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Essays from twenty-seven leading book editors: “Honest and unflinching accounts from publishing insiders . . . a valuable primer on the field.” —Publishers Weekly Editing is an invisible art in which the very best work goes undetected. Editors strive to create books that are enlightening, seamless, and pleasurable to read, all while giving credit to the author. This makes it all the more difficult to truly understand the range of roles they inhabit while shepherding a project from concept to publication. What Editors Do gathers essays from twenty-seven leading figures in book publishing about their work. Representing both large houses and small, and encompassing trade, textbook, academic, and children’s publishing, the contributors make the case for why editing remains a vital function to writers—and readers—everywhere. Ironically for an industry built on words, there has been a scarcity of written guidance on how to approach the work of editing. Serving as a compendium of professional advice and a portrait of what goes on behind the scenes, this book sheds light on how editors acquire books, what constitutes a strong author-editor relationship, and the editor’s vital role at each stage of the publishing process—a role that extends far beyond marking up the author’s text. This collection treats editing as both art and craft, and also as a career. It explores how editors balance passion against the economic realities of publishing—and shows why, in the face of a rapidly changing publishing landscape, editors are more important than ever. “Authoritative, entertaining, and informative.” —Copyediting
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.