The benchmark interest rate in the United States was last recorded at 4.50 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 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.

United States Interest Rates: 12 Months Expectation: Same data was reported at 22.500 % in Apr 2025. This records a decrease from the previous number of 23.300 % for Mar 2025. United States Interest Rates: 12 Months Expectation: Same data is updated monthly, averaging 29.700 % from Jun 1987 (Median) to Apr 2025, with 455 observations. The data reached an all-time high of 43.700 % in Dec 1997 and a record low of 13.600 % in Mar 1989. United States Interest Rates: 12 Months Expectation: Same data remains active status in CEIC and is reported by The Conference Board. The data is categorized under Global Database’s United States – Table US.H051: Consumer Confidence Index: Interest Rate Expectation. [COVID-19-IMPACT]

The benchmark interest rate in Sweden was last recorded at 2 percent. This dataset provides the latest reported value for - Sweden Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

United States Interest Rates: 12 Months Expectation: Higher data was reported at 56.100 % in Apr 2025. This records an increase from the previous number of 53.400 % for Mar 2025. United States Interest Rates: 12 Months Expectation: Higher data is updated monthly, averaging 55.200 % from Jun 1987 (Median) to Apr 2025, with 455 observations. The data reached an all-time high of 79.900 % in Mar 1989 and a record low of 23.400 % in Oct 2001. United States Interest Rates: 12 Months Expectation: Higher data remains active status in CEIC and is reported by The Conference Board. The data is categorized under Global Database’s United States – Table US.H051: Consumer Confidence Index: Interest Rate Expectation. [COVID-19-IMPACT]

While higher interest rates increase the cost of credit financing for businesses, this study finds that the direct impact of this traditional credit transmission mechanism on corporate bankruptcy risk is limited. Instead, our research reveals that changes in corporate behavior induced by rising debt financing costs are the root cause of bankruptcy risk. In the short term, an increase in interest rates drives businesses to substitute supply chain financing for credit financing in pursuit of profit maximization. This mismatch of short-term debt and long-term investments undermines the sustainability of the supply chain, ultimately reducing financial security—sacrificing safety for profitability. In the long term, higher interest rates exacerbate the overcapacity problem in industries, increasing the unsustainability of the production and sales balance. Using data from China’s construction industry, this study empirically tests these findings and, based on the main conclusions, provides policy suggestions regarding the long- and short-term effects of monetary policy on the sustainable development of China’s construction industry: (1) focus on short-term interest rate risks and be vigilant against commercial credit bubbles; (2) long-term monetary policy should prioritize industrial structure optimization.

This dataset contains news headlines relevant to key forex pairs: AUDUSD, EURCHF, EURUSD, GBPUSD, and USDJPY. The data was extracted from reputable platforms Forex Live and FXstreet over a period of 86 days, from January to May 2023. The dataset comprises 2,291 unique news headlines. Each headline includes an associated forex pair, timestamp, source, author, URL, and the corresponding article text. Data was collected using web scraping techniques executed via a custom service on a virtual machine. This service periodically retrieves the latest news for a specified forex pair (ticker) from each platform, parsing all available information. The collected data is then processed to extract details such as the article's timestamp, author, and URL. The URL is further used to retrieve the full text of each article. This data acquisition process repeats approximately every 15 minutes.

To ensure the reliability of the dataset, we manually annotated each headline for sentiment. Instead of solely focusing on the textual content, we ascertained sentiment based on the potential short-term impact of the headline on its corresponding forex pair. This method recognizes the currency market's acute sensitivity to economic news, which significantly influences many trading strategies. As such, this dataset could serve as an invaluable resource for fine-tuning sentiment analysis models in the financial realm.

We used three categories for annotation: 'positive', 'negative', and 'neutral', which correspond to bullish, bearish, and hold sentiments, respectively, for the forex pair linked to each headline. The following Table provides examples of annotated headlines along with brief explanations of the assigned sentiment.

