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Author : Mr.Yan Carriere-Swallow,Mr.Vikram Haksar
Publisher : International Monetary Fund
Page : 50 pages
File Size : 42,8 Mb
Release : 2019-09-23
Category : Computers
ISBN : 9781513511436
This SPR Departmental Paper will provide policymakers with a framework for studying changes to national data policy frameworks.
Author : Katharine G. Abraham,Ron S. Jarmin,Brian C. Moyer,Matthew D. Shapiro
Publisher : University of Chicago Press
Page : 502 pages
File Size : 54,6 Mb
Release : 2022-03-11
Category : Business & Economics
ISBN : 9780226801254
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 : Ajay Agrawal,Joshua Gans,Avi Goldfarb,Catherine Tucker
Publisher : University of Chicago Press
Page : 172 pages
File Size : 47,6 Mb
Release : 2024-03-05
Category : Business & Economics
ISBN : 9780226833125
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 : Cornelia Hammer,Ms.Diane C Kostroch,Mr.Gabriel Quiros
Publisher : International Monetary Fund
Page : 41 pages
File Size : 43,7 Mb
Release : 2017-09-13
Category : Business & Economics
ISBN : 9781484318973
Big data are part of a paradigm shift that is significantly transforming statistical agencies, processes, and data analysis. While administrative and satellite data are already well established, the statistical community is now experimenting with structured and unstructured human-sourced, process-mediated, and machine-generated big data. The proposed SDN sets out a typology of big data for statistics and highlights that opportunities to exploit big data for official statistics will vary across countries and statistical domains. To illustrate the former, examples from a diverse set of countries are presented. To provide a balanced assessment on big data, the proposed SDN also discusses the key challenges that come with proprietary data from the private sector with regard to accessibility, representativeness, and sustainability. It concludes by discussing the implications for the statistical community going forward.
Author : Sree Kumar,Warren B. Chik,See-Kiong Ng,Sin Gee Teo
Publisher : Routledge
Page : 116 pages
File Size : 43,6 Mb
Release : 2018-10-03
Category : Social Science
ISBN : 9780429782633
"The data economy" is a term used by many, but properly understood by few. Even more so the concept of "big data". Both terms embody the notion of a digital world in which many transactions and data flows animate a virtual space. This is the unseen world in which technology has become the master, with the hand of the human less visible. In fact, however, it is human interaction in and around technology that makes data so pervasive and important – the ability of the human mind to extract, manipulate and shape data that gives meaning to it. This book outlines the findings and conclusions of a multidisciplinary team of data scientists, lawyers, and economists tasked with studying both the possibilities of exploiting the rich data sets made available from many human–technology interactions and the practical and legal limitations of trying to do so. It revolves around a core case study of Singapore’s public transport system, using data from both the private company operating the contactless payment system (EZ-Link) and the government agency responsible for public transport infrastructure (Land Transport Authority). In analysing both the possibilities and the limitations of these data sets, the authors propose policy recommendations in terms of both the uses of large data sets and the legislation necessary to enable these uses while protecting the privacy of users.
Author : Chunlei Tang
Publisher : John Wiley & Sons
Page : 217 pages
File Size : 45,9 Mb
Release : 2016-06-13
Category : Mathematics
ISBN : 9781119138402
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 : Shoshana Zuboff
Publisher : PublicAffairs
Page : 658 pages
File Size : 52,6 Mb
Release : 2019-01-15
Category : Business & Economics
ISBN : 9781610395700
The challenges to humanity posed by the digital future, the first detailed examination of the unprecedented form of power called "surveillance capitalism," and the quest by powerful corporations to predict and control our behavior. In this masterwork of original thinking and research, Shoshana Zuboff provides startling insights into the phenomenon that she has named surveillance capitalism. The stakes could not be higher: a global architecture of behavior modification threatens human nature in the twenty-first century just as industrial capitalism disfigured the natural world in the twentieth. Zuboff vividly brings to life the consequences as surveillance capitalism advances from Silicon Valley into every economic sector. Vast wealth and power are accumulated in ominous new "behavioral futures markets," where predictions about our behavior are bought and sold, and the production of goods and services is subordinated to a new "means of behavioral modification." The threat has shifted from a totalitarian Big Brother state to a ubiquitous digital architecture: a "Big Other" operating in the interests of surveillance capital. Here is the crucible of an unprecedented form of power marked by extreme concentrations of knowledge and free from democratic oversight. Zuboff's comprehensive and moving analysis lays bare the threats to twenty-first century society: a controlled "hive" of total connection that seduces with promises of total certainty for maximum profit -- at the expense of democracy, freedom, and our human future. With little resistance from law or society, surveillance capitalism is on the verge of dominating the social order and shaping the digital future -- if we let it.
Author : OECD
Publisher : OECD Publishing
Page : 135 pages
File Size : 51,6 Mb
Release : 2019-11-26
Category : Electronic
ISBN : 9789264660656
This report examines the opportunities of enhancing access to and sharing of data (EASD) in the context of the growing importance of artificial intelligence and the Internet of Things. It discusses how EASD can maximise the social and economic value of data re-use and how the related risks and challenges can be addressed. It highlights the trade-offs, complementarities and possible unintended consequences of policy action – and inaction. It also provides examples of EASD approaches and policy initiatives in OECD countries and partner economies.
