The U.S. Census Bureau maintains an important portfolio of economic statistics programs, including quinquennial economic censuses, annual economic surveys, and quarterly and monthly indicator surveys. Government, corporate, and academic users rely on the data to understand the complexity and dynamism of the U.S. economy. Historically, the Bureau's economic statistics programs developed sector by sector (e.g., separate surveys of manufacturing, retail trade, and wholesale trade), and they continue to operate largely independently. Consequently, inconsistencies in questionnaire content, sample and survey design, and survey operations make the data not only more difficult to use, but also more costly to collect and process and more burdensome to the business community than they could be. This report reviews the Census Bureau's annual economic surveys. Specifically, it examines the design, operations, and products of 11 surveys and makes recommendations to enable them to better answer questions about the evolving economy.

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.

Since the early days of the nation, the federal government has collected information on the revenues, expenditures, and other features of state and local jurisdictions and their operations. Today, these data are collected primarily by the Governments Division of the U.S. Census Bureau, which has conducted a census of governments every 5 years since 1957. The division also manages a program of related annual and quarterly surveys, as well as a comprehensive directory of state and local governments. All of this work is now taking place in an environment of constrained resources, and there have been cutbacks in the availability and dissemination of the data. In this context, State and Local Government Statistics at a Crossroads documents the uses of the state and local data and assesses the quality of the data for those uses. This book provides in-depth consideration of the efficiency of the surveys; the user base; and the timeliness, relevance, and quality of the data series. It also provides valuable background information and analysis and offers suggestions for program improvements. This information will be valuable to policy makers, state and local government workers, government contractors, budget analysts, economists, demographers, and others who rely on these data on government at the state and local levels and have a stake in ensuring that limited resources do not compromise the quality of the data on which they rely.

U.S. business data are used broadly, providing the building blocks for key national-as well as regional and local-statistics measuring aggregate income and output, employment, investment, prices, and productivity. Beyond aggregate statistics, individual- and firm-level data are used for a wide range of microanalyses by academic researchers and by policy makers. In the United States, data collection and production efforts are conducted by a decentralized system of statistical agencies. This apparatus yields an extensive array of data that, particularly when made available in the form of microdata, provides an unparalleled resource for policy analysis and research on social issues and for the production of economic statistics. However, the decentralized nature of the statistical system also creates challenges to efficient data collection, to containment of respondent burden, and to maintaining consistency of terms and units of measurement. It is these challenges that raise to paramount importance the practice of effective data sharing among the statistical agencies. With this as the backdrop, the Bureau of Economic Analysis (BEA) asked the Committee on National Statistics of the National Academies to convene a workshop to discuss interagency business data sharing. The workshop was held October 21, 2005. This report is a summary of the discussions of that workshop. The workshop focused on the benefits of data sharing to two groups of stakeholders: the statistical agencies themselves and downstream data users. Presenters were asked to highlight untapped opportunities for productive data sharing that cannot yet be exploited because of regulatory or legislative constraints. The most prominently discussed example was that of tax data needed to reconcile the two primary business lists use by the statistical agencies.

Federal government statistics provide critical information to the country and serve a key role in a democracy. For decades, sample surveys with instruments carefully designed for particular data needs have been one of the primary methods for collecting data for federal statistics. However, the costs of conducting such surveys have been increasing while response rates have been declining, and many surveys are not able to fulfill growing demands for more timely information and for more detailed information at state and local levels. Innovations in Federal Statistics examines the opportunities and risks of using government administrative and private sector data sources to foster a paradigm shift in federal statistical programs that would combine diverse data sources in a secure manner to enhance federal statistics. This first publication of a two-part series discusses the challenges faced by the federal statistical system and the foundational elements needed for a new paradigm.

This open access book describes the differences in US census coverage, also referred to as “differential undercount”, by showing which groups have the highest net undercounts and which groups have the greatest undercount differentials, and discusses why such undercounts occur. In addition to focusing on measuring census coverage for several demographic characteristics, including age, gender, race, Hispanic origin status, and tenure, it also considers several of the main hard-to-count populations, such as immigrants, the homeless, the LBGT community, children in foster care, and the disabled. However, given the dearth of accurate undercount data for these groups, they are covered less comprehensively than those demographic groups for which there is reliable undercount data from the Census Bureau. This book is of interest to demographers, statisticians, survey methodologists, and all those interested in census coverage.

