Abstract

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems.

By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

Geographic coverage

The 2014 Global Findex Database covers around 150,000 adults in more than 140 economies and representing about 97 percent of the world's population. See Methodology document for country-specific geographic coverage details.

Analysis unit

Individual

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Sample survey data [ssd]

Frequency of data collection

Triennial

Sampling procedure

As in the first edition, the indicators in the 2014 Global Findex are drawn from survey data covering almost 150,000 people in more than 140 economies-representing more than 97 percent of the world's population. The survey was carried out over the 2014 calendar year by Gallup, Inc. as part of its Gallup World Poll, which since 2005 has continually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 140 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. The set of indicators will be collected again in 2017.

Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or is the customary methodology. In most economies the fieldwork is completed in two to four weeks. 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 by means of the Kish grid. In economies where cultural restrictions dictate gender matching, respondents are randomly selected through the Kish grid from among all eligible adults of the interviewer's gender.

In economies where telephone interviewing is employed, random digit dialing or a nationally representative list of phone numbers is used. In most economies where cell phone penetration is high, a dual sampling frame is used. Random selection of respondents is achieved by using either the latest birthday or Kish grid method. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Mode of data collection

Other [oth]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup Inc. also provided valuable input. The questionnaire was piloted in multiple countries, using focus groups, cognitive interviews, and field testing. The questionnaire is available in 142 languages upon request.

Questions on cash withdrawals, saving using an informal savings club or person outside the family, domestic remittances, school fees, and agricultural payments are only asked in developing economies and few other selected countries. The question on mobile money accounts was only asked in economies that were part of the Mobile Money for the Unbanked (MMU) database of the GSMA at the time the interviews were being held.

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 Asli Demirguc-Kunt, Leora Klapper, Dorothe Singer, and Peter Van Oudheusden, “The Global Findex Database 2014: Measuring Financial Inclusion around the World.” Policy Research Working Paper 7255, World Bank, Washington, D.C.

1) Data Introduction • The Anti Money Laundering Transaction Data (SAML-D) is a large tabular dataset that artificially generates more than 9.5 million transactions, 28 types of transactions (11 normal and 17 suspicious) and various characteristics for anti-money laundering (AML) research.

2) Data Utilization (1) Anti Money Laundering Transaction Data (SAML-D) has characteristics that: • Each transaction consists of 12 major variables: transaction date, transmission and reception account information, amount, payment method (credit card, cash, overseas remittance, etc.), transmission and reception bank location, currency, transaction type, and doubt. • Only about 0.1% of all transactions are labeled as suspicious transactions, and the transaction flow is represented by 15 network structures, enabling complex pattern detection and analysis. (2) Anti Money Laundering Transaction Data (SAML-D) can be used to: • Development of money laundering detection models: They can utilize different types of transactions and suspicious transaction labels to detect abnormal transactions, learn and evaluate AML machine learning models. • Financial Network and Pattern Analysis: applicable to research analyzing complex fund flows and suspicious patterns within financial networks based on transaction network structure and sender/receiver information.

Incentive compatible mechanisms for eliciting beliefs typically presume that theutility of money is state independent or that money is the only argument in utilityfunctions. However, subjects may have non-monetary objectives that confound thesemechanisms. In particular, psychologists have argued that people favour bets wheretheir ability is involved over equivalent random bets, a so-called preference for control.We propose a new belief elicitation method that mitigates the control preference. Usingthis method, we determine that under the ostensibly incentive compatible matchingprobabilities method, subjects report self-beliefs 18% higher than their true beliefs inorder to increase control. Non-monetary objectives account for at least 68% of whatwould normally be measured as overcon fidence. We also find that control manifestsitself only as a desire for betting on doing well; betting on doing badly is perceivedas a negative. Our mechanism can be used to yield better measurements of beliefs incontexts beyond the study of overcon fidence.

