The United States recorded a Government Debt to GDP of 124.30 percent of the country's Gross Domestic Product in 2024. This dataset provides - United States Government Debt To GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Households Debt in the United States decreased to 69.20 percent of GDP in the fourth quarter of 2024 from 70.50 percent of GDP in the third quarter of 2024. This dataset provides - United States Households Debt To Gdp- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Private Debt to GDP in the United States decreased to 142 percent in 2024 from 147.50 percent in 2023. United States Private Debt to GDP - values, historical data, forecasts and news - updated on July of 2025.

Government Debt in the United States decreased to 36211469 USD Million in June from 36215818 USD Million in May of 2025. This dataset provides - United States Government Debt- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Summarizes the U.S. government's total outstanding debt at the end of each fiscal year from 1789 to the current year.

India recorded a Government Debt to GDP of 81.59 percent of the country's Gross Domestic Product in 2023. This dataset provides - India Government Debt To GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This table shows data on the balance and Maastricht debt of general government. These figures are also known as EMU-balance and EMU-debt (EMU stands for the Economic and Monetary Union). In this table, yearly and quarterly figures are subdivided to subsectors of general government. Furthermore, this table shows the relation between the government balance and change in Maastricht debt.

Balance and debt are the most import indicators for the healthiness of government finances in the European Union. In the Maastricht treaty and the consequent Stability and Growth Pact, it was decided that government deficit may not exceed 3 percent of gross domestic product (GDP) and Maastricht debt may not be higher than 60 percent of GDP. If government deficit exceeds the threshold of 3 percent, the member state in question shall be subject to the excessive deficit procedure.

The terms and definitions used are in accordance with the framework of the national accounts. The national accounts are based on the international definitions of the European System of Accounts (ESA 2010). However, Maastricht debt is valued at face value whereas debt instruments in national accounts are valued at market value. Maastricht debt covers the following debt instruments: deposits, short term debt securities, long term debt securities, short term loans and long term loans.

Small temporary differences in data in this table with publications of the national accounts may occur due to the fact that the government finance statistics are sometimes more up to date.

Data available from: Yearly figures from 1995, quarterly figures from 1999.

Status of the figures: The figures for the period 1995-2021 are final. The quarterly figures for 2022 are provisional. The annual figures for 2022 are final. The figures for 2023 and 2024 are provisional.

Changes as of 24 June 2025: The figures for the first quarter of 2025 are available. Figures for 2023 and 2024 have been adjusted due to updated information. The quarterly figures for 2022 and the annual figures for 2023 are final now. In the context of the revision policy of National accounts, the dividend tax has been adjusted as of the fourth quarter of 2006. The revised registration aligns more closely with the accrual principle of ESA 2010.

Changes as of 10 April 2025: Due to an error made while processing the data, the initial preliminary figures for government expenditure in 2024 were calculated incorrectly, which means that the figure published for the general government balance was also incorrect. It concerns a decrease in government expenditure. Therefore, the general government balance is 2.3 billion euros higher than originally reported. This means the government deficit is equivalent to 0.9 percent of GDP, rather than the 1.1 percent published previously. The revision also impacts the transactions in other liabilities that are not part of Maastricht debt.

When will new figures be published? Provisional quarterly figures are published three months after the end of the quarter. In September the figures on the first quarter may be revised, in December the figures on the second quarter may be revised and in March the first three quarters may be revised. Yearly figures are published for the first time three months after the end of the year concerned. Yearly figures are revised two times: 6 and 18 months after the end of the year. Please note that there is a possibility that adjustments might take place at the end of March or September, in order to provide the European Commission with the latest figures. Revised yearly figures are published in June each year. Quarterly figures are aligned to revised years at the end of June. More information on the revision policy of Dutch national accounts and government finance statistics can be found under 'relevant articles' under paragraph 3.

World Development Indicators (WDI) is the primary World Bank collection of development indicators, compiled from officially recognized international sources. It presents the most current and accurate global development data available, and includes national, regional and global estimates. [Note: Even though Global Development Finance (GDF) is no longer listed in the WDI database name, all external debt and financial flows data continue to be included in WDI. The GDF publication has been renamed International Debt Statistics (IDS), and has its own separate database, as well.

