The OECD Family database is an on-line database on family outcomes and family policies with indicators for all OECD countries. Coverage also includes EU Member States that are not OECD members. To date the database brings together 58 indicators on family structure, labor market participation, public policies and child outcomes. When possible, indicators are updated on a regular basis.

The QoG Institute is an independent research institute within the Department of Political Science at the University of Gothenburg. The main objective of our research is to address the theoretical and empirical problem of how political institutions of high quality can be created and maintained.

To achieve said goal, the QoG Institute makes comparative data on QoG and its correlates publicly available. To accomplish this, we have compiled several datasets that draw on a number of freely available data sources, including aggregated individual-level data.

The QoG OECD Datasets focus exclusively on OECD member countries. They have a high data coverage in terms of geography and time. In the QoG OECD TS dataset, data from 1946 to 2021 is included and the unit of analysis is country-year (e.g., Sweden-1946, Sweden-1947, etc.).

In the QoG OECD Cross-Section dataset, data from and around 2018 is included. Data from 2018 is prioritized, however, if no data are available for a country for 2018, data for 2019 is included. If no data for 2019 exists, data for 2017 is included, and so on up to a maximum of +/- 3 years. In the QoG OECD Time-Series dataset, data from 1946 to 2021 are included and the unit of analysis is country-year (e.g. Sweden-1946, Sweden-1947 and so on).

The QoG OECD Datasets focus exclusively on OECD member countries. They have a high data coverage in terms of geography and time.

In the QoG OECD Cross-Section dataset, data from and around 2018 is included. Data from 2018 is prioritized, however, if no data are available for a country for 2018, data for 2019 is included. If no data for 2019 exists, data for 2017 is included, and so on up to a maximum of +/- 3 years. In the QoG OECD Time-Series dataset, data from 1946 to 2021 are included and the unit of analysis is country-year (e.g. Sweden-1946, Sweden-1947 and so on).

This table presents Gross Domestic Product (GDP) and its main components according to the expenditure approach. Data is presented as growth rates. In the expenditure approach, the components of GDP are: final consumption expenditure of households and non-profit institutions serving households (NPISH) plus final consumption expenditure of General Government plus gross fixed capital formation (or investment) plus net trade (exports minus imports).

When using the filters, please note that final consumption expenditure is shown separately for the Households/NPISH and General Government sectors, not for the whole economy. All other components of GDP are shown for the whole economy, not for the sector breakdowns.
The data is presented for G20 countries individually, as well as the OECD total, G20, G7, OECD Europe, United States - Mexico - Canada Agreement (USMCA), European Union and euro area.

These indicators were presented in the previous dissemination system in the QNA dataset.

See User Guide on Quarterly National Accounts (QNA) in OECD Data Explorer: QNA User guide
See QNA Calendar for information on advance release dates: QNA Calendar
See QNA Changes for information on changes in methodology: QNA Changes
See QNA TIPS for a better use of QNA data: QNA TIPS
Explore also the GDP and non-financial accounts webpage: GDP and non-financial accounts webpage
OECD statistics contact: STAT.Contact@oecd.org

This dataset FDI main aggregates, BMD4 is updated every quarter and includes quarterly and annual aggregate inward and outward Foreign Direct Investment (FDI) flows, positions and income for OECD reporting economies and for non-OECD G20 countries (Argentina, Brazil, China, India, Indonesia, Saudi Arabia and South Africa).

It is a simplified dataset with fewer breakdowns compared to the other separate datasets specifically dedicated to FDI flows, FDI positions or FDI income aggregates. In this dataset, FDI statistics are presented on directional basis only (unless otherwise specified, see metadata attached at the reporting country level) and resident Special Purpose Entities (SPEs), when they exist, are excluded (unless otherwise stated, see metadata attached at the reporting country level).

FDI aggregates are measured in USD millions, in millions of national currency and as a share of GDP.

This dataset supports FDI aggregates indicators available from the FDI in Figures.

