Manufacturing sector multifactor productivity declined 3.2 percent in 2016, the U.S. Bureau of Labor Statistics reported today.(See chart 1, table A.) This was the largest annual decline in manufacturing multifactor productivity since the series started in 1987. (See table 1.) The multifactor productivity decline in 2016 reflected a 0.4-percent increase in sectoral output and a 3.6-percent increase in combined inputs. The decrease in multifactor productivity followed a 1.5-percent decrease in 2015.
Multifactor productivity is calculated by dividing an index of real sectoral output by an index of combined units of labor input, capital services, and intermediate inputs. Multifactor productivity annual measures differ from BLS quarterly labor productivity or output per hour measures because the former also includes information on capital services, shifts in the composition of the workforce, and intermediate inputs.
Durable manufacturing sector multifactor productivity decreased 2.2 percent in 2016. The decline reflected a 1.2-percent decrease in sectoral output and a 1.0-percent increase in combined inputs. Nondurable manufacturing sector multifactor productivity decreased 3.6 percent in 2016. The decline reflected a 1.4-percent increase in sectoral output and a 5.2-percent increase in combined inputs. (See table C, table 3.)
Among the 18 manufacturing industries, 14 experienced declines in multifactor productivity in 2016. The largest declines in multifactor productivity were in the chemical products and apparel, leather, and allied products industries. Printing and related support activities and miscellaneous manufacturing industries showed the largest gains in multifactor productivity. Sectoral output increased in 8 industries and combined inputs increased in 12 industries in 2016. (See chart 2, table 3.) Chart 3 displays the contributions of three-digit manufacturing industries to manufacturing sector multifactor productivity. Contributions take into account the relative importance of each industry to total manufacturing output. Chemical products and food manufacturing made the largest negative contribution to manufacturing sector multifactor productivity. Miscellaneous manufacturing and primary metal products made the largest positive contribution to manufacturing sector multifactor productivity in 2016, partially offsetting the overall productivity decline.
Trends in the manufacturing sector
Manufacturing sector output grew in 2016 with an annual increase of 0.4 percent compared to the 0.6-percent decline in 2015. The 3.6-percent increase in combined inputs was driven by a 9.4-percent growth in materials, larger than the 0.9-percent increase in combined inputs in 2015. (See table 1.)
Multifactor productivity in the manufacturing sector grew at an average annual rate of 0.7 percent from 1987 to 2016 with sectoral output increasing at an average annual rate of 1.6 percent, faster than the 0.9-percent average annual rate of increase in combined inputs. During the same period, labor productivity grew at an average annual rate of 2.8 percent. (See table A.) Of the 2.8-percent average annual increase in labor productivity, multifactor productivity contributed 0.7 percent, capital intensity contributed 0.7 percent, intermediate inputs intensity contributed 1.2 percent, and labor composition contributed 0.2 percent. (See table B.)
For the most recent 2007-16 period, multifactor productivity declined at a 0.8-percent average annual rate as compared to an increase of 1.7 percent during the 2000-07 period. (See table A.) Sectoral output decreased at a 0.5-percent annual average rate and combined inputs rose at a 0.3-percent annual average rate over the 2007-16 period.
Annual rates of multifactor productivity and related series were revised historically for all three sectors. (See table D.) The revisions were a result of changes to BLS methodology for estimating intrasectoral transactions and labor composition, as well as revisions to the National Income and Product Accounts (NIPA) released on January 26, 2018 and the Gross Domestic Product by Industry data released on November 2, 2017. Additional information can be found at www.bls.gov/mfp/sectoraloutputrevisions.htm.
Over the 1987-2015 period, manufacturing multifactor productivity increased 0.8 percent as previously reported. Revisions over the 1987-2015 period resulted in a 0.1-percentage point upward revision to multifactor productivity in nondurable manufacturing, with a 0.1-percentage point downward revision to multifactor productivity growth in durable manufacturing.
In 2015, multifactor productivity in the manufacturing sector was revised upward 1.3 percentage points, the upward revision was due to a larger downward revision to combined inputs than to sectoral output. Multifactor productivity was revised upward 0.9 percent in the durable manufacturing sector and 1.5 percent in the nondurable manufacturing sector. Materials were revised downward 9.0 percent in manufacturing, 7.0 percent in durable manufacturing, and 6.2 percent in nondurable manufacturing. (See table D.)
1 Output per combined units of labor input, capital services, energy, materials, and purchased business services.
2 Output per hour worked.
3 The growth rate of each input is weighted by its share of current dollar costs.
4 Hours at work by age, education, and gender group are weighted by each group’s share of total wages.
5 Ratio of labor input to hours.
BLS includes a measure of the effects of changes in the composition of the work force for manufacturing sectors and industries. Labor input in manufacturing sectors and NAICS industry groups is obtained by chained superlative Tornqvist aggregation of the hours at work, classified by age, education, and gender with weights determined by each group’s share of total wages. The labor composition index estimates the effect of shifts in the age, education, and gender composition of the work force on hours worked.