Last Updated:01/28/2025

Data contains Following 20 Countries 'Argentina', 'Australia', 'Brazil', 'China', 'France', 'Germany', 'India', 'Indonesia', 'Italy', 'Japan', 'Korea, Rep.', 'Mexico', 'Netherlands', 'Russian Federation', 'Saudi Arabia', 'Spain', 'Switzerland', 'Turkiye', 'United Kingdom', 'United States'

Dataset contains below Development Indicators 'Adolescent fertility rate (births per 1,000 women ages 15-19)', 'Agriculture, forestry, and fishing, value added (% of GDP)', 'Annual freshwater withdrawals, total (% of internal resources)', 'Births attended by skilled health staff (% of total)', 'Contraceptive prevalence, any method (% of married women ages 15-49)', 'Domestic credit provided by financial sector (% of GDP)', 'Electric power consumption (kWh per capita)', 'Energy use (kg of oil equivalent per capita)', 'Exports of goods and services (% of GDP)', 'External debt stocks, total (DOD, current US$)', 'Fertility rate, total (births per woman)', 'Foreign direct investment, net inflows (BoP, current US$)', 'Forest area (sq. km)', 'GDP (current US$)', 'GDP growth (annual %)', 'GNI per capita, Atlas method (current US$)', 'GNI per capita, PPP (current international $)', 'GNI, Atlas method (current US$)', 'GNI, PPP (current international $)', 'Gross capital formation (% of GDP)', 'High-technology exports (% of manufactured exports)', 'Immunization, measles (% of children ages 12-23 months)', 'Imports of goods and services (% of GDP)', 'Income share held by lowest 20%', 'Industry (including construction), value added (% of GDP)', 'Inflation, GDP deflator (annual %)', 'Life expectancy at birth, total (years)', 'Merchandise trade (% of GDP)', 'Military expenditure (% of GDP)', 'Mobile cellular subscriptions (per 100 people)', 'Mortality rate, under-5 (per 1,000 live births)', 'Net barter terms of trade index (2015 = 100)', 'Net migration', 'Net official development assistance and official aid received (current US$)', 'Personal remittances, received (current US$)', 'Population density (people per sq. km of land area)', 'Population growth (annual %)', 'Population, total', 'Poverty headcount ratio at $2.15 a day (2017 PPP) (% of population)', 'Poverty headcount ratio at national poverty lines (% of population)', 'Prevalence of HIV, total (% of population ages 15-49)', 'Prevalence of underweight, weight for age (% of children under 5)', 'Primary completion rate, total (% of relevant age group)', 'Revenue, excluding grants (% of GDP)', 'School enrollment, primary (% gross)', 'School enrollment, primary and secondary (gross), gender parity index (GPI)', 'School enrollment, secondary (% gross)', 'Surface area (sq. km)', 'Tax revenue (% of GDP)', 'Terrestrial and marine protected areas (% of total territorial area)', 'Time required to start a business (days)', 'Total debt service (% of exports of goods, services and primary income)', 'Urban population growth (annual %)

Abstract

We have again updated the more popular data series from the Financial Structure database through 2008. Revised: April 2010. The revised dataset has some additional variables (two indicators of deposits in banks and in financial institutions relative to GDP added in 2007, and included in this latest update, some standard banking variables (ROE, ROA, cost-income ratio and z-score) as well as some measures of financial globalization: outstanding and net issues of international debt to GDP, loans from non-resident banks to GDP, off-shore deposits to domestic bank deposits, and remittance inflows to GDP.

We gratefully acknowledge the assistance of Pam Gill, Baybars Karacaovali and Edward Al-Hussainy with this update. Please note that most metrics have been recalculated for the entire time period to ensure consistency over time. The file contains a sheet with definitions and sources; for more detailed definitions and detailed description of the sources, please see the working paper attached as external resources.

This new database of indicators of financial development and structure across countries and over time is unique in that it unites a range of indicators that measure the size, activity, and efficiency of financial intermediaries and markets.

The compiled data permit the construction of financial structure indicators to measure whether, for example, a country's banks are larger, more active, and more efficient than its stock markets. These indicators can then be used to investigate the empirical link between the legal, regulatory, and policy environment and indicators of financial structure. They can also be used to analyze the implications of financial structure for economic growth.

Kind of data

Aggregate data [agg]

Brazil recorded a Government Debt to GDP of 76.50 percent of the country's Gross Domestic Product in 2024. This dataset provides - Brazil Government Debt To GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Analysis of ‘Macro-economic indicators - Indebtedness’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/ecb-macro-economic-indicators-indebtedness on 07 January 2022.

--- Dataset description provided by original source is as follows ---

Indebtedness refers to government debt-to-GDP ratio, government deficit-to-GDP ratio, households' debt-to-gross disposable income ratio and non-financial corporations' debt-to-gross domestic product ratio.

Government debt-to-GDP ratio refers to the Maastricht criteria. The government debt statistics used are those used in the context of the excessive deficit procedure. (Financial stocks at nominal value, percentage points, not seasonally or working day adjusted). The last two years presented in the graphs are based on European Commission forecasts (Directorate-General for Economic and Financial Affairs). The reference value set by the Maastricht criteria is 60%.

