The 2022 energy price shock, triggered by geopolitical disruptions, prompted governments to implement widespread policy interventions to support firms and accelerate the energy transition. Using nationally representative firm surveys from the United States and Germany, this paper examines how firms adjusted to the shock and their reported responses to fiscal incentives. We document that firms were generally resilient, relying on a combination of price pass-through, margin adjustment, and energy-saving investments. Responses differed across countries: firms in Germany - facing a larger and more persistent shock - were more likely to invest in energy efficiency and reduce energy consumption, while firms in the United States primarily absorbed costs through lower profits. Evidence from input-output simulations suggests that, absent policy intervention, energy-intensive sectors would have faced substantially larger price pressures. We also find that firms with pre-existing plans to reduce greenhouse gas emissions were more likely to report intending to utilize fiscal incentive programs, suggesting that policy targeting and credibility may play an important role in supporting the green transition. Overall, our findings provide descriptive evidence on firms' responses to energy price shocks and announced policy measures, offering insights for the design of targeted and predictable energy and climate policies in an increasingly fragmented geopolitical environment.
Although pollution is widespread, there is little evidence about how it might harm children’s long run outcomes. Using the detailed, geocoded data that follows national representative cohorts of children born to the National Longitudinal Survey of Youth respondents over time, I compare siblings who were gestating before versus after a Toxic Release Inventory site opened or closed within one mile of their home. I find that children who were exposed prenatally to industrial pollution have lower wages, are more likely to be in poverty as adults, have fewer years of completed education, and are less likely to graduate high school.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
Chest X-rays are an inexpensive and widely available imaging modality for diagnosing or monitoring a variety of medical conditions. Given their abundance, healthcare providers could greatly benefit from automated systems capable of screening healthy patients and supporting the diagnosis of pathological cases. Deep learning has become central to such decision-support systems, offering accurate and efficient image classification that can improve clinical workflows and reduce radiologist workload. However, despite the rapid evolution of general-purpose neural architectures, particularly attention-based models, their application to medical imaging remains constrained by limited incorporation of medical domain knowledge. Most existing attention mechanisms optimize only task-specific losses, disregarding crucial anatomical and lesion-location priors, which can hinder generalization and interpretability. In this work, we introduce a fully automated, attention-guided classification framework that integrates medical priors through an on-the-fly segmentation of the lungs, followed by a spatially aware attention loss that directs the network’s focus toward clinically relevant regions. The method requires minimal physician input—only a single annotated X-ray indicating potential lesion areas at initialization and generalizes effectively across patients without relying on absolute bounding-box coordinates. Gradient-based activation mapping is further employed to ensure alignment between attention and lesion-specific regions. Our approach is architecture-agnostic and integrates seamlessly into end-to-end pipelines. Experiments on two medical image datasets demonstrate that the proposed segmentation-enhanced attention loss improves both classification accuracy and representation interpretability compared to the standard cross-entropy loss. The code is available at: https://github.com/rcorizzo/cxr-segmentation-attention/ .
Supported decision-making is rapidly gaining attention as an alternative to guardianship and other forms of surrogate decision-making for people with cognitive disabilities. This article provides an overview of the legal status of supported decision-making in the United States, with particular attention to how states are recognizing supported decision-making in different legal contexts. It then explores how the law of supported decision-making applies to clinical research. It explains that although federal research regulations and guidance do not explicitly address formal supported decision-making, individuals may use supported decision-making to make decisions about participating in clinical research and researchers may be required to allow such use under federal laws that protect against disability discrimination. It then concludes by considering legal barriers to greater use of supported decision-making and how these barriers might be overcome.
