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

    <p>RBA Governor Lowe’s Testimony High inflation is damaging and corrosive </p>

    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.

The benchmark interest rate in Mexico was last recorded at 9.50 percent. This dataset provides - Mexico Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This data collection is one in a series of financial surveys of consumers conducted annually since 1946. In a nationally representative sample, the head of each family unit was interviewed. Starting in 1966, in order to examine the effect that increased car ownership was having on American families, the data collected in this series were organized so that they could be analyzed by both family unit and car unit. The 1968 data are based on car unit. Survey questions regarding automobiles included number of drivers and car owners in the family, make and model of each car, purchase method, car financing and installment debt, and expectations of car purchases in the coming year. Other questions in the 1968 survey covered the respondent's attitudes toward national economic conditions (e.g., the effect of income tax, interest rates, the stock market, Vietnam War involvement, and relations with other communist countries on United States business) and price activity, as well as the respondent's own financial situation. Other questions examined the family unit head's occupation, and the nature and amount of the family's income, debts, liquid assets, changes in liquid assets, savings, investment preferences, and actual and expected purchases of major durables. In addition, the survey explored in detail the subject of housing, e.g., previous and present home ownership, value of respondent's dwelling, and mortgage information. Personal data include age and education of head, household composition, and occupation. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR07448.v3. We highly recommend using the ICPSR version as have made this dataset available in multiple data formats.

This paper describes the relationship between central bank interest rates and exchange rates under a capital control regime. Higher interest rates may strengthen the currency by inducing owners of local currency assets not to sell local currency off shore. There is also an effect that goes in the opposite direction: higher interest rates may also increase the flow of interest income to foreigners through the current account, making the exchange rate fall. The historical financial crisis now under way in Iceland provides excellent testing grounds for the analysis. Overall, the experience does not suggest that cutting interest rates moderately from a very high level is likely to make a currency depreciate in a capital control regime, but it highlights the importance of effective enforcement of the controls.

This table contains 39 series, with data for starting from 1991 (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 item: Canada); Financial market statistics (39 items: Government of Canada Treasury Bills, 1-month (composite rates); Government of Canada Treasury Bills, 2-month (composite rates); Government of Canada Treasury Bills, 3-month (composite rates);Government of Canada Treasury Bills, 6-month (composite rates); ...).

This paper studies the impact of financial openness on the size of government, and other key economic variables, such as the consumption-wealth ratio, the growth rate of wealth, and welfare, in a two-country world, based on a portfolio approach, assuming that public spending is utility-enhancing. The model suggests that the size of government, the consumption-wealth ratio, and welfare should be higher in an open economy due to a higher productivity and/or less volatility through risk sharing. The theoretical results for the growth rate depend on differences on productivities and consumption-wealth ratios. The empirical evidence based on a sample of 50 countries for the period 1970-2009 broadly supports the main theoretical results of the model, even though the inclusion of Singapore distorts sometimes the broad picture.

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.

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).

I use a field experiment in rural Kenya to study how temporary incentives to save impact long-run economic outcomes. Study participants randomly selected to receive large temporary interest rates on an individual bank account had significantly more income and assets 2.5 years after the interest rates expired. These changes are much larger than the short-run impacts on experimental bank account use and almost entirely driven by growth in entrepreneurship. Temporary Interest rates directed to joint bank accounts had no detectable long-run impacts on entrepreneurship or income, but increased investment in household public goods and spousal consensus over finances.

Abstract

The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds.

The Global Findex is the world's most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of about 128,000 adults in more than 120 economies. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments.

The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals.

Geographic coverage

Tibet was excluded from the sample. The excluded areas represent less than 1 percent of the total population of China.

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19 related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Phone surveys were not a viable option in 17 economies that had been part of previous Global Findex surveys, however, because of low mobile phone ownership and surveying restrictions. Data for these economies will be collected in 2022 and released in 2023.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households. Each eligible household member is listed, and the hand-held survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used.

The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent).

For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Sample size for China is 3500.

