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Bureau of Labor Statistics > Productivity > Publications > Articles and Research

Experimental Labor Composition for Detailed Industries

On September 29, 2023, the Bureau of Labor Statistics (BLS) released experimental labor composition measures for detailed industries in manufacturing and air transportation for the years 2005 through 2021. The data include experimental total factor productivity (TFP) and costs measures that incorporate the labor composition adjustment. BLS is researching the reliability of these labor composition estimates to measure TFP more accurately at the detailed industry level.

About our measures:

What is labor composition?

Productivity estimates require a measure of labor input, which reflects the time and skill of the workforce used to produce output. A measure of labor input that only accounts for the time dimension of labor, meaning the number of hours worked, does not consider the varying degrees of effectiveness or skill among workers. For example, an hour worked by a newly hired employee is not likely to produce as much output as an hour worked by a highly experienced one. The effectiveness of the experienced worker’s hours is greater than that of the new employee’s.

Workers must be treated as heterogeneous, and their corresponding hours worked weighted by their effectiveness. This weighting adjustment is referred to as labor composition. Labor composition captures the characteristics of different groups of workers producing the output and measures the composition of their skill levels. In practice, workers are differentiated by their age, education level, and sex, and their skill level is estimated by their hourly wage. Therefore, by accounting for labor composition, changes in the educational attainment and experience of the workforce are incorporated into productivity estimates.

In BLS’ official measures of total factor productivity for detailed industries, labor input is estimated as the sum of hours worked by all workers. Consequently, the hours worked of all workers, regardless of their skill levels, are treated equally. In this experimental data release, BLS, for the first time, incorporates the labor composition adjustment to labor input in its measure of TFP for detailed industries in manufacturing and air transportation.

The new method

On November 18, 2022, BLS released data accompanying the Total Factor Productivity for Major Industries release that incorporated a new method for labor composition that uses both the Census Bureau’s American Community Survey (ACS) and the BLS Current Population Survey (CPS). This method is a result of a multi-year joint effort with the Bureau of Economic Analysis and improves upon the previous method primarily by using microdata from the ACS. Additional details on the new method can be found in the technical notes that accompanied the release.

Notably, the new method estimates labor composition at a detailed industry level and then aggregates up to the currently published major sectors and industries. BLS is releasing a subset of this underlying industry detail as an experimental series and is continuing research on the reliability of the entire series. This initial subset includes all the detailed industries for which BLS currently publishes total factor productivity measures, with the exception of the line-haul railroads industry. The experimental labor composition and TFP measures begin in 2005 because the ACS data is only available starting that year.

The impact of labor composition

The labor composition index adjusts total labor hours worked for the demographic composition of the workers. This can have an impact when considering the changes over time to labor input. From 2005 to 2021, the share of workers in the U.S. with a bachelor’s degree or more rose from 30% to 41%.[1]  All else being equal, the U.S. has a higher skilled workforce in 2021 compared to 2005. This would result in an increase to labor input over this period.

Total factor productivity is the change in output not explained by the change in the combined inputs used to produce the output in an industry.[2] Thus, if we hold the inputs of capital, intermediate inputs, and hours worked constant, in increase in labor composition is going to have inverse effect on TFP. As a result, an increase in labor input, because of an increase in labor composition, would then result in a decrease in total factor productivity. This impact can be seen across all industries in the experimental dataset.

Labor input in the semiconductors manufacturing industry

The new experimental series includes measures of labor hours worked, labor composition, and labor input for detailed industries in the manufacturing sector. As an example, we can take a closer look at the industry defined under NAICS 3344, semiconductors and other electronic components manufacturing.

Labor composition in the semiconductor manufacturing industry has had a general positive trend upwards since 2005. There is a notable dip (-1.0 percent) in labor composition in 2020, followed by a large increase (+2.4 percent) in 2021. The semiconductor manufacturing industry has a highly skilled work force due to the training required to design chips and to build tools that make the chips. This kind of work requires roles such as electrical engineers, industrial engineers, programmers, and technicians. In 2020, the industry’s initial response to the pandemic was to cut back on orders for chips. The hours worked by the highly skilled and experienced workers in the industry were largely and negatively impacted, leading to the drop in labor composition. In 2021, with an ensuing chip shortage and a slowly recovering economy, the hours worked by the highly skilled and experienced bounced up to meet demand.

The change in labor input is equal to the sum of the change in labor hours worked and the change in labor composition. Therefore, if labor composition increases, labor input increases. This intuitively makes sense since the increase in the skill composition of the work force means that there is higher labor input involved in producing the industry’s output. This impact can be seen in the experimental labor input measure for the semiconductors manufacturing industry.

Note that official measures of labor input for detailed industries equal hours worked because they do not include the labor composition adjustment. With an increasing trend over the 2005-2021 period, incorporating the labor composition adjustment into the labor input measure for semiconductors manufacturing has an overall positive impact. In 2021, the adjustment reversed the trend in labor input from a -1.4 percent decrease to a 0.9 percent increase.

In addition to providing a more accurate measure of labor input, the new experimental series includes data on labor composition at a more detailed industry level than what is currently published. This can provide a deeper insight into what’s driving labor composition for the published major industries and sectors. As an example, the decrease in labor composition in 2020 for NAICS 3344, semiconductor and other electronic component manufacturing, can be identified as the main driver of the decrease in labor composition in 2020 for NAICS 334, computer and electronic product manufacturing.

