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.

Context

The World Bank is an international financial institution that provides loans to countries of the world for capital projects. The World Bank's stated goal is the reduction of poverty. Source: https://en.wikipedia.org/wiki/World_Bank

Content

This dataset contains both national and regional debt statistics captured by over 200 economic indicators. Time series data is available for those indicators from 1970 to 2015 for reporting countries.

For more information, see the World Bank website.

Fork this kernel to get started with this dataset.

Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:world_bank_intl_debt

https://cloud.google.com/bigquery/public-data/world-bank-international-debt

Citation: The World Bank: International Debt Statistics

Dataset Source: World Bank. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by @till_indeman from Unplash.

Inspiration

What countries have the largest outstanding debt?

https://cloud.google.com/bigquery/images/outstanding-debt.png" alt="enter image description here"> https://cloud.google.com/bigquery/images/outstanding-debt.png

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.

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]

Total outstanding debt of the U.S. government reported daily. Includes a breakout of intragovernmental holdings (federal debt held by U.S. government) and debt held by the public (federal debt held by entities outside the U.S. government).

The Debt to the Penny dataset provides information about the total outstanding public debt and is reported each day. Debt to the Penny is made up of intragovernmental holdings and debt held by the public, including securities issued by the U.S. Treasury. Total public debt outstanding is composed of Treasury Bills, Notes, Bonds, Treasury Inflation-Protected Securities (TIPS), Floating Rate Notes (FRNs), and Federal Financing Bank (FFB) securities, as well as Domestic Series, Foreign Series, State and Local Government Series (SLGS), U.S. Savings Securities, and Government Account Series (GAS) securities. Debt to the Penny is updated at 3:00 PM EST each business day with data from the previous business day.

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.

Graph and download economic data for Federal Debt: Total Public Debt (GFDEBTN) from Q1 1966 to Q1 2025 about public, debt, federal, government, and USA.

External Debt in Egypt increased to 156689 USD Million in the first quarter of 2025 from 155093.40 USD Million in the fourth quarter of 2024. This dataset provides - Egypt External Debt - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Historical Debt Outstanding is a dataset that provides a summary of the U.S. government's total outstanding debt at the end of each fiscal year from 1789 to the current year. Between 1789 and 1842, the fiscal year began in January. From January 1842 until 1977, the fiscal year began in July. From July 1977 onwards, the fiscal year has started in October. Between 1789 and 1919, debt outstanding was presented as of the first day of the next fiscal year. From 1920 onwards, debt outstanding has been presented as of the final day of the fiscal year. This is a high-level summary of historical public debt and does not contain a breakdown of the debt components.

The Interest Expense on Debt Outstanding dataset provides monthly and fiscal year-to-date values for interest expenses on federal government debt, that is, the cost to the U.S. for borrowing money (calculated at a specified rate and period of time). U.S. debt includes Treasury notes and bonds, foreign and domestic series certificates of indebtedness, savings bonds, Government Account Series (GAS), State and Local Government Series (SLGS) and other special purpose securities. While interest expenses are what the government pays to investors who loan money to the government, how much the government pays in interest depends on both the total federal debt and the interest rate investors charged when they loaned the money. This dataset is useful for those who wish to track the cost of maintaining federal debt.

This dataset shows the External debt, 2012 - 2021 Footnote Offshore borrowing- Equivalent to the external debt as previously defined mainly foreign currency loan raised, and bonds as well as notes issued offshore Medium and long-term debt refers to debt with tenure of more than one year Public corporations includes both guaranteed and non-guaranteddebt of public corporations Short-term debt refers to debt with tenure of one year and below Comprise trade credits, IMF allocation of SDRs and miscellaneous Source Bank Negara Malaysia No. of Views : 139

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 %)

Analysis of ‘Evolution of debt vulnerabilities in Africa’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/evadrichter/evolution-of-debt-distress-in-hipc-countries on 28 January 2022.

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

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.

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

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.

China recorded a Government Debt to GDP of 88.30 percent of the country's Gross Domestic Product in 2024. This dataset provides - China Government Debt To GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

Description Utrecht in Figures is the online database of the municipality of Utrecht. Here the Research & Advice department publishes figures about the city of Utrecht and the Utrecht districts and neighbourhoods. With Utrecht in Figures we want to show everyone interested in Utrecht how things are actually going in Utrecht. Utrecht in Figures is divided into themes. Within the theme 'Debts (Work & income)' you will find information about the following 5 'indicators': - % who indicate that they have no debt now or in the past year - % who indicate that they have a problem debt now or in the past 12 months have - Number of households with registered problematic debts - Have you had any debts in the past 12 months? - Percentage of households with registered problematic debts #### Number of indicators Number of indicators under this theme: 5 #### Source CBS, Research & Advice, Municipality of Utrecht, Residents' Survey #### Update frequency Annual #### Related datasets Other datasets that fall within this theme: - Indicators Poverty (Work & Income) - Indicators Income & benefits (Work & income)

The Mortgage Debt Outstanding table is no longer being updated. All of the series that were published in this table can be found in the Financial Accounts of the United States.

