This book explores the relations between objective, media-related, and social attitudinal as well as behavioral realities of private, expert, and corporate agents in the traditions of mass communication, journalism studies and behavioral economics. Results based on time series analyses for German data show that the news reports in a volatile manner on the economy and may influence its development through third-person effects. Bad economic news does not cause a decrease in private purchase intentions. Bad news may lead to a change in corporate decisions, such as advertising expenditures, because corporate decision makers may presume changes in consumer behavior through news.

This book tells the story of how the news media can help the inattentive members of the public become better educated and knowledgeable ‘economic citizens’. The authors argue that changes in the economy, journalism and consumer culture have made economic news more visible, more mainstream and more accessible. They show how economic news not only affects economic perceptions, but also interest in the economy, knowledge about the economy, and economic voting. Relying on statistical analyses, the book provides a comprehensive and systematic study of the effects of economic news.

This book offers a clear exposition of introductory macroeconomic theory along with more than 600 one- or two-sentence "news clips" that serve as illustrations and exercises.

This Element provides a concise review of the existing literature on content, antecedents and consequences of economic news coverage. It tests and refines prominent assumptions and hypotheses in this area. Relying on communication science theories such as framing, news values and media dependency theories, we first outline and explain how media cover the economy. Additionally, it demonstrates that coverage has a fundamental impact above and beyond the state of the economy, both on economic perceptions and political attitudes of citizens, as well as on political decision makers and media reputation of a wide variety of organizations.

In Understanding China’s Economic Indicators, leading economist and Wall Street Journal columnist Thomas M. Orlik introduces 35 of China's most significant economic statistics. Orlik explains why each indicator matters, how it is collected and computed, and its impact on equity, commodity, and currency markets. As China has emerged as a central player in the global economy, more and more investors are seeking profitable opportunities there. To choose the right investments, it's crucial to understand China's economic environment–and that means finding, interpreting, and utilizing China's growing base of economic indicators. Orlik helps investors make sense of data on everything from Chinese GDP growth to inflation, unemployment, bond yields, electricity production, and aircraft passenger numbers. He draws on the best information supplied by the Chinese government's statistical agency, ministries, and industry associations, as well as private sources. Each indicator is clearly described, along with a practical discussion of its implications for investors.

In 2011 the World Bank—with funding from the Bill and Melinda Gates Foundation—launched the Global Findex database, the world's most comprehensive data set on how adults save, borrow, make payments, and manage risk. Drawing on survey data collected in collaboration with Gallup, Inc., the Global Findex database covers more than 140 economies around the world. The initial survey round was followed by a second one in 2014 and by a third in 2017. Compiled using nationally representative surveys of more than 150,000 adults age 15 and above in over 140 economies, The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution includes updated indicators on access to and use of formal and informal financial services. It has additional data on the use of financial technology (or fintech), including the use of mobile phones and the Internet to conduct financial transactions. The data reveal opportunities to expand access to financial services among people who do not have an account—the unbanked—as well as to promote greater use of digital financial services among those who do have an account. The Global Findex database has become a mainstay of global efforts to promote financial inclusion. In addition to being widely cited by scholars and development practitioners, Global Findex data are used to track progress toward the World Bank goal of Universal Financial Access by 2020 and the United Nations Sustainable Development Goals. The database, the full text of the report, and the underlying country-level data for all figures—along with the questionnaire, the survey methodology, and other relevant materials—are available at www.worldbank.org/globalfindex.

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.