Few United States government programs are as controversial as those designed to aid the poor. From tax credits to medical assistance, aid to needy families is surrounded by debate—on what benefits should be offered, what forms they should take, and how they should be administered. The past few decades, in fact, have seen this debate lead to broad transformations of aid programs themselves, with Aid to Families with Dependent Children replaced by Temporary Assistance to Needy Families, the Earned Income Tax Credit growing from a minor program to one of the most important for low-income families, and Medicaid greatly expanding its eligibility. This volume provides a remarkable overview of how such programs actually work, offering an impressive wealth of information on the nation's nine largest "means-tested" programs—that is, those in which some test of income forms the basis for participation. For each program, contributors describe origins and goals, summarize policy histories and current rules, and discuss the recipient's characteristics as well as the different types of benefits they receive. Each chapter then provides an overview of scholarly research on each program, bringing together the results of the field's most rigorous statistical examinations. The result is a fascinating portrayal of the evolution and current state of means-tested programs, one that charts a number of shifts in emphasis—the decline of cash assistance, for instance, and the increasing emphasis on work. This exemplary portrait of the nation's safety net will be an invaluable reference for anyone interested in American social policy.

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 addresses the rising productivity gap between the global frontier and other firms, and identifies a number of structural impediments constraining business start-ups, knowledge diffusion and resource allocation (such as barriers to up-scaling and relatively high rates of skill mismatch).

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.

"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"--

This volume presents six new papers on environmental and energy economics and policy in the United States. Rebecca Davis, J. Scott Holladay, and Charles Sims analyze recent trends in and forecasts of coal-fired power plant retirements with and without new climate policy. Severin Borenstein and James Bushnell examine the efficiency of pricing for electricity, natural gas, and gasoline. James Archsmith, Erich Muehlegger, and David Rapson provide a prospective analysis of future pathways for electric vehicle adoption. Kenneth Gillingham considers the consequences of such pathways for the design of fuel vehicle economy standards. Frank Wolak investigates the long-term resource adequacy in wholesale electricity markets with significant intermittent renewables. Finally, Barbara Annicchiarico, Stefano Carattini, Carolyn Fischer, and Garth Heutel review the state of research on the interactions between business cycles and environmental policy.

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.