The database contains three productivity variables: labor productivity levels, labor productivity growth rates, and total factor productivity (TFP) growth rates for up to 172 countries for 1980-2018. In addition, the database contains the contribution of capital deepening to the labor productivity growth rate.

For further details, please refer to https://thedocs.worldbank.org/en/doc/351491594482906845-0050022020/original/GlobalProductivityAggregateDatabase.pdf

Graph and download economic data for Total Factor Productivity at Constant National Prices for United States (RTFPNAUSA632NRUG) from 1954 to 2019 about production, price, and USA.

United States Total Factor Productivity: Labor Productivity data was reported at -1.155 % in Mar 2025. This records a decrease from the previous number of 3.413 % for Dec 2024. United States Total Factor Productivity: Labor Productivity data is updated quarterly, averaging 1.971 % from Jun 1947 (Median) to Mar 2025, with 312 observations. The data reached an all-time high of 17.789 % in Mar 1950 and a record low of -7.150 % in Jun 1960. United States Total Factor Productivity: Labor Productivity data remains active status in CEIC and is reported by Federal Reserve Bank of San Francisco. The data is categorized under Global Database’s United States – Table US.G109: Total Factor Productivity.

United States Total Factor Productivity: Output data was reported at -0.651 % in Mar 2025. This records a decrease from the previous number of 3.739 % for Dec 2024. United States Total Factor Productivity: Output data is updated quarterly, averaging 3.330 % from Jun 1947 (Median) to Mar 2025, with 312 observations. The data reached an all-time high of 34.927 % in Sep 2020 and a record low of -42.288 % in Jun 2020. United States Total Factor Productivity: Output data remains active status in CEIC and is reported by Federal Reserve Bank of San Francisco. The data is categorized under Global Database’s United States – Table US.G109: Total Factor Productivity.

Graph and download economic data for Private Business Sector: Total Factor Productivity (MPU4900013) from 1988 to 2024 about productivity, sector, business, private, rate, and USA.

Japan Total Factor Productivity data was reported at 0.175 % in Sep 2018. This records an increase from the previous number of 0.170 % for Mar 2018. Japan Total Factor Productivity data is updated semiannually, averaging 1.032 % from Sep 1983 (Median) to Sep 2018, with 71 observations. The data reached an all-time high of 1.457 % in Mar 1986 and a record low of 0.170 % in Mar 2018. Japan Total Factor Productivity data remains active status in CEIC and is reported by Bank of Japan. The data is categorized under Global Database’s Japan – Table JP.A053: SNA 2008: Potential Growth Rate and Output Gap.

Increasing the efficiency of agricultural production—getting more output from the same amount of resources—is critical for improving food security. To measure the efficiency of agricultural systems, we use total factor productivity (TFP). TFP is an indicator of how efficiently agricultural land, labor, capital, and materials (agricultural inputs) are used to produce a country’s crops and livestock (agricultural output)—it is calculated as the ratio of total agricultural output to total production inputs. When more output is produced from a constant amount of resources, meaning that resources are being used more efficiently, TFP increases. Measures of land and labor productivity—partial factor productivity (PFP) measures—are calculated as the ratio of total output to total agricultural area (land productivity) and to the number of economically active persons in agriculture (labor productivity). Because PFP measures are easy to estimate, they are often used to measure agricultural production performance. These measures normally show higher rates of growth than TFP, because growth in land and labor productivity can result not only from increases in TFP but also from a more intensive use of other inputs (such as fertilizer or machinery). Indicators of both TFP and PFP contribute to the understanding of agricultural systems needed for policy and investment decisions by enabling comparisons across time and across countries and regions. The data file provides estimates of IFPRI's TFP and PFP measures for developing countries for three-sub-periods between 1991 and 2014(1991-2000,2001-2010 and 2010-2014). These TFP and PFP estimates were generated using the most recent data from Economic Research Service of the United States Department of Agriculture (ERS-USDA), the FAOSTAT database of the Food and Agriculture Organization of the United Nations (FAO), and national statistical sources.