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Abstract copyright UK Data Service and data collection copyright owner. In the spring of 2011, the BBC Lab UK used the flagship consumer affairs programme Watchdog to launch an online survey, across the nation, of emotional and psychological relationships with money: the Big Money Test. Public response to the survey was good and over the following months more than 109,000 people completed the survey. The survey was designed by Mark Fenton-O’Creevy (Open University) and Adrian Furnham (University College London) to develop ways of characterising people’s psychological and emotional relationships with money and examine how they affect financial health. A good deal of public money and resources are devoted to providing people with the knowledge they need to manage their financial affairs. Regulatory regimes require providers of financial services to provide their customers with specific forms of financial knowledge. However, for the most part, these kinds of knowledge-based strategies have had limited success in improving how capable people are at managing their money. This study was founded in a concern that knowledge-focused approaches to financial behaviour miss a very important part of the picture, the emotional and attitudinal elements of our relationship to money. The researchers wanted to question whether it is lack of financial knowledge that most often makes the difference in successfully navigating financial difficulties or our habits, attitudes, beliefs and emotions about money. They wanted to look at whether there are useful ways in which we can characterise a person’s ‘financial personality’ and whether how people manage their emotions matters to their financial behaviour. Participants were told that by participating they would help scientists understand how and why different people think and feel about money in different ways. As an incentive for participation they were offered (automated) video and web feedback on key self-reported financial capability measures, and their score on a financial knowledge test on completion of the online questionnaire, followed by a video of a television presenter (Martin Lewis, who also advised on elements of the survey) offering them tips on personal financial management. Further information can be found on the BBC Lab UK Big Money Test webpage and the BBC Science Big Money Test results webpage. Some participants also took part in other studies hosted by the BBC’s Lab UK (with the same unique ID) so in principle matching is possible across data sets. This study also includes a combined dataset containing matched respondents who also completed the BBC Big Personality Test (held at the UK Data Archive under SN 7656). Main Topics: The main topics covered by the survey include: financial capability, money pathology, money attitudes, financial outcomes, emotion regulation, vigilance and avoidance, financial knowledge, behavioural approach system, behavioural avoidance system, and impulsive buying. Convenience sample Online (web-based) survey

Abstract

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems.

By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

Geographic coverage

National coverage

Analysis unit

Individual

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Observation data/ratings [obs]

Sampling procedure

The indicators in the 2017 Global Findex database are drawn from survey data covering almost 150,000 people in 144 economies-representing more than 97 percent of the world's population (see Table A.1 of the Global Findex Database 2017 Report). The survey was carried out over the 2017 calendar year by Gallup, Inc., as part of its Gallup World Poll, which since 2005 has annually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 150 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. Interview procedure Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or where this is the customary methodology. In most economies the fieldwork is completed in two to four weeks.

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 handheld 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 economies where telephone interviewing is employed, random digit dialing or a nationally representative list of phone numbers is used. In most economies where cell phone penetration is high, a dual sampling frame is used. Random selection of respondents is achieved by using either the latest birthday or household enumeration method. At least three attempts are made to reach a person in each household, spread over different days and times of day.

The sample size was 1000.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup Inc. also provided valuable input. The questionnaire was piloted in multiple countries, using focus groups, cognitive interviews, and field testing. The questionnaire is available in more than 140 languages upon request.

Questions on cash on delivery, saving using an informal savings club or person outside the family, domestic remittances, and agricultural payments are only asked in developing economies and few other selected countries. The question on mobile money accounts was only asked in economies that were part of the Mobile Money for the Unbanked (MMU) database of the GSMA at the time the interviews were being held.

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, and Jake Hess. 2018. The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution. Washington, DC: World Bank

Abstract

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems.

By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

Geographic coverage

See Methodology document for country-specific geographic coverage details.

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Observation data/ratings [obs]

Sampling procedure

The indicators in the 2017 Global Findex database are drawn from survey data covering almost 150,000 people in 144 economies-representing more than 97 percent of the world’s population (see Table A.1 of the Global Findex Database 2017 Report for a list of the economies included). The survey was carried out over the 2017 calendar year by Gallup, Inc., as part of its Gallup World Poll, which since 2005 has annually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 150 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. Interview procedure Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or where this is the customary methodology. In most economies the fieldwork is completed in two to four weeks.

