Chapter 13.
Employment Projections

For about 60 years, the Bureau of Labor Statistics (BLS) has developed long-term projections of likely employment patterns in the U.S. economy. Since the early 1970s, projections have been prepared on a 2-year cycle. The projections cover the future size and composition of the labor force, aggregate economic growth, detailed estimates of industry production, and industry and occupational employment. The resulting data serve a variety of users who need information about expected patterns of economic growth and the effects these patterns are expected to have on employment. for example, information about future employment opportunities by occupation is used by counselors, educators, and others helping people choose a career and by officials who plan education and training programs.

BLS previously developed projections in which the target year always ended in a zero or a five. Projections were prepared every other year, resulting in at least two and sometimes three sets of projections being prepared for the same target year. As a result, the projection horizons were as short as 10 years or as long as 15 years. In 1997, BLS has changed its procedures. Beginning with the 1996-2006 projections, BLS began developing projections for a 10-year period, still on a 2-year cycle.

Projection Procedures

Over the years, the procedures used to develop the employment projections have undergone many changes as new data series became available and as economic and statistical tools improved. Since the late 1970s, however, the basic methodology has remained largely the same. The procedures have centered around projections of an interindustry or input-output model that determines job requirements associated with production needs.

Projecting employment in industry and occupational detail requires projections of the total economy and its sectors. BLS develops its projections in a series of six steps, each of which is based on separate procedures and models, and on related assumptions. These six steps examine:

• The size and demographic composition of the labor force.
• Aggregate economic growth.
• Commodity final demand.
• Input-output.
• Industry output and employment.
• Occupational employment.

These components provide the analytical framework needed to develop detailed employment projections. BLS analysts solve each component sequentially. And each step includes several iterations to ensure internal consistency as assumptions and results are reviewed and revised.

Labor force

BLS projects the future supply of labor by applying population projections produced by the Census Bureau to the labor force participation rate projections made by BLS.

The Census Bureau carries out long-term population projections of the resident U.S. population. The projection of the resident population is based on the current size and composition of the population and includes assumptions about future fertility, mortality, and net international migration. The conversion from the resident population concept of the Census to the civilian noninstitutional population concept of the BLS Current Population Survey (CPS) takes place in three steps. First, the population of children under 16 years is subtracted from the total resident population. Then, the population of the Armed Forces, broken down into different age, sex, race, and ethnic categories, is also subtracted. Finally, the institutional population is subtracted from the civilian population for all the different categories.

For more than 136 age, sex, race and ethnic groups, BLS maintains a data base of annual averages of CPS labor force participation rates. BLS analysts examine the trends and past behavior of participation rates for all the categories. First, the historical participation rates for these groups are smoothed. Second, the smoothed data are transformed into logits, or natural log of the odds ratio¹. Finally, the logits of the participation rates are 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.

In addition, projected labor force participation rates are reviewed for consistency. The time-path, cross-section in the target year, and cohort patterns of participation are all reviewed and, if necessary, modified. Projected labor force participation rates are applied to the population projections, producing labor force projections for each of the different age, sex, race, and ethnicity groups. The groups are then summed to obtain the total civilian labor force, which becomes an input to the next stage of the projections.

Aggregate economic growth

The second stage of the BLS projections process develops projections of the gross domestic product (GDP) and the major categories of demand and income. The results of this stage provide aggregate measures that are consistent with each other and with the various assumptions and conditions of the projections. The values generated for each demand sector are then used in the next stage: developing detailed commodity purchases for personal consumption, business investment, foreign trade, and government.

Recent projections have been based on a macroeconomic model developed by St. Louis-based Macroeconomic Advisers, LLC (MA). This model has 744 variables descriptive of the U.S. economy. Of these, 134 are behavioral equations, 409 are identities, and the remaining 201 are exogenous variables, including key assumptions such as monetary policy, fiscal policy, energy prices and supply, and demographic changes.

Besides being governed by general assumptions, the projections are generally approached with specific goals or targets. The goals used to assess the behavior of a given set of projections include the rate of growth and demand composition of real GDP, the rate of growth of labor productivity, the rate of inflation, the unemployment rate, and international trade related issues. Many solution rounds may be necessary to arrive at a balanced set of assumptions, which yield a defensible set of results. When the aggregate economic projection is final, the components of GDP are then supplied to the commodity component of the projections process.