Labor productivity in the manufacturing sector was the highest in Lebanon in 2019 at about **** U.S. dollars. Countries in conflict and in transition showed smaller employment growth than the rest in the Middle East and North Africa (MENA) region.

Total Factor Productivity at Constant National Prices for United States was 1.01680 Index 2011=1 in January of 2019, according to the United States Federal Reserve. Historically, Total Factor Productivity at Constant National Prices for United States reached a record high of 1.01680 in January of 2019 and a record low of 0.56775 in January of 1950. Trading Economics provides the current actual value, an historical data chart and related indicators for Total Factor Productivity at Constant National Prices for United States - last updated from the United States Federal Reserve on August of 2025.

Graph and download economic data for Total Factor Productivity at Constant National Prices for Brazil (RTFPNABRA632NRUG) from 1954 to 2019 about Brazil, production, and price.

Colombia FP: VG: CS: ICT: Construction data was reported at 0.002 % in 2024. This records a decrease from the previous number of 0.010 % for 2023. Colombia FP: VG: CS: ICT: Construction data is updated yearly, averaging 0.020 % from Dec 2020 (Median) to 2024, with 4 observations. The data reached an all-time high of 0.033 % in 2022 and a record low of 0.002 % in 2024. Colombia FP: VG: CS: ICT: Construction data remains active status in CEIC and is reported by National Administrative Department of Statistics. The data is categorized under Global Database’s Colombia – Table CO.G075: Total Factor Productivity.

Colombia FP: VG: CF: Social, Communal & Personal Service Activities data was reported at 0.214 % in 2024. This records an increase from the previous number of 0.114 % for 2023. Colombia FP: VG: CF: Social, Communal & Personal Service Activities data is updated yearly, averaging 3.190 % from Dec 2020 (Median) to 2024, with 4 observations. The data reached an all-time high of 8.560 % in 2020 and a record low of 0.114 % in 2023. Colombia FP: VG: CF: Social, Communal & Personal Service Activities data remains active status in CEIC and is reported by National Administrative Department of Statistics. The data is categorized under Global Database’s Colombia – Table CO.G075: Total Factor Productivity.

The total factor productivity (TFP) in Tunisia in 2013 was the highest in the Middle East and North Africa (MENA) region among firms with above average TFP among the country's income group peers.

Graph and download economic data for Manufacturing Sector: Total Factor Productivity (MPU9900013) from 1988 to 2023 about productivity, sector, manufacturing, rate, and USA.

Factor price distortions and resource misallocation are important sources of productivity differences between regions. Promoting the free flow of factors of production is conducive to giving full play to the decisive role of the market in allocating resources, which is crucial to helping a country’s economy develop in a high-quality and sustainable manner. This paper proposes a new approach to measuring factor market distortions and establishes the relationship between factor price distortions and a country’s economic growth. This paper examines the resource misallocation and efficiency loss of 31 provinces in China from 2004 to 2020, and proposes an analytical framework for resource misallocation among regions, with which the Total Factor Productivity (TFP) and the factor price distortion of provinces in China are calculated. The calculation results indicate that the TFP of China’s provinces gradually declines from the eastern coast to the western inland. The resource allocation efficiency in the eastern and central areas is higher than that in the western areas, so is the factor price, and its distortion causes nearly 6% of loss of output value in China. China’s economic growth is still reliant on the increase of factor input and technological development and the improvement of resource allocation efficiency has no significant effect on growth.

