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.

In 21 prescriptive rather than descriptive treatments, well known academic economists set out how they think the discipline should be practiced both internally and in relation to other fields and arenas of society. They explore economics as a historical process and as a public science, realism in model buildings, social science, normative and positive aspects, extracting information from data, and worthwhile economics. Annotation copyright by Book News, Inc., Portland, OR.

Orthodox economics operates within a hypothesized world of perfect competition in which perfect consumers and firms act to bring about supposedly optimal outcomes. The discrepancies between this model and the reality it claims to address are then attributed to particular imperfections in reality itself. Most heterodox economists seize on this fact and insist that the world is characterized by imperfect competition. But this only ties them to the notion of perfect competition, which remains as their point of departure and base of comparison. There is no imperfection without perfection. In Capitalism, Anwar Shaikh takes a different approach. He demonstrates that most of the central propositions of economic analysis can be derived without any reference to standard devices such as hyperrationality, optimization, perfect competition, perfect information, representative agents, or so-called rational expectations. This perspective allows him to look afresh at virtually all the elements of economic analysis: the laws of demand and supply, the determination of wage and profit rates, technological change, relative prices, interest rates, bond and equity prices, exchange rates, terms and balance of trade, growth, unemployment, inflation, and long booms culminating in recurrent general crises. In every case, Shaikh's innovative theory is applied to modern empirical patterns and contrasted with neoclassical, Keynesian, and Post-Keynesian approaches to the same issues. Shaikh's object of analysis is the economics of capitalism, and he explores the subject in this expansive light. This is how the classical economists, as well as Keynes and Kalecki, approached the issue. Anyone interested in capitalism and economics in general can gain a wealth of knowledge from this ground-breaking text.

"Policy-makers often call for expanding public spending on infrastructure, which includes a broad range of investments from roads and bridges to digital networks that will expand access to high-speed broadband. Some point to near-term macro-economic benefits and job creation, others focus on long-term effects on productivity and economic growth. This volume explores the links between infrastructure spending and economic outcomes, as well as key economic issues in the funding and management of infrastructure projects. It draws together research studies that describe the short-run stimulus effects of infrastructure spending, develop new estimates of the stock of U.S. infrastructure capital, and explore the incentive aspects of public-private partnerships (PPPs). A salient issue is the treatment of risk in evaluating publicly-funded infrastructure projects and in connection with PPPs. The goal of the volume is to provide a reference for researchers seeking to expand research on infrastructure issues, and for policy-makers tasked with determining the appropriate level of infrastructure spending"--

This textbook articulates the elements of good craftsmanship in applied microeconomic research and demonstrates its effectiveness with multiple examples from economic literature. Empirical economic research is a combination of several elements: theory, econometric modelling, institutional analysis, data handling, estimation, inference, and interpretation. A large body of work demonstrates how to do many of these things correctly, but to date, there is no central resource available which articulates the essential principles involved and ties them together. In showing how these research elements can be best blended to maximize the credibility and impact of the findings that result, this book presents a basic framework for thinking about craftsmanship. This framework lays out the proper context within which the researcher should view the analysis, involving institutional factors, complementary policy instruments, and competing hypotheses that can influence or explain the phenomena being studied. It also emphasizes the interconnectedness of theory, econometric modeling, data, estimation, inference, and interpretation, arguing that good craftsmanship requires strong links between each. Once the framework has been set, the book devotes a chapter to each element of the analysis, providing robust instruction for each case. Assuming a working knowledge of econometrics, this text is aimed at graduate students and early-career academic researchers as well as empirical economists looking to improve their technique.

"The challenges facing agriculture are plenty. Along with the world's growing population and diminishing amounts of water and arable land, the gradual increase in severe weather presents new challenges and imperatives for producing new, more resilient crops to feed a more crowded planet in the twenty-first century. Innovation has historically helped agriculture keep pace with earth's social, population, and ecological changes. In the last 50 years, mechanical, biological, and chemical innovations have more than doubled agricultural output while barely changing input quantities. The ample investment behind these innovations was available because of a high rate of return: a 2007 paper found that the median ROI in agriculture was 45 percent between 1965 and 2005. This landscape has changed. Today many of the world's wealthier countries have scaled back their share of GDP devoted to agricultural R&D amid evidence of diminishing returns. Universities, which have historically been a major source of agricultural innovation, increasingly depend on funding from industry rather than government to fund their research. As Upton Sinclair wrote of the effects industry influences, "It is difficult to get a man to understand something when his salary depends upon his not understanding it." In this volume of the NBER Conference Report series, editor Petra Moser offers an empirical, applied-economic framework to the different elements of agricultural R&D, particularly as they relate to the shift from public to private funding. Individual chapters examine the sources of agricultural knowledge and investigate challenges for measuring the returns to the adoption of new agricultural technologies, examine knowledge spillovers from universities to agricultural innovation, and explore interactions between university engagement and scientific productivity. Additional analysis of agricultural venture capital point to it as an emerging and future source of resource in this essential domain"--

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.