Commodity final demand

The macroeconomic model provides forecasts of final demand sectors, including personal consumption expenditures (PCE), gross private fixed investment, change in private inventories, exports and imports of goods and services, Federal and state and local governments, and more detailed sectors within many of these categories. The next step in the projections process is to further disaggregate the results from the macro model into detailed categories that are supported by data from both the National Income and Product Accounts² and the Input-Output Accounts³. These accounts are used to break out the types of commodities produced or consumed within each of the categories. The process yields a matrix comprising about 200 rows of commodity sectors and 190 columns of final demand categories⁴.

For example, BLS forecasts 88 product classes within PCE using the Houthakker Tayler model. These sub-model estimates are then chain weighted⁵ to each of the six categories of the personal consumption sector from the macroeconomic model and adjusted as necessary to insure consistency between aggregate controls and the detailed estimates. The final step is to allocate these PCE product classes to detailed commodities via a commodity-by-final demand category matrix. The matrix allows the PCE analyst to provide for shifts in the commodity of a given demand category.

Because the demand for each of the foreign trade categories and change in private investment are estimated by the macroeconomic model, regression models are instead used to forecast demand directly within the detailed commodities. The commodities are then constrained to chain weight up to the macroeconomic model result. The methods used to derive the final demand data vary by GDP component and include economic methods and statistical techniques. The resulting detailed distribution of GDP provides the demand component of an interindustry model of the U.S. economy.

Input-output

The creation of an input-output model is the next stage in developing the 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; intermediate material inputs, such as the steel incorporated into cars, are not explicitly reflected in the GDP estimates. Therefore, to derive an industry-level estimate of the employment and capital needed to produce a given level of GDP, it is first necessary to translate that GDP to a total output concept. An input-output model provides just such a translation and, at the same time, allows BLS analysts to consider other expected phenomena, such as technological changes, shortages or surpluses, and any other factors that may affect the production process.

The BLS input-output model consists of two basic matrices for each year, a "use" table and a "make" table. Both tables are expressed in coefficient form. The "use" table, the principal one, shows the use of commodities by each industry as inputs into its production process. In coefficient form, each column of this table shows the pattern of commodity inputs per dollar of industry output. Projecting this table must take into account the changes in the input pattern or the way in which goods are produced or services are provided by each industry. In general, two types of changes in these input patterns are made in developing a future input-output table: those made to the inputs of a specific industry (such as the changes in inputs in the publishing industry) and those made to the inputs of a specific commodity in all or most industries (such as increased use of business services across a wide spectrum of industries).

The "make" table shows the commodity output of each industry. The table allocates commodity output to the industry in which it is the primary commodity output and to those industries where it is secondary. This table shows the industry distribution of production for each commodity. Unlike the "use" table, the "make" table is generally held constant or changed very little over the projections decade.

When projected values of the "use" and "make" relationships are available, the projection of commodity demand developed in preceding steps is converted into a projection of domestic industry output using the relationships obtained from the U.S. Department of Commerce's Bureau of Economic Analysis (BEA). These relationships are summarized briefly below.

g = D(I - BD)^-1e

where,

g = vector of domestic industry output by sector

B = "use" table in coefficient form

D = "make" table in coefficient form

I = identity matrix

e = vector of final demand by commodity sector

In sum, matrix multiplying the inverse of the coefficient forms of the "make" and "use" tables by a vector of final demand commodity distribution, as represented by "e" above, yields industry outputs.

Industry output and employment

The industry total output derived from the previous stage includes both sales to final users, as GDP, and to other industries as intermediate inputs. The detailed industry output is then used to derive the industry employment necessary to produce the given level of output. The BLS industry employment is modeled as a function of industry output, wages, prices, and time. Industry employment is then projected using the estimated historical relationship between the variables.

Industry employment is projected in both numbers of jobs and hours worked, both for wage and salary workers and for self-employed and unpaid family workers. Projections are developed according to the following procedure implemented for each industry.

Through a system of equations, employment for wage and salary workers is solved independently over the projections decade for each industry. The individual industry estimates of employment must be consistent with the total employment level derived from the macroeconomic solution. The employment equation relates an industry's labor demand (total hours) to its output, its wage rate relative to its output price, and a trend variable to capture technological change within that industry. A separate set of equations, describing average weekly hours for each industry, are estimated as a function of time and the unemployment rate. The equations are then used to predict average weekly hours over the projections decade. An identity relating average weekly hours, total hours, and employment yields a count of jobs by industry.

The number of self-employed and unpaid family workers is derived by first extrapolating the ratio of the self-employed to the total employment for each industry. This equation is a function of time and the unemployment rate. The extrapolated ratio is used to derive the level of self-employed and unpaid family workers given the number of wage and salary jobs in each industry. The total hours for self-employed and unpaid family workers are calculated by applying the estimated annual average weekly hours to the employment levels for each industry. Finally, total hours for each industry are derived by summing hours for wage and salary workers and for self-employed and unpaid family worker hours.

