This dataset provides values for GOVERNMENT DEBT TO GDP reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

This dataset provides values for EXTERNAL DEBT reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

This dataset provides values for HOUSEHOLDS DEBT TO INCOME reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Context

The Global Debt Database (GDD) is the outcome of an extensive investigative process initiated with the October 2016 Fiscal Monitor. This dataset encapsulates the total gross debt of the nonfinancial sector (both private and public) for an unbalanced panel of 190 advanced economies, emerging market economies, and low-income countries, with records dating back to 1950. The Global Debt Dataset aggregates information from diverse sources to offer a comprehensive view of both public and private debt metrics. It includes data on government debt, corporate debt, household debt, and external debt, enabling users to delve into trends, patterns, and interrelationships among different debt categories.

Content

The dataset furnishes crucial information for comprehending global debt trends. Key columns encompass the country name, inflation indicator type, and annual average debt percentages from 1950 to 2022. This dataset empowers researchers and policymakers for thorough analyses, allowing exploration of relationships between country-specific indicators and debt percentages. Through meticulous examination, users can unveil patterns in the financial landscapes of diverse economies over the past seven decades. This historical record stands as a valuable tool, providing insights into the complexities of global economic dynamics.

Dataset Structure:

This dataset (central_government_debt.csv) spanning from 1950 to 2022 comprises the following columns:

Additionally, there are four other datasets following the same data structure: general_government_debt.csv, household_debt.csv, non-financial_corporate_debt.csv, and private_debt.

Acknowledgment

The primary dataset was sourced from the International Monetary Fund. I extend sincere gratitude to the team for providing the core data used in this dataset.

Reference: Mbaye, S., Moreno-Badia, M., and K. Chae. 2018. “Global Debt Database: Methodology and Sources,” IMF Working Paper, International Monetary Fund, Washington, DC

© Image credit: Freepik

This dataset provides values for PRIVATE DEBT TO GDP reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

International Debt Statistics (IDS), successor to Global Development Finance and World Debt Tables, is designed to respond to user demand for timely, comprehensive data on trends in external debt in low- and middle-income countries. The World Bank's Debtor Reporting System (DRS), from which the aggregate and country tables presented in this report are drawn, was established in 1951. World Debt Tables, the first publication that included DRS external debt data, appeared in 1973 and gained increased attention during the debt crisis of the 1980s. Since then, the publication and data have undergone numerous revisions and iterations to address the challenges and demands posed by the global economic conditions.

For further details, please refer to https://www.worldbank.org/en/programs/debt-statistics/ids

Context

Now in its forty-eighth year, International Debt Statistics (IDS) supports policymakers and analysts by monitoring aggregate and country-specific trends in external debt in low- and middle-income countries. It provides a comprehensive picture of external borrowing and sources of lending by type of borrower and creditor with information on data availability and comparability. To further enhance debt transparency, this year’s report introduces additional features, such additional information on average lending terms by creditor country and the currency composition of debt stock. The Central Bank is also featured separately in the borrower composition along with its debt instruments. In addition, the IDS-DSSI database includes the actual debt service deferred in 2020 by each bilateral creditor and the projected monthly debt-service payments owed to all bilateral creditors for year 2021. The IDS 2022 publication provides a select set of indicators, with an expanded data set available online.

International Debt Statistics (IDS), successor to Global Development Finance and World Debt Tables, is designed to respond to user demand for timely, comprehensive data on trends in external debt in low- and middle-income countries. The World Bank’s Debtor Reporting System (DRS), from which the aggregate and country tables presented in this report are drawn, was established in 1951. World Debt.

Source

https://datacatalog.worldbank.org/search/dataset/0038015/international-debt-statistics

Download Links:

API Excel CSV

Metadata

• Coverage & Extent Geographical Coverage : East Asia and Pacific, Europe and Central Asia, Middle East and North Africa, South Asia Granularity List : National Temporal Coverage: 1970 - 2027 Periodicity : Annual

Additional Metadata

Acronym IDS Recommended Citation International Debt Statistics, The World Bank Languages Supported English Source Type World Bank Group Source: International Debt Statistics, The World Bank Harvest Source World Bank Data API Harvest System ID 6

Dates

First Published Date: Dec 19, 2012 Update Frequency: Annual Update Schedule: October

Evolution of Debt Vulnerability Classifications in Sub-Saharan African Heavily Indebted Poor Countries

This data contains debt distress vulnerability classifications for thirty Sub-Saharan African countries that have been granted debt relief under the Heavily Indebted Poor Countries (HIPC) initiative. At the turn of the century, heavily indebted countries (most of which were located in Sub-Saharan Africa) were granted large-scale cancellations of external debt owed to the World Bank, International Monetary Fund, and African Development Bank. Since then, the debt sustainability of these countries has been closely monitored by the IMF and World Bank under the Debt Sustainability Analysis for Low Income Countries (DSA for LIC). This DSA has been conducted in Low-Income countries since 2005.

