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- Volume 15, 2023
Annual Review of Resource Economics - Volume 15, 2023
Volume 15, 2023
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Heterogeneous Effects of Obesity on Life Expectancy: A Global Perspective
Vol. 15 (2023), pp. 433–554More LessBased on an extensive literature review and publicly available data, this article provides insights into the differences in prevalence, sociodemographics, contributing factors, socioeconomic consequences, health effects, and public policies related to obesity between developed and developing countries. Most importantly, it explores the relationship between obesity and life expectancy and identifies potential mechanisms through which obesity affects mortality, highlighting the differences between developed and developing countries and by gender. It also examines how the associations between obesity and life expectancy differ at the population level compared with the individual level. The evidence shows a negative association between obesity and longevity, as well as an increased risk of various diseases with the rising rates of obesity. The findings contribute to a better understanding of the heterogeneous effects of obesity on life expectancy between developed and developing countries and by gender. The article also discusses the effectiveness of various policies adopted to address obesity and provides suggestions to address obesity problems and improve health and well-being in these countries.
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Advances in Causal Inference at the Intersection of Air Pollution and Health Outcomes
Vol. 15 (2023), pp. 455–469More LessThis article provides an overview of the recent economics literature analyzing the effect of air pollution on health outcomes. We review the common approaches to measuring and modeling air pollution exposures and the epidemiological and biological literature on health outcomes that undergird federal air regulations in the United States. The article contrasts the methods used in the epidemiology literature with the causal inference framework used in economics. In particular, we review the common sources of estimation bias in epidemiological approaches that the economics literature has sought to overcome with research designs that take advantage of natural experiments. We review new promising research designs for estimating concentration-response functions and identify areas for further research.
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Slow Magic: Agricultural Versus Industrial R&D Lag Models
Vol. 15 (2023), pp. 471–493More LessR&D is slow magic. It takes many years before research investments begin to affect productivity, but then they can affect productivity for a long time. Many economists get this wrong. Here, we revisit the conceptual foundations for R&D lag models used to represent the temporal links between research investments and impact, review prevalent practice, and document and discuss a range of evidence on R&D lags in agriculture and other industries. Our theory and evidence consistently support the use of longer lags with a different overall lag profile than is typically imposed in studies of industrial R&D and government compilations of R&D knowledge stocks. Many studies systematically fail to recognize the many years of investment and effort typically required to create a new technology and bring it to market and the subsequent years as the technology is diffused and adopted. Consequential distortions in the measures and economic understanding are implied.
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The Rigor Revolution: New Standards of Evidence for Impact Assessment of International Agricultural Research
Vol. 15 (2023), pp. 495–515More LessWe take stock of the major changes in methodology for studying the impacts of international agricultural research, focusing on the period 2006–2020. Impact assessment of agricultural research has a long and recognized tradition. Until the mid-2000s, such assessments were dominated by a model of demand for and supply of agricultural products in partial equilibrium. The basic ideas for this approach were sketched out by Griliches more than half a century ago. We describe the implications of heightened standards of evidence for good practice in three domains of research design: causal inference, valid measurement, and statistical representativeness. We document advances in each of these domains and review recent evidence that demonstrates the lessons that can be learned from adopting these practices, emphasizing the importance of evidence at-scale, the need to consider portfolios of innovations at a national level, and the challenges of accounting for innovations that are promoted as bundles.
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