Handbook of Methods Employment Projections Concepts
Employment Projections: Concepts
The main objective of the Employment Projections (EP) program is to provide estimated employment and occupational trends over a 10-year projection period. The employment projections are developed in a series of six steps that examine: the labor force, aggregate economic growth, commodity final demand, input-output data, industry output and employment, and finally occupational employment and openings. Each step is based on separate procedures, models, and related assumptions. Together, the six components provide the analytical framework used to develop the detailed employment projections. The following are brief discussions of key concepts.
Labor force
The Current Population Survey (CPS) provides the historical labor force data. The labor force consists of individuals age 16 or older who are employed or unemployed. People with jobs are employed. People who are jobless are unemployed if they are available to work and have actively looked for work in the prior 4 weeks or are waiting to be recalled to a job from which they have been temporarily laid off. Any person who is neither employed nor unemployed is not in the labor force. The labor force participation rate is the ratio of the number in the labor force to the civilian noninstitutional population age 16 or older. Labor force participation rates may be calculated by demographic categories, including age, sex, and race.
National income and product accounts
National income and product accounts (NIPAs) are provided by the Bureau of Economic Analysis (BEA) within the Department of Commerce. The NIPAs are a national accounting framework to track economic activity within the United States. The EP program uses the NIPAs in analyzing historical macroeconomic activity and to project the overall aggregate economic trend. One component of the NIPAs is the aggregate gross domestic product (GDP), also known as final demand. The NIPAs also provide more detailed information on final demand, which EP uses to estimate commodity based final demand in the context of an input-output system.
EP uses the input-output accounting framework to model the relationship between final demand, employment, and occupational data.1 The input-output accounting framework tracks the flow of intermediate goods and services in the production of final goods and services. The input-output data are provided by BEA and are an important component in producing the NIPAs. Through the input-output system, EP can estimate the employment level required to produce a given level of final demand.
Staffing patterns
After estimating industry employment, EP staff use the National Employment Matrix to break industry employment out into detailed occupations. This matrix shows the distribution of occupational employment by industries for wage and salary workers, as well as the distribution of self-employed workers by occupation. The distribution of occupations by industry represents the occupational staffing pattern for each industry. The main source data for the staffing patterns is the BLS Occupational Employment and Wage Statistics (OEWS) survey.
Occupational openings
In addition to projecting employment change, EP also projects occupational separations, an estimate of the number of workers who will leave an occupation due to retirement, career changes, or other factors. When combined with projections of employment change, this provides an estimate of opportunities for new workers to enter an occupation. These estimates of occupational openings are used for career information and workforce development purposes to give a more complete picture of the demand for workers.
Classification systems
EP staff use a variety of data sources that are categorized by two classification systems, providing structure to the output, employment, and occupation data.2The North American Industry Classification System (NAICS) is used for classifying industry output and employment, while the Standard Occupational Classification System (SOC) is used for all EP occupation data.
Assumptions
BLS employment projections are a projection and not a forecast. The distinction emphasizes purpose and results. Projections use a set of assumptions to determine long-term underlying trends, whereas forecasts focus on predicting actual outcomes in the near term. The assumptions that underlie projections are designed to provide a neutral backdrop so that a focused analysis of the long-term trends can take place. For example, BLS does not forecast business cycle activity, but rather is concerned with the long-term growth path of the aggregate economy. Because the purpose of a forecast is prediction, the forecast user will be interested in the actual forecast values. A projection, however, supplies the user with a plausible scenario to help them understand the ramifications of the long-term trends.
The following assumptions underlie the BLS employment projections:
- Broad social and demographic trends will continue
- New major armed conflicts will not develop
- There will be no major natural disasters, including pandemics
- The projected U.S. economy will be at approximately full employment/potential output3
- Existing laws and policies with significant impacts on economic trends are assumed to hold throughout the projection period
In addition to these general assumptions, individual components of the projections may incorporate specific assumptions, or exogenous inputs. For example, the labor force model projects labor force participation rates and then applies them to the Census Bureau population projections to derive the labor force level. These assumptions are discussed in the relevant sections of the calculations section.
Finally, unexpected events will occur and have unforeseen influences. These events may include wars, disasters, and changes in technology, or social dynamics. In this context, BLS employment projections should be considered as likely outcomes based on specified assumptions, and not definite outcomes.
Last Modified Date: September 08, 2021
United States Department of Labor