In 2014, many countries implemented the latest international standards for Foreign Direct Investment (FDI) statistics:

• the OECD’s Benchmark Definition of FDI, 4th edition (BMD4); and
• the IMF’s Balance of Payments and International Investment Position Manual, 6th edition (BPM6)

This OECD database was launched in March 2015 which includes the data series reported by national experts according to BMD4. The data are for the most part based on balance of payments statistics published by Central Banks and Statistical Offices following the recommendations of the IMF’s BPM6 and the OECD’s BMD4. However, some of the data relate to other sources such as notifications or approvals.

Historical and unrevised series of FDI aggregates under the previous BMD3 methodology can be accessed in the archived dataset FDI series of BOP and IIP aggregates

The Better Life Index is an initiative created by the OECD to compare the well-being priorities of people around the world. It consists of 11 social indicators: “housing, income, jobs, community, education, environment, governance, health, life satisfaction, safety, work-life balance” that contribute to well-being in OECD countries. This initiative aims to involve citizens in the debate on measuring the well-being of societies, and to empower them to become more informed and engaged in the policy-making process that shapes all our lives.

The 11 indicators in turn are composed of 20 sub-indicators through averaging and normalization. The visualization tool is available here. By selecting a set of weights to the sub-indicators, a user can rank countries according to their weighted sum.

Abstract copyright UK Data Service and data collection copyright owner. The Organisation for Economic Co-operation and Development (OECD) Health Statistics offers the most comprehensive source of comparable statistics on health and health systems across OECD countries. It is an essential tool for health researchers and policy advisors in governments, the private sector and the academic community, to carry out comparative analyses and draw lessons from international comparisons of diverse health care systems. Within UKDS.Stat the data are presented in the following databases: Health status This datasets presents internationally comparable statistics on morbidity and mortality with variables such as life expectancy, causes of mortality, maternal and infant mortality, potential years of life lost, perceived health status, infant health, dental health, communicable diseases, cancer, injuries, absence from work due to illness. The annual data begins in 2000. Non-medical determinants of health This dataset examines the non-medical determinants of health by comparing food, alcohol, tobacco consumption and body weight amongst countries. The data are expressed in different measures such as calories, grammes, kilo, gender, population. The data begins in 1960. Healthcare resources This dataset includes comparative tables analyzing various health care resources such as total health and social employment, physicians by age, gender, categories, midwives, nurses, caring personnel, personal care workers, dentists, pharmacists, physiotherapists, hospital employment, graduates, remuneration of health professionals, hospitals, hospital beds, medical technology with their respective subsets. The statistics are expressed in different units of measure such as number of persons, salaried, self-employed, per population. The annual data begins in 1960. Healthcare utilisation This dataset includes statistics comparing different countries’ level of health care utilisation in terms of prevention, immunisation, screening, diagnostics exams, consultations, in-patient utilisation, average length of stay, diagnostic categories, acute care, in-patient care, discharge rates, transplants, dialyses, ICD-9-CM. The data is comparable with respect to units of measures such as days, percentages, population, number per capita, procedures, and available beds. Health Care Quality Indicators This dataset includes comparative tables analyzing various health care quality indicators such as cancer care, care for acute exacerbation of chronic conditions, care for chronic conditions and care for mental disorders. The annual data begins in 1995. Pharmaceutical market This dataset focuses on the pharmaceutical market comparing countries in terms of pharmaceutical consumption, drugs, pharmaceutical sales, pharmaceutical market, revenues, statistics. The annual data begins in 1960. Long-term care resources and utilisation This dataset provides statistics comparing long-term care resources and utilisation by country in terms of workers, beds in nursing and residential care facilities and care recipients. In this table data is expressed in different measures such as gender, age and population. The annual data begins in 1960. Health expenditure and financing This dataset compares countries in terms of their current and total expenditures on health by comparing how they allocate their budget with respect to different health care functions while looking at different financing agents and providers. The data covers the years starting from 1960 extending until 2010. The countries covered are Australia, Austria, Belgium, Canada, Chile, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Israel, Italy, Japan, Korea, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Turkey, United Kingdom, and United States. Social protection This dataset introduces the different health care coverage systems such as the government/social health insurance and private health insurance. The statistics are expressed in percentage of the population covered or number of persons. The annual data begins in 1960. Demographic references This dataset provides statistics regarding general demographic references in terms of population, age structure, gender, but also in term of labour force. The annual data begins in 1960. Economic references This dataset presents main economic indicators such as GDP and Purchasing power parities (PPP) and compares countries in terms of those macroeconomic references as well as currency rates, average annual wages. The annual data begins in 1960. These data were first provided by the UK Data Service in November 2014.

