Inter Country Input Output table from OECD

Sources: OECD (2022), OECD FDI Statistics Database, https://stats.oecd.org/. OECD (2021), OECD Inter-Country Input-Output Database, https://oe.cd/icio. OECD (2018), OECD Analytical AMNE Database, https://www.oecd.org/sti/ind/analytical-amne-database.htm. IMF staff calculations.Category: Greenhouse Gas (GHG) EmissionsData series:CO₂ emissions in exports of domestic controlled enterprisesCO₂ emissions in exports of foreign controlled multinational enterprisesCO₂ emissions in output of domestic controlled enterprisesCO₂ emissions in output of foreign controlled multinational enterprisesCO₂ emissions per unit of output for foreign controlled multinational enterprisesCO₂ emissions per unit of output of domestic controlled enterprisesCO₂ emissions in gross fixed capital formation of direct investment in resident operating units (non-SPEs)CO₂ emissions in gross fixed capital formation of direct investment in resident operating units (non-SPEs) to final demand ratioMetadata:CO₂ emissions in gross fixed capital formation of direct investment in resident operating units (non-SPEs). Source: IMF CO₂ emissions multipliers; OECD Input-Output tables, ISIC Rev.4; OECD FDI by country and economic activity_BMD4 and historical BMD3 series; OECD Capital formation by activity ISIC rev4; and IMF calculations.CO₂ emissions in gross fixed capital formation of direct investment in resident operating units (non-SPEs) to final demand ratio. Source: IMF CO₂ emissions in Gross Fixed capital formation of Direct Investment in resident operating units (non-SPEs); IMF CO₂ emissions multipliers; OECD Output tables, ISIC Rev.4.; and IMF calculations.CO₂ emissions per unit of output for foreign controlled multinational enterprises. Source: IEA CO₂ emission; and OECD Inter-Country Input-Output tables split according to ownership (domestic-owned and foreign-owned firms).- ISIC Rev 4.; and IMF calculations.CO₂ emissions per unit of output of domestic controlled enterprises. Source: IEA CO₂ emissions; and OECD Inter-Country Input-Output tables split according to ownership (domestic-owned and foreign-owned firms).- ISIC Rev 4.; and IMF calculations. CO₂ emissions in output of foreign controlled multinational enterprises. Source: IMF CO₂ emission intensities of output of foreign controlled multinational enterprises; and OECD Inter-Country Input-Output tables split according to ownership (domestic-owned and foreign-owned firms).- ISIC Rev 4.; and IMF calculations.CO₂ emissions in exports of foreign controlled multinational enterprises. Source: IMF CO₂ emission intensities of output of foreign controlled multinational enterprises; and OECD Inter-Country Input-Output tables split according to ownership (domestic-owned and foreign-owned firms).- ISIC Rev 4.; and IMF calculations.CO₂ emissions in output of domestic controlled enterprises. Source: IMF CO₂ emission intensities of output of domestic controlled enterprises; OECD Inter-Country Input-Output tables split according to ownership (domestic-owned and foreign-owned firms)., ISIC rev4.; and IMF calculations.CO₂ emissions in exports of domestic controlled enterprises. Source: IMF CO₂ emission intensities of output of domestic controlled enterprises; OECD Inter-Country Input-Output tables split according to ownership (domestic-owned and foreign-owned firms)., ISIC rev4.; and IMF calculations.Methodology:CO₂ emissions in gross fixed capital formation of direct investment in resident operating units (non-SPEs): CO₂ emission multipliers (reflecting both direct and indirect CO₂ emissions including CO₂ emissions from fuel combustion and CO₂ emissions embodied in goods and services used as inputs during the production process) were multiplied by the output used in gross fixed capital formation to obtain CO₂ emissions. The CO₂ emissions obtained were apportioned to direct investment using the share of direct investment in resident operating units (non-SPEs) to gross fixed capital formation.CO₂ emissions in gross fixed capital formation of direct investment in resident operating units (non-SPEs) to final demand ratio: Estimates of CO₂ emissions in gross fixed capital formation of direct investment in resident operating units (non-SPEs) are divided by final demand.CO₂ emissions per unit of output of foreign controlled multinational enterprises: Direct CO₂ emission intensities of output (reflecting CO₂ emissions emitted during the production of goods and services by industry from the combustion of fuel) were multiplied by calculated output multipliers of foreign controlled multinational enterprises.CO₂ emissions per unit of output of domestic controlled enterprises: Direct CO₂ emission intensities of output (reflecting CO₂ emissions emitted during the production of goods and services by industry from the combustion of fuel) were multiplied by calculated output multipliers of domestic controlled enterprises.CO₂ emissions in output of foreign controlled multinational enterprises: CO₂ emission intensities of output (reflecting CO₂ emissions emitted during the production of goods and services by industry from the combustion of fuel) of foreign controlled multinational enterprises were multiplied by final demand of products of foreign controlled multinational enterprises.CO₂ emissions in exports of foreign controlled multinational enterprises: CO₂ emission intensities of output (reflecting CO₂ emissions emitted during the production of goods and services by industry from the combustion of fuel) of foreign controlled multinational enterprises were multiplied by exports for final use of foreign controlled multinational enterprises.CO₂ emissions in output of domestic controlled enterprises: CO₂ emission intensities of output (reflecting CO₂ emissions emitted during the production of goods and services by industry from the combustion of fuel) of domestic controlled enterprises were multiplied by final demand of products of domestic owned enterprises.CO₂ emissions in exports of domestic controlled enterprises: CO₂ emission intensities of output (reflecting CO₂ emissions emitted during the production of goods and services by industry from the combustion of fuel) of domestic controlled enterprises were multiplied by exports for final use of domestic owned enterprises.Methodology Attachment