Author : Chunlei Tang
Publisher : Springer Nature
Page : 376 pages
File Size : 41,7 Mb
Release : 2021-01-31
Category : Political Science
ISBN : 9783030601928
This book defines and develops the concept of data capital. Using an interdisciplinary perspective, this book focuses on the key features of the data economy, systematically presenting the economic aspects of data science. The book (1) introduces an alternative interpretation on economists’ observation of which capital has changed radically since the twentieth century; (2) elaborates on the composition of data capital and it as a factor of production; (3) describes morphological changes in data capital that influence its accumulation and circulation; (4) explains the rise of data capital as an underappreciated cause of phenomena from data sovereign, economic inequality, to stagnating productivity; (5) discusses hopes and challenges for industrial circles, the government and academia when an intangible wealth brought by data (and information or knowledge as well); (6) proposes the development of criteria for measuring regulating data capital in the twenty-first century for regulatory purposes by looking at the prospects for data capital and possible impact on future society. Providing the first a thorough introduction to the theory of data as capital, this book will be useful for those studying economics, data science, and business, as well as those in the financial industry who own, control, or wish to work with data resources.
Author : Gábor Békés,Gábor Kézdi
Publisher : Cambridge University Press
Page : 741 pages
File Size : 50,9 Mb
Release : 2021-05-06
Category : Business & Economics
ISBN : 9781108483018
A comprehensive textbook on data analysis for business, applied economics and public policy that uses case studies with real-world data.
Author : Alina Mihaela Dima,Mihaela Kelemen
Publisher : Springer Nature
Page : 242 pages
File Size : 45,5 Mb
Release : 2022-03-05
Category : Business & Economics
ISBN : 9783030932862
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 : OECD
Publisher : OECD Publishing
Page : 456 pages
File Size : 45,8 Mb
Release : 2015-10-06
Category : Electronic
ISBN : 9789264229358
This report improves the evidence base on the role of Data Driven Innovation for promoting growth and well-being, and provide policy guidance on how to maximise the benefits of DDI and mitigate the associated economic and societal risks.
Author : Sergio Consoli,Diego Reforgiato Recupero,Michaela Saisana
Publisher : Springer Nature
Page : 357 pages
File Size : 53,7 Mb
Release : 2021
Category : Application software
ISBN : 9783030668914
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.
Author : Jan Krämer,Pierre Senellart ,Alexandre de Streel
Publisher : Centre on Regulation in Europe asbl (CERRE)
Page : 103 pages
File Size : 55,7 Mb
Release : 2020-06-15
Category : Political Science
ISBN : 8210379456XXX
This study provides recommendations on how to make personal data portability more effective. This will truly empower consumers to use the services they want and share their data with whoever they wish and stimulate innovation in Europe. With the entry into force of the GDPR, European citizens gained new rights, notably with data portability. But two years later, there is still little sign of people exercising this right, and of companies offering an easy and convenient service for data portability. While the European Commission is finalising its evaluation of the GDPR and closes its consultation on the European data strategy, the authors, professors Jan Krämer, Pierre Senellart and Alexandre de Streel*, warn that the current legal framework requires clarifications to better empower European citizens in a data-driven society. In this study, they identify barriers to data portability, including the lack of possibilities to import data as well as the lack of common standards and tools to access data as easy as the click of a button. The ability to provide users with a centralised dashboard for monitoring and controlling the flow of their data is also critically missing. “Today, consumers do not widely use data portability for reasons that can and should be overcome. Making data portability more effective is better for competition, for innovation and to empower users,” stress the authors. “There should be no second-guessing on whether to make data portability more effective, the time to act is now.” The current EU framework encourages data portability, but there are legal gaps that the EU should fill. The authors insist on the need for detailed guidance on how data portability can be facilitated and on which data is subject to data portability without violating privacy rights. They advocate that data provided by users when using a service, such as search history (i.e. “observed data”) should clearly be included under the scope of data portability. The authors consider it essential that the obligation to offer standardised Application Programming Interfaces (APIs) be much more widespread to enable consumers to continuously port their data. “We believe that standardised APIs that enable continuous data portability is a prerequisite for encouraging more organisations to import personal data, and for encouraging more consumers to initiate such transfers,” explain the authors. Projects, such as the Data Transfer Project have highlighted that continuous data portability is technically feasible. The authors argue that Personal Management Information Systems (PIMSs) facilitate the complex consent management and offer users a centralised dashboard for monitoring and controlling the flow of their data will have a crucial role to play for the wider adoption of data portability. “It must be as easy as clicking a button for consumers to continuously share data they created with one provider to another provider. This may also require educating and informing users on their rights through information campaigns alongside clear policy measures,” explain the authors. Nevertheless, they stress that PIMSs are not likely to find a sustainable business model, and thus, policy makers should support the emergence of open-source projects by setting common standards for data transfers, consent management, and identity management.
Author : Alessandro Acquisti,Stefanos Gritzalis,Costos Lambrinoudakis,Sabrina di Vimercati
Publisher : CRC Press
Page : 494 pages
File Size : 48,8 Mb
Release : 2007-12-22
Category : Business & Economics
ISBN : 9781420052183
During recent years, a continuously increasing amount of personal data has been made available through different websites around the world. Although the availability of personal information has created several advantages, it can be easily misused and may lead to violations of privacy. With growing interest in this area, Digital Privacy: Theory, Technologies, and Practices addresses this timely issue, providing information on state-of-the-art technologies, best practices, and research results, as well as legal, regulatory, and ethical issues. This book features contributions from experts in academia, industry, and government.