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 handheld 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 economies where telephone interviewing is employed, random digit dialing or a nationally representative list of phone numbers is used. In most economies where cell phone penetration is high, a dual sampling frame is used. Random selection of respondents is achieved by using either the latest birthday or household enumeration method. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Mode of data collection

Other [oth]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup Inc. also provided valuable input. The questionnaire was piloted in multiple countries, using focus groups, cognitive interviews, and field testing. The questionnaire is available in more than 140 languages upon request.

Questions on cash on delivery, saving using an informal savings club or person outside the family, domestic remittances, and agricultural payments are only asked in developing economies and few other selected countries. The question on mobile money accounts was only asked in economies that were part of the Mobile Money for the Unbanked (MMU) database of the GSMA at the time the interviews were being held.

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, and Jake Hess. 2018. The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution. Washington, DC: World Bank

With the present study, we tested whether generosity changes dependent on money or time being shared. During the experiment, participants N = 371 (MAge = 37.5 years, 38.8% female) completed questionnaires measuring social value orientation, moral identity centrality, and honesty-humility. The opportunity cost of time spent on a real effort task was measured with an incentivized method. Then, participants played two versions of a dictator game: either in a standard dictator game, where participants could share payoffs from the real effort task; or in a time dictator game, where participants decided how long they want to work for another participant’s payoff. We tested three hypotheses. (a) Time and money are not equivalent, and participants are more generous with time than with money. (b) Giving time results in higher positive affect than giving money. (c) Participants’ social value orientation, moral identity centrality, and honesty-humility explain the difference between the donations of time and money, and personality traits will have a stronger impact on time decisions than on monetary decisions. We found that approximately 50% of participants were more generous when giving time, this effect was not dependent on the opportunity cost of time. We think that our experiment is the first experiment to unambiguously show this effect. Furthermore, generosity was not related to positive affect and we found no moderating effect of personality traits.

Participants completed an online survey about their preferences over ways of reducing their risks of dying over time such that they obtained gains in life expectancy. The dataset includes the options they faced and their choices. It also includes some demographic information and other related preference questions (e.g. time preferences, risk preferences, sequence preferences).A key role of the UK government is to address causes of premature fatality. In the UK, air pollution leads to the loss of 340,000 years of life each year and workplace cancers led to the loss of over 140,000 years of life in 2010. Government policies can address the many causes of premature fatality, but these policies need to be evaluated to ensure they make the best use of public money. The question then becomes: what is the value of increasing a person's life expectancy? To address this question, researchers have introduced the concept of the Value Of a Life Year (VOLY). This VOLY is used in government policy evaluations as a measure of the benefits of policies including air pollution mitigation and workplace safety regulation, and thus it is crucial it is measured accurately. The VOLY is estimated using surveys of members of the public, in which people state how much they would pay for a given reduction in their risk of dying, or for a given increase in their life expectancy. The benefits being valued occur in the future. Crucially then, a key component of the VOLY is the effect of timing. Put simply, the further in the future something is, the less we tend to care about it. So a reduction in our risk of dying this year might be more valuable than a reduction in our risk of dying in the future, even if the effect on our overall life expectancy is the same. Unless we understand the influence of this 'discounting' for changes in life expectancy, we cannot accurately disentangle it from the true VOLY. This is the problem we aim to solve with our research. To solve it, our team of experimental economists will use an innovative mixture of experiments and surveys. Participants will play experimental games designed to include simplified models of the air pollution policies, so our team can learn the best ways to describe and measure discounting as it relates to delayed changes in risk. The survey will use the insights from the experiment and elicit individuals' preferences for reductions in their risks at different points in the future. Taken together, the experiments and survey will provide the first major investigation into how people discount their future life expectancy in the context of the VOLY. Our results will be important for policymakers in two ways. First, unless we can account for the effects of discounting on the VOLY, then policy estimates of the VOLY taken from current surveys might be wrong. If these incorrect estimates are used in the evaluation of policies aimed at improving life expectancy, then the value of the policies will be over- or under-estimated, which means public money is likely to be spent on the wrong policies. Second, when the government is evaluating policies where improvements in life expectancy happen in the future, as is the case for air pollution policies, they have to apply discounting to the value of the benefits. Our research will provide evidence about how governments should discount future gains in life expectancy, to make sure that public preferences are reflected in policymaking. Our research is also academically cutting-edge. It combines models from economics with insights from psychology to generate new methodological and empirical evidence about how discounting influences preferences for changes in risk, both for money outcomes (in the experiments) and for fatality risks (in the surveys). It also forges a new methodological agenda, which is the incorporation of incentivised experiments into policy-driven research projects. Overall, our research aims to provide the basis for changing the VOLY used in government policy, challenge existing guidance for discounting fatality risk reductions, and ultimately change how government money is spent, so that the policies implemented are those that improve the wellbeing of society. Survey programmed by the research team in o-tree and conducted online using a sample of respondents recruited on prolific.ac. The sample sex and age band distribution was selected to match those of the UK population (although the respondents were not restricted to be UK residents).

