Credit card debt in the United States has been growing at a fast pace between 2021 and 2025. In the fourth quarter of 2024, the overall amount of credit card debt reached its highest value throughout the timeline considered here. COVID-19 had a big impact on the indebtedness of Americans, as credit card debt decreased from *** billion U.S. dollars in the last quarter of 2019 to *** billion U.S. dollars in the first quarter of 2021. What portion of Americans use credit cards? A substantial portion of Americans had at least one credit card in 2025. That year, the penetration rate of credit cards in the United States was ** percent. This number increased by nearly seven percentage points since 2014. The primary factors behind the high utilization of credit cards in the United States are a prevalent culture of convenience, a wide range of reward schemes, and consumer preferences for postponed payments. Which companies dominate the credit card issuing market? In 2024, the leading credit card issuers in the U.S. by volume were JPMorgan Chase & Co. and American Express. Both firms recorded transactions worth over one trillion U.S. dollars that year. Citi and Capital One were the next banks in that ranking, with the transactions made with their credit cards amounting to over half a trillion U.S. dollars that year. Those industry giants, along with other prominent brand names in the industry such as Bank of America, Synchrony Financial, Wells Fargo, and others, dominate the credit card market. Due to their extensive customer base, appealing rewards, and competitive offerings, they have gained a significant market share, making them the preferred choice for consumers.

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.

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.

Daily, weekly and monthly data showing seasonally adjusted and non-seasonally adjusted UK spending using debit and credit cards. These are official statistics in development. Source: CHAPS, Bank of England.

Negative Equity in U.S. Housing Market

Measuring Home Values, Debt, and Credit Risk

By Zillow Data [source]

About this dataset

This dataset, Negative Equity in the US Housing Market, provides an in-depth look into the negative equity occurring across the United States during this single quarter. Included are metrics such as total amount of negative equity in millions of dollars, total number of homes in negative equity, percentage of homes with mortgages that are in negative equity and more. These data points provide helpful insights into both regional and national trends regarding the prevalence and rate of home mortgage delinquency stemming from a diminishment of value from peak levels.

Home types available for analysis include 'all homes', condos/co-ops, multifamily units containing five or more housing units as well as duplexes/triplexes. Additionally, Cash buyers rates for particular areas can also be determined by referencing this collection. Further metrics such as mortgage affordability rates and impacts on overall indebtedness are readily calculated using information related to Zillow's Home Value Index (ZHVI) forecast methodology and TransUnion data respectively.

Other variables featured within this dataset include characteristics like region type (i.e city, county ..etc), size rank based on population values , percentage change in ZHVI since peak levels as well as loan-to-value ratio greater than 200 across all regions constituted herein (NE). Moreover Zillow's own Secondary Mortgage Market Survey data is utilized to acquire average mortgage quote rates while correlative Census Bureau NCHS median household income figures represent typical assessable proportions between wages and debt obligations . So whether you're looking to assess effects along metro lines or detailed buffering through zip codes , this database should prove sufficient for insightful explorations! Nonetheless users must strictly adhere to all conditions encompassed within Terms Of Use commitments put forth by our lead provider before accessing any resources included herewith

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

Research Ideas

• Analyzing regional and state trends in negative equity: Analyze geographic differences in the percentage of mortgages “underwater”, total amount of negative equity, number of homes at least 90 days late, and other key indicators to provide insight into the factors influencing negative equity across regions, states and cities.
• Tracking the recovery rate over time: Track short-term changes in numbers related to negative equity (e.g., region or area ZHVI Change from Peak) to monitor recovery rates over time as well as how different policy interventions are affecting homeownership levels in affected areas.
• Exploring best practices for promoting housing affordability: Compare affordability metrics (e.g., mortgage payments, price-to-income ratios) across different geographic locations over time to identify best practices for empowering homeowners and promoting stability within the housing market while reducing local inequality impacts related to availability of affordable housing options and access to credit markets like mortgages/loans etc

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: NESummary_2017Q1_Public.csv | Column name | Description | |:------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------| | RegionType | The type of region (e.g., city, county, metro etc.) (String) | | City | Name of the city (String) | | County | Name of the county (String) | | State | Name of the state (String) | | Metro ...

