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, a total of 136 separate categories.
The U.S. Census Bureau prepares projections of the resident population. The size and composition of the population are affected by the interaction of three variables: births, deaths, and net immigration. More information about population projections is available on the
Census Bureau website. BLS converts these population projections 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.
For this latest round of projections, the Census Bureau's 2014 population projections based on Census 2010 population weights were used as the base for the labor force projections. The size and composition of the population affect not only the labor force projections, but the projected composition of GDP and the demand for workers in various industries and occupations.
BLS currently disaggregates the various race and ethnic categories into 5-year age groups by gender. Participation rates for these groups are smoothed, using a robust-resistant nonlinear filter and then transformed into logits. The logits of the participation rates are then extrapolated linearly by regressing against time and then extending the fitted series to or beyond the target year. When the series are transformed back into participation rates, the projected path is nonlinear.
After the labor force participation rates have been projected, they are reviewed from the perspectives of the time path, the cross section in the target year, and cohort patterns of participation. 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.
BLS' macroeconomic projections are produced using the MA/US model, licensed from Macroeconomic Advisers, LLC (MA). The 2012–22 projections were the first to employ this model, which was introduced in late 2012; previously the Bureau relied on MA's Washington University Macro Model (WUMM). MA/US has the same foundations as WUMM: consumption follows a life-cycle model and investment is based on a neoclassical model. Foreign sector estimates rely on forecasts from Oxford Economics. However, many improvements were made; most notably, the model is explicitly designed to reach a full-employment solution in the target years. Within MA/US, a sub-model calculates an estimate of potential output from the nonfarm business sector, based upon full-employment estimates of the sector's hours worked and output per hour. Error correction models are embedded into MA/US to align the model's solution with the full-employment submodel.
Certain critical variables set the parameters for the nation's economic growth and determine in large part the trend that GDP will follow. In developing the macroeconomic projections, BLS elects to determine these critical variables through research and modeling, and then supplies them to the MA/US model as exogenous variables. The in-house labor force projections, described above, are of particular importance, as they are the primary constraint on future economic growth. Other fundamental exogenous variables in the model include energy prices and assumptions about fiscal and monetary policy. Initial estimates of key economic variables, as well as the underlying exogenous assumptions, are reviewed by a panel of Federal economists. The final solution is evaluated for consistency with the detailed output and employment projections. The specific assumptions and target variables for projections are presented in the October 2017 Monthly Labor Review.
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 155 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. Solutions supplied by MA/US include the aggregates projections of: personal consumption expenditures (PCE), private investment in equipment (PEQ), private investment in intellectual property products (IPP), residential and nonresidential construction, change in private inventories (CIPI), exports and imports of goods and services, as well as consumption and investment of federal defense, nondefense, and state and local government. BLS uses several behavioral models as well as distributional trends and/or assumptions in breaking out these eleven categories of GDP supplied by the macro model to the detailed matrix of final demand data.
Personal consumption expenditures (PCE) and Private Business Investment (PEQ and IPP) are projected using the Houthakker-Taylor model(1) and the Modified Neoclassical Model of Investment. Projections are made for 78 PCE product categories and 32 investment asset groups consistent with the national income and product account (NIPA) data published by the Bureau of Economic Analysis (BEA).
These column totals for PCE and investment are adjusted as necessary to ensure consistency between the aggregate projection from the MA/US solution and the detailed estimates based on the Houthakker-Taylor and Modified Neoclassical models. Column totals for the remaining components of final demand are output directly from the MA/US software. Adjustments are made to the MA/US projections of trade goods and services to account for re-exports and re-imports, effectively transforming the data from a NIPA based estimate to one consistent with the input-output framework. These adjustments preserve the projected level of net exports from MA/US.
Bridge tables are developed based on the most recent Benchmark and annual Input-Output Accounts published by BEA. For some columns the bridge table is held constant from the last historical year while other components project the bridge table based on trends over the historical series. The bridge tables are used to distribute projected column controls out to the roughly 200 commodity groups or rows within the final demand matrix. Exceptions to the use of bridge tables occur in the columns of Change in Private Inventories (CIPI) and imports and exports of goods and services. Business inventories by detailed commodities are projected based on a two stage least squares model where inventories are regressed on lagged values of both inventories and commodity output. Results are aggregated and adjusted to conform to the projection of CIPI from the macro model. Imports and exports are also projected separately at the detailed commodity level. Exports for each commodity are regressed on the projected aggregate export value and the lagged value of exports for the commodity. The same is done for imports. Results are then aggregated and adjusted to conform to the adjusted trade projections from the macro model.
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 (ie. 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. For example, in buying a sweater, the margin column would subtract the retail markup by the vendor from the textile commodity row and move that value to the retail trade commodity row. In estimating employment, it is helpful to know the producer value of the data as it helps separate employment in the wholesale, retail, and transportation industries from the remaining economy.
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.