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Global Movie Franchise Revenue and Budget Data

Tracks Lifetime Gross, Budgets, Ratings, and Release Dates

By Emma Culwell [source]

About this dataset

This dataset offers an extensive look at some of the most popular movie franchises in history, shedding light on their financial success and public reception. It includes data on the lifetime gross sales, budgets, ratings, and release dates of each featured movie. Furthermore, this dataset provides invaluable insights into how different elements such as ratings and runtime can affect the performance of a film at the box office. Whether you are an aspiring or established filmmaker looking for inspiration to craft your own successful blockbuster or simply a fan curious about these films’ inner workings, this dataset offers an unprecedented level of detail regarding many beloved franchises

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How to use the dataset

This dataset provides comprehensive information on movie franchises released worldwide between 2000 and 2020. It includes data such as lifetime gross, budget, rating, runtime, release date and vote count/average. This dataset can be used to gain insights on the global movie industry trends over this time period.

The data can be explored in various ways to identify patterns of success or failure among movie franchises across countries, genres or decades. For example, you may want to examine the average budget for movies released each year or calculate the average number of votes received by movies of a particular genre. Additionally, you could use this dataset to compare different types of media (e.g., cable vs streaming) and understand how they impact box-office performance.

To get the most out of this data set it is essential that you first familiarize yourself with all the columns provided: Title: The title of the movie; Lifetime Gross: Total amount money earned by a franchise in all territories; Year: The year in which it was first made available publicly; Studio: The production company behind the production; Rating: Classification given by MPAA/BBFC; Runtime: Length in minutes/hours; Budget: Amount spent producing it ; Release Date : Date when publically announced Availability ; Vote Average : Average ratings based on user reviews ; Vote Count : Number people who rated franchise).
Once you have become comfortable with these variables then feel free to try out some larger analysis techniques such as predictive analytics (predicting future success based on existing trends) or clustering (grouping similar outcomes together). No matter which methods you decide to utilize it is important that you remember – always validate your assumptions! Good luck exploring!

Research Ideas

• A comparison of movie budget to box office returns, to identify over/underperforming movies.
• A study of the correlation between movie rating and viewership.
• An analysis of what types of movies tend to become franchise success stories (big budget, PG-13 rating, 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: MovieFranchises.csv | Column name | Description | |:-------------------|:------------------------------------------------------------------------| | Title | The title of the movie. (String) | | Lifetime Gross | The total amount of money the movie has made in its lifetime. (Integer) | | Year | The year the movie was released. (Integer) | | Studio | The studio that produced the movie. (String) | | Rating | The rating of the movie (e.g. PG-13, R, etc). (String) | | Runtime | The length of the movie in minutes. (Integer) | | Budget | The budget of the movie in USD. (Integer) | | ReleaseDate | The date the movie was released. (Date) | | VoteAvg | The average rating of the movie from users. (Float) | | VoteCount | The total number of votes the movie has received from users. (Integer) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Emma Culwell.

Abstract

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems.

By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

Geographic coverage

National Coverage. Sample excludes Abkhazia and South Ossetia because of security concerns. The excluded areas represent approximately 7% of the population.