Abstract copyright UK Data Service and data collection copyright owner. The Wealth and Assets Survey (WAS) is a longitudinal survey, which aims to address gaps identified in data about the economic well-being of households by gathering information on level of assets, savings and debt; saving for retirement; how wealth is distributed among households or individuals; and factors that affect financial planning. Private households in Great Britain were sampled for the survey (meaning that people in residential institutions, such as retirement homes, nursing homes, prisons, barracks or university halls of residence, and also homeless people were not included).The WAS commenced in July 2006, with a first wave of interviews carried out over two years, to June 2008. Interviews were achieved with 30,595 households at Wave 1. Those households were approached again for a Wave 2 interview between July 2008 and June 2010, and 20,170 households took part. Wave 3 covered July 2010 - June 2012, Wave 4 covered July 2012 - June 2014 and Wave 5 covered July 2014 - June 2016. Revisions to previous waves' data mean that small differences may occur between originally published estimates and estimates from the datasets held by the UK Data Service. These revisions are due to improvements in the imputation methodology.Note from the WAS team - November 2023:"The Office for National Statistics has identified a very small number of outlier cases present in the seventh round of the Wealth and Assets Survey covering the period April 2018 to March 2020. Our current approach is to treat cases where we have reasonable evidence to suggest the values provided for specific variables are outliers. This approach did not occur for two individuals for several variables involved in the estimation of their pension wealth. While we estimate any impacts are very small overall and median pension wealth and median total wealth estimates are unaffected, this will affect the accuracy of the breakdowns of the pension wealth within the wealthiest decile, and data derived from them. We are urging caution in the interpretation of more detailed estimates."Survey Periodicity - "Waves" to "Rounds"Due to the survey periodicity moving from "Waves" (July, ending in June two years later) to “Rounds” (April, ending in March two years later), interviews using the ‘Wave 6’ questionnaire started in July 2016 and were conducted for 21 months, finishing in March 2018. Data for round 6 covers the period April 2016 to March 2018. This comprises of the last three months of Wave 5 (April to June 2016) and 21 months of Wave 6 (July 2016 to March 2018). Round 5 and Round 6 datasets are based on a mixture of original wave-based datasets. Each wave of the survey has a unique questionnaire and therefore each of these round-based datasets are based on two questionnaires. While there may be some changes in the questionnaires, the derived variables for the key wealth estimates have not changed over this period. The aim is to collect the same data, though in some cases the exact questions asked may differ slightly. Detailed information on Moving the Wealth and Assets Survey onto a financial years’ basis was published on the ONS website in July 2019.Further information and documentation may be found on the ONS Wealth and Assets Survey webpage. Users are advised to the check the page for updates before commencing analysis.Users should note that issues with linking have been reported and the WAS team are currently investigating.Secure Access WAS dataThe Secure Access version of the WAS includes additional, detailed geographical variables not included in the End User Licence (EUL) version (SN 7215). These include:WardsParliamentary Constituency Areas for Wave 1 onlyCensus Output AreasLower Layer Super Output AreasLocal AuthoritiesLocal Education AuthoritiesProspective users of the Secure Access version of the WAS will need to fulfil additional requirements, including completion of face-to-face training, and agreement to the Secure Access User Agreement and Licence Compliance Policy, in order to obtain permission to use that version (see 'Access' section below). Users are therefore strongly encouraged to download the EUL version (SN 7215) to see if it contains sufficient detail for their needs, before considering making an application for the Secure Access version.Latest Edition InformationFor the ninth edition (October 2022), the Round 7 person and household data have been updated. The Round 7 Wave 1 Variable Catalogue Excel file has also been updated. Main Topics: The WAS questionnaire was divided into two parts with all adults aged 16 years and over (excluding those aged 16 to 18 currently in full-time education) being interviewed in each responding household. Household schedule: This was completed by one person in the household (usually the head of household or their partner) and predominantly collected household level information such as the number, demographics and relationship of individuals to each other, as well as information about the ownership, value and mortgages on the residence and other household assets. Individual schedule: This was given to each adult in the household and asked questions about economic status, education and employment, business assets, benefits and tax credits, saving attitudes and behaviour, attitudes to debt, insolvency, major items of expenditure, retirement, attitudes to saving for retirement, pensions, financial assets, non-mortgage debt, investments and other income. Multi-stage stratified random sample Face-to-face interview 2006 2020 ADOPTION PAY AGE AIRCRAFT ALIMONY ASSETS ATTITUDES TO SAVING BANK ACCOUNTS BEDROOMS BICYCLES BOATS BONDS BUSINESS OWNERSHIP BUSINESS RECORDS BUSINESSES CARAVANS CARE OF DEPENDANTS CARERS BENEFITS CARS CHILD BENEFITS CHILD SUPPORT PAYMENTS CHILD TRUST FUNDS COHABITING COMMERCIAL BUILDINGS COST OF LIVING COSTS CREDIT CARD USE DEBILITATIVE ILLNESS DEBTS DISABILITIES EARLY RETIREMENT ECONOMIC ACTIVITY EDUCATIONAL BACKGROUND EDUCATIONAL COURSES EDUCATIONAL FEES EDUCATIONAL GRANTS EDUCATIONAL STATUS EMPLOYEES EMPLOYMENT EMPLOYMENT HISTORY EMPLOYMENT PROGRAMMES ENDOWMENT ASSURANCE ESTATES ETHNIC GROUPS EXPENDITURE FAMILY BENEFITS FAMILY INCOME FAMILY MEMBERS FINANCIAL ADVICE FINANCIAL COMPENSATION FINANCIAL DIFFICULTIES FINANCIAL SERVICES FREQUENCY OF PAY FRINGE BENEFITS FULL TIME EMPLOYMENT FURNISHED ACCOMMODA... GENDER GIFTS Great Britain HEALTH HEALTH STATUS HIRE PURCHASE HOME BUILDINGS INSU... HOME BUYING HOME CONTENTS INSUR... HOME OWNERSHIP HOUSE PRICES HOUSEHOLD BUDGETS HOUSEHOLD HEAD S EC... HOUSEHOLD HEAD S SO... HOUSEHOLD INCOME HOUSEHOLDERS HOUSEHOLDS HOUSING HOUSING AGE HOUSING ECONOMICS HOUSING FINANCE HOUSING TENURE ILL HEALTH INCOME INCOME TAX INCONTINENCE INFORMAL CARE INHERITANCE INSOLVENCIES INSURANCE CLAIMS INTELLECTUAL IMPAIR... INTEREST FINANCE INVESTMENT Income JOB HUNTING JOB SEEKER S ALLOWANCE LAND OWNERSHIP LAND VALUE LANDLORDS LIFE INSURANCE LOANS Labour and employment MAIL ORDER SERVICES MARITAL STATUS MATERNITY BENEFITS MATERNITY PAY MATHEMATICS MOBILE HOMES MORTGAGE ARREARS MORTGAGE PROTECTION... MORTGAGES MOTOR VEHICLE VALUE MOTOR VEHICLES MOTORCYCLES OCCUPATIONAL PENSIONS OCCUPATIONAL QUALIF... OCCUPATIONS OLD AGE BENEFITS ONE PARENT FAMILIES OVERDRAFTS PART TIME EMPLOYMENT PARTNERSHIPS BUSINESS PATERNITY BENEFITS PATERNITY PAY PENSION BENEFITS PENSION CONTRIBUTIONS PENSIONS PERSONAL DEBT REPAY... PERSONAL FINANCE MA... PHYSICAL MOBILITY PLACE OF BIRTH PRIVATE PENSIONS PRIVATE PERSONAL PE... PROFIT SHARING PROFITS QUALIFICATIONS REDUNDANCY PAY RELIGIOUS AFFILIATION RELIGIOUS ATTENDANCE RENTED ACCOMMODATION RENTS RESIDENTIAL BUILDINGS RETIREMENT RETIREMENT AGE ROYALTIES SAVINGS SAVINGS ACCOUNTS AN... SECOND HOMES SELF EMPLOYED SELLING SHARED HOME OWNERSHIP SHARES SICK PAY SICKNESS AND DISABI... SOCIAL HOUSING SOCIAL SECURITY SOCIAL SECURITY BEN... SOCIO ECONOMIC STATUS SPOUSES STAKEHOLDER PENSIONS STATE RETIREMENT PE... STATUS IN EMPLOYMENT STUDENT LOANS SUBSIDIARY EMPLOYMENT SUPERVISORY STATUS SURVIVORS BENEFITS TAX RELIEF TAXATION TENANTS HOME PURCHA... TIED HOUSING TOP MANAGEMENT TRANSPORT FARES TRUSTS UNEARNED INCOME UNEMPLOYED UNFURNISHED ACCOMMO... UNWAGED WORKERS WAGES WAR VETERANS BENEFITS WEALTH WILLS WINNINGS WORKPLACE property and invest...