Mode of data collection

Mobile telephone

Research instrument

Questionnaires are available on the website.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

Annual Budget 2024 Table A FCC. Published by Fingal County Council. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).This dataset contains the data from the Council’s Annual Budget. The budget is comprised of Tables A to F and Appendix 1 & 2. Each table is represented by a separate data file.Table A is the Calculation of the Annual Rate on Valuation for the Financial Year. It is comprised of a number of sections and a series of calculations to determine the Annual Rate on Valuation.The data in this dataset is best interpreted by comparison with Table A in the published Annual Budget document which can be found at www.fingal.ieSection 1 of Table A contains the Budgeted ‘Expenditure’ and ‘Income’ per Council Division and the ‘Estimated Outturn’ per Council Division for the previous Financial Year.The ‘Gross Revenue Expenditure and Income’ is the total of section 1Section 2 of Table A contains ‘Provision of Debit Balance’The ‘Adjusted Gross Expenditure and Income’ is the total of Section 1 and Section 2Section 3 of Table A contains ‘Provision for Credit Balance’, ‘Local Property Tax’ and ‘Pension Related Deduction’The ‘Amount of Rates to be Levied’ is the ‘Adjusted Gross Expenditure and Income’ minus the total of Section 3Section 4 of Table A contains ‘Net Effective Valuation’The ‘General Annual Rate on Valuation’ is the ‘Amount of Rates to be Levied’ divided by the ‘Net Effective Valuation’Data fields for Table A are as follows –Doc : Table ReferenceHeading : Indicates sections in the Table - Table A is comprised of four sections; each section is represented by a sequential number in the heading field i.e. Heading = 1 for all records in the first section; etc.Ref : Item Reference (In section 1 = Division Reference; In other sections, DB = Provision for Debit Balance; CB = Provision for Credit Balance; LPT = Local Property Tax; PRD = Pension Related Deduction; NEV = Net Effective Valuation)Description : Item DescriptionExpenditure : Expenditure for this ItemIncome : Income for this ItemPY : Estimated Outturn for this Item for previous Financial YearABP-PUB-989...

The research delves into the underexplored area of how production network structures influence the severity of economic downturns, particularly during the last financial crisis. Utilizing the RSTAN database from the OECD, we meticulously derived critical measures from the input-output matrices for 61 economies. Our methodology entailed a panel analysis spanning from 2008 to 2010, which is a period marked by significant recessionary pressures. This analysis aimed to correlate economic performance with various production network metrics, taking into account control factors such as interest rates and the prevalence of service sectors. The findings reveal a noteworthy positive correlation between the density of production networks and economic resilience during the crisis, which remained consistent across multiple model specifications. Conversely, as anticipated, higher interest rates were linked to poorer economic performance, highlighting the critical interplay between monetary policy and economic outcomes during periods of financial instability. Given these insights, we propose a policy recommendation emphasizing the strategic enhancement of production network density as a potential buffer against economic downturns. This approach suggests that policymakers should consider the structural aspects of production networks in designing economic stability and growth strategies, thus potentially mitigating the impacts of future financial crises.

India Money Market: Call Money: Weighted Avg Rate data was reported at 6.300 % pa in 25 Mar 2025. This records a decrease from the previous number of 6.310 % pa for 24 Mar 2025. India Money Market: Call Money: Weighted Avg Rate data is updated daily, averaging 6.340 % pa from Apr 2006 (Median) to 25 Mar 2025, with 5276 observations. The data reached an all-time high of 13.480 % pa in 07 Sep 2013 and a record low of 2.480 % pa in 04 Jul 2020. India Money Market: Call Money: Weighted Avg Rate data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under High Frequency Database’s Monetary – Table IN.KAD001: Money Market. [COVID-19-IMPACT]

If you’re a senior with low income, you may qualify for monthly Guaranteed Annual Income System payments.

Maximum payment and allowable private income amounts:

The guaranteed income levels for July 1, 2024 to June 30, 2025 are:

• $87 monthly for single seniors (maximum monthly payment amount), your annual private income must be less than $4,176
• $174 monthly for senior couples (maximum monthly payment amount), your annual private income must be less than $8,352

The data is organized by private income levels. GAINS payments are provided on top of the Old Age Security (OAS) pension and the Guaranteed Income Supplement (GIS) payments you may receive from the federal government.