This dataset contains perceived health status statistics for countries members of OECD (The Organization for Economic Co-operation and Development) and countries in accession negotiations with OECD. The perceived health status data cover periods from 1980 to 2015.

OECD countries full macroeconomic indicators

TRA210 - Extent to which respondents trust people and institutions across OECD participant countries. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Extent to which respondents trust people and institutions across OECD participant countries...

Out of all OECD countries, Cost Rica had the highest poverty rate as of 2022, at over 20 percent. The country with the second highest poverty rate was the United States, with 18 percent. On the other end of the scale, Czechia had the lowest poverty rate at 6.4 percent, followed by Denmark.

The significance of the OECD

The OECD, or the Organisation for Economic Co-operation and Development, was founded in 1948 and is made up of 38 member countries. It seeks to improve the economic and social well-being of countries and their populations. The OECD looks at issues that impact people’s everyday lives and proposes policies that can help to improve the quality of life.

Poverty in the United States

In 2022, there were nearly 38 million people living below the poverty line in the U.S.. About one fourth of the Native American population lived in poverty in 2022, the most out of any ethnicity. In addition, the rate was higher among young women than young men. It is clear that poverty in the United States is a complex, multi-faceted issue that affects millions of people and is even more complex to solve.

The OECD Gender Data Portal, www.oecd.org/gender/data, includes 40+ selected indicators shedding light on gender inequalities in education, employment and entrepreneurship. Data and metadata for all the indicators are easily and freely accessible and displayed through interactive visualizations. The Gender Data Portal is one of the main outputs of the OECD Gender Initiative, launched in 2010 to improve policies and promote gender equality in the economy in both OECD and non-OECD countries. The Portal is part of the new OECD Gender Equality website www.oecd.org/gender, which also features Closing the Gender Gap: Act Now, a publication that presents new analysis of the productivity losses caused by gender inequality and proposes policy solutions to close the gender gaps. While much progress has been accomplished in recent years, there are still relevant dimensions of gender inequalities that are poorly monitored and measured. The OECD Gender Portal is thus a work in progress, that aims at progressively filling these gaps through new indicators. The last data release, for Women's Day 2013, includes new gender-sensitive indicators of job quality, timely indicators of labor market participation, indicators on top and low-achieving students in different subjects and on entrepreneurial culture. The data cover OECD member countries, as well as Russia, Brazil, China, India, Indonesia, and South Africa.

Abstract copyright UK Data Service and data collection copyright owner.

The OECD International Trade in Services provides annual data and is presented in the following tables:

Trade in services - EBOPS 2010
This dataset aims to assemble and disseminate balance of payments data on trade in services at the most detailed partner-country and service-category level available. To the extent that countries report them, data are also broken down by type of service according to the EBOPS classification. These data cover international trade in services between residents and non-residents of countries and are reported within the framework of the sixth edition of the IMF's Balance of Payments Manual and the Extended Balance of Payments Services Classification (EBOPS2010), which is consistent with the balance of payments classification but is more detailed. Statistics by partner country and service category on international trade in services such as transportation, communication services, financial services, government services are recorded for Australia and Chile from 1999 onwards and shown in US dollars.

Trade in services - EBOPS 2002
This dataset provides statistics on international trade in services by service category and partner country for 34 OECD countries plus the European Union (EU27), the euro area (EA17), the Russian Federation and Hong Kong, China as well as definitions and methodological notes. These data cover international trade in services between residents and non-residents of countries and are reported within the framework of the fifth edition of the IMF's Balance of Payments Manual and the Extended Balance of Payments Services Classification (EBOPS 2002), which is consistent with the balance of payments classification but is more detailed. To the extent that countries report them, data are also broken down by type of service according to the EBOPS classification. Series are shown in national currency, euros and US dollars and are recorded from 1970 onwards.