Company Information:

The data set is based upon https://www.kaggle.com/prateikmahendra/loan-data"> Lending Club Information . - TheIrish Dummy Banks is a peer to peer lending bank based in the ireland, in which bank provide funds for potential borrowers and bank earn a profit depending on the risk they take (the borrowers credit score). Irish Fake bank provides loan to their loyal customers. The complete data set is borrowed from Lending Club For more basic information about the company please check out the wikipedia article about the company. This dataset is copied and clean from kaggle but it has been changed. The any kind of similarity is just for learning purposes. I dont have any intention for Plagiarism I just like to be clear myself.

<a src="https://en.wikipedia.org/wiki/Lending_Club"> Lending Club Information </a>

The central idea and coding is abstract from Kevin mark ham youtube video series, Introduction to machine learning with scikit-learn video series. You can find link under resources section.

Data Description

• LoanStatNew Description

• addr_state The state provided by the borrower in the loan application

• annual_inc The self-reported annual income provided by the borrower during registration.

• annual_inc_joint The combined self-reported annual income provided by the co-borrowers during registration

• application_type Indicates whether the loan is an individual application or a joint application with two co-borrowers

• collection_recovery_fee post charge off collection fee

• collections_12_mths_ex_med Number of collections in 12 months excluding medical collections

• delinq_2yrs The number of 30+ days past-due incidences of delinquency in the borrower's credit file for the past 2 years

• desc Loan description provided by the borrower

• dti A ratio calculated using the borrower’s total monthly debt payments on the total debt obligations, - - - excluding mortgage and the requested LC loan, divided by the borrower’s self-reported monthly income.

• dti_joint A ratio calculated using the co-borrowers' total monthly payments on the total debt obligations, - excluding mortgages and the requested LC loan, divided by the co-borrowers' combined self-reported monthly income

• earliest_cr_line The month the borrower's earliest reported credit line was opened

• emp_length Employment length in years. Possible values are between 0 and 10 where 0 means less than one year

• and 10 means ten or more years.

• emp_title The job title supplied by the Borrower when applying for the loan.*

• fico_range_high The upper boundary range the borrower’s FICO at loan origination belongs to.

• fico_range_low The lower boundary range the borrower’s FICO at loan origination belongs to.

• funded_amnt The total amount committed to that loan at that point in time.

• funded_amnt_inv The total amount committed by investors for that loan at that point in time.

• grade LC assigned loan grade

• home_ownership The home ownership status provided by the borrower during registration. Our values are: RENT, OWN, MORTGAGE, OTHER.

Abstract copyright UK Data Service and data collection copyright owner. The project aimed to collect detailed oral testimony from 40 retired working-class people on their experiences of credit, debt and consumerism. The interviews were to designed to gather innovative historical information on the changing circumstances of financial management in working-class homes. Information was sought on the impact of family gender, generation, neighbourhood, occupation, income, and religion on financial decision making. The project also set out to identify and describe the various forms of credit used by working-class consumers in Belfast and to explain changes over time. Amongst these forms of credit was the credit union and information was sought on this form of financial institution. However, as very few of the interviewees provided detailed information on credit unions, it was necessary to conduct a small number of interviews with individuals below retirement age. This also helped to establish factors of change and continuity within the working class economies of Belfast. The opportunity also arose to interview two licensed moneylenders from families with long experience of providing loans to working-class communities in Northern Ireland. As well as aiming to gather testimony on experiences of, and attitudes towards, credit and debt, the project was designed to excavate and explore the forms of social memory that exist within working-class Belfast. The examination of credit and debt, with their association with factors such as levels of poverty or affluence and respectability offered an excellent vehicle through which to pursue this aim. Interviewees addressed issues of money management in families, pawnbroking, moneylending, check trading, the role of the Co-op, mail order catalogues, hire puchase, banking, credit unions, mortgages, credit cards amongst other methods of financial management. Main Topics: The dataset is of 30 separate interviews with a total of 32 individuals. The majority probe the life histories of the interviewees through the prism of their familial and personal experiences of credit, debt and money management. As the interviewees range in age from their forties to one hundred years of age there is a broad range of experiences from corner shop tick through to credit cards and credit unions. The majority of interviews took place in four Belfast day centres and the interviews hail from Catholic and Protestant backgrounds and a range of working-class occupations The interviews reveal the attitudes towards credit and debt and how they were mediated in every day practice. Frequently, for example, interviewees do not identify various forms of credit as credit and, as a result, narrate a life history where debt has been avoided. Thus there is a strong degree of ambivalence and ambiguity in many of the testimonies that reflects the censure that was placed, in the past on `bad managers' (particularly the wife of course) in the working class community. Cultural attitudes towards various forms of credit impacted upon their use, but, of course, levels of income, size of family and, particularly, the spending habits of the male breadwinner were all important elements in levels of credit use. Two interviews were also conducted with licensed moneylenders who are from families with long experience of providing loans in working class communities. Their testimony offered a different perspective on credit and debt in working class Belfast and helps establish the practices and thinking of creditors. Purposive selection/case studies Face-to-face interview

