Labor force projections are based on projections 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 web site. 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.
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 level of the labor force is also compared with the labor force derived from an econometric model that projects only the total civilian labor force.
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 aggregate economic projections are developed using a commercially provided econometric model of the U.S. economy—the Macroeconomic Advisers, LLC WUMMSIM Model of the U.S. Economy (MA or macro model). The MA model comprises 134 behavioral equations, 409 identities, and 201 exogenous assumptions (variables) for a total of 744 variables which describe all facets of aggregate economic performance. Estimates for exogenous variables are provided to the model and a solution of the behavioral and identity equations generated. Finally, the results are evaluated with regard to previously formulated targets for various key indicators of economic behavior.
The principal exogenous variables supplied to the MA model fall into the categories of monetary policy, fiscal policy, energy prices and supply, global economic growth, the unemployment rate associated with a full employment economy, and demographic related projections. Primary targets, or variables used to assess the behavior of a given set of projections, include the rate of growth and demand composition of real GDP, the labor productivity growth rate, and the inflation rate. Initial results for key target and assumption variables for the 2020 projections were reviewed by a panel of Federal economists. Many solution rounds were necessary to arrive at a balanced set of assumptions which yielded a defensible set of results consistent with both the detailed output and employment projections as well as any comfort zones agreed upon by the panel. The specific assumptions and target variables for the 2020 projections are presented in the January 2012 Monthly Labor Review [PDF].
(PCE) are projected in the MA model at an aggregate level. The Houthakker Taylor model(1) is used to project consumption expenditures for 75 detailed national income and product account categories for the 2010-2020 period. These detailed category estimates are then aggregated to the level of total PCE from the macro model and adjusted as necessary to ensure consistency between aggregate PCE and the detailed estimates. A bridge table based on the most recent Benchmark and annual Input-Output Accounts published by the Bureau of Economic Analysis (BEA) is then used to distribute consumption spending for each of the 75 categories among the roughly 200 commodity groups for the 2010-2020 period.
Gross private domestic investment is initially projected by the MA model for private investment in equipment and software (PIES), nonresidential and residential structures, and business inventories. The PIES categories are estimated in greater detail using a system of regression equations that set GDP, capital stock, and the cost of capital as explanatory variables. In all, projections are made for 28 categories of private investment in equipment and software. The estimates are then aggregated to the level of the macro model control and adjusted as necessary to ensure consistency between the macro model aggregate and the detailed estimates. Much like PCE, detailed data from the BEA Benchmark and annual I-O tables are used to breakout investment category estimates to detailed commodity sectors
Business inventories, on a commodity basis, are extrapolated based on lagged values of commodity output. These also are aggregated and adjusted to conform to the macro model aggregate of the change in inventories. The controls for nonresidential and residential structures are taken directly from the macro model. All the category controls, with the exception of business inventories, are then distributed to producing sectors using projected bridge tables.
is initially projected by the MA macro model for export goods and services, and import goods and services. Adjustments are made to the initial MA model forecast in order to account for re-exports and re-imports and move the data from a NIPA based estimate to an I-O basis. Distributional models are used to allocate the adjusted forecasted macro model data to a commodity basis. Other factors also are considered, including energy forecasts, existing and expected shares of the domestic market, expected world economic conditions, and known trade agreements.
is projected by the MA model for three major government categories: Federal defense, Federal nondefense, and State and local government. Projections for each major category include estimates for two expenditure categories: consumption and gross investment. These are further disaggregated based upon past trends and expected government political and policy changes. Finally, each of the six expenditure categories is allocated to detailed commodity sectors, such as electric utilities or hospitals, based on relationships from BEA's benchmark and annual I-O accounts.
The components of final demand, personal consumption expenditures, gross private domestic investment, foreign trade, and government, are then compiled into a final demand matrix of about 200 rows of commodities and 200 columns of demand categories. The resulting final demand matrix is an input into the projection of industry output.
Industry output is derived using a set of projected input-output tables consisting of two basic matrices for each year, a "use" and a "make" table. The use table consists of final demand, from the preceding step, together with intermediate demand and value added. The use table shows the use of commodities by each industry as inputs into its production process. The make table allocates commodity output to the industry in which it is the primary commodity output and to those industries in which it is secondary. In percentage form, the use table provides the direct requirements table and the make table becomes a market share table. These two tables are then used to create total requirements tables which yield the projected levels of industry and commodity output required to satisfy projected final demand.
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 and unpaid family workers is extrapolated using historical data. These data are derived from the ratio of self-employed and unpaid family workers 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 unpaid family workers and total employment by industry. Projected average weekly hours and total hours for self-employed and unpaid family workers 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.
To allocate projected industry employment to occupations, a set of industry-occupation matrices are developed. These include a base-year employment matrix for 2010 and a projected-year employment matrix for 2020. These matrices, referred to collectively as the National Employment Matrix, constitute a comprehensive employment database. For each occupation, the Matrix provides a detailed breakdown of employment by industry and class of worker. Similarly, for each industry and class of worker, the Matrix provides a detailed breakdown of occupational employment.