Trade in services: national classification items
This dataset contains additional national data on international trade in services for Australia, Canada, New Zealand, Turkey and the United States. The data are reported within the framework of the fifth and sixth editions of the IMF's Balance of Payments Manual and the Extended Balance of Payments Services Classification (EBOPS 2002 and EBOPS 2010), which is consistent with the balance of payments classification but is more detailed. Series are shown in US dollars and are recorded from 1970 onwards.

These data were first provided by the UK Data Service in November 2004.

Main Topics:

The database covers:

• trade in services
• financial services
• business services
• maintenance services
• insurance services
• travel services
• balance of payments
• imports
• exports

In the literature, the consensus about the importance of the independence of the central banks towards stable economic growth has been proven (e.g. Barro and Gordon 1983). The empirical papers studying this problem follow Cukierman, Webb, and Neyapti (1992) central bank independence index which does not include some important factors such as rule of law in the given country and defines the components of independence too generally. In this project, we aim to build an alternative index that will measure the central bank independence in more detail which will account for the rule of law and other relevant aspects that indirectly affect the true independence of the central bank. Obtaining the index, we will study whether the independence of the central bank has a positive effect on maintaining the given central bank's primary target. Furthermore, in this research, we aim to test whether our results differ from the previous findings in the empirical literature on the central bank independence and its effect on stable prices. Based on the obtained results, an optimal legislature of the central bank's independence shall be suggested. This panel dataset provides an assessment of the independence of central banks in 21 OECD countries (excluding the Eurozone), focusing on their monetary policy autonomy as determined by legislation in 2010, 2015, and 2020. Our data collection adopts a novel approach, building upon theinnovating methodology proposed by Cukierman et al. (1992), while incorporating revised components of the index that place greater emphasis on current standards of central bank independence. Additionally, we introduce new criteria to evaluate budgetary independence, an important aspect of central bank autonomy (Swinburne and Castello-Branco, 1991). The dataset serves as a valuable resource for empirical studies seeking to analyze the impact of monetary policy independence on economic performance. Furthermore, policymakers can draw insights from this index to enhance legislative frameworks and promote stronger performance in central bank independence.

