Government demand 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 compensation, consumption of general government fixed capital, and all other expenditures. These are further disaggregated based upon past trends and expected government political and policy changes. For Federal defense and nondefense, projections are made for compensation of general government employees, consumption, and gross investment. For State and local government, expenditures are subdivided between education and noneducation functions, each of which also features projections of compensation of general government employees, consumption, and gross investment. Finally, each of the twelve expenditure categories is allocated to the appropriate industry sector or sectors.

Industrial activity

Employment by industry

The initial projections of industry employment are developed according to the following procedure implemented for each industry:

1. The demand for wage and salary hours in millions is projected using an estimated regression equation derived from the first order conditions of a constant elasticity of substitution production function modified to include a time variable. The time variable is meant to capture disembodied technical change or shifts in the production function arising from long term increased efficiencies in the use of inputs.
2. Annual average weekly wage and salary hours are estimated as a function of time and the unemployment rate. The same technique is also used to estimate annual average weekly self-employed and unpaid family worker hours.
3. The number of wage and salary jobs in thousands is then derived from the estimation of hours using the estimated annual average weekly wage and salary hours:

[Jobs = (Hours/AWH)/0.052],

where AWH = average weekly hours

4. The number of self-employed and unpaid family workers is derived by first extrapolating the logit of the ratio of self-employed and unpaid family workers to the total for each industry as a function of time and the unemployment rate. The extrapolated ratio is then used to derive the level of self-employed and unpaid family workers from the number of wage and salary jobs by first calculating the total number of jobs and then subtracting the number of wage and salary jobs from the total:

[SEUFW = ( WS/(1-SEUFWRatio) - WS)],

where SEUFW = self-employed and unpaid family workers, and

WS = wage and salary jobs.

5. The hours for self-employed and unemployed family workers are then calculated by applying the estimated annual average weekly self-employed and unpaid family workers hours to the self-employed and unpaid family workers levels:

• SEHrs = self-employed and unpaid family worker hours,
• SEUFW = self-employed and unpaid family workers, and
• SEAWH = self-employed and unpaid family workers average weekly hours.

1. Finally, total hours for each industry are derived by summing wage and salary and self-employed and unpaid family workers hours.

The results produced by these procedures are then reviewed together with industry output and labor productivity to insure consistency with historical trends. At the same time attempts are made to identify industries which may be expected to deviate from past behavior because of changes in technology, demand or other factors. Where appropriate, changes to the initial employment estimates are made either by modifying the employment demand relationships themselves or by modifying results from earlier steps of the projections process.

Employment by occupation

An industry-occupation matrix is used to project employment for wage and salary workers. The matrix shows occupational staffing patterns—each occupation as a percent of the work force in every industry. It includes 284 detailed industries and 725 detailed occupations. Data for current staffing patterns in the matrix come primarily from the BLS Occupational Employment Statistics surveys, which collect data from employers on a 3-year cycle.

The occupational staffing patterns for each industry are projected based on anticipated changes in the way goods and services are produced, then applied to projected industry employment, and the resulting employment summed across industries to get total wage and salary employment by occupation. Using this method, employment is projected to grow faster than average in those occupations concentrated in fast-growing industries and more slowly in slow-growing industries. For example, health care workers are expected to grow rapidly, as the health care industries grow rapidly.

Employment in an occupation also may grow or decline as a result of many other factors. For example, rapid growth is expected among social and human service assistants as employers increasingly rely on these workers to undertake greater responsibility for delivering services to clients. Rapid growth is also expected among computer systems analysts as technology advances and organizations place more emphasis on network applications and maximizing the efficiency of their computer systems. On the contrary, automation, the expanding use of computers, and developments in computer software enhance productivity and will result in slower than average growth among office and administrative support workers, machine operators, and assemblers—thus lowering their proportion of the labor force. The projected-year matrix incorporates these expected changes.

Data on self-employed workers, unpaid family workers, and workers who have a second wage and salary job in agricultural production, forestry, fishing, or private households in each occupation come from the Current Population Survey. Workers in these groups for each occupation are projected separately for the economy as a whole rather than by industry, and are added to the projections of wage and salary workers to obtain total projected employment for each occupation.

Last Modified Date: February 11, 2004