Examples of Annotated Headlines


    Forex Pair
    Headline
    Sentiment
    Explanation




    GBPUSD 
    Diminishing bets for a move to 12400 
    Neutral
    Lack of strong sentiment in either direction


    GBPUSD 
    No reasons to dislike Cable in the very near term as long as the Dollar momentum remains soft 
    Positive
    Positive sentiment towards GBPUSD (Cable) in the near term


    GBPUSD 
    When are the UK jobs and how could they affect GBPUSD 
    Neutral
    Poses a question and does not express a clear sentiment


    JPYUSD
    Appropriate to continue monetary easing to achieve 2% inflation target with wage growth 
    Positive
    Monetary easing from Bank of Japan (BoJ) could lead to a weaker JPY in the short term due to increased money supply


    USDJPY
    Dollar rebounds despite US data. Yen gains amid lower yields 
    Neutral
    Since both the USD and JPY are gaining, the effects on the USDJPY forex pair might offset each other


    USDJPY
    USDJPY to reach 124 by Q4 as the likelihood of a BoJ policy shift should accelerate Yen gains 
    Negative
    USDJPY is expected to reach a lower value, with the USD losing value against the JPY


    AUDUSD

RBA Governor Lowe’s Testimony High inflation is damaging and corrosive

    Positive
    Reserve Bank of Australia (RBA) expresses concerns about inflation. Typically, central banks combat high inflation with higher interest rates, which could strengthen AUD.

Moreover, the dataset includes two columns with the predicted sentiment class and score as predicted by the FinBERT model. Specifically, the FinBERT model outputs a set of probabilities for each sentiment class (positive, negative, and neutral), representing the model's confidence in associating the input headline with each sentiment category. These probabilities are used to determine the predicted class and a sentiment score for each headline. The sentiment score is computed by subtracting the negative class probability from the positive one.

EGPB - An Event-based Gold Price Benchmark Dataset

This benchmark dataset consists of 8030 rows and 36 variables sourced from multiple credible economic websites, covering a period from January 2001 to December 2022. This dataset can be utilized to predict gold prices specifically or to aid any economic field that is influenced by the variables in this dataset.

Key variables & Features include:

• Previous gold prices

• Future gold prices with predictions for one day, one week, and one month

• Oil prices

• Standard & Poor's 500 Index (S&P 500)

• Dow Jones Industrial (DJI)

• US dollar index

• US treasury

• Inflation rate

• Consumer price index (CPI)

• Federal funds rate

• Silver prices

• Copper prices

• Iron prices

• Platinum prices

• Palladium prices

Additionally, the dataset considers global events that may impact gold prices, which were categorized into groups and collected from three distinct sources: the Al-Jazeera website spanning from 2022 to 2019, the Investing website spanning from 2018 to 2016, and the Yahoo Finance website spanning from 2007 to 2001.

These events data were then divided into multiple groups:

• Economic data

• Politics

• logistics

• Oil

• OPEC

• Dollar currency

• Sterling pound currency

• Russian ruble currency

• Yen currency

• Euro currency

• US stocks

• Global stocks

• Inflation

• Job reports

• Unemployment rates

• CPI rate

• Interest rates

• Bonds

These events were encoded using a numeric value, where 0 represented no events, 1 represented low events, 2 represented high events, 3 represented stable events, 4 represented unstable events, and 5 represented events that were observed during the day but had no effect on the dataset.

Cite this dataset: Farah Mansour and Wael Etaiwi, "EGPBD: An Event-based Gold Price Benchmark Dataset," 2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), Tenerife, Canary Islands, Spain, 2023, pp. 1-7, doi: 10.1109/ICECCME57830.2023.10252987.

@INPROCEEDINGS{10252987, author={Mansour, Farah and Etaiwi, Wael}, booktitle={2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)}, title={EGPBD: An Event-based Gold Price Benchmark Dataset}, year={2023}, volume={}, number={}, pages={1-7}, doi={10.1109/ICECCME57830.2023.10252987}}

The benchmark interest rate in Brazil was last recorded at 15 percent. This dataset provides - Brazil Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Indonesia Banking Survey: Loan Interest Rate: Whole Year Estimation: in USD: Investment data was reported at 6.566 % in Mar 2025. This records an increase from the previous number of 6.446 % for Dec 2024. Indonesia Banking Survey: Loan Interest Rate: Whole Year Estimation: in USD: Investment data is updated quarterly, averaging 6.330 % from Mar 2012 (Median) to Mar 2025, with 53 observations. The data reached an all-time high of 6.961 % in Sep 2023 and a record low of 4.454 % in Mar 2022. Indonesia Banking Survey: Loan Interest Rate: Whole Year Estimation: in USD: Investment data remains active status in CEIC and is reported by Bank Indonesia. The data is categorized under Indonesia Premium Database’s Business and Economic Survey – Table ID.SE003: Banking Survey: Interest Rate. [COVID-19-IMPACT]

