A key economic indicator is real output. To get this right, we need to measure accurately both the value of nominal GDP (done by Bureau of Economic Analaysis) and key price indexes (done mostly by Bureau of Labor Statisticcs). All of us have worked on these measurements while at the BLS and the BEA. In this article, we explore some of the thorny statistical and conceptual issues related to measuring a dynamic economy. An often-stated concern is that the national economic accounts miss some of the value of some goods and services arising from the growing digital economy. We agree that measurement problems related to quality changes and new goods have likely caused growth of real output and productivity to be understated. Nevertheless, these measurement issues are far from new, and, based on the magnitude and timing of recent changes, we conclude that it is unlikely that they can account for the pattern of slower growth in recent years. First we discuss how the Bureau of Labor Statistics currently adjusts price indexes to reduce the bias from quality changes and the introduction of new goods, along with some alternative methods that have been proposed. We then present estimates of the extent of remaining bias in real GDP growth that stem from potential biases in growth of consumption and investment. And we take a look at potential biases that could result from challenges in measuring nominal GDP, including those involving the digital economy. Finally, we review ongoing work at BLS and BEA to reduce potential biases and further improve measurement.
High-quality data are accurate, relevant, and timely. Large national health surveys have always balanced the implementation of these quality dimensions to meet the needs of diverse users. The COVID-19 pandemic shifted these balances, with both disrupted survey operations and a critical need for relevant and timely health data for decision-making. The National Health Interview Survey (NHIS) responded to these challenges with several operational changes to continue production in 2020. However, data files from the 2020 NHIS were not expected to be publicly available until fall 2021. To fill the gap, the National Center for Health Statistics (NCHS) turned to 2 online data collection platforms—the Census Bureau’s Household Pulse Survey (HPS) and the NCHS Research and Development Survey (RANDS)—to collect COVID-19‒related data more quickly. This article describes the adaptations of NHIS and the use of HPS and RANDS during the pandemic in the context of the recently released Framework for Data Quality from the Federal Committee on Statistical Methodology. (Am J Public Health. 2021;111(12):2167–2175. https://doi.org/10.2105/AJPH.2021.306516 )
Smith, and all other staff of the Industry Accounts Directorate at BEA, who made significant contributions directly and indirectly to the development of this paper. We thank Jack Triplett, Eric Bartelsman, and other participants of the NBER/CRIW conference in April 2004 for their many helpful comments on the direction of this research. The views expressed herein are those of the author(s) and do not necessarily reflect the views of the National Bureau of Economic Research.
Official statistical data on the structure, evolution and performance of the U.S. economy are produced by a variety federal, state and local agencies. Much of the methodology, policy frameworks and infrastructure for U.S. economic measurement have been in place for decades. There are growing concerns that the economy is evolving more rapidly than are the economic statistics we use to monitor it. We discuss both the challenges and opportunities to modernizing federal economic statistics. We describe an incremental approach that federal statistics agencies can follow to build a 21st century economic measurement system.
Aggregate measures of real GDP growth obtained from the GDP by Industry Accounts often differ from the featured measure of real GDP growth obtained from the National Income and Product Accounts (NIPAs). We find that differences in source data account for most of the difference in aggregate real output growth rates; very little is due to the treatment of the statistical discrepancy, differences in aggregation methods, or the contributions formula. Moreover, we demonstrate that with consistent data, use of BEA's Fisher-Ideal aggregation procedures to aggregate value added over industries yields the same estimate of real GDP as aggregation over final commodities. Thus, two major approaches to measuring real GDP-the "expenditures" approach used in the NIPAs and the "production" or "industry" approach used in the Industry Accounts-give the same answer under certain conditions. This result enables us to show that the "exact contributions" formula that the NIPAs use to calculate commodity contributions to change in real GDP can also be used to calculate consistent industry contributions to change in real GDP. We also find that using some newly developed datasets would help to bring the aggregate real output measures into closer alignment.
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