Because of the role of innovation as a driver of economic productivity and growth and as a mechanism for improving people's well-being in other ways, understanding the nature,determinants, and impacts of innovation has become increasingly important to policy makers. To be effective, investment in innovation requires this understanding, which, in turn, requires measurement of the underlying inputs and subsequent outcomes of innovation processes. In May 2016, at the request of the National Center for Science and Engineering Statistics of the National Science Foundation, the Committee on National Statistics of the National Academies of Sciences, Engineering, and Medicine convened a workshop - bringing together academic researchers, private and public sector experts, and representatives from public policy agencies - to develop strategies for broadening and modernizing innovation information systems.This publication summarizes the presentation and discussion of the event.

The Consumer Expenditure (CE) surveys are the only source of information on the complete range of consumers' expenditures and incomes in the United States, as well as the characteristics of those consumers. The CE consists of two separate surveys: (1) a national sample of households interviewed five times at three-month intervals; and (2) a separate national sample of households that complete two consecutive one-week expenditure diaries. For more than 40 years, these surveys, the responsibility of the Bureau of Labor Statistics (BLS), have been the principal source of knowledge about changing patterns of consumer spending in the U.S. population. In February 2009, BLS initiated the Gemini Project, the aim of which is to redesign the CE surveys to improve data quality through a verifiable reduction in measurement error with a particular focus on underreporting. The Gemini Project initiated a series of information-gathering meetings, conference sessions, forums, and workshops to identify appropriate strategies for improving CE data quality. As part of this effort, BLS requested the National Research Council's Committee on National Statistics (CNSTAT) to convene an expert panel to build on the Gemini Project by conducting further investigations and proposing redesign options for the CE surveys. The charge to the Panel on Redesigning the BLS Consumer Expenditure Surveys includes reviewing the output of a Gemini-convened data user needs forum and methods workshop and convening its own household survey producers workshop to obtain further input. In addition, the panel was tasked to commission options from contractors for consideration in recommending possible redesigns. The panel was further asked by BLS to create potential redesigns that would put a greater emphasis on proactive data collection to improve the measurement of consumer expenditures. Measuring What We Spend summarizes the deliberations and activities of the panel, discusses the conclusions about the uses of the CE surveys and why a redesign is needed, as well as recommendations for the future.

The Survey of Income and Program Participation (SIPP) is a national, longitudinal household survey conducted by the Census Bureau. SIPP serves as a tool to evaluate the effectiveness of government-sponsored social programs and to analyze the impacts of actual or proposed modifications to those programs. SIPP was designed to fill a need for data that would give policy makers and researchers a much better grasp of how effectively government programs were reaching their target populations, how participation in different programs overlapped, and to what extent and under what circumstances people transitioned into and out of these programs. SIPP was also designed to answer questions about the short-term dynamics of employment, living arrangements, and economic well-being. The Census Bureau has reengineered SIPPâ€"fielding the initial redesigned survey in 2014. This report evaluates the new design compared with the old design. It compares key estimates across the two designs, evaluates the content of the redesigned SIPP and the impact of the new design on respondent burden, and considers content changes for future improvement of SIPP.

Recently, social science has had numerous episodes of influential research that was found invalid when placed under rigorous scrutiny. The growing sense that many published results are potentially erroneous has made those conducting social science research more determined to ensure the underlying research is sound. Transparent and Reproducible Social Science Research is the first book to summarize and synthesize new approaches to combat false positives and non-reproducible findings in social science research, document the underlying problems in research practices, and teach a new generation of students and scholars how to overcome them. Understanding that social science research has real consequences for individuals when used by professionals in public policy, health, law enforcement, and other fields, the book crystallizes new insights, practices, and methods that help ensure greater research transparency, openness, and reproducibility. Readers are guided through well-known problems and are encouraged to work through new solutions and practices to improve the openness of their research. Created with both experienced and novice researchers in mind, Transparent and Reproducible Social Science Research serves as an indispensable resource for the production of high quality social science research.