The data comprises of qualitative semi-structured interviews with two groups. The first is individuals in the North East of England who have accessed High Cost Short Term Credit through digital interfaces such as laptops and smart phones. Discussions focus on how the design of these digital interfaces and their mobile nature influenced people's decision to access credit and how they went on to manage this debt. The second is individuals working in the debt charity and regulation sector. These interviews focused on how charities and regulators understood the role digital interfaces played in the decision making processes around accessing credit.The HCSTC market in the UK has shown huge growth over the past five years. The cash and pay day loan market (a well publicised part of HCSTC) is now estimated to be worth two billion pounds a year (CMA, 2015). Part of the expansion of the HCSTC market is a shift in how this credit is accessed. In the case of cash and pay day loans, previously, customers would have to phone or call into a branch of a cash or pay day loan company to apply for a loan. With the rise of internet enabled devices and internet access, HCSTC companies have developed websites and mobile applications, where customers can apply through automated systems and receive decisions about the status of their loan very quickly. The ease and speed of using these systems has led to a situation in which 82% of all cash and pay day loans in the UK are applied for and approved online (Competition and Markets Authority, 2015). This project seeks to understand how the design of HCSTC websites and applications as well as the spaces and times in which these loans are applied for influence consumers decision making processes when applying for loans. Understanding the relationship between digital interfaces used to access HCSTC and decision making processes associated with these devices is important due to problematic nature of much HCSTC debt. HCSTC is problematic because it provides easy credit to those that can least afford it and is incredibly expensive, with high APR's and penalties for late payment. Indeed recent legislation has been created to regulate the financial terms of parts of the HCSTC market, such as cash and pay day loan companies. This regulation has included limits on the total APR and total cost of penalties for late payment of loans. However, no in-depth academic research has been conducted on: 1. How HCSTC websites and apps (including cash, pay day, guarantor and log book loans) are designed to shape decision making processes around taking a loan. 2. How the design of HCSTC websites and appsencourage, smooth or normalize processes of taking a loan in practice. 3. How the spaces and times where digital devices are used to access these sites and applications might impact these decision making processes in practice. This is an important omission, considering that 82% of these loans are accessed via digital interfaces (CMA, 2015). To investigate this issue, the research uses qualitative methods to study three groups. 1. Website and app designers. This part of the project will investigate the techniques involved in designing HCSTC websites and apps. Through 2 days of observation of designers at the App World and Consumer Credit conferences and 10 interviews the project will understand how designers discuss and reflect upon their own practices in relation to mobile website and app design. Through interviews with designers the project will investigate how websites are designed in an attempt to prime consumer decision making processes regarding taking a loan or buying a product. 2. Staff from debt advice charities, including The Debt Advice Foundation, Citizen's Advice Bureau and financial regulators including The Financial Conduct Authority (FCA) and Competition and Markets Authority (CMA). Through 10 interviews, this part of the project will seek to understand how members of staff from debt advice services understand the role of digital interfaces in shaping their clients practices and whether they give advice regarding the use of digital devices to their clients. 3. HCSTC website and app users in Newcastle Upon Tyne, UK. This aspect of the project will seek to understand how people experience and use HCSTC products. Through data gathered in 40 interviews the project will fill the gap in knowledge around how these interfaces influence decision making processes of customers and produce evidence-based recommendations regarding the implications of digital interface design on forms of problem debt enabled by HCSTC. Data was collected through qualitative semi-structured interviews, with a focus on conversational interview approaches. Participants were sourced through snowballing of contacts with debt advice charities and advertising throughout Newcastle Upon Tyne. All interviews with users have been anonymised to protect participants and details of individual names, locations, and organisations have been removed to avoid identification by association, apart from charities and regulators, which agreed to being named.

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.

Analysis of ‘Loan Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/itssuru/loan-data on 28 January 2022.

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

About the data and what to do…

publicly available data from LendingClub.com. Lending Club connects people who need money (borrowers) with people who have money (investors). Hopefully, as an investor you would want to invest in people who showed a profile of having a high probability of paying you back.

We will use lending data from 2007-2010 and be trying to classify and predict whether or not the borrower paid back their loan in full. You can download the data from here.

Here are what the columns represent:

credit.policy: 1 if the customer meets the credit underwriting criteria of LendingClub.com, and 0 otherwise. purpose: The purpose of the loan (takes values "credit_card", "debt_consolidation", "educational", "major_purchase", "small_business", and "all_other"). int.rate: The interest rate of the loan, as a proportion (a rate of 11% would be stored as 0.11). Borrowers judged by LendingClub.com to be more risky are assigned higher interest rates. installment: The monthly installments owed by the borrower if the loan is funded. log.annual.inc: The natural log of the self-reported annual income of the borrower. dti: The debt-to-income ratio of the borrower (amount of debt divided by annual income). fico: The FICO credit score of the borrower. days.with.cr.line: The number of days the borrower has had a credit line. revol.bal: The borrower's revolving balance (amount unpaid at the end of the credit card billing cycle). revol.util: The borrower's revolving line utilization rate (the amount of the credit line used relative to total credit available). inq.last.6mths: The borrower's number of inquiries by creditors in the last 6 months. delinq.2yrs: The number of times the borrower had been 30+ days past due on a payment in the past 2 years. pub.rec: The borrower's number of derogatory public records (bankruptcy filings, tax liens, or judgments).

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

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 August of 2025.