Abstract copyright UK Data Service and data collection copyright owner. The OECD Banking statistics database includes data from 1979 to 2009 on classification of bank assets and liabilities, income statement and balance sheet and structure of the financial system for OECD countries. The OECD have discontinued this dataset, so no further updates will be made. The OECD Banking Statistics are presented in the following tables (some tables will include missing data): Classification of bank assets and liabilities This dataset provides the composition of bank assets and liabilities of residents and non-residents denominated in domestic and foreign currencies based on financial statements of banks in each OECD member country and Russia. Data are reported at current prices in millions of national currency and in millions of Euros for OECD countries. The data covers the years starting from 2005 extending until 2009. The countries covered are Austria, Belgium, Canada, Chile, Czech Republic, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Israel, Italy, Japan, Korea, Luxembourg, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Turkey, United Kingdom, and Russian Federation. Income statement and balance sheet This comparative tables comprises statistics on country’s financial profiles by presenting their respective extensive income statements, balance sheets and capital adequacy by banking group that can be further analyzed by type of financial institution such as commercial banks, savings banks co-operative banks and other monetary institutions. This dataset provides information on income statements, balance sheets and capital adequacy by banking group. Data are reported at current prices in millions of national currency. The data covers the years starting from 1979 extending until 2009. The countries covered are Austria, Belgium, Canada, Chile, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Israel, Italy, Japan, Korea, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Turkey, United Kingdom, United States and Russian Federation. Structure of the financial system This dataset provides information on the overall structure of the financial system per country by type of institution and their components: Central banks, other monetary institutions, other financial institutions and insurance institutions. Data relate to number of institutions, number of branches, number of employees, total assets and liabilities and total financial assets. The data covers the years starting from 1979 extending until 2009. The countries covered are Austria, Belgium, Canada, Chile, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Israel, Italy, Japan, Korea, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Turkey, United Kingdom, United States and Russian Federation. These data were first provided by the UK Data Service in December 2014. Main Topics: • Banking • Financial statement • Financial structure • Financial system • Monetary institutions • Monetary system 1979 2009 ACCOUNTING ASSETS Afghanistan Albania Algeria Andorra Angola Antigua and Barbuda Argentina Armenia Aruba Australia Austria Azerbaijan BANKS Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bermuda Bhutan Bolivia Bosnia and Herzegovina Botswana Brazil Brunei Darussalam Bulgaria Burkina Faso Burundi CURRENCIES Cambodia Cameroon Canada Cape Verde Cayman Islands Central African Rep... Chad Channel Islands Chile China Colombia Comoros Congo Costa Rica Croatia Cuba Curacao Cyprus Czech Republic Denmark Djibouti Dominica Dominican Republic Economic conditions... Ecuador Egypt El Salvador Equatorial Guinea Estonia Ethiopia Europe European Union Coun... FINANCIAL INSTITUTIONS Faroe Islands Finland France Gabon Gambia Georgia Germany October 1990 Ghana Gibraltar Greece Grenada Guatemala Guinea Guinea Bissau Honduras Hong Kong Hungary INSURANCE Iceland India Indonesia Iran Iraq Ireland Isle of Man Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Kosovo Kuwait Kyrgyzstan Latvia Lebanon Lesotho Liberia Lithuania Luxembourg MONETARY ECONOMICS Macao Macedonia Madagascar Malawi Malaysia Mali Malta Mauritania Mauritius Mexico Moldova Montenegro Morocco Mozambique Multi nation Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Romania Russia Rwanda Saint Lucia Saint Martin Saint Vincent Saotome Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore Slovakia Slovenia Solomon Islands Somalia South Africa South Korea Spain Sri Lanka Sudan Surinam Swaziland Switzerland Tajikistan Tanzania Thailand Togo Trinidad and Tobago Turkey Turkmenistan Uganda Ukraine United Kingdom United States Uruguay Uzbekistan Vanuatu Venezuela Vietnam Virgin Islands USA Zambia Zimbabwe

Japan JP: Foreign Direct Investment Financial Flows: Inward: Total: Africa data was reported at 789.986 JPY mn in 2023. This records an increase from the previous number of -1,099.157 JPY mn for 2022. Japan JP: Foreign Direct Investment Financial Flows: Inward: Total: Africa data is updated yearly, averaging 762.148 JPY mn from Dec 2014 (Median) to 2023, with 10 observations. The data reached an all-time high of 62,516.768 JPY mn in 2019 and a record low of -4,656.314 JPY mn in 2020. Japan JP: Foreign Direct Investment Financial Flows: Inward: Total: Africa data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Japan – Table JP.OECD.FDI: Foreign Direct Investment Financial Flows: by Region and Country: OECD Member: Annual. Reverse investment: Netting of reverse investment in equity (when a direct investment enterprise acquires less than 10% equity ownership in its parent) and reverse investment in debt (when a direct investment enterprise extends a loan to its parent) is applied in the recording of total inward and outward FDI transactions and positions. Treatment of debt FDI transactions and positions between fellow enterprises: directional basis according to the residency of the direct investor. FDI financial flows, income flows and positions include, if they exist, resident Special Purpose Entities (SPEs) which cannot be identified separately. Valuation method used for listed inward and outward equity positions: Own funds at book value, Accumulation of FDI equity flows. Valuation method used for unlisted inward and outward equity positions: Own funds at book value, Accumulation of FDI equity flows. Valuation method used for inward and outward debt positions: Nominal value .; FDI statistics are available by geographic allocation, vis-à-vis single partner countries worldwide and geographical and economic zones aggregates. Partner country allocation can be subject to confidentiality restrictions. Geographic allocation of inward and outward FDI transactions and positions is according to the immediate counterparty. Inward FDI positions according to the ultimate counterparty (the ultimate investing country) are also available and publishable. In the dataset 'FDI statistics by parner country and by industry - Summary', inward FDI positions are showed according to the UIC. Intercompany debt between related financial intermediaries, including permanent debt, are excluded from FDI transactions and positions. Direct investment relationships are identified according to the criteria of the Framework for Direct Investment Relationships (FDIR) method. Debt between fellow enterprises are completely covered . Collective investment institutions are covered as direct investment enterprises. Non-profit institutions serving households are covered as direct investors. FDI statistics are available by industry sectors according to ISIC4 classification. Industry sector allocation can be subject to confidentiality restrictions. Inward FDI transactions and positions are allocated to the activity of the resident direct investment enterprise. Outward FDI transactions are allocated according to the activity of the non resident direct investment enterprise. Statistical unit: Enterprise.

