supply- and demand-side wage pressures, demographic turnover, and data-driven occupational labor demand. To identify the contribution of each mechanism, we compare six model variants of increasing complexity against national and occupationally disaggregated labor market data. Dynamic application effort and on-the-job search competition unequivocally improve model fit, capturing the cyclicality of search effort, long-term unemployment dynamics, and post-displacement wage losses across the business cycle. Reservation wages improve fit for higher-wage occupations while worsening fit for others, suggesting that reference-dependent wage preferences vary in salience across the wage distribution. Finally, we assess the labor market impacts of preferential hiring policies for long-term unemployed and new-entrant workers, finding that such policies improve employment outcomes for targeted workers and matching efficiency overall, though the magnitude of these effects is likely overestimated without the presence of behavioral frictions.

Wage growth is a key driver of local wealth accumulation, enabling greater household and community investment in public goods. Yet local wages vary substantially across US regions due to differences in industrial composition and exposure to macroeconomic shocks. Given the structure of local public finances, regions exposed to favorable wage shocks likely benefit from deepened tax bases whereas the fiscal capacity of others is eroded. This work estimates the effect of local wage shocks on education spending, exploiting variation in commuting zone industrial composition interacted with national industry value added shocks. We estimate that a 10\% increase in local wages generates a 2.1\% short-run increase in per-pupil education expenditure, with a long-run effect of approximately 4.4\%. This result is driven by a handful of states, whereas others exhibit limited within-state wage responsiveness. Although states exhibiting a weak or insignificant wage-expenditure relationship rely less on local taxation, the cleaner cross-state regularity runs through state fiscal policy: legally binding redistributive policies interrupting the fiscal transmission channel from wages to education spending, insulating local school budgets from wage fluctuations. Finally, we find that the wage-expenditure link is strongest in regions with historically low wage growth, indicating that economic stagnation may beget further stagnation through weaker fiscal capacity.

Prediction markets mobilize financial incentives to forecast binary event outcomes through the aggregation of dispersed beliefs and heterogeneous information. Their growing popularity and demonstrated predictive accuracy in political elections have raised speculation and concern regarding their susceptibility to manipulation and the potential consequences for democratic processes. Using agent-based simulations combined with an analytic characterization of price dynamics, we study how high-budget agents can introduce price distortions in prediction markets. We explore the persistence and stability of these distortions in the presence of herding or stubborn agents, and analyze how agent expertise affects market-price variance. Firstly we propose an agent-based model of a prediction market in which bettors with heterogeneous expertise, noisy private information, variable learning rates and budgets observe the evolution of public opinion on a binary election outcome to inform their betting strategies in the market. The model exhibits stability across a broad parameter space, with complex agent behaviors and price interactions producing self-regulatory price discovery. Second, using this simulation framework, we investigate the conditions under which a highly resourced minority, or ‘whale’ agent, with a biased valuation can distort the market price, and for how long. We find that biased whales can temporarily shift prices, with the magnitude and duration of distortion increasing when non-whale bettors exhibit herding behavior and slow learning. Our theoretical analysis corroborates these results, showing that whales can shift prices proportionally to their share of market capital, with distortion duration depending on non-whale learning rates and herding intensity.

An update from the Climate Econometrics team on strategy and tactics for Net Zero for the Royal Economic Society Newsletter.