The tables and interactive maps below allow users to explore the ratio of debt to income by state, metropolitan statistical area, and county for each year since 1999. Household debt is calculated from Federal Reserve Bank of New York (FRBNY) Consumer Credit Panel/Equifax Data, and household income is reported by the Bureau of Labor Statistics.

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.

Content

More details about each file are in the individual file descriptions.

Context

This is a dataset hosted by the World Bank. The organization has an open data platform found here and they update their information according the amount of data that is brought in. Explore the World Bank using Kaggle and all of the data sources available through the World Bank organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using the World Bank's APIs and Kaggle's API.

Cover photo by Markus Spiske on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

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,
• the archive of Hacker News available at Google's BigQuery (available at https://console.cloud.google.com/marketplace/details/y-combinator/hacker-news), and the Stack Exchange data dump (available at https://archive.org/details/stackexchange).
• the GHTorrent project
• the GH Archive

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:

{
 "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 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 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.

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.

This dataset has various calculations of debt burden. The data is represented as percentages and will be updated twice per year in the Preliminary and Executive Budgets.

Debt service ratios, interest and obligated principal payments on debt, and related statistics for households, Canada.

Debt Balance Credit Cards in the United States decreased to 1.18 Trillion USD in the first quarter of 2025 from 1.21 Trillion USD in the fourth quarter of 2024. This dataset includes a chart with historical data for the United States Debt Balance Credit Cards.

Content

More details about each file are in the individual file descriptions.

Context

This is a dataset from the Federal Reserve Bank of St. Louis hosted by the Federal Reserve Economic Database (FRED). FRED has a data platform found here and they update their information according to the frequency that the data updates. Explore the Federal Reserve Bank of St. Louis using Kaggle and all of the data sources available through the St. Louis Fed organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using FRED's API and Kaggle's API.

Cover photo by Noah Silliman on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

Households Debt in France decreased to 95.35 percent of gross income in 2023 from 101.84 percent in 2022. This dataset provides - France Households Debt To Income- actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Family Resources Survey (FRS) has been running continuously since 1992 to meet the information needs of the Department for Work and Pensions (DWP). It is almost wholly funded by DWP.

The FRS collects information from a large, and representative sample of private households in the United Kingdom (prior to 2002, it covered Great Britain only). The interview year runs from April to March.

The focus of the survey is on income, and how much comes from the many possible sources (such as employee earnings, self-employed earnings or profits from businesses, and dividends; individual pensions; state benefits, including Universal Credit and the State Pension; and other sources such as savings and investments). Specific items of expenditure, such as rent or mortgage, Council Tax and water bills, are also covered.

Many other topics are covered and the dataset has a very wide range of personal characteristics, at the adult or child, family and then household levels. These include education, caring, childcare and disability. The dataset also captures material deprivation, household food security and (new for 2021/22) household food bank usage.

The FRS is a national statistic whose results are published on the gov.uk website. It is also possible to create your own tables from FRS data, using DWP’s Stat Xplore tool. Further information can be found on the gov.uk Family Resources Survey webpage.

Secure Access FRS data
In addition to the standard End User Licence (EUL) version, Secure Access datasets, containing unrounded data and additional variables, are also available for FRS from 2005/06 onwards - see SN 9256. Prospective users of the Secure Access version of the FRS will need to fulfil additional requirements beyond those associated with the EUL datasets. Full details of the application requirements are available from http://ukdataservice.ac.uk/media/178323/secure_frs_application_guidance.pdf" style="background-color: rgb(255, 255, 255);">Guidance on applying for the Family Resources Survey: Secure Access.

FRS, HBAI and PI
The FRS underpins the related Households Below Average Income (HBAI) dataset, which focuses on poverty in the UK, and the related Pensioners' Incomes (PI) dataset. The EUL versions of HBAI and PI are held under SNs 5828 and 8503, respectively. The Secure Access versions are held under SN 7196 and 9257 (see above).

FRS 2023-24

Alongside the usual topics covered, the 2023-2024 FRS includes new variables on veterans (ex-armed forces, former regulars and reserves); care leavers (where young adults were previously living in care, during their teenage years); and, for the self-employed, length of time in that occupation. For doctors, we add clarifying variables for NHS vs private earnings streams. There are new variables on food support from friends/relatives, which complement the existing food bank and household food security set. 2023-2024 also includes Cost of Living Payment variables, including those on certain state benefits and the Warm Homes Discount scheme.

The achieved sample was over 16,500 households (28,500+ adults). A large majority of interviews were face-to-face with a minority being by telephone.

