A timely investigation of the potential economic effects, both realized and unrealized, of artificial intelligence within the United States healthcare system. In sweeping conversations about the impact of artificial intelligence on many sectors of the economy, healthcare has received relatively little attention. Yet it seems unlikely that an industry that represents nearly one-fifth of the economy could escape the efficiency and cost-driven disruptions of AI. The Economics of Artificial Intelligence: Health Care Challenges brings together contributions from health economists, physicians, philosophers, and scholars in law, public health, and machine learning to identify the primary barriers to entry of AI in the healthcare sector. Across original papers and in wide-ranging responses, the contributors analyze barriers of four types: incentives, management, data availability, and regulation. They also suggest that AI has the potential to improve outcomes and lower costs. Understanding both the benefits of and barriers to AI adoption is essential for designing policies that will affect the evolution of the healthcare system.
This book provides a careful historical analysis of the co-evolution of educational attainment and the wage structure in the United States through the twentieth century. The authors propose that the twentieth century was not only the American Century but also the Human Capital Century. That is, the American educational system is what made America the richest nation in the world. Its educational system had always been less elite than that of most European nations. By 1900 the U.S. had begun to educate its masses at the secondary level, not just in the primary schools that had remarkable success in the nineteenth century. The book argues that technological change, education, and inequality have been involved in a kind of race. During the first eight decades of the twentieth century, the increase of educated workers was higher than the demand for them. This had the effect of boosting income for most people and lowering inequality. However, the reverse has been true since about 1980. This educational slowdown was accompanied by rising inequality. The authors discuss the complex reasons for this, and what might be done to ameliorate it.
We study the long-term impact of climate change on economic activity across countries, using a stochastic growth model where labor productivity is affected by country-specific climate variables—defined as deviations of temperature and precipitation from their historical norms. Using a panel data set of 174 countries over the years 1960 to 2014, we find that per-capita real output growth is adversely affected by persistent changes in the temperature above or below its historical norm, but we do not obtain any statistically significant effects for changes in precipitation. Our counterfactual analysis suggests that a persistent increase in average global temperature by 0.04°C per year, in the absence of mitigation policies, reduces world real GDP per capita by more than 7 percent by 2100. On the other hand, abiding by the Paris Agreement, thereby limiting the temperature increase to 0.01°C per annum, reduces the loss substantially to about 1 percent. These effects vary significantly across countries depending on the pace of temperature increases and variability of climate conditions. We also provide supplementary evidence using data on a sample of 48 U.S. states between 1963 and 2016, and show that climate change has a long-lasting adverse impact on real output in various states and economic sectors, and on labor productivity and employment.
"Innovation and entrepreneurship are ubiquitous today, both as fields of study and as starting points for conversations among experts in government and economic development. But while these areas on continue to attract public and private investments, many measurements of their resulting economic growth-including productivity growth and business dynamism-have remained modest. Why this difference? Because not all business sectors are the same, and the transformative gains of some industries have been offset by stagnation or contraction in others. Accordingly, a nuanced understanding of the economy requires a nuanced understanding of where innovation and entrepreneurship occur and where they matter. Answering these questions allows for strategic public investment and the infrastructure for economic growth.The Role of Innovation and Entrepreneurship in Economic Growth, the latest entry in the NBER conference series, seeks to codify these answers. The editors leverage industry studies to identify specific examples of productivity improvements enabled by innovation and entrepreneurship, including those from new production technologies, increased competition, new organizational forms, and other means. Taken together, the volume illuminates whether the contribution of innovation and entrepreneurship to economic growth is likely to be concentrated, be it selected sectors or more broadly"--
The twenty-eighth edition of the NBER Macroeconomics Annual continues its tradition of featuring theoretical and empirical research on central issues in contemporary macroeconomics. As in previous years, this volume not only addresses recent developments in macroeconomics, but also takes up important policy-relevant questions and opens new debates that will continue for years to come. The first two papers in this year’s issue tackle fiscal and monetary policy, asking how interest rates and inflation can remain low despite fiscal policy behavior that appears inconsistent with a monetary policy regime focused only on inflation and output and not on fiscal balances as recently observed in the U.S. The third examines the implications of reference-dependent preferences and moral hazard in employment fluctuations in the labor market. The fourth paper addresses money and inflation, analyzing the long run inflation rate, the coexistence of money with pledgeable and money-like assets, and why inflation did not increase in response to business-cycle fluctuations in productivity. And the fifth looks at the stock market and how it relates to the real economy. The final chapter discusses the large and public shift towards more expansionary monetary policy that has recently occurred in Japan.