Together with the industry output projections, the employment results provide a measure of labor productivity. BLS analysts examine the implied growth rates in productivity for consistency with historical trends. At the same time, analysts attempt to identify industries that may deviate from past behavior because of changes in technology or other factors. Where appropriate, changes to the employment estimates are made by modifying either the employment demand itself or the results from earlier steps in the projections process.

The final estimates of the projected employment for about 200 industries are then used as inputs to determine the occupational employment over the projections decade.

Occupational employment

The technique for developing the occupational employment projections is based on an industry-occupation matrix showing the distribution of employment for over 300 industries and 750 detailed occupations. Occupational staffing patterns for the industries are based on Occupational Employment Statistics (OES) data collected by State Workforce Agencies and analyzed by BLS. In coefficient terms this matrix represents industry staffing patterns where each column represents the occupational distribution of employment in a specific industry. The change in occupational requirements is jointly determined by shifts in these coefficients and by the structure of industry employment developed in the preceding step.

Because staffing patterns of industries may change over time, the projection method must account for shifts through a series of steps. First, historical data are reviewed to identify trends. Next, factors underlying these trends are then identified through analytical studies of specific industries and occupations, technological change, and a variety of other economic data. Finally, projected staffing patterns are produced based on judgments of how the pattern is expected to change in the future. Numerous factors underlying these changes include technological developments affecting production and products, innovations in the ways business is conducted, modifications of organizational patterns, responses to government policies, and decisions to add new products and services or to stop offering existing ones.

Some expected trends may not be evident in the historical data. For example, an analysis of the past would not point toward the impact of radio frequency identification (RFID) on staffing patterns for cashiers because this technology has not been widely used in most industries. However, as more stores track their inventory and purchases through RFID, this technology may have a significant impact on cashiers, especially in industries in which RFID is easiest to implement.

The projected change in a specific occupation's share of industry employment may be small, moderate, or large; the precise percentage reflects the judgment of the BLS analyst who studies that occupation. In general, changes in coefficients of 5 to 14 percent are considered small; changes of 15 to 27 percent are moderate; and changes of 28 percent or more are large. Documentation released with the projections provides detail on the assumptions developed for each of the occupations for which changes to the base-year coefficients are made.

When projected staffing patterns are available they are used to allocate each industry's projected employment to detailed occupations. These estimates can then be summed across industries to yield total employment for each detailed occupation as follows:

o = Sl

where

o = vector of wage and salary employment by occupation

l = vector of wage and salary employment by industry

S = staffing pattern matrix in which each column contains the allocation of industry employment to occupations in percent terms.

The estimates described above relate only to wage and salary employees. Other classes of workers, primarily the self-employed, are analyzed separately. The analyses of these other workers are then combined with that of wage and salary workers to produce a projection of total occupational demand for the United States.

Final review

An important element of the projection system is its comprehensive structure. To ensure internal consistency and reasonableness of this large structure, the BLS projections process encompasses detailed review and analysis of the results at each stage. For example, the close relationship between changes in staffing patterns in the occupational model to changes in technology is also an important factor in determining industry labor productivity. Specialists in many different areas from inside and outside the projection group review all of the relevant results from their particular perspective. In short, the final results reflect innumerable interactions among BLS analysts who focus on particular sectors in the model. Through this review, the projection process at BLS converges into an internally consistent set of employment projections across a substantial number of industries and occupations.

Footnotes
¹For more information on labor force methodology, see Paul F. Velleman, "Definition and Comparison of Robust Nonlinear Data Smoothing Algorithms," Journal of the American Statistical Association, September 1980, Volume 75, Number 372, Theory and Methods Section, pp. 609-615.
²For more detailed discussion on concepts and methods of the U.S. National Income and Product Accounts, see the publication on the Internet at www.bea.gov/national/pdf/NIPAhandbookch1-4.pdf.
³For more detailed discussion on concepts and methods of the U.S. Input-Output Accounts, see the publication on the Internet at www.bea.gov/papers/pdf/IOmanual_092906.pdf.
⁴The number of commodity sectors and final demand categories may vary from one projection study to the next depending on data availability.
⁵The U.S. National Income and Product Accounts have adopted a chain-weighted Fisher Index to calculate real aggregates. The chain-weighted methodology calculated the prices of goods and services in order to use weights that are appropriate for the specific periods or years being measured. As a result, for a particular year, the details do not necessarily add to their higher level aggregates.

Next: Assumptions