The Eora global supply chain database consists of a multi-region input-output table (MRIO) model that provides a time series of high-resolution IO tables with matching environmental and social satellite accounts for 190 countries.

Global Inflation-to-Output Price Elasticity Database (GIOPED)

This paper introduces a new empirical model of international trade flows based on an import intensity-adjusted measure of aggregate demand. We compute the import intensity of demand components by using the OECD Input-Output tables. We argue that the composition of demand plays a key role in trade dynamics because of the relatively larger movements in the most import-intensive categories of expenditure (especially investment, but also exports). We provide evidence in favor of these mechanisms for a panel of 18 OECD countries, paying particular attention to the 2008-2009 Great Trade Collapse.

Sources: OECD (2021), OECD Inter-Country Input-Output Database, https://oe.cd/icio; International Monetary Fund (IMF), Statistics Department Questionnaire; IMF staff calculations.Category: Climate FinanceData series:Carbon Footprint of Bank Loans (Based on emission intensities)Carbon Footprint of Bank Loans (Based on emission intensities - normalized)Carbon Footprint of Bank Loans (Based on emission multipliers)Carbon Footprint of Bank Loans (Based on emission multipliers - normalized)Metadata:For relevant literature see Guan, Rong, Haitao Zheng, Jie Hu, Qi Fang, and Ruoen Ren. 2017. “The Higher Carbon Intensity of Loans, the Higher Non-Performing Loan Ratio: The Case of China.” Sustainability 9 (4) (April 22): 667. https://dx.doi.org/10.3390/su9040667.Methodology:The IMF has developed the Carbon Footprint of Bank Loans (CFBL) indicator for selected countries. CFBL indicator requires (i) deposit takers’ domestic loans by industry data, and (ii) the estimation of carbon emission factors (CEFs) by industry.The IMF has conducted a data collection exercise to obtain deposit takers’ domestic loans by industry data. The CEFs are calculated based on (i) direct metric tons of carbon emissions from fuel consumption per million $US of output by country and industry (CO2 emission intensities), and (ii) direct and indirect carbon emissions from fuel consumption per million $US of output by country (CO2 emission multipliers). The output multipliers and carbon emission intensities for 66 countries and 45 industries are sourced from the OECD Input-Output Database. Direct and indirect carbon emission factors are calculated by multiplying the Leontief inverse (also known as input-output multipliers) from the OECD World Input-Output Table by the carbon emissions from fuel consumption intensities.CFBL indicator is obtained by multiplying domestic loans to a specific industry by their corresponding carbon emission factors, summing over all industries and dividing the final result by total domestic loans. For a limited number of countries, updated CFBL information until 2018 will be posted in due course. CFBL is an experimental indicator. The index requires a nuanced reading. For instance, a sharp increase in the share of a brown industry in the deposit takers’ loans portfolio may create a negative impact on this indicator in the short term, but longer term results could diverge significantly if these loans were allocated for transition to low carbon environment or for continuing unsustainable brown activities. The emission coefficients applied to loans related to the emissions of the industry and not the emissions resulting from the consumption of the goods the industry produces. Also, the estimation methodology has a number of limitations. First, class level ISIC data could be more appropriate for the CFBL estimation, as it offers more detailed information to evaluate carbon footprint by industry. However, carbon emission factors are not available at this granularity. Also, the ISIC structure is not fully aligned with the needs of climate finance.Second, the granularity of the deposit takers’ domestic loans by industry data availability is not fully consistent across jurisdictions. It is not possible to obtain the loans by industry data at the same level of granularity from all participating countries. Third, the country coverage is limited as carbon intensity factors are available for only 66 countries. Fourth, input-output multipliers have limiting assumptions. Input-output multipliers are static (i.e., assume that there is a fixed input structure and fixed ratios for production for each industry) and do not take into account supply-side constraints or budget constraints. Please see additional information in this link.

