|Unit||Mil. USD, SAAR|
|Adjustments||Seasonally Adjusted at Annual Rate|
|Labor Force||Jan 2020||164,606||164,556||Ths. #, SA||Monthly|
|Labor Force Employment||Jan 2020||158,714||158,803||Ths. #, SA||Monthly|
|Manufacturing Employment||Jan 2020||12,851||12,863||Ths. #, SA||Monthly|
|Total Employment Non-Ag||Jan 2020||152,186||151,961||Ths. #, SA||Monthly|
|Unemployment||Jan 2020||5,892||5,753||Ths. #, SA||Monthly|
|Unemployment Rate||Jan 2020||3.6||3.5||%, SA||Monthly|
|Wage & Salaries||2019 Q4||9,453,053||9,353,955||Mil. USD, SAAR||Quarterly|
|Primary Industries Employment||Jun 2019||1,376,233||1,313,346||#, NSA||Monthly|
The National Income and Product Accounts (NIPA) are a measure of production and the distribution of incomes earned in production. Their goal is to present a reliable and complete picture of the United States economy by including government-, consumer-, and industry-specific data.
For most NIPA components, the current-dollar estimates are derived from source data that are “value data,” where value = price x quantity. Frequently, BEA—which does not collect much of its own data—must adjust the data that are collected by others, primarily government agencies, trade associations, and international organizations. Most source data are collected for purposes other than the estimation of the NIPAs, and therefore use definitions, population parameters, or time periods that differ from NIPA concepts. Much of the data must be adjusted by filling gaps in coverage or by using available data as proxies for the desired NIPA measure.
For periods for which data are not available, NIPA estimates may be derived using existing estimates. For example, when annual data are available and the quarterly are not, the quarterly data are often estimated by interpolation. For the periods beyond those covered by annual estimates (such as the most recent quarter), the quarterly estimates are derived by extrapolation. These interpolations and extrapolations are often based on “indicators”—related data that are used to approximate movements in the NIPA measures.
Quantity and price estimates
Changes over time in the current-dollar measures provided in the NIPAs may reflect a change in quantity, a change in price, or a combination of both. For some analyses, it is important to know these separate effects—for instance, to know how much of the change in GDP is due to changes in the quantities of goods and services without the influence of price changes.
Therefore, the NIPAs provide separate estimates of changes in quantities and prices, derived as indexes that provide information on the change from some reference period. BEA describes estimates of quantities as “real” expenditures—for example “real GDP” or “real PCE.” Note that the level of an index in any single period is not in itself meaningful. Instead, it is the relation of that index level to the index level in another period—that is, it is the change in the index over time—that is important. Indeed, the change in real GDP over time is the featured measure of economic activity. In addition, BEA provides measures of the contributions of various components (such as personal consumption expenditures or investment) to GDP growth.
BEA also provides quantity measures in value terms—called chained dollars—by scaling the index. Specifically, the index in the reference year is set equal to the current-dollar level in the same year, and the change in the index in successive and previous periods is multiplied by the current-dollar level to form a time series in monetary terms.
To facilitate the analysis of the drivers of change in the real estimates, BEA provides measures of the contributions of real components to the percent change in real aggregates. These are provided because the chained-dollar measures of components are not additive,and therefore, accurate measures of a component’s contribution to change cannot be derived from the chained-dollar measures.
Moody's Analytics History Extension of Real Series
Motivation: The U.S. Bureau of Economic Analysis (BEA) reports many real NIPA series beginning only in 1995 to avoid misleading analysis (citation). That is, real (inflation-adjusted) figures are estimates, and distortions increase with distance from the base year (2005).
Response: For convenience of our users, Moody's Analytics has extended the real series backwards, as far as 1947, by using the reported quantity index and nominal series.
Annual estimates of GDP that are first available as the sum of the quarterly estimates are revised in the “annual revision” each July and in the following two annual revisions. Annual revisions are timed to incorporate newly available annual source data and quarterly data that are released too late to be used in the current quarterly estimates.
The revision cycle culminates, at about 5-year intervals, in a comprehensive revision of the NIPAs. Comprehensive revisions differ from annual revisions in a variety of ways. First, the data used for comprehensive revisions are based in large part on censuses of economic activity, while the monthly, quarterly, and annual data discussed above are generally based on sample surveys. Second, comprehensive revisions have traditionally been used to introduce major improvements in definitions, estimating methods, and data presentations into the accounts. Finally, the estimates may be revised back as far as 1929.