Government deficit-to-GDP ratio refers to the Maastricht criteria. The government deficit statistics include settlements under swaps (non-financial flows, current prices, percentage points, not seasonally or working day adjusted). The last two years presented in the graphs are based on European Commission forecasts (Directorate-General for Economic and Financial Affairs). The reference value for the budget deficit under the Stability and Growth Pact is 3%.

Households' debt-to-gross disposable income ratio. Gross disposable income is adjusted for the change in net equity of households in pension fund reserves (non-consolidated, current prices, percentage points, not seasonally or working day adjusted).

Non-financial corporations' debt-to-gross domestic product ratio. The statistics are taken from the national accounts statistics. Debt includes pension reserve liabilities and excludes financial derivatives owing to a lack of comparability across countries (non-consolidated, market prices, percentage points, not seasonally or working day adjusted).

--- Original source retains full ownership of the source dataset ---

Aggregate indicators at the level of the country for 7 countries of the East Bloc from the areas of economy, defense, population and society.

Topics: 1. Population and society: population density; population growth from 1970 to 1978; infant mortality and life expectancy; degree of urbanization; rate of provision with running water and sanitary facilities; residential furnishings and housing conditions; hospital beds and doctors per capita; proportion of children in kindergartens; proportion of women in various branchs of the economy; religious affiliation; divorce rate; training level of the population; education expenditures; employees in technology and science; scientific book production; social mobility.

1. Economy: growth rate of the gross national product; GNP per capita; public investments; merchandise import and export; proportion of employees and proportion of production in the individual sectors of the economy; average income; meat consumption and supply of calories; trade with Comecon countries, capitalist and under-developed countries; trade deficit and foreign debt; growth of import and export as well as of income; work productivity; working hours needed for selected goods; capital intensity; provision of households with telephone, television, cars and other durable economic goods; energy import and energy use; employee-worker relationship; development of real income as well as prices; private savings; income concentration; retail trade index; hectare yields and proportion of private agriculture.

2. Military: defense expenditures; export of weapons; strength of military forces; proportion of defense expenditures in gross national product; number of disturbances and protest demonstrations; armed attacks and persons killed; sanctions of the government; internal security forces.

3. Miscellaneous: content analysis of newspapers regarding reports about human rights, disarmament, economic as well as technical cooperation and conflicts after adoption of the final agreement of Helsinki and Belgrad.

Italy recorded a Government Debt to GDP of 135.30 percent of the country's Gross Domestic Product in 2024. This dataset provides - Italy Government Debt To GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Affirm (AFRM) - Buy Now, Pay Later: Growth and Profitability on the Horizon

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Tesla Stock Price Predicted to Reach $350 in One Year

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Cloudflare (NET) Navigates the Web of Growth

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This study attempts to explore the impact of external debt ($Debt), foreign reserves ($Reserves), and political stability & absence of violence/terrorism (PS&AVT) on the current financial crisis in Sri Lanka. Using data from 1996 to 2022 obtained from the World Bank (WB) and the Central Bank of Sri Lanka (CBSL), a regression analysis is conducted, with a composite variable named "CRISIS," which accounts for interest rate, inflation, currency devaluation adjusted to GDP growth, as the dependent variable. The findings indicate that, collectively, these predictors significantly contribute to explaining the variance in the financial crisis, although their impact is relatively minor. While the direct influence of PS&AVT on the financial crisis is not statistically significant, it indirectly affects the crisis through its considerable impact on debt and reserves. Granger causality tests showed predictive value for $Debt and $Reserve in relation to CRISIS, but the reverse relationship was not significant. Regression analysis using the error term and scatter plots supports the absence of endogeneity issues in the model. These findings suggest that while external debt and foreign reserves are more directly related to financial crises, political stability and the absence of violence/terrorism can influence the crisis indirectly through their effects on debt accumulation and reserve levels. This study represents a pioneering effort in investigating the impact of external debt, foreign reserves, and political stability on the financial crises in Sri Lanka. By utilizing a comprehensive dataset and applying a regression analysis, it sheds light on the complex interactions between these variables and their influence on the country’s financial stability.

Pakistan recorded a Government Debt to GDP of 80 percent of the country's Gross Domestic Product in 2024. This dataset provides - Pakistan Government Debt To GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Gross Domestic Product (GDP) in the United States was worth 29184.89 billion US dollars in 2024, according to official data from the World Bank. The GDP value of the United States represents 27.49 percent of the world economy. This dataset provides - United States GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Japan recorded a Government Debt to GDP of 236.70 percent of the country's Gross Domestic Product in 2024. This dataset provides the latest reported value for - Japan Government Debt to GDP - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.