The benchmark interest rate in the United States was last recorded at 4.25 percent. This dataset provides the latest reported value for - United States Fed Funds Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The benchmark interest rate in Canada was last recorded at 2.50 percent. This dataset provides - Canada Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Abstract

The Survey on Interest Rate Controls 2020 was conducted as a World Bank Group study on interest rate controls (IRCs) in lending and deposit markets around the world. The study aims to identify the different types of formal (or de jure) controls, the countries that apply then, how they implement them, and the reasons for doing so. The objective of the study is to advance knowledge on this topic by providing an evidence base for investigating the impact of IRCs on economic outcomes.

The survey investigates present IRCs in each surveyed country, the reasons why they have been applied, the framework and resources associated with their application and the details as to their level and functioning. The focus is on legal forms of control (i.e. codified into law) as opposed to de facto controls. The new database on interest rate controls, a popular form of financial repression is based on a survey of 108 countries, representing 88 percent of global gross domestic product. The interest rate controls presented in this dataset were in effect in 2019.

Geographic coverage

Global Survey, covering 108 countries, representing 88 percent of global GDP.

Analysis unit

Regulation at the national level.

Universe

Banking supervisors and Local Banking Associations.

Kind of data

Sample survey data [ssd]

Mode of data collection

Mail Questionnaire [mail]

Research instrument

Bank supervisors and banking associations were provided with a standard excel file with five parts. The survey was structured in five parts, each placed in a different excel sheet. Part A: Introduction. Countries with no IRCs in place were asked to only answer this sheet and leave the rest blank. Part B: Presented the definitions of controls, institutions, products and additional aspects that will be covered in the survey. Part C: Introduced a set of qualitative questions to describe the IRCs in place. Part D: Displayed a set of tables to quantitatively describe the IRCs in place. Part E: Laid out the final set of questions, covering sanctions and control mechanisms that support the IRCs' enforcement. The questionnaire is provided in the Documentation section in pdf and excel.

The Survey on Interest Rate Controls 2020 was conducted as a World Bank Group study on interest rate controls (IRCs) in lending and deposit markets around the world. The study aims to identify the different types of formal (or de jure) controls, the countries that apply then, how they implement them, and the reasons for doing so. The objective of the study is to advance knowledge on this topic by providing an evidence base for investigating the impact of IRCs on economic outcomes.The survey investigates present IRCs in each surveyed country, the reasons why they have been applied, the framework and resources associated with their application and the details as to their level and functioning. The focus is on legal forms of control (i.e. codified into law) as opposed to de facto controls. The new database on interest rate controls, a popular form of financial repression is based on a survey of 108 countries, representing 88 percent of global gross domestic product. The interest rate controls presented in this dataset were in effect in 2019.

30 Year Mortgage Rate in the United States increased to 6.34 percent in October 2 from 6.30 percent in the previous week. This dataset includes a chart with historical data for the United States 30 Year Mortgage Rate.

The benchmark interest rate in Japan was last recorded at 0.50 percent. This dataset provides - Japan Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Interest rates on deposits in the following foreign currencies: the US dollar, the euro, the British pound and the Swiss franc, for the following periods: 1 month, 3 months, 6 months and 12 months.

Historical dataset of the 12 month LIBOR rate back to 1986. The London Interbank Offered Rate is the average interest rate at which leading banks borrow funds from other banks in the London market. LIBOR is the most widely used global "benchmark" or reference rate for short term interest rates.

Lending Club offers peer-to-peer (P2P) loans through a technological platform for various personal finance purposes and is today one of the companies that dominate the US P2P lending market. The original dataset is publicly available on Kaggle and corresponds to all the loans issued by Lending Club between 2007 and 2018. The present version of the dataset is for constructing a granting model, that is, a model designed to make decisions on whether to grant a loan based on information available at the time of the loan application. Consequently, our dataset only has a selection of variables from the original one, which are the variables known at the moment the loan request is made. Furthermore, the target variable of a granting model represents the final status of the loan, that are "default" or "fully paid". Thus, we filtered out from the original dataset all the loans in transitory states. Our dataset comprises 1,347,681 records or obligations (approximately 60% of the original) and it was also cleaned for completeness and consistency (less than 1% of our dataset was filtered out).