Abstract copyright UK Data Service and data collection copyright owner.The Opinions and Lifestyle Survey (formerly known as the ONS Opinions Survey or Omnibus) is an omnibus survey that began in 1990, collecting data on a range of subjects commissioned by both the ONS internally and external clients (limited to other government departments, charities, non-profit organisations and academia).Data are collected from one individual aged 16 or over, selected from each sampled private household. Personal data include data on the individual, their family, address, household, income and education, plus responses and opinions on a variety of subjects within commissioned modules. The questionnaire collects timely data for research and policy analysis evaluation on the social impacts of recent topics of national importance, such as the coronavirus (COVID-19) pandemic and the cost of living, on individuals and households in Great Britain. From April 2018 to November 2019, the design of the OPN changed from face-to-face to a mixed-mode design (online first with telephone interviewing where necessary). Mixed-mode collection allows respondents to complete the survey more flexibly and provides a more cost-effective service for customers. In March 2020, the OPN was adapted to become a weekly survey used to collect data on the social impacts of the coronavirus (COVID-19) pandemic on the lives of people of Great Britain. These data are held in the Secure Access study, SN 8635, ONS Opinions and Lifestyle Survey, Covid-19 Module, 2020-2022: Secure Access. From August 2021, as coronavirus (COVID-19) restrictions were lifting across Great Britain, the OPN moved to fortnightly data collection, sampling around 5,000 households in each survey wave to ensure the survey remains sustainable. The OPN has since expanded to include questions on other topics of national importance, such as health and the cost of living. For more information about the survey and its methodology, see the ONS OPN Quality and Methodology Information webpage.Secure Access Opinions and Lifestyle Survey dataOther Secure Access OPN data cover modules run at various points from 1997-2019, on Census religion (SN 8078), cervical cancer screening (SN 8080), contact after separation (SN 8089), contraception (SN 8095), disability (SNs 8680 and 8096), general lifestyle (SN 8092), illness and activity (SN 8094), and non-resident parental contact (SN 8093). See Opinions and Lifestyle Survey: Secure Access for details. Main Topics:Each month's questionnaire consists of two elements: core questions, covering demographic information, are asked each month together with non-core questions that vary from month to month. The non-core questions for this month were: SunSmart (Module 327): this module was asked on behalf of Cancer Research UK to find out whether respondents had heard of SunSmart 2002 and what the main messages of the campaign were. This module has been run since February 2003 to monitor awareness levels pre and post-campaign. Financial capability (Module 336): the purpose of this module was to gain a general view of how respondents who have a mortgage or rent their property would cope with a change to their circumstances, such as an increase to their mortgage or rent payment or a rise in interest rates. It also asks all respondents about type and amount of debt and how the individual or family who have a mortgage or rent their property would cope with 'shock' changes to income. Disability monitoring (Module 363): the Special Licence version of this module is held under SN 6470. Use of HRT (Module 368): the National Health Service is interested in women's use of cancer screening services, in particular breast cancer screening and cervical cancer screening. The module also asks about the use of hormone replacement therapy (HRT). Road accidents (Module MAU): this module was asked on behalf of the Department for Transport which is interested in finding out about road accidents that people have been involved in. Travel horizons (Module MAV): this module was asked on behalf of the Department for Transport which is interested in groups of people who are less likely than others to travel and how this relates to work patterns and finding work. Multi-stage stratified random sample Face-to-face interview 2006 ADULTS AGE BREAST SCREENING BUSES BUSINESS OWNERSHIP BUSINESSES CARS CERVICAL SMEARS CHILDREN COHABITATION COMMUNITIES COMMUTING CONTRACEPTIVE DEVICES CREDIT CARD USE CYCLISTS DEBTS DISABILITIES ECONOMIC ACTIVITY EDUCATIONAL BACKGROUND EDUCATIONAL CERTIFI... EDUCATIONAL STATUS EMPLOYEES EMPLOYERS EMPLOYMENT EMPLOYMENT HISTORY EMPLOYMENT PROGRAMMES ETHNIC GROUPS EXPOSURE TO THE SUN FAMILY MEMBERS FINANCIAL COMMITMENTS FULL TIME EMPLOYMENT FURNISHED ACCOMMODA... GENDER General health and ... HEADS OF HOUSEHOLD HOME OWNERSHIP HOME SELLING HORMONE REPLACEMENT... HOUSE PRICES HOUSEHOLDS HOUSING TENURE HYSTERECTOMY ILL HEALTH INCOME INDUSTRIES INJURIES INSOLVENCIES INSURANCE CLAIMS INTEREST RATES Income JOB DESCRIPTION JOB HUNTING LANDLORDS LOANS LOCAL COMMUNITY FAC... MARITAL STATUS MEDICAL CARE MENOPAUSE MORTGAGE ARREARS MORTGAGES MOTORCYCLES Media NATIONAL IDENTITY OCCUPATIONS PARENTS PART TIME EMPLOYMENT PEDESTRIANS PERSONAL DEBT REPAY... POLICE SERVICES PUBLIC HEALTH RISKS PUBLIC INFORMATION PUBLIC TRANSPORT RENTED ACCOMMODATION RENTS ROAD ACCIDENTS ROAD USERS ROAD VEHICLES SAVINGS SHARED HOME OWNERSHIP SKIN CANCER SKIN TYPES SOCIAL HOUSING STATUS IN EMPLOYMENT STUDENT LOANS STUDENTS SUN PROTECTION SUNBATHING SUNBURN SUNTANNING SUPERVISORY STATUS Social behaviour an... Specific diseases TELEVISION TIED HOUSING TRAINS TRANSPORT TRAVEL TRAVELLING TIME Transport and travel UNDERGROUND RAILWAYS UNEMPLOYED UNFURNISHED ACCOMMO... UNWAGED WORKERS Wounds and injuries disorders and medic... property and invest...