The financial indicators are based on data compiled according to the 2008 SNA "System of National Accounts, 2008". Many indicators are expressed as a percentage of Gross Domestic Product (GDP) or as a percentage of Gross Disposable Income (GDI) when referring to the Households and NPISHs sector. The definition of GDP and GDI are the following:

Gross Domestic Product:
Gross Domestic Product (GDP) is derived from the concept of value added. Gross value added is the difference of output and intermediate consumption. GDP is the sum of gross value added of all resident producer units plus that part (possibly the total) of taxes on products, less subsidies on products, that is not included in the valuation of output [System of National Accounts, 2008, par. 2.138]. GDP is also equal to the sum of final uses of goods and services (all uses except intermediate consumption) measured at purchasers’ prices, less the value of imports of goods and services [System of National Accounts, 2008, par. 2.139]. GDP is also equal to the sum of primary incomes distributed by producer units [System of National Accounts, 2008, par. 2.140].

Gross Disposable Income:
Gross Disposable Income (GDI) is equal to net disposable income which is the balancing item of the secondary distribution income account plus the consumption of fixed capital. The use of the Gross Disposable Income (GDI), rather than net disposable income, is preferable for analytical purposes because there are uncertainty and comparability problems with the calculation of consumption of fixed capital. GDI measures the income available to the total economy for final consumption and gross saving [System of National Accounts, 2008, par. 2.145].

Definition of Debt:
Debt is a commonly used concept, defined as a specific subset of liabilities identified according to the types of financial instruments included or excluded. Generally, debt is defined as all liabilities that require payment or payments of interest or principal by the debtor to the creditor at a date or dates in the future. Consequently, all debt instruments are liabilities, but some liabilities such as shares, equity and financial derivatives are not debt [System of National Accounts, 2008, par. 22.104]. According to the SNA, most debt instruments are valued at market prices. However, some countries do not apply this valuation, in particular for securities other than shares, except financial derivatives (AF33). In this dataset, for financial indicators referring to debt, the concept of debt is the one adopted by the SNA 2008 as well as by the International Monetary Fund in “Public Sector Debt Statistics – Guide for compilers and users” (Pre-publication draft, May 2011). Debt is thus obtained as the sum of the following liability categories, whenever available / applicable in the financial balance sheet of the institutional sector:special drawing rights (AF12), currency and deposits (AF2), debt securities (AF3), loans (AF4), insurance, pension, and standardised guarantees (AF6), and other accounts payable (AF8). This definition differs from the definition of debt applied under the Maastricht Treaty for European countries. First, gross debt according to the Maastricht definition excludes not only financial derivatives and employee stock options (AF7) and equity and investment fund shares (AF5) but also insurance pensions and standardised guarantees (AF6) and other accounts payable (AF8). Second, debt according to Maastricht definition is valued at nominal prices and not at market prices.