Long term growth in total factor productivity

Incorporating the labor composition measure into labor input dampens long-term TFP growth for all 86 4-digit manufacturing industries and air transportation. The table below shows the impact of the labor composition adjustment to the annual percent change[3] in TFP for 2005-2021. 

Table 1. Impact of labor composition on TFP annual percent change (%) for 2005-2021
NAICS Industry Official Experimental


Animal food manufacturing -1.3 -1.3


Grain and oilseed milling 0.3 0.3


Sugar and confectionery products -1.0 -1.1


Fruit and vegetable preserving and specialty food 0.2 0.1


Dairy products 0.2 0.1


Animal slaughtering and processing -0.3 -0.4


Seafood product preparation and packaging 0.1 0.1


Bakeries and tortilla manufacturing -0.6 -0.7


Other food products 0.1 0.1


Beverages -0.2 -0.3


Tobacco manufacturing -1.7 -1.7


Fiber, yarn, and thread mills -0.3 -0.4


Fabric mills -0.3 -0.5


Textile and fabric finishing and fabric coating mills -1.3 -1.3


Textile furnishings mills -1.2 -1.3


Other textile product mills 0.2 0.0


Apparel knitting mills -0.9 -1.1


Cut and sew apparel -1.4 -1.8


Apparel accessories and other apparel manufacturing -1.6 -1.9


Leather and hide tanning and finishing 4.1 4.0


Footwear manufacturing -0.9 -1.1


Other leather and allied product manufacturing -0.1 -0.2


Sawmills and wood preservation 1.4 1.4


Veneer, plywood, and engineered wood product manufacturing -0.2 -0.3


Other wood products 0.2 0.1


Pulp, paper, and paperboard mills 0.3 0.2


Converted paper products -0.2 -0.3


Printing and related support activities 0.6 0.5


Petroleum and coal products -0.8 -0.8


Basic chemicals 0.7 0.7


Resin, synthetic rubber, and artificial synthetic fibers and filaments -0.4 -0.4


Pesticides, fertilizers, and other agricultural chemicals -0.1 -0.1


Pharmaceuticals and medicine -2.0 -2.1


Paint, coatings, and adhesives -0.5 -0.5


Soaps, cleaning compounds, and toilet preparations -0.4 -0.4


Other chemical products and preparations 0.7 0.6


Plastics products -0.1 -0.2


Rubber products 0.4 0.3


Clay products and refractories -0.7 -0.9


Glass and glass products -0.4 -0.5


Cement and concrete products -0.9 -1.0


Lime and gypsum products -2.0 -2.1


Other nonmetallic mineral products -0.1 -0.2


Iron and steel mills and ferroalloy production -0.2 -0.3


Steel products from purchased steel 0.1 0.1


Alumina and aluminum production and processing 1.4 1.3


Nonferrous metal (except aluminum) production and processing 1.7 1.6


Foundries -0.1 -0.2


Forging and stamping 0.8 0.7


Cutlery and handtools 1.4 1.2


Architectural and structural metals 0.2 0.1


Boilers, tanks, and shipping containers 0.3 0.2


Hardware -0.5 -0.6


Spring and wire products 0.2 0.1


Machine shops; turned products; and screws, nuts, and bolts 0.5 0.4


Coating, engraving, heat treating, and allied activities 0.8 0.7


Other fabricated metal products -0.6 -0.7


Agriculture, construction, and mining machinery -0.2 -0.3


Industrial machinery 0.5 0.4


Commercial and service industry machinery 0.6 0.5


HVAC and commercial refrigeration equipment -0.2 -0.3


Metalworking machinery 1.5 1.4


Engine, turbine, and power transmission equipment 0.6 0.5


Other general purpose machinery -0.1 -0.2


Computer and peripheral equipment 3.5 3.4


Communications equipment 0.2 0.1


Audio and video equipment manufacturing 2.7 2.7


Semiconductors and other electronic components 2.0 1.9


Electronic instruments 0.0 -0.2


Manufacturing and reproducing magnetic and optical media -1.5 -1.7


Electric lighting equipment 1.2 1.0


Household appliances 1.5 1.4


Electrical equipment 0.2 0.1


Other electrical equipment and components 0.5 0.4


Motor vehicles 0.0 -0.1


Motor vehicle bodies and trailers 0.1 0.1


Motor vehicle parts 1.2 1.2


Aerospace products and parts -0.9 -0.9


Railroad rolling stock manufacturing 0.5 0.4


Ship and boat building 1.2 1.1


Other transportation equipment 1.3 1.2


Household and institutional furniture and kitchen cabinets 0.2 0.1


Office furniture (including fixtures) -0.5 -0.6


Other furniture related products -0.3 -0.3


Medical equipment and supplies -0.2 -0.4


Other miscellaneous manufacturing 0.3 0.1


Air transportation -0.3 -0.4

Source: U.S. Bureau of Labor Statistics and Bureau of Economic Analysis


[1] Source: Census Bureau’s American Community Survey, 2005-2021.

[2] It’s important in productivity measurement to distinguish the effects of labor composition on output from the effects on labor input. More data on how output is calculated for detailed industries can be found in the Handbook of Methods.

[3] The annual percent change is the compound annual growth rate in an index series over a period of more than one year. The change of an index series varies from year to year. However, the annual percent change is the constant rate that can be applied to each year in a period, from the start to the end, that would give the same total result. It is calculated as (Ending Value/Starting Value)^(1/Number of Years)-1.

Last Modified Date: September 29, 2023