| Author | : Mr.Yan Carriere-Swallow |
| Publisher | : International Monetary Fund |
| Total Pages | : 50 |
| Release | : 2019-09-23 |
| Genre | : Computers |
| ISBN | : 1513514814 |
This SPR Departmental Paper will provide policymakers with a framework for studying changes to national data policy frameworks.
| Author | : Ajay Agrawal |
| Publisher | : University of Chicago Press |
| Total Pages | : 172 |
| Release | : 2024-03-05 |
| Genre | : Business & Economics |
| ISBN | : 0226833127 |
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.
| Author | : Katharine G. Abraham |
| Publisher | : University of Chicago Press |
| Total Pages | : 502 |
| Release | : 2022-03-11 |
| Genre | : Business & Economics |
| ISBN | : 022680125X |
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.
| Author | : Mr.Yan Carriere-Swallow |
| Publisher | : International Monetary Fund |
| Total Pages | : 50 |
| Release | : 2019-09-23 |
| Genre | : Computers |
| ISBN | : 1513511432 |
This SPR Departmental Paper will provide policymakers with a framework for studying changes to national data policy frameworks.
| Author | : Edward L. Glaeser |
| Publisher | : University of Chicago Press |
| Total Pages | : 479 |
| Release | : 2021-11-11 |
| Genre | : Business & Economics |
| ISBN | : 022680058X |
"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"--
| Author | : Avi Goldfarb |
| Publisher | : University of Chicago Press |
| Total Pages | : 510 |
| Release | : 2015-05-08 |
| Genre | : Business & Economics |
| ISBN | : 022620684X |
There is a small and growing literature that explores the impact of digitization in a variety of contexts, but its economic consequences, surprisingly, remain poorly understood. This volume aims to set the agenda for research in the economics of digitization, with each chapter identifying a promising area of research. "Economics of Digitization "identifies urgent topics with research already underway that warrant further exploration from economists. In addition to the growing importance of digitization itself, digital technologies have some features that suggest that many well-studied economic models may not apply and, indeed, so many aspects of the digital economy throw normal economics in a loop. "Economics of Digitization" will be one of the first to focus on the economic implications of digitization and to bring together leading scholars in the economics of digitization to explore emerging research.
| Author | : Alina Mihaela Dima |
| Publisher | : Springer Nature |
| Total Pages | : 242 |
| Release | : 2022-03-05 |
| Genre | : Business & Economics |
| ISBN | : 3030932869 |
This book highlights the economic and social science perspectives in light of COVID-19. During 2020, leaders found themselves at historic crossroads, taking decisions under remarkable pressures and uncertainties. However, windows of opportunity are being created to shape the economic recovery, restore the health of the environment, develop sustainable business models, strengthen regional development, revitalize global cooperation, harness Industry 4.0, and redesign the social contracts, skills, and jobs. This book is an excellent resource for all those interested in economics and social sciences perspectives on digitalization and big data, especially in the light of the recent crisis determined by COVID-19. The chapters cover topics related to new models in entrepreneurship and innovation, sustainability and education, data science and digitalization, marketing and finance, etc., that will develop innovative instruments for countries, businesses, and education to revive after the crisis.
| Author | : Chunlei Tang |
| Publisher | : John Wiley & Sons |
| Total Pages | : 217 |
| Release | : 2016-06-13 |
| Genre | : Mathematics |
| ISBN | : 111913840X |
Provides an introduction of the data industry to the field of economics This book bridges the gap between economics and data science to help data scientists understand the economics of big data, and enable economists to analyze the data industry. It begins by explaining data resources and introduces the data asset. This book defines a data industry chain, enumerates data enterprises’ business models versus operating models, and proposes a mode of industrial development for the data industry. The author describes five types of enterprise agglomerations, and multiple industrial cluster effects. A discussion on the establishment and development of data industry related laws and regulations is provided. In addition, this book discusses several scenarios on how to convert data driving forces into productivity that can then serve society. This book is designed to serve as a reference and training guide for ata scientists, data-oriented managers and executives, entrepreneurs, scholars, and government employees. Defines and develops the concept of a “Data Industry,” and explains the economics of data to data scientists and statisticians Includes numerous case studies and examples from a variety of industries and disciplines Serves as a useful guide for practitioners and entrepreneurs in the business of data technology The Data Industry: The Business and Economics of Information and Big Data is a resource for practitioners in the data science industry, government, and students in economics, business, and statistics. CHUNLEI TANG, Ph.D., is a research fellow at Harvard University. She is the co-founder of Fudan’s Institute for Data Industry and proposed the concept of the “data industry”. She received a Ph.D. in Computer and Software Theory in 2012 and a Master of Software Engineering in 2006 from Fudan University, Shanghai, China.
| Author | : Sergio Consoli |
| Publisher | : Springer Nature |
| Total Pages | : 357 |
| Release | : 2021 |
| Genre | : Application software |
| ISBN | : 3030668916 |
This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models. The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis. This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications.