Monetary policy is generally regarded as a central element in the attempts of policy makers to attenuate business-cycle fluctuations. According to the New Keynesian paradigm, central banks are able to stimulate or depress aggregate demand in the short run by adjusting their nominal interest rate targets. The effects of interest rate changes on aggregate consumption, the largest component of aggregate demand, are well understood in the context of this paradigm, on which the canonical "workhorse'' model used in monetary policy analysis is grounded. A key feature of the model is that aggregate consumption is fully described by the amount of goods consumed by a representative household. A decline in the policy rate for instance implies that the real interest rate declines, the representative household saves less and hence increase its demand for consumption. At the same time, general equilibrium effects let labour income grow causing consumption to increase further. However, the mechanism outlined above ignores a considerable amount of empirically-observed heterogeneity among households. For example, households with a higher earnings elasticity to interest rate changes benefit more from a rate cut than those with a lower elasticity; households with large debt positions are at a relative advantage over households with large bond holdings; and households with low exposure to inflation are relatively better off than those holding a sizeable amount of nominal assets. As a result, the contribution to the aggregate consumption response differs substantially across households, implying that monetary expansions and tightenings produce relative "winners'' and relative "losers''. The aim of the project laid out in this proposal is to give a disaggregated account of the heterogeneous effects of monetary-policy induced interest rate changes on household consumption and a detailed analysis of the channels underlying them. Additionally, it seeks to draw conclusions about the determinants of the strength of the transmission mechanism of monetary policy. To do so, it relies on a large panel comprising detailed data from the universe of all households residing in Norway between 1993 and 2015 supplemented with additional micro-data provided by the European Commission. I will be assisted by two project partners, Pascal Paul who is a member of the Research Department of the Federal Reserve Bank of San Francisco and Martin Holm who is affiliated with the Research Unit of Statistics Norway and the University of Oslo. In addition, I would like to collaborate with and help train a doctoral student based at the University of Lausanne on this project. Existing empirical studies of the consumption response to monetary policy at the micro level rely on survey data. Therefore, they are subject to a number of severe data limitations. The surveys employed typically have either no or only a short panel dimension, suffer from attrition, include only limited information on income and wealth, are top-coded, and contain a significant amount of measurement error. The administrative data set provided to us by Statistics Norway suffers from none of these issues, implying that we are in a unique position to evaluate the household-level effects of policy rate changes. In a first step, we use forecasts published by the Norwegian central bank to derive monetary policy shocks that are robust to the simultaneity problem inherent in the identification of the effects of monetary policy following Romer and Romer (2004). We then confront the micro-data with the estimated shocks to study the consumption response along different segments of the income and wealth distribution and to test the importance of heterogeneity in labour earnings, financial income, liquid assets, inflation exposure and interest rate exposure among others. The findings will be of high relevance as they will not only allow us to evaluate channels hypothesised in the analytical literature, improve our understanding of the monetary policy transmission mechanism and its distributional consequences but also serve as a benchmark for structural models built both by theorists and practitioners.

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.

Key information about India Long Term Interest Rate

• India Government Securities Yield: 10 Years was reported at 6.71 % pa in Jan 2025, compared with 6.78 % pa in the previous month.
• India Long Term Interest Rate data is updated monthly, available from May 1996 to Jan 2025.
• The data reached an all-time high of 13.96 % pa in May 1996 and a record low of 5.11 % pa in Oct 2003.
• Long Term Interest Rate is reported by reported by Reserve Bank of India.

• In the latest reports, India Short Term Interest Rate: Month End: India: MIBOR: 3 Months was reported at 7.19 % pa in Jun 2023.
• The cash rate (Policy Rate: Month End: Repo Rate) was set at 6.50 % pa in Jan 2025.
• India Exchange Rate against USD averaged 82.23 (USD/INR) in Jun 2023.