Collection consists of data collected within the loan application form that the credit unions use to assess loan applications. The aim was to investigate if contemplation can improve the financial information that credit union loan applicants provide? Our rationale for focusing on this financial behavior in this group was four-fold. Firstly, people generally (see above; Santander, 2016) and this group specifically (as identified by staff at the credit union) have a tendency to underestimate their expenditure. Secondly, contemplation potentially encourages a degree of self-reflection, a process associated with greater self-control and self-regulation (Howell and Shepperd, 2013). This suggests that after contemplation, decision-making will be more thorough, detailed and personally beneficial (Yeung and Summerfield, 2012). In other words, contemplating expenditure may encourage people to give more accurate estimates of expenditure. Thirdly, staff used expenditure to help decide if the client could afford the loan they were applying for. Thus, a more accurate estimate of expenditure would benefit the staff in terms of the expediency of the loan application process. Finally, we propose that using such a sample was a more vigorous test of contemplation than occurred in our earlier studies. In that, the people who went to this credit union likely had more complex financial and social histories than the students and university staff who participated in Studies 1 and 2. For example, ~87% of credit loan applicants were in receipt of child benefit, 58% were not employed (vs. 5.1% National Average; Office for National Statistics, 2016), 63% had been in receipt of a social fund loan, and 67% have used high cost lenders (see Table 1). Unfortunately, an analysis of peoples financial histories and behaviors has identified a relationship between these demographic factors and poor financial management (i.e., ineffective planning for financial event, and having less self-efficacy and confidence in financial management; Money Advice Service, 2015), with financial illiteracy further related to poor financial outcomes (Hastings and Tejeda-Ashton, 2008). Thus, an intervention that improves financial estimations will be of benefit to credit union loan applicants and staff alike. With these points in mind we proposed the following applied hypothesis: Prompting credit union loan applicants to contemplate their expenditure would improve their estimates of expenditure. We expected that this improvement would occur in three areas: (i) Thoroughness: more expenditure information would be provided; (ii) Totals: larger estimates of expenditure, i.e., clients give an estimate of expenditure that more closely matches what they actually spend; (iii) Discrepancies between clients and staff : greater agreement between clients and staff for the above two measures.Despite personal debt being an ever increasing problem within our society the psychological understanding of debt and interventions to the problem remain elusive. The present project provides a novel solution by using insights from those with Obsessive-Compulsive Disorder, who are known to excessively monitor (eg, "Did I turn the oven off?"), and apply this to those who don’t monitor their finances. The research will examine which cognitive factors explain why debtors fail to adequately monitor their debt. Then examine debtors’ attentional biases with debt-related stimuli and how this relates to how they monitor their finances. This information will be used to modify how debtors interact with debt-related stimuli, and quantify its influence on financial behaviours. Finally, this will be applied to the design of a Manage Your Debt Application System (MYDAS) mobile phone intervention which aims to improve how debtors monitor their debt. This research will have the following implications: (1) Science: By providing an empirical understanding of the thought process of debtors and an intervention to change those thought processes key to debt. (2) Society: By providing new tools to identify problem debtors and interventions (MYDAS) the research will benefit debtors (reduce debt), creditors (repayment) and debt agencies. The data was collected within the loan application form that the credit union used to assess each applicants loan application request.

Running between 2019 and 2022, the project ‘Depleted by Debt? Focusing a gendered lens on climate resilience, credit and nutrition in Cambodia and South India’ has undertaken cutting-edge interdisciplinary research during the COVID-19 pandemic on some of the most pressing issues impacting rural communities today. The data collected - via quantitative surveys, semi-structured qualitative interviews, and photo-elicitation -evidences how household over-indebtedness needs to be understood and tackled in tandem with the climate crisis and the negative impacts these are both having on people’s health and well-being.Small-scale credit is exalted in mainstream development thinking as a key means of supporting women and their families in dealing with daily, ongoing, and often slow-onset climate disasters. Facing growing crises of agricultural productivity from droughts and floods, and taking primary responsibility for the nutritional wellbeing of their households, women are targeted as credit borrowers globally. Credit provisioning therefore speaks to the push for 'resilience' against climate disasters that is central to Sustainable Development Goal (SDG) 13, 'Take urgent action to combat climate change and its impacts', and which has serious implications for SDG 5 'Achieve gender equality and empower all women and girls' that prioritises the valuing and recognition of women's unpaid care and domestic work. How do we ensure, then, that 'climate resilience' does not come at the cost of women's emotional and bodily depletion through processes of household nutrition provisioning? This is the key concern motivating the project which asks: (1) In what ways is credit, as a form of climate resilience, shaping nutritional provisioning? (2) How are the dynamics of nutrition provisioning and credit-taking in a changing climate being experienced and visualised? (3) What are the gender and social reproductive dynamics of the climate-credit-nutrition nexus? (4) What lessons can be learned to deliver improved and more equitable credit provisioning and nutritional outcomes to households and communities affected by slow-onset climate disasters? The project is set within the political economy contexts of Cambodia and Tamil Nadu, India. Data have been collected via various methods and sampling procedures including quantitative surveys, qualitative interviews, and photo elicitation; further information about the collection methods is available in the document 85779_data_collection_methods.

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.