Introduction.Big data for twenty-first-century economic statistics: the future is now /Katharine G. Abraham, Ron S. Jarmin, Brian C. Moyer, and Matthew D. Shapiro --Toward comprehensive use of big data in economic statistics.Reengineering key national economic indicators /Gabriel Ehrlich, John Haltiwanger, Ron S. Jarmin, David Johnson, and Matthew D. Shapiro ;Big data in the US consumer price index: experiences and plans /Crystal G. Konny, Brendan K. Williams, and David M. Friedman ;Improving retail trade data products using alternative data sources /Rebecca J. Hutchinson ;From transaction data to economic statistics: constructing real-time, high-frequency, geographic measures of consumer spending /Aditya Aladangady, Shifrah Aron-Dine, Wendy Dunn, Laura Feiveson, Paul Lengermann, and Claudia Sahm ;Improving the accuracy of economic measurement with multiple data sources: the case of payroll employment data /Tomaz Cajner, Leland D. Crane, Ryan A. Decker, Adrian Hamins-Puertolas, and Christopher Kurz --Uses of big data for classification.Transforming naturally occurring text data into economic statistics: the case of online job vacancy postings /Arthur Turrell, Bradley Speigner, Jyldyz Djumalieva, David Copple, and James Thurgood ;Automating response evaluation for franchising questions on the 2017 economic census /Joseph Staudt, Yifang Wei, Lisa Singh, Shawn Klimek, J. Bradford Jensen, and Andrew Baer ;Using public data to generate industrial classification codes /John Cuffe, Sudip Bhattacharjee, Ugochukwu Etudo, Justin C. Smith, Nevada Basdeo, Nathaniel Burbank, and Shawn R. Roberts --Uses of big data for sectoral measurement.Nowcasting the local economy: using Yelp data to measure economic activity /Edward L. Glaeser, Hyunjin Kim, and Michael Luca ;Unit values for import and export price indexes: a proof of concept /Don A. Fast and Susan E. Fleck ;Quantifying productivity growth in the delivery of important episodes of care within the Medicare program using insurance claims and administrative data /John A. Romley, Abe Dunn, Dana Goldman, and Neeraj Sood ;Valuing housing services in the era of big data: a user cost approach leveraging Zillow microdata /Marina Gindelsky, Jeremy G. Moulton, and Scott A. Wentland --Methodological challenges and advances.Off to the races: a comparison of machine learning and alternative data for predicting economic indicators /Jeffrey C. Chen, Abe Dunn, Kyle Hood, Alexander Driessen, and Andrea Batch ;A machine learning analysis of seasonal and cyclical sales in weekly scanner data /Rishab Guha and Serena Ng ;Estimating the benefits of new products /W. Erwin Diewert and Robert C. Feenstra.
A calculation of the social returns to innovation /Benjamin F. Jones and Lawrence H. Summers --Innovation and human capital policy /John Van Reenen --Immigration policy levers for US innovation and start-ups /Sari Pekkala Kerr and William R. Kerr --Scientific grant funding /Pierre Azoulay and Danielle Li --Tax policy for innovation /Bronwyn H. Hall --Taxation and innovation: what do we know? /Ufuk Akcigit and Stefanie Stantcheva --Government incentives for entrepreneurship /Josh Lerner.