To view other related indicator datasets, please refer to:
Institutional Investors Indicators [add link]
Household Dashboard [add link]

Product Market Regulation (PMR) indicators assess the extent to which public policies promote or inhibit market forces in several areas of product markets. Each of the areas addressed within the PMR methodology sheds light on specific restrictions of the regulatory framework both economy-wide and in key sectors of the economy on twelve topics: electricity; gas; telecom; post; transport; water; retail distribution; professional services; other sectors; administrative requirements for business start-ups; treatment of foreign parties; and others, such as governance of public-controlled enterprises or antitrust exclusions and exemptions. The information included in this dataset was collected as part of a partnership between the Markets & Competition Policy Global team of the World Bank Group (WBG) and the Economics Division of the Organisation for Economic Co-operation and Development (OECD) to produce PMR indicators for 10 LAC countries (Brazil, Costa Rica, Chile, Colombia, Dominican Republic, El Salvador, Honduras, Jamaica, Mexico, Nicaragua, Peru and Uruguay), later supported by the Inter-American Development Bank to produce indicators for 5 additional countries in the region (Bolivia, Ecuador, Guatemala, Panama and Paraguay). For further details on the PMR methodology, see the Product Market Regulation Indicators Homepage of the OECD.

Foreign Direct Investment Position: Inward: Total: ODA Recipients - Africa data was reported at 288.714 EUR mn in 2023. This records an increase from the previous number of 203.901 EUR mn for 2022. Foreign Direct Investment Position: Inward: Total: ODA Recipients - Africa data is updated yearly, averaging 203.901 EUR mn from Dec 2019 (Median) to 2023, with 5 observations. The data reached an all-time high of 288.714 EUR mn in 2023 and a record low of 98.862 EUR mn in 2020. Foreign Direct Investment Position: Inward: Total: ODA Recipients - Africa data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Ireland – Table IE.OECD.FDI: Foreign Direct Investment Position: by Region and Country: OECD Member: Annual. Reverse investment: Netting of reverse investment in equity (when a direct investment enterprise acquires less than 10% equity ownership in its parent) and reverse investment in debt (when a direct investment enterprise extends a loan to its parent) is applied in the recording of total inward and outward FDI transactions and positions. Treatment of debt FDI transactions and positions between fellow enterprises: directional basis according to the residency of the ultimate controlling parent (extended directional principle). FDI transactions and positions by partner country and/or by industry are available excluding and including resident Special Purpose Entities (SPEs). The dataset 'FDI statistics by parner country and by industry - Summary' contains series including resident SPEs only. Valuation method used for listed inward and outward equity positions: Market value, Own funds at book value. Valuation method used for unlisted inward and outward equity positions: Own funds at book value. Valuation method used for inward and outward debt positions: Market value .; FDI statistics are available by geographic allocation, vis-à-vis single partner countries worldwide and geographical and economic zones aggregates. Partner country allocation can be subject to confidentiality restrictions. Geographic allocation of inward and outward FDI transactions and positions is according to the immediate counterparty. Intercompany debt between related financial intermediaries, including permanent debt, are not excluded from FDI transactions and positions. Direct investment relationships are identified according to the criteria of the Framework for Direct Investment Relationships (FDIR) method. Debt between fellow enterprises are completely covered. Collective investment institutions are not covered as direct investment enterprises. Non-profit institutions serving households are covered as direct investors. FDI statistics are available by industry sectors according to ISIC4 classification. Industry sector allocation can be subject to confidentiality restrictions. Inward FDI transactions and positions are allocated to the activity of the resident direct investment enterprise. Outward FDI transactions and positions are allocated according to the activity of the resident direct investor. Statistical unit: Enterprise.; Countries from AFRICA recipients of Offical Development Assistance (ODA), 55 countries: Algeria, Egypt, Libya, Morocco, Tunisia, Benin, Burkina Faso, Cape Verde, Côte d'Ivoire, Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Mali, Mauritania, Niger, Nigeria, Saint Helena, Senegal, Sierra Leone, Togo, Angola, Cameroon, Central African Republic, Chad, Congo, Congo, the Democratic Republic of the , Equatorial Guinea, Gabon, Sao Tome and Principe, Burundi, Comoros, Djibouti, Eritrea, Ethiopia, Kenya, Madagascar, Malawi, Mauritius, Mozambique, Rwanda, Seychelles, Somalia, Sudan, South Sudan, Tanzania, United Republic of, Uganda, Zambia, Zimbabwe, Botswana, Lesotho, Namibia, South Africa, Swaziland