DOI Abstract copyright UK Data Service and data collection copyright owner. The aim of this survey was to provide information about the living standards of unemployed people and their families in Great Britain by looking at the implications of levels of income, savings and debts for the material living standards of the families concerned. A particular focus of interest was the extent to which living standards change during a spell of unemployment. The survey was designed to monitor changes that took place during the first three months of unemployment, and changes that took place during the subsequent year, from three to fifteen months after the first signing on. Main Topics: Variables Informants' work history and living standards before starting to sign on; changes in living standards during the first three months of signing on; the situation after three months of signing on; labour market experience between the three-month point and the fifteen-month point; changes in income, savings, and debt between the three-month point and the fifteen-month point; the evolution of living standards; the experience of unemployment in terms of psychological well-being. Measurement Scales Living standards; psychological well-being. Multi-stage stratified random sample two-stage Face-to-face interview 1983 1984 ADVICE AGE ALCOHOL USE ALCOHOLIC DRINKS ALLOTMENTS ANXIETY ASSETS ATTITUDES BANK ACCOUNTS BASIC NEEDS BED COVERINGS BEDROOMS BEREAVEMENT BINGO BONDS BOOK READERSHIP BUILDING MAINTENANCE CARPETS CENTRAL HEATING CHARITABLE ORGANIZA... CHILD BENEFITS CHILD CARE CHILD MINDING CHILDBIRTH CHILDREN CLOTHING COLOUR TELEVISION R... COMMUNITY CENTRES COMMUTING COMPANY CARS CONDITIONS OF EMPLO... CONFECTIONERY COOKING COSTS CREDIT CULTURAL PARTICIPATION Consumption and con... DEBTS DEPRESSION DISC RECORDINGS DISTANCE MEASUREMENT DOMESTIC APPLIANCES DOMESTIC RESPONSIBI... DRIVING ECONOMIC ACTIVITY ECONOMIC VALUE EDUCATIONAL FEES EDUCATIONAL GRANTS EDUCATIONAL VISITS ELECTRIC POWER SUPPLY EMOTIONAL STATES EMPLOYMENT EMPLOYMENT HISTORY EMPLOYMENT PROGRAMMES EMPLOYMENT SERVICES ENDOWMENT ASSURANCE EXERCISE PHYSICAL A... EXPENDITURE FAMILY COHESION FAMILY LIFE FAMILY MEMBERS FINANCIAL COMMITMENTS FINANCIAL INSTITUTIONS FINANCIAL RESOURCES FINANCIAL SUPPORT FINANCING FOOD FOOD AND NUTRITION FOOD SUPPLEMENTS FOSSIL FUELS FRIENDS FRINGE BENEFITS FRUIT FULL TIME EMPLOYMENT FURNITURE FURTHER EDUCATION GARDEN EQUIPMENT GARDENING GAS SUPPLY GENDER GIFTS GRANTS Great Britain HEADS OF HOUSEHOLD HEATING SYSTEMS HIRE PURCHASE HOLIDAYS HOLIDAYS ABROAD HOME BUYING HOME OWNERSHIP HOME SHARING HOUSEHOLD BUDGETS HOUSEHOLDS HOUSEWORK HOUSING HOUSING CONDITIONS HOUSING FINANCE HOUSING TENURE HUNGER INCOME INCOME TAX INDUSTRIES INSURANCE INSURANCE PREMIUMS INTERPERSONAL CONFLICT INVESTMENT JOB HUNTING KITCHENS LANDLORDS LEISURE TIME LEISURE TIME ACTIVI... LIBRARIES LIFE INSURANCE LISTENING LIVING CONDITIONS LOANS LODGERS MANAGERS MARITAL STATUS MARRIED WOMEN WORKERS MEALS MEAT MILK MORTGAGES MOTOR VEHICLES NEIGHBOURS OCCUPATIONAL PENSIONS OCCUPATIONS PARENT CHILD RELATI... PARENT PARTICIPATION PART TIME EMPLOYMENT PAWNSHOPS PENSION CONTRIBUTIONS PLEASURE POCKET MONEY POVERTY PRISONERS PRIVATE GARDENS PRIVATE PERSONAL PE... PURCHASING QUALITY OF LIFE RADIO LISTENING RADIO RECEIVERS RATES READING ACTIVITY REBATES REDUNDANCY PAY RELIGIOUS ATTENDANCE RENTED ACCOMMODATION RENTS RESIDENTIAL MOBILITY RESIDENTS OF INSTIT... ROOM SHARING ROOMS SATISFACTION SAVINGS SCHOOL MEALS SELF EMPLOYED SHARES SHOPPING SICK PERSONS SMOKING SOCIAL ACTIVITIES L... SOCIAL DISADVANTAGE SOCIAL HOUSING SOCIAL PROBLEMS SOCIAL SECURITY SOCIAL SECURITY BEN... SOCIAL SECURITY CON... SOCIAL SUPPORT SOCIAL WELFARE SPORT SPORT SPECTATORSHIP SPOUSE S ECONOMIC A... SPOUSE S OCCUPATION SPOUSE S WAGES SPOUSES STANDARD OF LIVING STATE RETIREMENT PE... STRESS PSYCHOLOGICAL STUDENTS SUPERVISORS Social conditions a... TAPE RECORDERS TAPE RECORDINGS TAX RELIEF TELEPHONES TELEVISION RECEIVERS TELEVISION VIEWING TEMPORARY EMPLOYMENT TIED HOUSING TOURIST ACCOMMODATION TRAVEL UNEMPLOYED UNEMPLOYMENT UNEMPLOYMENT BENEFITS Unemployment VEGETABLES VIDEO RECORDERS VISITS PERSONAL VISITS TO RECREATIO... VITAMINS VOLUNTARY ORGANIZAT... VOLUNTARY WORK WAGES YOUTH EMPLOYMENT

Explore the Saudi Arabia World Development Indicators dataset , including key indicators such as Access to clean fuels, Adjusted net enrollment rate, CO2 emissions, and more. Find valuable insights and trends for Saudi Arabia, Bahrain, Kuwait, Oman, Qatar, China, and India.

