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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 )

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.

This chapter presents new collaborative research work by the BEA and BLS toward an integrated industry-level production account that covers 1947 to 2016, the entire time span of the GDP by Industry accounts. The prototype estimates that we have constructed reveal that for the past decade and a half, relatively slow input growth (in capital and labor services) has curtailed US economic growth, even relative to the long slump between 1973 and 1995. The low contribution of capital input was concentrated in the Finance, insurance, real estate, rental and leasing and Manufacturing sectors, while the low contribution from labor was spread more equally across sectors.

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.

The Bureau of Labor Statistics (BLS) prepares the Consumer Price Index for All Urban Consumers (CPI-U), and the Bureau of Economic Analysis prepares the Personal Consumption Expenditures (PCE) chain-type price index. Both indexes measure the prices paid by consumers for goods and services. Because the two indexes are based on different underlying concepts, they are constructed differently, and tend to behave differently over time. From the first quarter of 2002 through the second quarter of 2007, the CPI-U increased 0.4 percentage point per year faster than the PCE price index. This paper details and quantifies the differences in growth rates between the CPI-U and the PCE price index; it provides a quarterly reconciliation of growth rates for the 2002:Q1- 2007:Q2 time period. There are several factors that explain the differences in growth rates between the CPI and the PCE price index. First, the indexes are based on difference index-number formulas. The CPI-U is based on a Laspeyres index; the PCE price index is based on a Fisher-Ideal index. Second, the relative weights assigned to the detailed item prices in each index are different because they are based on different data sources. The weights used in the CPIU are based on a household survey, while the weights used in the PCE price index are based on business surveys. Third, there are scope differences between the two indexes— that is, there are items in the CPI-U that are out-of-scope of the PCE price index, and there are items in the PCE price index that are out-of-scope of the CPI-U. And finally, there are differences in the seasonal-adjustment routines and in the detailed price indexes used to construct the two indexes. Over the 2002:Q1-2007:Q2 time period, this analysis finds that almost half of the 0.4 percentage point difference in growth rates between the CPI-U and the PCE price index was explained by differences in index-number formulas. After adjusting for formula differences, differences in relative weights—primarily “rent of shelter”—more than accounted for the remaining difference in growth rates. Net scope differences, in contrast, partly offset the effect of relative weight differences.

The integration of the annual I-O accounts with the GDP-by-industry accounts is the most recent in a series of improvements to the industry accounts provided by the BEA in recent years. BEA prepares two sets of national industry accounts: The I-O accounts, which consist of the benchmark I-O accounts and the annual I-O accounts, and the GDPby- industry accounts. Both the I-O accounts and the GDP-by-industry accounts present measures of gross output, intermediate inputs, and value added by industry. However, in the past, they were inconsistent because of the use of different methodologies, classification frameworks, and source data. The integration of these accounts eliminated these inconsistencies and improved the accuracy of both sets of accounts. The integration of the annual industry accounts represents a major advance in the timeliness, accuracy, and consistency of these accounts, and is a result of significant improvements in BEA's estimating methods. The paper describes the new methodology, and the future steps required to integrate the industry accounts with the NIPAs. The new methodology combines source data between the two industry accounts to improve accuracy; it prepares the newly integrated accounts within an I-O framework that balances and reconciles industry production with commodity usage. Moreover, the new methodology allows the acceleration of the release of the annual I-O accounts by 2 years and for the first time, provides a consistent time series of annual I-O accounts. Three appendices are provided: A description of the probability-based method to rank source data by quality; a description of the new balancing produced for producing the annual I-O accounts; and a description of the computation method used to estimate chaintype price and quantity indexes in the GDP-by-industry accounts.

The integration of the annual I-O accounts with the GDP-by-industry accounts is the most recent in a series of improvements to the industry accounts provided by the BEA in recent years. BEA prepares two sets of national industry accounts: The I-O accounts, which consist of the benchmark I-O accounts and the annual I-O accounts, and the GDPby- industry accounts. Both the I-O accounts and the GDP-by-industry accounts present measures of gross output, intermediate inputs, and value added by industry. However, in the past, they were inconsistent because of the use of different methodologies, classification frameworks, and source data. The integration of these accounts eliminated these inconsistencies and improved the accuracy of both sets of accounts. The integration of the annual industry accounts represents a major advance in the timeliness, accuracy, and consistency of these accounts, and is a result of significant improvements in BEA's estimating methods. The paper describes the new methodology, and the future steps required to integrate the industry accounts with the NIPAs. The new methodology combines source data between the two industry accounts to improve accuracy; it prepares the newly integrated accounts within an I-O framework that balances and reconciles industry production with commodity usage. Moreover, the new methodology allows the acceleration of the release of the annual I-O accounts by 2 years and for the first time, provides a consistent time series of annual I-O accounts. Three appendices are provided: A description of the probability-based method to rank source data by quality; a description of the new balancing produced for producing the annual I-O accounts; and a description of the computation method used to estimate chaintype price and quantity indexes in the GDP-by-industry accounts.

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 -- "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.

The Gross Domestic Product (GDP) by industry accounts for the United States provide industry estimates of value added, gross output, and intermediate inputs based, in part, on data from the benchmark and annual input-output (I-O) accounts for the United States. The GDP by industry data provide a decomposition of an industry’s gross output into expenditures on primary inputs--that is, value-added inputs--and expenditures on total intermediate inputs. Recently, these data have been widely used in studies of structural change and economic growth in the U.S. economy. This paper extends the information available for such studies by introducing intermediate inputs decomposed into the cost categories of energy, materials, and purchased services using a time-series of I-O “use” tables. It develops a conceptual framework for measuring intermediate-inputs price and quantity growth and then uses this framework to prepare nominal estimates, chain-type price indexes, and chain-type quantity indexes for intermediate inputs by industry and by cost category. It also presents contributions by each cost category to growth in the chain-type price and quantity indexes of gross output. Data are consistent with the published GDP by industry accounts and are presented for the years 1992-2000.