Total factor productivity (TFP) is the ratio of total output (crop and livestock products) to total production inputs (land, labor, capital and materials). An increase in TFP implies that more output is being produced from a constant amount of resources used in the production process. In the long run, TFP is the main driver of growth in agriculture and can be affected by policies and investment. Partial factor productivity (PFP) measures, such as labor and land productivity, are often used to measure agricultural prodcution performance because they are easy to estimate. These measures of productivity normally show higher rates of growth than TFP because growth in land and labor productivity could result from more intensive use of inputs, including fertilizer and machinery, rather than TFP increase. If productivity increases without the addition of more inputs, then the only source of growth is TFP. The data file provides estimates of IFPRI's TFP and PFP measures for developing countries for three-sub-periods between 1990 and 2011(1991-2000,2001-2007 and 2008-2013). These TFP and PFP estimates were generated using data from the Food and Agriculture Organization of the United Nations (FAO) on outputs and inputs. The output values are the FAO-constructed gross agricultural outputs, measured in constant 2004-2006 US dollars and smoothed using the Hodrick-Prescott filter. Each output v alue is a composite of 190 crop and livestock commodities aggregated using a constant set of global average prices from 2004-2006. Inputs include agricultural land, measured by the sum, in hectares, of cropland and permanent pasture; labor, measured by the number of animals in cattle equivalents; machinery, measured by the total amount of horsepower available from four-wheel tractors, pedestrian-operated tractors, and combine-threshers in use; and fertilizer, measured by tons of fertilizer nutrients used. The dataset of outputs and inputs was checked and cleaned using different statistical techniques. TFP estimates were obtained using Data Envelopment Analysis (DEA) techniques. These techniques have been extensively used because they make TFPs easy to compute, do not involve restrictive assumptions regarding economic behavior, such as cost minimization or profit maximization. On the other hand, DEA productivity estimates are sensitive to data noise and outliers and can suffer from the probel of ""unusual"" weights that are higher or lower than expected when aggregating inputs to meas ure TFP. Given these limitations, outlier detection methods were used to determine influential observations in the dataset and input weights were allowed to vary only within a certain range of expected values because specific lower and upper bounds were imposed for each input in different regions. Results are also afected by data characteristics and quality issues. In particular, the data series on fertilizer and machinery show high volatility and could result in high variablity of TFP estimates for some countries.

Increasing the efficiency of agricultural production—getting more output from the same amount of resources—is critical for improving food security. To measure the efficiency of agricultural systems, we use total factor productivity (TFP). TFP is an indicator of how efficiently agricultural land, labor, capital, and materials (agricultural inputs) are used to produce a country’s crops and livestock (agricultural output)—it is calculated as the ratio of total agricultural output to total production inputs. When more output is produced from a constant amount of resources, meaning that resources are being used more efficiently, TFP increases. Measures of land and labor productivity—partial factor productivity (PFP) measures—are calculated as the ratio of total output to total agricultural area (land productivity) and to the number of economically active persons in agriculture (labor productivity). Because PFP measures are easy to estimate, they are often used to measure agricultural production performance. These measures normally show higher rates of growth than TFP, because growth in land and labor productivity can result not only from increases in TFP but also from a more intensive use of other inputs (such as fertilizer or machinery). Indicators of both TFP and PFP contribute to the understanding of agricultural systems needed for policy and investment decisions by allowing for comparisons across time and across countries and regions. The data include estimates of TFP and land and labor productivity measures for developing countries and regions for three-sub-periods between 2000 and 2016. These use the most recent data on outputs and inputs from the Economic Research Service of the US Department of Agriculture (ERS-USDA), an internationally consistent and comparable dataset on production and input quantities built using data from the FAOSTAT database of the Food and Agriculture Organization of the United Nations (FAO), supplemented with data from national statistical sources (for more on data and methodology).

FP: VG: LS: HW: Agriculture, Livestock, Hunting, Forestry & Fishing data was reported at -0.682 % in 2024. This records a decrease from the previous number of 0.766 % for 2023. FP: VG: LS: HW: Agriculture, Livestock, Hunting, Forestry & Fishing data is updated yearly, averaging -0.544 % from Dec 2020 (Median) to 2024, with 4 observations. The data reached an all-time high of 0.766 % in 2023 and a record low of -3.535 % in 2020. FP: VG: LS: HW: Agriculture, Livestock, Hunting, Forestry & Fishing data remains active status in CEIC and is reported by National Administrative Department of Statistics. The data is categorized under Global Database’s Colombia – Table CO.G075: Total Factor Productivity.

Employment elasticity in the hotel and restaurant sector was the highest in Morocco in 2019 at about **** points. Countries in conflict and in transition showed smaller employment growth than the rest in the Middle East and North Africa (MENA) region.

The employment growth in Morocco in 2019 was approximately *** percent. Countries in conflict and in transition showed smaller growth than the rest in the Middle East and North Africa (MENA) region.