The creation of an input-output model is the next stage in developing BLS projections. Each industry within the economy relies on other industries to supply inputs–intermediate products or services–for further processing. By definition, GDP reflects only sales to final purchasers, such as car buyers for personal use and businesses for equipment. Intermediate material inputs, such as the steel incorporated into cars, 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. BLS develops a historical time series of I-O tables.
In 2009, BEA published a comprehensive revision to its I-O framework, bringing about a standardized and consistent framework between the benchmark I-O data and the annual I-O systems, and updated annually. Within the framework of these revisions, BLS developed a comprehensive historical detailed set of I-O tables, using the 1997, 2002 and 2007 benchmark I-O tables as the basis, scaling the BEA benchmark tables (393 industries) to the BLS sector plan (205 industries), and making adjustments conforming to BLS assumptions and methods, BLS utilized the tables to create pattern structures that would be used to develop the detailed sector I-O tables for the nonbenchmark-year (or BEA Annual - 1997-2015) I-O tables. Under this methodology, and based on the pattern structures developed from the scaled benchmark I-O tables, the BEA annual I-O tables, detailing only 71 industries, were expanded. The years between the benchmarks (i.e., 1998-2001 and 2003-2006) included interpolation factors to accommodate changes to the patterns between the benchmark years. Years subsequent to the 2007 benchmark year utilized the 2007 pattern structure. Because no annual I-O tables were available for the 2016 year, these I-O tables were developed on the basis of the patterns of the 2007 benchmark year and on industry and commodity outputs for the 205 BLS sector industries. After all of the tables were developed under the BLS sector plan, each table was RAS balanced (iteratively scaled) to ensure consistency and conformity. In addition, on initial RAS balancing, the BLS data were compressed to the Annual I-O levels (71 industries) and raked to the Annual I-O values, then re-expanded to the BLS sector plan. These data were then RAS balanced to ensure conformity and consistency.
The BLS I-O model consists of two basic matrices for each year: a "use" table and a "make" table. Once balanced, both tables are converted to coefficient form. The converted "use" table, or the direct requirements table, shows the use of commodities by each industry as inputs into its production process. Each column of this table displays the pattern of commodity inputs per dollar of industry output. The converted "make" table, or the market share table, shows the commodity output of each industry. This table allocates commodity output to the industry in which it is the primary commodity output and to those industries in which it is secondary. The "make" table also shows the industry distribution of production for each commodity. 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. The BEA relationships are summarized in the following formula:
g = D(I-BD)-1e,
g = vector of domestic industry output by sector,
B = "use" table in coefficient form,
D = "make" table in coefficient form,
I = identity matrix, and
e = vector of final demand by commodity sector.
In sum, the matrix product of the inverse of the coefficient forms of the "make" and "use" tables (or total requirements tables) and a vector e of final demand commodity distribution, yields industry outputs.
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. Additionally, average weekly hours are derived as a time trend for each industry. From these hours' data, projected wage and salary employment by industry is derived.
For each industry, the share of self-employed is extrapolated using historical data. These data are derived from the ratio of self-employed to total employment and extrapolated based on time and the unemployment rate. The 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.
Factors Affecting Industry Employment
Many assumptions underlie the BLS projections of the aggregate economy and of industry output, productivity, and employment. Often, these assumptions bear specifically on econometric factors, such as the aggregate unemployment rate, the anticipated time path of labor productivity, and expectations regarding the Federal budget surplus or deficit. Other assumptions deal with factors that affect industry-specific measures of economic activity.
Detailed industry employment projections are based largely on econometric models, which, by their very nature, project future economic behavior on the basis of a continuation of economic relationships that held in the past. For the most part, the determinants of industry employment are expressed both in the structure of the models' equations and as adjustments imposed on the specific equations to ensure that the models are indeed making a smooth transition from actual historical data to projected results. However, one of the most important steps associated with the preparation of the BLS projections is a detailed review of the results by analysts who have studied recent economic trends in specific industries. In some cases, the results of the aggregate and industry models are modified because of the analysts’ judgment that historical relationships need to be redefined in some manner.
Table 2.7 Employment and Output by Industry presents historical and projected information about employment and output for aggregate and detailed industries. Industry sector employment projections prepared in the Division of Industry Employment Projections (DIEP) used comprehensive modeling techniques that estimate output as well as employment.
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 matrix does not include employment for every possible combination of industry and occupation. Some data are not released to protect the confidentiality of the businesses or individuals providing the data and others are not released for quality reasons. All employment data in the National Employment Matrix are presented in thousands, rounded to one decimal place. The detailed data in the matrix may not sum to summaries because of rounding or data which are not released.
The coverage of the National Employment Matrix can be divided into two groups:
Nonfarm wage and salary employment is by far the larger of the two groups. These jobs are grouped into industries and occupations that generally match those released in the OES data. The data used in the matrix to describe the base-year for these jobs come from three sources. Job counts by industry come from the CES, which covers nonfarm, payroll jobs. In places where the matrix is more detailed than CES industry data, QCEW data are used as weights to provide further detail. Industry employment is split into individual occupations using OES data that describe what share of industry employment is held by which occupations.