Base-year employment data for wage and salary workers, self-employed workers, and unpaid family workers come from a variety of sources, and measure total employment as a count of jobs, not a count of individual workers. This concept is different from that used by another measure familiar to many readers, the Current Population Survey’s total employment as a count of the number of workers. The Matrix’s total employment concept is also different from the BLS Current Employment Statistics (CES) total employment measure. Although the CES measure is also a count of jobs, it covers nonfarm payroll jobs, whereas the Matrix includes all jobs.
The Matrix does not include employment estimates for every industry which employs an occupation, or every occupation employed within an industry. Some data are not released due to confidentiality and/or quality reasons(2). Beginning with the 2010-20 projections, employment data in the National Employment Matrix are presented in thousands. Detailed data may not sum to totals because of rounding or because some data are not released.
2010 Base-Year Employment
For most industries, the Occupational Employment Statistics (OES) survey provides data for the occupational staffing patterns—the distribution of wage and salary employment by occupation in each industry—and Current Employment Statistics (CES) data provide information on total wage and salary employment in each nonfarm industry. Estimates of occupational employment for each industry are derived by multiplying each occupation’s proportion–or ratio–of employment in each industry, based on OES survey data, by CES industry employment.
BLS staff obtains industry and occupational employment data for workers in all agricultural industries except logging(3) workers in private households, self-employed workers, or unpaid family workers from the Current Population Survey. Data are obtained for workers holding primary and secondary jobs to yield a broader employment measure. CPS data are coded using the 2002 Census occupation classification system. Although the Census system is based on the Standard Occupational Classification (SOC) system used by OES, it does not provide the same level of detail. CPS employment data were proportionally distributed to detailed SOC occupations using the employment distribution from the OES data.
Total base-year employment for an occupation is the sum of employment across all industries and class-of-worker categories—the combination of wage and salary, self-employed, and unpaid family workers. Occupational employment within each industry, divided by total wage and salary employment in each industry, yields the occupational distribution ratios used to project occupational employment. These ratios, referred to as staffing patterns, show occupational utilization by industry.
2020 Projected-Year Employment
Projected-year employment data for industries and class-of-worker categories are first developed at a higher level of aggregation, and then distributed to corresponding detailed Matrix industries and by class of worker. To project employment for workers holding secondary jobs, BLS applies the growth rate of the corresponding primary jobs category to the base-year estimate.
To derive projected-year staffing patterns, BLS economists place base-year staffing patterns under an iterative process of qualitative and quantitative analyses. They examine historical staffing pattern data and conduct research on factors that may affect occupational utilization within given industries during the projection decade. Such factors include shifts in product mix, and changes in technology or business practices. Once these factors are identified, change factors are developed which give the proportional change in an occupation’s share of industry employment over the 10-year projection period. These change factors are applied to the 2010 occupational staffing patterns to derive projected staffing patterns. An occupation’s projected share of an industry may increase, decrease, or remain the same, depending on the change factors and underlying rationales.
For each industry, the projected-year employment is multiplied by the projected-year occupational ratio to yield projected-year wage and salary occupational employment for the industry(4). Occupational employment data for self-employed and unpaid family workers are projected separately. Total projected-year occupational employment is the sum of the projected employment figures for wage and salary, self-employed, and unpaid family workers.
Factors Affecting Occupational Utilization
BLS projections of wage and salary employment are developed within the framework of an industry-occupation matrix, which shows the occupational distribution in each industry—the proportion of each industry’s employment which each occupation comprises. Historical data indicate that the occupational distribution within industries shifts over time as the utilization of some occupations changes relative to that of other occupations.
Among the various factors that can affect the utilization of workers in an occupation in particular industries are technology, business practices, the mix of goods and services produced, the size of business establishments, and offshore outsourcing. BLS staff analyzes each occupation in the matrix to identify the factors that are likely to cause an increase or decrease in utilization of that occupation within particular industries. Their analyses incorporate judgments about new trends that may influence occupational utilization, such as the use of the Internet and electronic commerce. Table 5.1 Factors Affecting Occupational Utilization contains brief descriptions of the factors underlying changes in occupational utilization within industries that are projected to occur between 2010 and 2020. 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 published industries with the highest share of an occupation’s employment. Some factors apply to only one industry, while others may apply to many or all industries in which the occupation is employed.
Changes in occupational utilization, the proportion of an industry’s employment which an occupation comprises, are classified using one of eight descriptors:
Very large increase
Very large decrease
Estimating Replacement Needs
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 leave their occupations and need to be replaced. In most occupations, these replacement needs provide more job openings than employment growth does. Further detail is presented in Estimating Occupational Replacement Needs technical documentation (February 2012).
BLS provides information about education and training requirements for hundreds of occupations. In the education and training system, each of the occupations for which the office publishes projections data is assigned separate categories for education, work experience, and on-the-job training. Occupations can be grouped in order 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 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. Further detail is presented in Measures of Education and Training technical documentation (February 2012).