Learn more about the Ontario Guaranteed Annual Income System

This data is related to The Retirement Income System in Canada

Brazil Loans: Stock: Non Financial Corporations: by Indexers: Reference Rate - TR: Piauí data was reported at 23,746,432.090 BRL in Apr 2023. This records an increase from the previous number of 22,209,699.670 BRL for Mar 2023. Brazil Loans: Stock: Non Financial Corporations: by Indexers: Reference Rate - TR: Piauí data is updated monthly, averaging 60,740,850.370 BRL from Apr 2014 to Apr 2023, with 109 observations. The data reached an all-time high of 198,002,989.750 BRL in Aug 2015 and a record low of 5,384,091.320 BRL in Jan 2022. Brazil Loans: Stock: Non Financial Corporations: by Indexers: Reference Rate - TR: Piauí data remains active status in CEIC and is reported by Central Bank of Brazil. The data is categorized under Brazil Premium Database’s Monetary – Table BR.KAB115: Loans: Stock: Non Financial Corporations: by Indexers: Reference Rate - TR. [COVID-19-IMPACT]

Loans: Stock: Non Financial Corporations: by Indexers: Reference Rate - TR: Paraná data was reported at 2,321,490,305.810 BRL in Sep 2024. This records an increase from the previous number of 2,270,240,814.260 BRL for Aug 2024. Loans: Stock: Non Financial Corporations: by Indexers: Reference Rate - TR: Paraná data is updated monthly, averaging 1,473,997,310.290 BRL from Apr 2014 (Median) to Sep 2024, with 126 observations. The data reached an all-time high of 2,425,885,998.870 BRL in Nov 2016 and a record low of 816,672,678.760 BRL in Dec 2021. Loans: Stock: Non Financial Corporations: by Indexers: Reference Rate - TR: Paraná data remains active status in CEIC and is reported by Central Bank of Brazil. The data is categorized under Brazil Premium Database’s Monetary – Table BR.KAB115: Loans: Stock: Non Financial Corporations: by Indexers: Reference Rate - TR. [COVID-19-IMPACT]

Loans: Contracting: Non Financial Corporations: by Indexers: Reference Rate - TR: Pará data was reported at 26,600,000.000 BRL in Sep 2024. This records an increase from the previous number of 0.010 BRL for Aug 2024. Loans: Contracting: Non Financial Corporations: by Indexers: Reference Rate - TR: Pará data is updated monthly, averaging 10,166,451.270 BRL from Apr 2014 (Median) to Sep 2024, with 85 observations. The data reached an all-time high of 93,406,411.960 BRL in Sep 2023 and a record low of 0.010 BRL in Aug 2024. Loans: Contracting: Non Financial Corporations: by Indexers: Reference Rate - TR: Pará data remains active status in CEIC and is reported by Central Bank of Brazil. The data is categorized under Brazil Premium Database’s Monetary – Table BR.KAB066: Loans: Contracting: Non Financial Corporations: by Indexers: Reference Rate - TR. [COVID-19-IMPACT]

Money Supply M2 in the United States increased to 21447.60 USD Billion in November from 21311.20 USD Billion in October of 2024. This dataset provides - United States Money Supply M2 - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate in Argentina was last recorded at 32 percent. This dataset provides the latest reported value for - Argentina Money Market Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Loans: Contracting: Non Financial Corporations: by Indexers: Reference Rate - TR: Piauí data was reported at 60,000,000.000 BRL in Sep 2024. This records an increase from the previous number of 41,765,540.330 BRL for Aug 2024. Loans: Contracting: Non Financial Corporations: by Indexers: Reference Rate - TR: Piauí data is updated monthly, averaging 11,470,824.940 BRL from Apr 2014 (Median) to Sep 2024, with 50 observations. The data reached an all-time high of 283,745,849.470 BRL in Jul 2024 and a record low of 0.010 BRL in Nov 2023. Loans: Contracting: Non Financial Corporations: by Indexers: Reference Rate - TR: Piauí data remains active status in CEIC and is reported by Central Bank of Brazil. The data is categorized under Brazil Premium Database’s Monetary – Table BR.KAB066: Loans: Contracting: Non Financial Corporations: by Indexers: Reference Rate - TR. [COVID-19-IMPACT]