Self-employed workers, workers employed by private households, and agricultural workers (excluding the logging industry) account for a small share of total employment. Because these workers are not captured by establishment survey programs like OES and CES, the matrix uses data from CPS, which is a household survey that collects data directly from the workers. Unlike data from OES, CES, and QCEW, CPS data used by the matrix are a count of workers, not jobs.
One additional note on matrix use of CPS data: CPS data are coded using the Census Occupation Classification System, which has fewer, more broadly defined occupations than the Standard Occupational Classification System (SOC). Both OES and the National Employment Matrix use SOC. To make CPS occupational data comparable with SOC, OES occupational data are applied as weights to divide CPS occupations into the more detailed SOC occupations.
Projected Wage and Salary Employment
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.
The sum of shares of industry employment for all the occupations in an industry must add up to 100% for the occupational employment within an industry to match the over-all industry's projected employment. As a result, changes to one or more occupations' shares of industry can scale the shares of other occupations in that industry. To prevent unintended changes, the scaled shares of industry employment are reviewed extensively to ensure that changes in each industry are consistent with each other and that individual changes support the broader industry's narrative and projection.
Factors Affecting Demand for Occupations
Each occupation in the matrix is analyzed to identify factors that are likely to cause an increase or decrease in demand for that occupation within particular industries. This analysis incorporates judgments about new trends that may influence occupational demand, such as expanding use of new manufacturing techniques like 3D printing that might change the productivity of particular manufacturing occupations, or shifts in customer preferences between different building materials which may affect demand for specific construction occupations.
Among the various factors that can affect the demand for an occupation in an industry are:
Changes in business practices or production methods
Replacement of one product or service by another
Organizational restructuring of work
Changes to the size of business establishments
Offshore and domestic outsourcing
Expected employment change in a segment of an industry where an occupation is more concentrated relative to expected employment changes in other segments of the same industry
The results of this qualitative analysis form the quantitative basis for making changes to occupational shares of industry employment. The structural changes suggested by different trends are compared to determine if they will cause demand to grow or shrink, and if so, by how much. The effects of the projected trends are then combined into an overall numerical estimate which describes the change in an occupation's share of industry employment.
Table 5.1 Factors Affecting Occupational Utilization contains brief descriptions of the factors underlying changes in the demand for occupations within industries that are projected to occur between 2016 and 2026. Occupations appear in order by Standard Occupational Classification code. Although all detailed occupations were analyzed, utilization of many occupations is projected to remain unchanged. These occupations are not included in the table. In addition, factors are discussed only for those industries with releasable employment. Some factors apply to only one industry, while others may apply to many or all industries in which the occupation is employed.
Projected Self-Employed Workers
Projected-year data for self-employment are created using a modified version of the wage and salary employment method. Wage and salary employment is analyzed at an occupation-by-industry level but self-employment data at that same level result in estimates which are too small to analyze reliably. Additional difficulties finding sufficient qualitative information about self-employment by occupation and industry make analysis at this level impractical even if robust data were available.
To provide more usable estimates, self-employment data are initially projected at the occupation-by-industry level but aggregated to the occupational level for analysis. That is, the details about individual occupations in specific industries for self-employed workers are combined to show the growth or decline of self-employed workers overall for each occupation. While this broader view of self-employed workers does not provide the same detail as wage and salary, the rate of growth or decline for self-employed occupations does incorporate the underlying industry detail, providing a reasonable estimate for analysis. As with other CPS occupational data, these estimates are based on the Census Occupation Classification System and the matrix uses OES employment as weights to provide the more detailed SOC structure for analysis and publishing.
The methods used for wage and salary and self-employed worker projections are similar in their reliance on qualitative and quantitative research. Both examine available data and other resources. Both make changes when the information available suggests a structural change is happening or is likely to occur before the projected year. Both incorporate changing industry demand for the occupation, but self-employment does this at a less detailed level than wage and salary.
Projections of job growth provide valuable insight into future employment opportunities because each new job created is an opening for a worker entering an occupation. However, opportunities also result when workers separate from their jobs, either to find employment in other occupations or to leave the labor force entirely. In most occupations, the openings caused by separating employees provide more job openings than employment growth does. Further detail is presented in Occupational Separations Methodology. Note: BLS adopted separations methodology with the 2016–26 projections. This replaced the previous method of Estimating Occupational Replacement Needs.
BLS provides information about education and training requirements for each projected occupation. In the BLS education and training system, each of the occupations is assigned separate categories for education, work experience, and on-the-job training. Occupations can be grouped to create estimates of the education and training needs for the labor force as a whole and estimates of the outlook for occupations with various education or training needs. In addition, educational attainment data for each occupation are presented to show the level of education achieved by current workers. Further detail is presented in Measures of Education and Training technical documentation.