Global Inflation-to-Output Price Elasticity Database (GIOPED)

Summary CO₂ emissions; CO₂ direct and indirect emissions per unit of output by industry and by country. CO₂ emissions by industry, in aggregate terms and in terms of output by industry.

Annual country-level estimates for 66 countries for the three indicators are presented by the industry for 45 industries, for the years 1995-2018.

CO₂ emissions from fuel consumption are in millions of metric tons of CO₂. CO₂ emissions intensities are in metric tons of CO₂ emissions per $1 million USD of output. CO₂ emissions multipliers are in metric tons of CO₂ emissions per $1 million USD of output.

Category: Economic Activity Indicators

Data series: CO2 emissions CO2 emissions intensities CO2 emissions multipliers Metadata:

Input-Output tables and Carbon Emissions for 66 Countries and 45 industries have been taken from the OECD’s compilation of indicators on “Carbon dioxide emissions embodied in international trade” (2021 ed.) which combines the Input-Output Database and Trade in embodied CO₂ (TeCO2) Database. In this release of TeCO2 sourced from OECD, emissions from fuels used for international aviation and maritime transport (i.e. aviation and marine bunkers) are also considered. The data series “CO₂ emissions, emission intensities; emission multipliers” was earlier referred to as “Carbon emissions from fuel combustion per unit of output” in the previous vintage of the Climate Change Indicator Dashboard.

Methodology:

CO₂ emission intensities are calculated by dividing the CO₂ emissions from fuel consumption by output from the OECD Inter-Country Input-Output (ICIO) Tables and multiplying the result by 1 million for scaling purposes. CO₂ emission multipliers are calculated by multiplying the Leontief inverse (also known as output multipliers matrix) from the OECD Inter-Country Input-Output (ICIO) Tables by the CO₂ emission intensities.