This dataset contains the external debt distress classifications for 30 Sub-Saharan African countries that have been granted debt reductions under the HIPC scheme from 2005 to 2019. If there was no DSA conducted in a year, the DSA classification of the previous year is shown.

Acknowledgements

Data collected by me from documents on https://www.imf.org/en/Publications/DSA.

Monthly and long-term Russia Public Debt data: historical series and analyst forecasts curated by FocusEconomics.

Time series data for the statistic External_Debt_Stocks_Total_$ and country Belarus. Indicator Definition:Total external debt is debt owed to nonresidents repayable in currency, goods, or services. Total external debt is the sum of public, publicly guaranteed, and private nonguaranteed long-term debt, use of IMF credit, and short-term debt. Short-term debt includes all debt having an original maturity of one year or less and interest in arrears on long-term debt. Data are in current U.S. dollars.The statistic "External Debt Stocks Total $" stands at 36,704,544,069.50 Belarusian Rubles as of 12/31/2023, the lowest value since 12/31/2013. Regarding the One-Year-Change of the series, the current value constitutes a decrease of -7.91 percent compared to the value the year prior.The 1 year change in percent is -7.91.The 3 year change in percent is -12.17.The 5 year change in percent is -5.32.The 10 year change in percent is -7.25.The Serie's long term average value is 20,079,514,490.41 Belarusian Rubles. It's latest available value, on 12/31/2023, is 82.80 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/1993, to it's latest available value, on 12/31/2023, is +3,690.68%.The Serie's change in percent from it's maximum value, on 12/31/2020, to it's latest available value, on 12/31/2023, is -12.17%.

Government Debt in the United States increased to 38040094 USD Million in October from 37637553 USD Million in September of 2025. This dataset provides - United States Government Debt- 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.

Time series data for the statistic External_Debt_Stocks_Total_$ and country Iraq. Indicator Definition:Total external debt is debt owed to nonresidents repayable in currency, goods, or services. Total external debt is the sum of public, publicly guaranteed, and private nonguaranteed long-term debt, use of IMF credit, and short-term debt. Short-term debt includes all debt having an original maturity of one year or less and interest in arrears on long-term debt. Data are in current U.S. dollars.The statistic "External Debt Stocks Total $" stands at 20,331,392,379.40 Iraqi Dinars as of 12/31/2023, the lowest value since 12/31/2016. Regarding the One-Year-Change of the series, the current value constitutes a decrease of -10.54 percent compared to the value the year prior.The 1 year change in percent is -10.54.The 3 year change in percent is -22.78.The 5 year change in percent is -26.86.The Serie's long term average value is 24,512,204,559.07 Iraqi Dinars. It's latest available value, on 12/31/2023, is 17.06 percent lower, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/2015, to it's latest available value, on 12/31/2023, is +6.25%.The Serie's change in percent from it's maximum value, on 12/31/2017, to it's latest available value, on 12/31/2023, is -27.60%.

📖 Dataset Overview

This dataset is designed for financial risk assessment and loan default prediction using machine learning techniques. It includes 300 records, each representing an individual with financial attributes that influence the likelihood of loan default.

📊 Features & Data Structure

The dataset contains the following columns:

• Highly Left-Skewed Column: Retirement Age – Most people retire at older ages, with fewer early retirees.
• Highly Right-Skewed Column: Debt Amount – Most people have low debt, but a few have very high debt.
• Totally Symmetric Column: Monthly Savings – Normally distributed around an average.

📌 Data Generation & Logic

The dataset was synthetically created using statistical distributions that mimic real-world financial behavior:

🔹 Retirement Age (Left-Skewed): Generated using a transformed normal distribution to ensure most values are high (60-85).
🔹 Debt Amount (Right-Skewed): Generated using a log-normal distribution, where most people have low debt, but a few have very high debt.
🔹 Monthly Savings (Symmetric): Normally distributed with mean $2000$ and standard deviation $500$, clipped between $500-$5000.
🔹 Loan Default Risk (Target Variable): Computed using a logistic function, where:
- Lower retirement age ⬆ default risk
- Higher debt ⬆ default risk
- Higher savings ⬇ default risk
- The probability threshold was adjusted to balance 0s and 1s.