The BENUNIT table contains a raft of variables on the new material deprivation question set; see GOV.UK for background.

This version of the dataset (End User Licence) adds the DEBT table for the first time this year. The table contains responses on credit card debt, loan debt, hire purchase debt and store card debt.

Please send any feedback directly to the FRS Team Inbox: team.frs@dwp.gov.uk

Documentation

Many variables in the data files are fully labelled, but additional details can be found in the frs2324_variable_listing_eul.xlsx document.

Households Debt in India increased to 41.90 percent of GDP in the fourth quarter of 2024 from 41.30 percent of GDP in the third quarter of 2024. This dataset provides - India Households Debt To Gdp- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Households Debt in Canada decreased to 99.58 percent of GDP in the first quarter of 2025 from 100.39 percent of GDP in the fourth quarter of 2024. This dataset provides - Canada Households Debt To Gdp- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Households Debt in Mexico increased to 16.70 percent of GDP in the fourth quarter of 2024 from 16.50 percent of GDP in the third quarter of 2024. This dataset provides - Mexico Households Debt To Gdp- actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset contains debts, obligations, and orders placed by Washington State Candidates and Political committees for the last 10 years as reported to the PDC on Schedule B to the C4 Summary Report. Loans are not included in this dataset. Loans, however, are a debt but are contained in the Loan dataset. For candidates, the number of years is determined by the year of the election, not necessarily the year the expenditure was reported. For political committees, the number of years is determined by the calendar year of the reporting period. Candidates and political committees choosing to file under "mini reporting" are not included in this dataset. See WAC 390-16-105 for information regarding eligibility. This dataset is a best-effort by the PDC to provide a complete set of records as described herewith and may contain incomplete or incorrect information. The PDC provides access to the original reports for the purpose of record verification. Descriptions attached to this dataset do not constitute legal definitions; please consult RCW 42.17A and WAC Title 390 for legal definitions and additional information regarding political finance disclosure requirements. CONDITION OF RELEASE: This publication constitutes a list of individuals prepared by the Washington State Public Disclosure Commission and may not be used for commercial purposes. This list is provided on the condition and with the understanding that the persons receiving it agree to this statutorily imposed limitation on its use. See RCW 42.56.070(9) and AGO 1975 No. 15.

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.

The World Bank Central Government Debt dataset contains information on the amount of debt owed by central governments in countries around the world. This dataset provides information on the amount of debt owed by central governments.The dataset contains data for more than 130 countries and covers a period of several decades. The data is collected from official government sources, such as national central banks, finance ministries, and debt management offices.

Some potential uses of this dataset include: * Analyzing the relationship between central government debt levels and economic indicators, such as GDP, inflation, and interest rates. * Studying the impact of different types of debt instruments on central government debt levels. * Comparing central government debt levels across different countries and regions. * Forecasting central government debt levels and analyzing the factors that drive changes in debt levels.

Context

After a debt has been legally declared "uncollectable" by a bank, the account is considered "charged-off." But that doesn't mean the bank walks away from the debt. They still want to collect some of the money they are owed. The bank will score the account to assess the expected recovery amount, that is, the expected amount that the bank may be able to receive from the customer in the future. This amount is a function of the probability of the customer paying, the total debt, and other factors that impact the ability and willingness to pay.

The bank has implemented different recovery strategies at different thresholds ($1000, $2000, etc.) where the greater the expected recovery amount, the more effort the bank puts into contacting the customer. For low recovery amounts (Level 0), the bank just adds the customer's contact information to their automatic dialer and emailing system. For higher recovery strategies, the bank incurs more costs as they leverage human resources in more efforts to obtain payments. Each additional level of recovery strategy requires an additional $50 per customer so that customers in the Recovery Strategy Level 1 cost the company $50 more than those in Level 0. Customers in Level 2 cost $50 more than those in Level 1, etc.

Content

The big question: does the extra amount that is recovered at the higher strategy level exceed the extra $50 in costs? In other words, was there a jump (also called a "discontinuity") of more than $50 in the amount recovered at the higher strategy level? We'll find out in this notebook.

Note. k = number of analyses, N = number of participants, ESr = (mean) effect size correlation r.+p

Households Debt in Brazil increased to 36.40 percent of GDP in the fourth quarter of 2024 from 35.80 percent of GDP in the third quarter of 2024. This dataset provides - Brazil Households Debt To Gdp- actual values, historical data, forecast, chart, statistics, economic calendar and news.