Stock Price Time Series for Japan Real Estate Investment Corp. Japan Real Estate Investment Corporation (the "Company") was established on May 11, 2001 pursuant to Japan's Act on Investment Trusts and Investment Corporations ("ITA"). The Company was listed on the real estate investment trust market of the Tokyo Stock Exchange ("TSE") on September 10, 2001 (Securities Code: 8952). Since its IPO, the size of the Company's assets (total acquisition price) has grown steadily, expanding from 92.8 billion yen to 1,167.7 billion yen as of March 31, 2025. Over the same period, the Company's portfolio has also increased from 20 properties to 77 properties. During the March 2025 period (October 1, 2024 to March 31, 2025), the Japanese economy continued to demonstrate a gradual recovery, despite some lingering stagnation in capital investment and personal consumption due to inflation and other factors. On the other hand, given the policy rate hikes by the Bank of Japan, the shift in global interest rates to a lowering phase, the impact of U.S. policy trends, such as trade policy and other factors, interest rate trends, overseas political and economic developments, and price trends, including resource prices, will continue to bear watching. In the office leasing market, demand continues to grow for leases driven by business expansion and relocations aimed at improving location. As a result, the vacancy rate in central Tokyo continues to decline gradually. In addition, rent levels are rising at an accelerating rate. In light of the prevailing conditions in the leasing market, the Company is striving to attract new tenants through strategic leasing activities and to further enhance the satisfaction level of existing tenants by adding value to its portfolio properties with the aim of maintaining and improving the occupancy rate and realizing sustainable income growth across the entire portfolio. In the real estate trading market, despite the Bank of Japan normalizing its monetary policy, the appetite for property acquisition among both domestic and foreign investors remains firm, backed ma

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]

Begin-Period-Cashflow Time Series for Japan Real Estate Investment Corp. Japan Real Estate Investment Corporation (the "Company") was established on May 11, 2001 pursuant to Japan's Act on Investment Trusts and Investment Corporations ("ITA"). The Company was listed on the real estate investment trust market of the Tokyo Stock Exchange ("TSE") on September 10, 2001 (Securities Code: 8952). Since its IPO, the size of the Company's assets (total acquisition price) has grown steadily, expanding from 92.8 billion yen to 1,167.7 billion yen as of March 31, 2025. Over the same period, the Company's portfolio has also increased from 20 properties to 77 properties. During the March 2025 period (October 1, 2024 to March 31, 2025), the Japanese economy continued to demonstrate a gradual recovery, despite some lingering stagnation in capital investment and personal consumption due to inflation and other factors. On the other hand, given the policy rate hikes by the Bank of Japan, the shift in global interest rates to a lowering phase, the impact of U.S. policy trends, such as trade policy and other factors, interest rate trends, overseas political and economic developments, and price trends, including resource prices, will continue to bear watching. In the office leasing market, demand continues to grow for leases driven by business expansion and relocations aimed at improving location. As a result, the vacancy rate in central Tokyo continues to decline gradually. In addition, rent levels are rising at an accelerating rate. In light of the prevailing conditions in the leasing market, the Company is striving to attract new tenants through strategic leasing activities and to further enhance the satisfaction level of existing tenants by adding value to its portfolio properties with the aim of maintaining and improving the occupancy rate and realizing sustainable income growth across the entire portfolio. In the real estate trading market, despite the Bank of Japan normalizing its monetary policy, the appetite for property acquisition among both domestic and foreign investors remains firm, backed ma

The benchmark interest rate in Mexico was last recorded at 7.75 percent. This dataset provides - Mexico Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Abstract

To educate consumers about responsible use of financial products, many governments, non-profit organizations and financial institutions have started to provide financial literacy courses. However, participation rates for non-compulsory financial education programs are typically extremely low.

Researchers from the World Bank conducted randomized experiments around a large-scale financial literacy course in Mexico City to understand the reasons for low take-up among a general population, and to measure the impact of this financial education course. The free, 4-hour financial literacy course was offered by a major financial institution and covered savings, retirement, and credit use. Motivated by different theoretical and logistics reasons why individuals may not attend training, researchers randomized the treatment group into different subgroups, which received incentives designed to provide evidence on some key barriers to take-up. These incentives included monetary payments for attendance equivalent to $36 or $72 USD, a one-month deferred payment of $36 USD, free cost transportation to the training location, and a video CD with positive testimonials about the training.

A follow-up survey conducted on clients of financial institutions six months after the course was used to measure the impacts of the training on financial knowledge, behaviors and outcomes, all relating to topics covered in the course.

The baseline dataset documented here is administrative data received from a screener that was used to get people to enroll in the financial course. The follow-up dataset contains data from the follow-up questionnaire.

Geographic coverage

Mexico City

Analysis unit

-Individuals

Universe

Participants in a financial education evaluation

Kind of data

Sample survey data [ssd]

Sampling procedure

Researchers used three different approaches to obtain a sample for the experiment.

The first one was to send 40,000 invitation letters from a collaborating financial institution asking about interest in participating. However, only 42 clients (0.1 percent) expressed interest.

The second approach was to advertise through Facebook, with an ad displayed 16 million times to individuals residing in Mexico City, receiving 119 responses.