# TDMentions: A Dataset of Technical Debt Mentions in Online Posts (version 1.0)

TDMentions is a dataset that contains mentions of technical debt from Reddit, Hacker News, and Stack Exchange. It also contains a list of blog posts on Medium that were tagged as technical debt. The dataset currently contains approximately 35,000 items.

## Data collection and processing

The dataset is mainly collected from existing datasets. We used data from:

- the archive of Reddit posts by Jason Baumgartner (available at [https://pushshift.io](https://pushshift.io),
- the archive of Hacker News available at Google's BigQuery (available at [https://console.cloud.google.com/marketplace/details/y-combinator/hacker-news](https://console.cloud.google.com/marketplace/details/y-combinator/hacker-news)), and the Stack Exchange data dump (available at [https://archive.org/details/stackexchange](https://archive.org/details/stackexchange)).
- the [GHTorrent](http://ghtorrent.org) project
- the [GH Archive](https://www.gharchive.org)

The data set currently contains data from the start of each source/service until 2018-12-31. For GitHub, we currently only include data from 2015-01-01.

We use the regular expression `tech(nical)?[\s\-_]*?debt` to find mentions in all sources except for Medium. We decided to limit our matches to variations of technical debt and tech debt. Other shorter forms, such as TD, can result in too many false positives. For Medium, we used the tag `technical-debt`.

## Data Format

The dataset is stored as a compressed (bzip2) JSON file with one JSON object per line. Each mention is represented as a JSON object with the following keys.

- `id`: the id used in the original source. We use the URL path to identify Medium posts.
- `body`: the text that contains the mention. This is either the comment or the title of the post. For Medium posts this is the title and subtitle (which might not mention technical debt, since posts are identified by the tag).
- `created_utc`: the time the item was posted in seconds since epoch in UTC.
- `author`: the author of the item. We use the username or userid from the source.
- `source`: where the item was posted. Valid sources are:
- HackerNews Comment
- HackerNews Job
- HackerNews Submission
- Reddit Comment
- Reddit Submission
- StackExchange Answer
- StackExchange Comment
- StackExchange Question
- Medium Post
- `meta`: Additional information about the item specific to the source. This includes, e.g., the subreddit a Reddit submission or comment was posted to, the score, etc. We try to use the same names, e.g., `score` and `num_comments` for keys that have the same meaning/information across multiple sources.

This is a sample item from Reddit:

```JSON
{
"id": "ab8auf",
"body": "Technical Debt Explained (x-post r/Eve)",
"created_utc": 1546271789,
"author": "totally_100_human",
"source": "Reddit Submission",
"meta": {
"title": "Technical Debt Explained (x-post r/Eve)",
"score": 1,
"num_comments": 0,
"url": "http://jestertrek.com/eve/technical-debt-2.png",
"subreddit": "RCBRedditBot"
}
}
```

## Sample Analyses

We decided to use JSON to store the data, since it is easy to work with from multiple programming languages. In the following examples, we use [`jq`](https://stedolan.github.io/jq/) to process the JSON.

### How many items are there for each source?

```
lbzip2 -cd postscomments.json.bz2 | jq '.source' | sort | uniq -c
```

### How many submissions that mentioned technical debt were posted each month?

```
lbzip2 -cd postscomments.json.bz2 | jq 'select(.source == "Reddit Submission") | .created_utc | strftime("%Y-%m")' | sort | uniq -c
```

### What are the titles of items that link (`meta.url`) to PDF documents?

```
lbzip2 -cd postscomments.json.bz2 | jq '. as $r | select(.meta.url?) | .meta.url | select(endswith(".pdf")) | $r.body'
```

### Please, I want CSV!

```
lbzip2 -cd postscomments.json.bz2 | jq -r '[.id, .body, .author] | @csv'
```

Note that you need to specify the keys you want to include for the CSV, so it is easier to either ignore the meta information or process each source.

Please see [https://github.com/sse-lnu/tdmentions](https://github.com/sse-lnu/tdmentions) for more analyses

# Limitations and Future updates

The current version of the dataset lacks GitHub data and Medium comments. GitHub data will be added in the next update. Medium comments (responses) will be added in a future update if we find a good way to represent these.