TARGET VARIABLE

The dataset includes a target variable based on the final resolution of the credit: the default category corresponds to the event charged off and the non-default category to the event fully paid. It does not consider other values in the loan status variable since this variable represents the state of the loan at the end of the considered time window. Thus, there are no loans in transitory states. The original dataset includes the target variable “loan status”, which contains several categories ('Fully Paid', 'Current', 'Charged Off', 'In Grace Period', 'Late (31-120 days)', 'Late (16-30 days)', 'Default'). However, in our dataset, we just consider loans that are either “Fully Paid” or “Default” and transform this variable into a binary variable called “Default”, with a 0 for fully paid loans and a 1 for defaulted loans.

EXPLANATORY VARIABLES

The explanatory variables that we use correspond only to the information available at the time of the application. Variables such as the interest rate, grade, or subgrade are generated by the company as a result of a credit risk assessment process, so they were filtered out from the dataset as they must not be considered in risk models to predict the default in granting of credit.

FULL LIST OF VARIABLES

Loan identification variables:

• id: Loan id (unique identifier).

• issue_d: Month and year in which the loan was approved.

Quantitative variables:

• revenue: Borrower's self-declared annual income during registration.

• dti_n: Indebtedness ratio for obligations excluding mortgage. Monthly information. This ratio has been calculated considering the indebtedness of the whole group of applicants. It is estimated as the ratio calculated using the co-borrowers’ total payments on the total debt obligations divided by the co-borrowers’ combined monthly income.

• loan_amnt: Amount of credit requested by the borrower.

• fico_n: Defined between 300 and 850, reported by Fair Isaac Corporation as a risk measure based on historical credit information reported at the time of application. This value has been calculated as the average of the variables “fico_range_low” and “fico_range_high” in the original dataset.

• experience_c: Binary variable that indicates whether the borrower is new to the entity. This variable is constructed from the credit date of the previous obligation in LC and the credit date of the current obligation; if the difference between dates is positive, it is not considered as a new experience with LC.

Categorical variables:

• emp_length: Categorical variable with the employment length of the borrower (includes the no information category)

• purpose: Credit purpose category for the loan request.

• home_ownership_n: Homeownership status provided by the borrower in the registration process. Categories defined by LC: “mortgage”, “rent”, “own”, “other”, “any”, “none”. We merged the categories “other”, “any” and “none” as “other”.

• addr_state: Borrower's residence state from the USA.

• zip_code: Zip code of the borrower's residence.

Textual variables

• title: Title of the credit request description provided by the borrower.

• desc: Description of the credit request provided by the borrower.

We cleaned the textual variables. First, we removed all those descriptions that contained the default description provided by Lending Club on its web form (“Tell your story. What is your loan for?”). Moreover, we removed the prefix “Borrower added on DD/MM/YYYY >” from the descriptions to avoid any temporal background on them. Finally, as these descriptions came from a web form, we substituted all the HTML elements by their character (e.g. “&” was substituted by “&”, “<” was substituted by “<”, etc.).

RELATED WORKS

This dataset has been used in the following academic articles:

• Sanz-Guerrero, M. Arroyo, J. (2024). Credit Risk Meets Large Language Models: Building a Risk Indicator from Loan Descriptions in P2P Lending. arXiv preprint arXiv:2401.16458. https://doi.org/10.48550/arXiv.2401.16458
• Ariza-Garzón, M.J., Arroyo, J., Caparrini, A., Segovia-Vargas, M.J. (2020). Explainability of a machine learning granting scoring model in peer-to-peer lending. IEEE Access 8, 64873 - 64890. https://doi.org/10.1109/ACCESS.2020.2984412

This dataset contains year, quarter and interest rate range wise number of accounts, credit limit and outstanding amount of scheduled commercial banks

Note: 1. From the year 2020–21(Q4) onward, the interest rates classifications have been updated 2. Data excludes Regional Rural Banks

The benchmark interest rate in the United Kingdom was last recorded at 4 percent. This dataset provides - United Kingdom Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate in China was last recorded at 3 percent. This dataset provides the latest reported value for - China Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This table contains 102 series, with data starting from 2013, and some select series starting from 2016. This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada), Components (51 items: Total, funds advanced, residential mortgages, insured; Variable rate, insured; Fixed rate, insured, less than 1 year; Fixed rate, insured, from 1 to less than 3 years; ...), and Unit of measure (2 items: Dollars; Interest rate). For additional clarification on the component dimension, please visit the OSFI website for the Report on New and Existing Lending.