The third approach was to conduct screener surveys on streets in Mexico City and outside branches of the partner institution. Together this yielded a total sample of 3,503 people. Researchers divided this sample into a control group of 1,752 individuals, and a treatment group of 1,751 individuals, using stratified randomization. A key variable used in stratification was whether or not individuals were financial institution clients. The analysis of treatment impacts is based on the sample of 2,178 individuals who were financial institution clients.

The treatment group received an invitation to participate in the financial education course and the control group did not receive this invitation. Those who were selected for treatment were given a reminder call the day before their training session, which was at a day and time of their choosing.

Mode of data collection

Face-to-face [f2f]

Research instrument

The follow-up survey was conducted between February and July 2012 to measure post-training financial knowledge, behavior and outcomes. The questionnaire was relatively short (about 15 minutes) to encourage participation.

Interviewers first attempted to conduct the follow-up survey over the phone. If the person did not respond to the survey during the first attempt, researchers offered one a 500 pesos (US$36) Walmart gift card for completing the survey during the second attempt. If the person was still unavailable for the phone interview, a surveyor visited his/her house to conduct a face-to-face interview. If the participant was not at home, the surveyor delivered a letter with information about the study and instructions for how to participate in the survey and to receive the Walmart gift card. Surveyors made two more attempts (three attempts in total) to conduct a face-to-face interview if a respondent was not at home.

Response rate

72.8 percent of the sample was interviewed in the follow-up survey. The attrition rate was slightly higher in the treatment group (29 percent) than in the control group (25.3 percent).

Access to finance of small and medium enterprises. Topics: most important problem of the company; introduction (in the last twelve months) of new or significantly improved: products or services, production processes, organisational methods, marketing strategies; development of the following indicators in the last six months: turnover, labour cost, other cost, net interest expenses, profit, mark up; development of the amount of debt compared to the assets in the last six months; use of selected sources of financing in the last six months: internal funds, grants from public sources, bank or credit cards overdraft, bank loan, trade credit, other loan, leasing, issue of debt securities, subordinated loans, equity issuance, other; development of the need for the following types of external financing in the last six months: bank loans, trade credit, equity investment, issue of debt securities, other; impact of selected issues on the company’s need for external financing in the last six months: fixed investment, inventories and working capital, internal funds, corporate restructuring; application for selected sources of external financing in the last six months: bank loan, trade credit, other; success of the application for the aforementioned means of financing: received all financing requested, received only part of the financing requested, refused because of too high cost, refusal of application; development of the availability of the following means of financing for the own company over the last six months: bank loans, trade credit, equity investments, issue of debt securities, other; development of selected issues regarding terms and conditions of bank financing: level of interest rates, level of other cost, available size of loan or credit line, available loan maturity, collateral requirements, other; development of the following factors over the last six months: general economic outlook, access to public financial support, company-specific outlook, company’s capital, company’s credit history, willingness of banks to provide loans, willingness of business partners to provide trade credits, willingness of investors to invest in equity or debt securities issued by the company; size of last loan; provider of last loan; purpose of the loan; development of turnover in the last three years; expected development of turnover in the next three years; confidence to obtain desired results with regard to financing from: banks, equity investors; preferred type of external financing; aimed amount of financing; most important limiting factor with regard to financing; expected development of selected types of financing over the next six months: internal funds, bank loans, equity investments, trade credit, issue of debt securities, other; aims to be listed on a stock market within the next two years; main obstacles to be listed on a stock market. Demography: information about the company: number of employees, company size, kind of enterprise, main activity of the company, company sector, year of company registration, ownership structure; turnover of the company in the own country in 2008. Additionally coded was: respondent ID; country; NACE-Code; weighting factor. Unternehmensfinanzierung. Nutzung von Krediten. Schwierigkeiten bei Kreditaufnahme. Themen: Wichtigstes Problem des Unternehmens; Innovation im letzten Jahr: Einführung eines neuen Produkts, Verbesserung des Produktionsprozesses, neue Organisation des Managements oder neuer Vertriebsweg; finanzielle Situation des Unternehmens; Veränderung der Unternehmensindikatoren wie Lohnsteuer, Umsatz, Materialkosten, Zinskosten, Gewinn und Marge, Veränderung zwischen Fremdkapital und Unternehmensvermögen; Nutzung von internen oder externen Finanzierungsquellen (Eigenmittel, Überziehungs- und Bankkredite etc.); Veränderungen in der Nutzung externer Finanzierungsquellen; Einfluss folgender Finanzierungsmittel auf die Notwendigkeit externer Finanzierung: Anlageinvestitionen, Vorratsinvestitionen oder mangelnde Eigenmittel; Beantragung von Bank- und Handelskrediten oder sonstige Außenfinanzierung; Erhalt der kompletten oder nur Teile der beantragten Finanzmittel; Veränderung in der Verfügbarkeit von Finanzmitteln; Veränderung der Bankfinanzierung in preislichen und nichtpreislichen Konditionen; Beurteilung der Veränderung der Verfügbarkeit von Finanzmitteln durch die Wirtschaftslage, unternehmerische Situation oder die Einstellung der Kreditgeber; Höhe des letzten Kreditantrags; Erhalt des letzten Kredits von einer Bank oder einer Privatperson; Verwendungszweck des Kredits; Unternehmenswachstum in den letzten drei Jahren; Wahrscheinlichkeit des zukünftigen Umsatzwachstums; Verhandlung mit Kapitalanlegern/Venture-Capital-Firmen; präferierte Form der Außenfinanzierung (Bankkredit, Kredit aus anderer Quelle, Kapitalbeteiligung, Darlehen); Höhe des gewünschten Finanzierungsbeitrags; Hauptgrund für mögliche Ablehnung einer gewünschten Finanzierung; erwartete Veränderung der verfügbaren Finanzierungsmittel des Unternehmens; geplanter Börsengang des Unternehmens; Haupthindernis für einen Börsengang. Demographie: Angaben zum Unternehmen: Anzahl der Mitarbeiter, Unternehmensgröße, Art des Unternehmens, Hauptgeschäftsfeld des Unternehmens, Branche, Jahr der Eintragung, Eigentümerstruktur; Jahresumsatz im eigenen Land in 2008. Zusätzlich verkodet wurde: Befragten-ID; Land; NACE-Code; Gewichtungsfaktor.