Capital services are the services derived from the stock of physical assets and intellectual property assets. There are 90 asset types for fixed business equipment, structures, inventories, land, and intellectual property products. The aggregate capital services measures are obtained by Tornqvist aggregation of the capital stocks for each asset type within each of the eighteen manufacturing NAICS industry groupings using estimated rental prices for each asset type. Each rental price reflects the nominal rate of return to all assets within the industry and rates of economic depreciation and revaluation for the specific asset; rental prices are adjusted for the effects of taxes. Data on investment for fixed assets are obtained from BEA. Data on inventories are estimated using data from BEA and additional information from IRS Corporation Income Returns. Data for land in the farm sector are obtained from USDA. Nonfarm industry detail for land is based on IRS book value data. Current-dollar value-added data, obtained from BEA, are used in estimating capital rental prices.
Labor input in manufacturing sectors and industries is obtained by chained superlative Tornqvist aggregation of the hours at work, classified by age, education, and gender with weights determined by each group’s share of total wages. The labor composition index estimates the effect of shifts in the age, education, and gender composition of the work force on hours worked. Hours at work data reflect Productivity and Costs data as of the February 1, 2018 “Productivity and Costs” news release (USDL-18-0153). The growth rate of labor composition is defined as the difference between the growth rate of weighted labor input and the growth rate of the hours. The growth rate of labor composition in manufacturing may be underestimated due to limitations in the source data. The education proxy does not include training certifications and licensing. The proxy only includes number of years of schooling.
Additional information concerning data sources and methods of measuring labor composition can be found in “Changes in the Composition of Labor for BLS Multifactor Productivity Measures, 2014” (www.bls.gov/mfp/mprlabor.pdf).
In manufacturing, intermediate inputs consist of energy, materials, and purchased business services, and represent a large share of production costs. Research has shown that substitution among inputs, including intermediate inputs, affects productivity change. Therefore, it is important to account for intermediate inputs in productivity measures at the industry level. In contrast, the more aggregate productivity measures compare “value-added” output with two classes of inputs, capital and labor. Because of these differences in concepts and methodology, productivity change in manufacturing cannot be directly compared with changes in private business or private nonfarm business. Data on intermediate inputs are obtained from BEA based on BEA annual input-output tables. Tornqvist indexes of each of these three input classes are derived at the three-digit NAICS level and then aggregated to the manufacturing sectors. Materials inputs are adjusted to exclude transactions between establishments within the same sector.
The five input indexes (capital services, labor, energy, materials, and purchased business services) are combined using chained superlative Tornqvist aggregation, applying weights that represent each component’s share of total costs. Total costs are defined as the current dollar value of manufacturing sectoral output. Most taxes on production and imports, such as excise taxes, are excluded from costs; however, property and motor vehicle taxes remain in total costs.
Capital intensity is the ratio of capital services to hours worked in the production process. The higher the capital to hours ratio, the more capital intensive the production process is.
In a production process, profit maximizing/cost-minimizing firms adjust the factor proportions of capital and labor if the price of one factor falls relative to the price of the other factor; there would be a tendency for the firms to substitute the less expensive factor for the more expensive one. In the short run, changes in hours worked are more variable than changes in capital services. Changes in hours worked in business cycles can result in volatility of the capital intensity ratio over short periods of time. In the long run an increase in wages relative to the price of capital will induce the firm to substitute capital for labor, resulting in an increase in capital intensity. Rising labor costs are, in fact, an incentive for firms to introduce automated production processes.
Industry estimates of capital to hours ratios can be obtained at http://www.bls.gov/mfp/mprdload.htm.
The output concept used for multifactor productivity in manufacturing is “sectoral output”. Sectoral output equals gross output (sales, receipts, and other operating income, plus commodity taxes plus changes in inventories), excluding transactions between establishments within the same sector. In contrast, the output concept used for private business and private nonfarm business is “real value-added”. Real value-added output in private business equals gross domestic product less general government, government enterprises, private households (including the rental value of owner-occupied real estate), and non-profit institutions. Real value-added output excludes intermediate transactions between businesses.
The output index for manufacturing is constructed using a chained superlative index (Tornqvist) of three-digit NAICS industry outputs. Industry output is measured as sectoral output, the total value of goods and services leaving the industry. The indexes of industry output are calculated with the Tornqvist index formula. This index formula aggregates the growth rates of the various industry outputs between two periods, using their relative shares in industry value of production averaged over the two periods as weights. BLS industry output measures for manufacturing industries are constructed using data from the economic censuses and annual surveys of the Bureau of the Census, U.S. Department of Commerce, together with information on price changes, primarily from BLS.
The manufacturing multifactor productivity measures describe the relationship between output in real terms and the inputs involved in its production. Multifactor productivity measures are not intended to capture the specific contributions of labor, capital, or intermediate inputs. Rather, they are designed to measure the joint influences on economic growth of technological change, efficiency improvements, returns to scale, reallocation of resources and other factors of economic growth, allowing for the effects of capital, labor, and intermediate inputs. The multifactor productivity indexes are derived by dividing an output index by an index of the combined inputs of labor, capital services, energy, non-energy materials, and purchased business services.