This dataset is designed to simulate rural financial system risk analysis, integrating various financial indicators, risk factors, and economic conditions that contribute to the systemic risk level of rural banking systems. It contains 10,293 rows, each representing data points from different rural financial institutions, including variables such as capital asset pricing, equities, insurance coverage, loan amounts, liquidity ratios, financial leverage, and agricultural performance. The dataset is structured to predict the systemic risk level (Low, Medium, High) of these financial institutions based on the input features.

The features in this dataset are normalized for consistency and scaled for machine learning applications. The target variable, "Systemic_Risk_Level," is a categorical variable that signifies the financial risk status of a rural bank, aiding in the detection and mitigation of financial risks. This dataset is designed to support research in AI-assisted rural financial planning, providing valuable insights for financial institutions, policy makers, and researchers working in risk management and rural economic development.

Key Features:

Capital_Asset_Pricing_Model: Reflects the relationship between a bank's expected return and market risks. Equities: The value of stocks held by the bank, representing ownership in businesses. Insurance_Coverage: The total amount of coverage for the bank’s insured assets. Loan_Amounts: The total loan amounts provided by the bank to customers. Deposit_Amount: The total deposits made by customers in the bank. Risk_Factor_Equities: A factor indicating the risk associated with the bank's equities investments. Risk_Factor_Loans: A factor indicating the risk associated with the bank's loan portfolio. Bank_Assets: The total value of the assets held by the bank. Bank_Liabilities: The total obligations (debts) owed by the bank. Liquidity_Ratio: A financial metric used to assess the bank's ability to cover its short-term obligations. Capital_Ratio: Measures the bank's capital against its risk-weighted assets to ensure its solvency. Operating_Cost: The total costs involved in the day-to-day operations of the bank. Financial_Leverage: The ratio of the bank's total debt to its equity, indicating the degree of financial risk. Agricultural_Performance: The performance of the agricultural sector in the region, affecting rural bank lending. Economic_Indicators: Key metrics indicating the overall economic health in the rural area. Natural_Disasters: The number of natural disasters (e.g., floods, earthquakes) affecting the region. Employment_Rate: The percentage of the workforce that is employed in the rural area. Inflation_Rate: The annual rate at which prices in the rural economy are rising. Credit_Risk: The probability that a borrower will default on loans, impacting the bank’s financial health. Systemic_Risk_Level (Target): The risk level assigned to the rural financial institution based on various economic and financial factors (Low, Medium, High).

This dataset provides values for GOVERNMENT DEBT TO GDP reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Euro Area recorded a Government Debt to GDP of 87.10 percent of the country's Gross Domestic Product in 2024. This dataset provides the latest reported value for - Euro Area Government Debt to GDP - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Turkey recorded a Government Debt to GDP of 24.70 percent of the country's Gross Domestic Product in 2024. This dataset provides the latest reported value for - Turkey Government Debt to GDP - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Greece recorded a Government Debt to GDP of 153.60 percent of the country's Gross Domestic Product in 2024. This dataset provides the latest reported value for - Greece Government Debt to GDP - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Long-Term-Debt Time Series for Harbin Jiuzhou Electrical Co Ltd. Harbin Jiuzhou Group Co.,Ltd. manufactures and supplies electrical equipment and energy efficiency management solutions in China and internationally. The company offers power products, including batteries; and power quality management products, comprising high and low voltage reactive power compensation products. It also provides transformers and reactors, prefabricated box types, busbars and bridges, terminals, and distribution automation products; low voltage switch complete set equipment; medium voltage switchgears comprising gas insulated medium voltage and air insulation medium voltage switchgears; and electrical components that include medium voltage switching components and low voltage dual power transfer switches. In addition, the company offers high-voltage motor drive products, including high voltage and low voltage soft starters, and high and low voltage inverters; new energy grid-connected converters and supporting products, such as megawatt level wind power converters; and power engineering installation and construction products. Further, it offers energy saving and energy efficiency management, city and infrastructure, rail transportation, distribution automation, building technology, industry, mine, new energy, data center, and reactive power compensation solutions. The company was formerly known as Harbin Jiuzhou Electrical Co.,Ltd and changed its name to Harbin Jiuzhou Group Co.,Ltd. in July 2020. Harbin Jiuzhou Group Co.,Ltd. was founded in 1993 and is based in Harbin, China.