Abstract copyright UK Data Service and data collection copyright owner. The purpose of this study was to produce quantitative data necessary for a critical evaluation of regulatory techniques. Main Topics: Attitudinal/Behavioural Questions Durable goods purchased, advertising (i.e. where product was advertised, content of advertisement, misrepresentation etc), where goods were purchased (i.e. shop/garage, mail order - 8 categories), contact made by seller (in particular, techniques used by salesman), information given by salesman/seller/agent etc (particularly whether this was misleading and whether respondent received the exact goods ordered). Respondent satisfaction with goods purchased and the sales transaction is recorded together with his knowledge of Which? magazine. Detailed information is available for: arrangements for return of goods or cancellation of the deal - if the respondent attempted to do one of these, how he went about it and the outcome are noted; complaints (i.e. from whom advice was sought, action taken, seller's reaction and outcome are given); the agreement (i.e. content, let-out clauses, terms of deposit, credit and interest, etc and surety required for credit are recorded). A section is included particularly on the purchase of motor vehicles. This includes: length of time respondent had been driving when he bought vehicle, age of vehicle bought and whether it had a current MOT Test Certificate, whether respondent had any mechanical check made, whether delivery date was met by seller, whether vehicle was in good condition (whether all accessories ordered were included), whether it was under guarantee (a list of defects is given together with a note of money spent on repairs since purchase and complaints made to the dealer about this). Data on loans: loan company money borrowed from, advertising used (accuracy noted), any contacts between the loan company and the seller of the goods purchased, amount borrowed by respondent (including interest rate, total amount repaid, etc). Content of agreement signed considered in detail and respondent dissatisfaction with loan transaction recorded. If payment became overdue, reason is given and the outcome noted (including a note of the technique used by the company to obtain money-particularly harrassment, court action, etc).Similar data for other sources of credit are also available. Background Variables Household composition, sex, age when ordered goods, occupation and social grade, fluency in English, whether respondent is obviously a member of a minority racial or ethnic group, type of accommodation (i.e. house, flat, etc) and length of time respondent had been resident at address when goods were purchased. A 2-stage probability sample. First stage: 60 sampling points allocated among the Registrar General's 10 standard regions in proportion to the adult population of each region. Within each standard region, a sampling frame was used that listed all the local authority areas in England and Wales stratified by area type (conurbations, non-conurbation urban areas, non-conurbation rural areas). Within each type by: conurbation; by high employment area/low employment areas, non-conurbation: by high population density/low population density. Within each of the resultant cells, local authority areas were arranged in descending order of socio-economic index derived from the 1966 census of population. Second stage: within each selected polling district, a sample of 25 addresses was drawn from the current Electoral Register by applying a fixed sampling interval to a random start Face-to-face interview