Employment Projections Methodology

Bureau of Labor Statistics projections of industry and occupational employment are developed in a series of six interrelated steps, each of which is based on a different procedure or model and assumptions: labor force, aggregate economy, final demand (GDP) by consuming sector and product, industry output, industry employment, and employment and openings by occupation. The results produced by each step are key inputs to following steps, and the sequence may be repeated multiple times to allow feedback and to ensure consistency. Further detail is presented in the BLS Handbook of Methods. This flow chart illustrates the six step process:

Labor Force
Total and by age, sex, race and ethnicity

Aggregate Economy
GDP, total employment, and major demand categories

Industry Final Demand
Sales to consumers, businesses, government and foreigners

Occupational Employment
Job openings due to growth & separations

Industry Employment
Labor productivity, average weekly hours, wage & salary employment

Industry Output
Use and Make Relationships, Total Requirements Tables

Labor Force

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Labor force projections are based on expectations of the future size and composition of the population, as well as on the trends in labor force participation rates of different age, gender, race, and ethnic groups.

BLS converts resident population projections prepared by the U.S. Census Bureau to the civilian noninstitutional population concept, a basis for labor force projections. BLS develops participation rate projections using data from the Current Population Survey (CPS) conducted for BLS by the Census Bureau.

The projected participation rate for each age, gender, race, and ethnicity group is multiplied by the corresponding projection of the civilian noninstitutional population to obtain the labor force projection for that group. The groups are then summed to obtain the total civilian labor force. The labor force outlook plays a critical role in long run macroeconomic trends and is therefore the most important exogenous data within the BLS macroeconomic projections.

For more information on Labor Force, please see the Handbook of Methods. View Labor Force data.

Aggregate Economy

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BLS' macroeconomic projections are produced using the MA/US model, licensed from Macroeconomic Advisers by IHS Markit. The foundation of the MA/US model is that consumption follows a life-cycle model and investment is based on a neoclassical model. The MA/US model is also explicitly designed to reach a full-employment solution in the target years.

The BLS supplies multiple critical variables, determined through research and modeling, and the in-house labor force projections described above to the MA/US model as exogenous variables and constraints, respectively, on future economic growth. Other fundamental exogenous variables in the model include energy prices and assumptions about fiscal and monetary policy.

For more information on the Aggregate Economy, please see the Handbook of Methods. View Aggregate Economy data.

Final Demand

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Demand is the key determinant in explaining future jobs. Therefore, underlying the projections of employment by industries and occupations, BLS publishes a projected final demand matrix consisting of roughly 153 demand categories by 205 commodity groups. Aggregate gross domestic product (GDP) as well as some underlying subcomponent categories are determined by the MA/US model and serve as constraints to BLS' more detailed projections of GDP.

Bridge tables are developed based on the most recent Benchmark and annual Input-Output Accounts published by BEA. The bridge tables are used to distribute projected column controls out to the roughly 200 commodity groups or rows within the final demand matrix, with exceptions for Change in Private Inventories (CIPI) and imports and exports of goods and services.

As a last step, data are converted from purchaser value to producer value. Margin columns are projected for each component of final demand. Summing across the rows of a particular component (i.e. PCE) with its related margin columns (consisting of transportation costs as well as wholesale and retail markups) results in a vector of producer value data by detailed commodity. Adjustments to the initial estimates of the final demand matrix are made based on research and analysis by industry experts including information pertaining to energy forecasts, existing and expected shares of the domestic output, known changes to trade agreements, expected government political and policy changes, and so forth.

For more information on Final Demand, please see the Handbook of Methods. View Final Demand matrix data.

Industry Output

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The creation of an input-output model is the next stage in developing BLS projections. By definition, GDP reflects only sales to final purchasers. Intermediate material inputs are not explicitly reflected in the GDP estimates. An input-output (I-O) model provides a means to derive an industry-level estimate of the output and employment needed to produce a given level of GDP. The BLS develops a comprehensive historical detailed set of I-O tables, using the benchmark I-O tables as the basis, scaling the BEA benchmark tables (393 industries) to the BLS sector plan (205 industries).

The BLS I-O model consists of two basic matrices for each year: a "use" table and a "make" table. The “use” table, or the direct requirements table, shows the use of commodities by each industry as inputs into its production process. The “make” table, or the market share table, shows the commodity output of each industry. Initial estimates of the projected I-O tables are based on historical relationships and the projected final demand tables. Results are then reviewed and revised in order to take into account changing trends in the input patterns, or the way in which goods are produced or services provided by each industry.

When projected values of the "use" and "make" relationships are available, BLS uses the relationships derived by BEA to convert the projection of commodity demand developed in preceding steps into a projection of domestic industry output.

For more information on Industry Output, please see the Handbook of Methods. View Industry Output and Employment data.

Industry Employment

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The next step is to project the industry employment necessary to produce the projected output. To do so, projected output is used in regression analysis to estimate hours worked by industry. The regression model utilizes industry output, industry wage rate relative to industry output price, and time. From these hours' data, projected wage and salary employment by industry is derived. The ratio of self-employed to total employment is extrapolated using historical data. This ratio, along with the projected level of wage and salary employment is then used to derive the projected number of self-employed and total employment by industry. Projected average weekly hours and total hours for self-employed also are derived from these data.

Implied output per hour (labor productivity) is calculated for each industry for both the total and for wage and salary employees. These data are used to evaluate the projected output and employment.

For more information on Industry Employment, please see the Handbook of Methods. View Industry Output and Employment data.

Occupational Employment

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BLS creates occupational employment projections in a product called the National Employment Matrix. This matrix describes the employment of detailed occupations within detailed wage and salary industries and different classes-of-workers, including those who are self-employed or employed by a private household. The matrix provides a comprehensive count of nonfarm wage and salary jobs—which is different from a count of workers since a single worker may hold more than one job—and a count of self-employed workers, agricultural industry workers, and workers employed in private households. These counts are provided for a base-year and a projected-year which is ten years in the future.

The base-year employment is derived from the data in the Occupational Employment Statistics program (OES), the Current Employment Statistics program (CES), the Quarterly Census of Employment and Wages (QCEW), and the Current Population Survey (CPS).

Projected-year employment data for wage and salary jobs, including all agricultural workers, and workers employed by private households are developed using a conceptual framework which divides industry employment between occupations based on expected, structural changes in the demand for those occupations within a given industry. To project these changes in occupational demand, BLS economists thoroughly review qualitative sources such as scholarly articles, expert interviews, and news stories, as well as quantitative resources such as historical data and externally produced projections. These reviews identify structural changes in the economy which are expected to change an occupation's share of industry employment. Projected-year employment data for self-employed workers are developed similarly, but at a less detailed level than wage and salary employment.

Occupational Separations, job openings resulting from workers separating from their jobs either to find employment in other occupations or to leave the labor force entirely, are also estimated, details of which are presented in Occupational Separations Methodology.

For more information on Occupational Employment, please see the Handbook of Methods. View Occupational Employment data.

Measures of Education and Training

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BLS provides information about education and training requirements for each of the occupations for which it publishes projections data. This approach allows occupations to be grouped in order to create estimates of the outlook for occupations with various types of education or training needs. In addition, educational attainment data for each occupation are presented to show the level of education achieved by current workers.

For more information on Occupational Employment, please see the Handbook of Methods. View Education and Training data. View Education and Training data definitions.

Last Modified Date: September 4, 2019