Industry-level measures of export quality that take global input-output linkages into account and associated code; as described in Wacker et al. (2025). The main repository folders are: - codes/: a folder with python codes performing the four steps explained in the Usage Notes below. - data/QualEst/: a folder with zipped CSV files (QualEst_YYYY.zip, where YYYY stands for the year), storing the quality estimate for bilateral traded products () based on Trenczek and Wacker [15]. This folder will also store the quality aggregations generated as intermediate products in the codes. Those estimates are ultimately based on the BACI database for bilateral trade flows [13], version 202301-HS07. Quality estimates and trade flows are available for bilateral trade between countries and administrative regions for the entire world, and data for each year are provided in separated CSV files with the following columns: identifier of importing and exporting countries (i and j), product code in 6-digit HS2007 classification (hs6digit), value of export in thousand US dollars (v), and quality estimates (qual_idx). Country identifiers are integers that follow the classification in the BACI database; more details can be obtained from https://cepii.fr/CEPII/en/bdd_modele/bdd_modele_item.asp?id=37. - data/ICIO/: An empty folder, to which one should download and unzip the Regular ICIO input-output tables from the official OECD website (https://oe.cd/icio) and place them in this folder. We used the 2023 December release of Regular ICIO input-output tables from OECD. ICIO are available for 76 countries and regions, plus a "rest-of-world" entry covering all other economies, and 46 industries based on 2-digit ISIC Rev. 4 classification. The input-output tables are provided in CSV format. More details on the data structure and the definitions for rows and columns can be found in https://oe.cd/icio - data/keys/: folder with correspondence tables for country and product-industry identifiers used in the export quality dataset and ICIO input-output tables; both are in CSV format. The file for product-industry identifiers includes a column (TYPE) that describe the property of traded products: 0: intermediates, 1: mixed-use products, 2: final products for consumption or capital investment only. Further details can be found in the ReadMe.txt file placed in the root folder of the replication package.

Inspired by the positive impact of service outsourcing in Chery and other enterprises on human resources, this paper explores the impact of service outsourcing on labor income share. This paper introduces a framework to analyze how value added is distributed between capital and labor along the mix of inputs from different countries and sectors participating in global value chains and examines the effect of service outsourcing on the labor share income. Using the World Input-Output Database (WIOD) and OECD Inter-Country Input-Output (OECD- ICIO) table, this paper utilizes the WWZ decomposition method of global value chains (GVCs) to quantify labor share income. The results show that: (1) service outsourcing significantly contributes to the increase in labor share income; (2) Offshore outsourcing had a statistically stronger effect on labor share income after the financial crisis, both compared to the past and to onshore outsourcing; (3) Offshore outsourcing has a higher coefficient in countries with low technology. For ease of comparison, only onshore outsourcing shows a statistically significant difference among various service types; (4) The analysis using Chinese data reveals that the coefficient of offshore outsourcing is negative and statistically significant, indicating that industries with higher levels of service outsourcing have a lower labor share income.

Inspired by the positive impact of service outsourcing in Chery and other enterprises on human resources, this paper explores the impact of service outsourcing on labor income share. This paper introduces a framework to analyze how value added is distributed between capital and labor along the mix of inputs from different countries and sectors participating in global value chains and examines the effect of service outsourcing on the labor share income. Using the World Input-Output Database (WIOD) and OECD Inter-Country Input-Output (OECD- ICIO) table, this paper utilizes the WWZ decomposition method of global value chains (GVCs) to quantify labor share income. The results show that: (1) service outsourcing significantly contributes to the increase in labor share income; (2) Offshore outsourcing had a statistically stronger effect on labor share income after the financial crisis, both compared to the past and to onshore outsourcing; (3) Offshore outsourcing has a higher coefficient in countries with low technology. For ease of comparison, only onshore outsourcing shows a statistically significant difference among various service types; (4) The analysis using Chinese data reveals that the coefficient of offshore outsourcing is negative and statistically significant, indicating that industries with higher levels of service outsourcing have a lower labor share income.

China GDP: % of GDP: Gross Value Added: Agriculture, Forestry, and Fishing data was reported at 7.120 % in 2023. This records a decrease from the previous number of 7.322 % for 2022. China GDP: % of GDP: Gross Value Added: Agriculture, Forestry, and Fishing data is updated yearly, averaging 22.252 % from Dec 1960 (Median) to 2023, with 64 observations. The data reached an all-time high of 41.643 % in 1968 and a record low of 7.043 % in 2018. China GDP: % of GDP: Gross Value Added: Agriculture, Forestry, and Fishing data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s China – Table CN.World Bank.WDI: Gross Domestic Product: Share of GDP. Agriculture, forestry, and fishing corresponds to ISIC divisions 1-3 and includes forestry, hunting, and fishing, as well as cultivation of crops and livestock production. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 4. Note: For VAB countries, gross value added at factor cost is used as the denominator.;World Bank national accounts data, and OECD National Accounts data files.;Weighted average;Note: Data for OECD countries are based on ISIC, revision 4.