Indicator, Access to clean fuels and technologies for cooking, rural (% of rural population), Access to electricity (% of population), Adjusted net enrollment rate, primary, female (% of primary school age children), Adjusted net national income (annual % growth), Adjusted savings: education expenditure (% of GNI), Adjusted savings: mineral depletion (current US$), Adjusted savings: natural resources depletion (% of GNI), Adjusted savings: net national savings (current US$), Adolescents out of school (% of lower secondary school age), Adolescents out of school, female (% of female lower secondary school age), Age dependency ratio (% of working-age population), Agricultural methane emissions (% of total), Agriculture, forestry, and fishing, value added (current US$), Agriculture, forestry, and fishing, value added per worker (constant 2015 US$), Alternative and nuclear energy (% of total energy use), Annualized average growth rate in per capita real survey mean consumption or income, total population (%), Arms exports (SIPRI trend indicator values), Arms imports (SIPRI trend indicator values), Average working hours of children, working only, ages 7-14 (hours per week), Average working hours of children, working only, male, ages 7-14 (hours per week), Cause of death, by injury (% of total), Cereal yield (kg per hectare), Changes in inventories (current US$), Chemicals (% of value added in manufacturing), Child employment in agriculture (% of economically active children ages 7-14), Child employment in manufacturing, female (% of female economically active children ages 7-14), Child employment in manufacturing, male (% of male economically active children ages 7-14), Child employment in services (% of economically active children ages 7-14), Child employment in services, female (% of female economically active children ages 7-14), Children (ages 0-14) newly infected with HIV, Children in employment, study and work (% of children in employment, ages 7-14), Children in employment, unpaid family workers (% of children in employment, ages 7-14), Children in employment, wage workers (% of children in employment, ages 7-14), Children out of school, primary, Children out of school, primary, male, Claims on other sectors of the domestic economy (annual growth as % of broad money), CO2 emissions (kg per 2015 US$ of GDP), CO2 emissions (kt), CO2 emissions from other sectors, excluding residential buildings and commercial and public services (% of total fuel combustion), CO2 emissions from transport (% of total fuel combustion), Communications, computer, etc. (% of service exports, BoP), Condom use, population ages 15-24, female (% of females ages 15-24), Container port traffic (TEU: 20 foot equivalent units), Contraceptive prevalence, any method (% of married women ages 15-49), Control of Corruption: Estimate, Control of Corruption: Percentile Rank, Upper Bound of 90% Confidence Interval, Control of Corruption: Standard Error, Coverage of social insurance programs in 4th quintile (% of population), CPIA building human resources rating (1=low to 6=high), CPIA debt policy rating (1=low to 6=high), CPIA policies for social inclusion/equity cluster average (1=low to 6=high), CPIA public sector management and institutions cluster average (1=low to 6=high), CPIA quality of budgetary and financial management rating (1=low to 6=high), CPIA transparency, accountability, and corruption in the public sector rating (1=low to 6=high), Current education expenditure, secondary (% of total expenditure in secondary public institutions), DEC alternative conversion factor (LCU per US$), Deposit interest rate (%), Depth of credit information index (0=low to 8=high), Diarrhea treatment (% of children under 5 who received ORS packet), Discrepancy in expenditure estimate of GDP (current LCU), Domestic private health expenditure per capita, PPP (current international $), Droughts, floods, extreme temperatures (% of population, average 1990-2009), Educational attainment, at least Bachelor's or equivalent, population 25+, female (%) (cumulative), Educational attainment, at least Bachelor's or equivalent, population 25+, male (%) (cumulative), Educational attainment, at least completed lower secondary, population 25+, female (%) (cumulative), Educational attainment, at least completed primary, population 25+ years, total (%) (cumulative), Educational attainment, at least Master's or equivalent, population 25+, male (%) (cumulative), Educational attainment, at least Master's or equivalent, population 25+, total (%) (cumulative), Electricity production from coal sources (% of total), Electricity production from nuclear sources (% of total), Employers, total (% of total employment) (modeled ILO estimate), Employment in industry (% of total employment) (modeled ILO estimate), Employment in services, female (% of female employment) (modeled ILO estimate), Employment to population ratio, 15+, male (%) (modeled ILO estimate), Employment to population ratio, ages 15-24, total (%) (national estimate), Energy use (kg of oil equivalent per capita), Export unit value index (2015 = 100), Exports of goods and services (% of GDP), Exports of goods, services and primary income (BoP, current US$), External debt stocks (% of GNI), External health expenditure (% of current health expenditure), Female primary school age children out-of-school (%), Female share of employment in senior and middle management (%), Final consumption expenditure (constant 2015 US$), Firms expected to give gifts in meetings with tax officials (% of firms), Firms experiencing losses due to theft and vandalism (% of firms), Firms formally registered when operations started (% of firms), Fixed broadband subscriptions, Fixed telephone subscriptions (per 100 people), Foreign direct investment, net outflows (% of GDP), Forest area (% of land area), Forest area (sq. km), Forest rents (% of GDP), GDP growth (annual %), GDP per capita (constant LCU), GDP per unit of energy use (PPP $ per kg of oil equivalent), GDP, PPP (constant 2017 international $), General government final consumption expenditure (current LCU), GHG net emissions/removals by LUCF (Mt of CO2 equivalent), GNI growth (annual %), GNI per capita (constant LCU), GNI, PPP (current international $), Goods and services expense (current LCU), Government Effectiveness: Percentile Rank, Government Effectiveness: Percentile Rank, Lower Bound of 90% Confidence Interval, Government Effectiveness: Standard Error, Gross capital formation (annual % growth), Gross capital formation (constant 2015 US$), Gross capital formation (current LCU), Gross fixed capital formation, private sector (% of GDP), Gross intake ratio in first grade of primary education, male (% of relevant age group), Gross intake ratio in first grade of primary education, total (% of relevant age group), Gross national expenditure (current LCU), Gross national expenditure (current US$), Households and NPISHs Final consumption expenditure (constant LCU), Households and NPISHs Final consumption expenditure (current US$), Households and NPISHs Final consumption expenditure, PPP (constant 2017 international $), Households and NPISHs final consumption expenditure: linked series (current LCU), Human capital index (HCI) (scale 0-1), Human capital index (HCI), male (scale 0-1), Immunization, DPT (% of children ages 12-23 months), Import value index (2015 = 100), Imports of goods and services (% of GDP), Incidence of HIV, ages 15-24 (per 1,000 uninfected population ages 15-24), Incidence of HIV, all (per 1,000 uninfected population), Income share held by highest 20%, Income share held by lowest 20%, Income share held by third 20%, Individuals using the Internet (% of population), Industry (including construction), value added (constant LCU), Informal payments to public officials (% of firms), Intentional homicides, male (per 100,000 male), Interest payments (% of expense), Interest rate spread (lending rate minus deposit rate, %), Internally displaced persons, new displacement associated with conflict and violence (number of cases), International tourism, expenditures for passenger transport items (current US$), International tourism, expenditures for travel items (current US$), Investment in energy with private participation (current US$), Labor force participation rate for ages 15-24, female (%) (modeled ILO estimate), Development

Saudi Arabia, Bahrain, Kuwait, Oman, Qatar, China, India Follow data.kapsarc.org for timely data to advance energy economics research..

Credit Card Spending in New Zealand decreased to 6450 NZD Million in May from 6460 NZD Million in April of 2025. This dataset provides - New Zealand Credit Card Spending- actual values, historical data, forecast, chart, statistics, economic calendar and news.

External Debt in Pakistan decreased to 130310 USD Million in the first quarter of 2025 from 130921 USD Million in the fourth quarter of 2024. This dataset provides - Pakistan External Debt - actual values, historical data, forecast, chart, statistics, economic calendar and news.