Series Name: Net official development assistance (ODA) to small island states (SIDS) as a percentage of OECD-DAC donors' GNI by donor countries (percent)Series Code: DC_ODA_SIDSGRelease Version: 2020.Q2.G.03 This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 17.2.1: Net official development assistance, total and to least developed countries, as a proportion of the Organization for Economic Cooperation and Development (OECD) Development Assistance Committee donors’ gross national income (GNI)Target 17.2: Developed countries to implement fully their official development assistance commitments, including the commitment by many developed countries to achieve the target of 0.7 per cent of gross national income for official development assistance (ODA/GNI) to developing countries and 0.15 to 0.20 per cent of ODA/GNI to least developed countries; ODA providers are encouraged to consider setting a target to provide at least 0.20 per cent of ODA/GNI to least developed countriesGoal 17: Strengthen the means of implementation and revitalize the Global Partnership for Sustainable DevelopmentFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

In the project we used secondary data used for the construction of the Economic Democracy Index. The sources were the Organisation for Economic Cooperation and Development (OECD), International Monetary Fund, World Bank, International Labour Organisation (ILOSTAT), and Institutional Characteristics of Trade Unions, Wage Setting, State Intervention and Social Pacts database (ICTWSS, Amsterdam Institute for Advanced Labour Studies), European Association of Cooperation Banks, European Values Study, World Values Survey, World Wealth and Income Database, and Worldwide Governance Indicators. In terms of geographical coverage, the dataset covers the OECD member countries and, depending on data availability, some indicators go back until 1970.

The research project centres around the basic proposition that societies with strong and effective forms of economic democracy are more likely to achieve crucial public policy goals; such as combating climate change, reducing inequalities and creating more sustainable forms of economic activity. The research will construct an index of economic democracy (EDI) as a tool to test the basic proposition. As such, the research proposed fits directly within two of the ESRC's current priority areas of economic performance, and creating a vibrant and fair society.

A key argument advanced here is that economic decision-making in many countries is becoming increasingly monopolised by a core of financial and political elites at the expense of the broader population. An increasingly narrow range of interests are therefore dominating economic decision making and policy failing to reflect the broad and diverse interest groups that constitute advanced capitalist societies. Not only is this leading to a democratic deficit in the management of society's resources and assets but it is argued that there are considerable negative public policy effects in terms of greater income and wealth inequalities, increasing susceptibility to financial crises and fragility, and arguably a failure to effectively address the causes of climate change. This leads to another central proposition, that greater economic democracy - more diversity and plurality in economic decision-making - will lead to better policy outcomes in terms of better taking into account critical economic, social and environmental issues.

The research proposed here will be pioneering in developing an inter-disciplinary conceptual framework, drawing upon scholars as diverse as Ostrom, Sandel, Olin Wright, Dewey and Sen who argue for the importance of collective action and public discourse in economic decision making for advancing the common good over vested interests, and for promoting individual economic and social rights.

The research takes a broad definition of economic democracy - employing four dimensions: (i) workplace (nature and structure of employment relations, levels of co-determination, etc); (ii) degree of associational economic governance (e.g. level of cooperatives within economy, number and extent of business and labour associations in economic policy forums); (iii) distribution of decision-making powers across space and sector between different economic and political governance institutions (e.g. ownership structure of the economy, diversity ; (iv) engagement of broader population in macro-economic decision-making (e.g. nature of economic policy formulation, governance structures in economic policy formation at national and subnational levels, role and participation of different interest groups).

Research Aims and Objectives The research would construct an Economic Democracy Index (EDI), and use it to test several key questions about the relationship between levels of economic democracy and three key public policy goals (see below). Key questions are: what is the level of public engagement and deliberation in economic decision-making and how does this vary internationally? What is the relationship between different levels and types of economic democracy and achieving key public policy goals around sustainable economic development and social justice?