This table contains 71 series, with data starting from 1934 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Rates (71 items: Bank rate; last Tuesday or last Thursday; Bank rate; Chartered bank administered interest rates - prime business; Chartered bank - consumer loan rate ...).

GENERAL INFORMATION

Title of Dataset: A dataset from a survey investigating disciplinary differences in data citation

Date of data collection: January to March 2022

Collection instrument: SurveyMonkey

Funding: Alfred P. Sloan Foundation

SHARING/ACCESS INFORMATION

Licenses/restrictions placed on the data: These data are available under a CC BY 4.0 license

Links to publications that cite or use the data:

Gregory, K., Ninkov, A., Ripp, C., Peters, I., & Haustein, S. (2022). Surveying practices of data citation and reuse across disciplines. Proceedings of the 26th International Conference on Science and Technology Indicators. International Conference on Science and Technology Indicators, Granada, Spain. https://doi.org/10.5281/ZENODO.6951437

Gregory, K., Ninkov, A., Ripp, C., Roblin, E., Peters, I., & Haustein, S. (2023). Tracing data:
A survey investigating disciplinary differences in data citation. Zenodo. https://doi.org/10.5281/zenodo.7555266

DATA & FILE OVERVIEW

File List

• Filename: MDCDatacitationReuse2021Codebook.pdf
  Codebook
• Filename: MDCDataCitationReuse2021surveydata.csv
  Dataset format in csv
• Filename: MDCDataCitationReuse2021surveydata.sav
  Dataset format in SPSS
• Filename: MDCDataCitationReuseSurvey2021QNR.pdf
  Questionnaire

Additional related data collected that was not included in the current data package: Open ended questions asked to respondents

METHODOLOGICAL INFORMATION

Description of methods used for collection/generation of data:

The development of the questionnaire (Gregory et al., 2022) was centered around the creation of two main branches of questions for the primary groups of interest in our study: researchers that reuse data (33 questions in total) and researchers that do not reuse data (16 questions in total). The population of interest for this survey consists of researchers from all disciplines and countries, sampled from the corresponding authors of papers indexed in the Web of Science (WoS) between 2016 and 2020.

Received 3,632 responses, 2,509 of which were completed, representing a completion rate of 68.6%. Incomplete responses were excluded from the dataset. The final total contains 2,492 complete responses and an uncorrected response rate of 1.57%. Controlling for invalid emails, bounced emails and opt-outs (n=5,201) produced a response rate of 1.62%, similar to surveys using comparable recruitment methods (Gregory et al., 2020).

Methods for processing the data:

Results were downloaded from SurveyMonkey in CSV format and were prepared for analysis using Excel and SPSS by recoding ordinal and multiple choice questions and by removing missing values.

Instrument- or software-specific information needed to interpret the data:

The dataset is provided in SPSS format, which requires IBM SPSS Statistics. The dataset is also available in a coded format in CSV. The Codebook is required to interpret to values.

DATA-SPECIFIC INFORMATION FOR: MDCDataCitationReuse2021surveydata

Number of variables: 94

Number of cases/rows: 2,492

Missing data codes: 999 Not asked

Refer to MDCDatacitationReuse2021Codebook.pdf for detailed variable information.

The benchmark interest rate in Russia was last recorded at 17 percent. This dataset provides the latest reported value for - Russia Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The benchmark interest rate in Norway was last recorded at 4 percent. This dataset provides the latest reported value for - Norway Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

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: Financial capability (Module 336): this module was asked on behalf of the Financial Services Authority. The purpose of the 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). Older workers (Module MAW): this module was asked on behalf of the Centre for Research into the Older Workforce (CROW) and examines work based training opportunities for older workers. Multi-stage stratified random sample Face-to-face interview

India Repo Rate data was reported at 6.000 % pa in 18 May 2025. This stayed constant from the previous number of 6.000 % pa for 17 May 2025. India Repo Rate data is updated daily, averaging 6.250 % pa from Apr 2001 (Median) to 18 May 2025, with 8788 observations. The data reached an all-time high of 7.500 % pa in 01 Jun 2015 and a record low of 4.000 % pa in 03 May 2022. India Repo Rate data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under High Frequency Database’s Lending Rates – Table IN.MB001: Bank Interest Rate. [COVID-19-IMPACT]

The benchmark interest rate In the Euro Area was last recorded at 2.15 percent. This dataset provides - Euro Area Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.