Analysis unit

Individual

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Sample survey data [ssd]

Frequency of data collection

Triennial

Sampling procedure

As in the first edition, the indicators in the 2014 Global Findex are drawn from survey data covering almost 150,000 people in more than 140 economies-representing more than 97 percent of the world's population. The survey was carried out over the 2014 calendar year by Gallup, Inc. as part of its Gallup World Poll, which since 2005 has continually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 140 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. The set of indicators will be collected again in 2017.

Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or is the customary methodology. In most economies the fieldwork is completed in two to four weeks. 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 by means of the Kish grid. In economies where cultural restrictions dictate gender matching, respondents are randomly selected through the Kish grid from among all eligible adults of the interviewer's gender.

In economies where telephone interviewing is employed, random digit dialing or a nationally representative list of phone numbers is used. In most economies where cell phone penetration is high, a dual sampling frame is used. Random selection of respondents is achieved by using either the latest birthday or Kish grid method. At least three attempts are made to reach a person in each household, spread over different days and times of day.

The sample size in Georgia was 1,000 individuals.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup Inc. also provided valuable input. The questionnaire was piloted in multiple countries, using focus groups, cognitive interviews, and field testing. The questionnaire is available in 142 languages upon request.

Questions on cash withdrawals, saving using an informal savings club or person outside the family, domestic remittances, school fees, and agricultural payments are only asked in developing economies and few other selected countries. The question on mobile money accounts was only asked in economies that were part of the Mobile Money for the Unbanked (MMU) database of the GSMA at the time the interviews were being held.

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 Asli Demirguc-Kunt, Leora Klapper, Dorothe Singer, and Peter Van Oudheusden, “The Global Findex Database 2014: Measuring Financial Inclusion around the World.” Policy Research Working Paper 7255, World Bank, Washington, D.C.

Corporate Profits in the United States decreased to 3203.60 USD Billion in the first quarter of 2025 from 3312 USD Billion in the fourth quarter of 2024. This dataset provides the latest reported value for - United States Corporate Profits - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Abstract

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems.

By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

Geographic coverage

National Coverage

Analysis unit

Individual

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Sample survey data [ssd]

Frequency of data collection

Triennial

Sampling procedure

As in the first edition, the indicators in the 2014 Global Findex are drawn from survey data covering almost 150,000 people in more than 140 economies-representing more than 97 percent of the world's population. The survey was carried out over the 2014 calendar year by Gallup, Inc. as part of its Gallup World Poll, which since 2005 has continually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 140 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. The set of indicators will be collected again in 2017.

Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or is the customary methodology. In most economies the fieldwork is completed in two to four weeks. 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 by means of the Kish grid. In economies where cultural restrictions dictate gender matching, respondents are randomly selected through the Kish grid from among all eligible adults of the interviewer's gender.

In economies where telephone interviewing is employed, random digit dialing or a nationally representative list of phone numbers is used. In most economies where cell phone penetration is high, a dual sampling frame is used. Random selection of respondents is achieved by using either the latest birthday or Kish grid method. At least three attempts are made to reach a person in each household, spread over different days and times of day.

The sample size in Kenya was 1,000 individuals.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup Inc. also provided valuable input. The questionnaire was piloted in multiple countries, using focus groups, cognitive interviews, and field testing. The questionnaire is available in 142 languages upon request.

Questions on cash withdrawals, saving using an informal savings club or person outside the family, domestic remittances, school fees, and agricultural payments are only asked in developing economies and few other selected countries. The question on mobile money accounts was only asked in economies that were part of the Mobile Money for the Unbanked (MMU) database of the GSMA at the time the interviews were being held.

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 Asli Demirguc-Kunt, Leora Klapper, Dorothe Singer, and Peter Van Oudheusden, “The Global Findex Database 2014: Measuring Financial Inclusion around the World.” Policy Research Working Paper 7255, World Bank, Washington, D.C.

Abstract

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems.

By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

Geographic coverage

Sample excludes the Federal Dependencies because of remoteness and difficulty of access, as well as some additional areas because of security concerns.The excluded areas represent about 5% of the population.

Analysis unit

Individual

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Observation data/ratings [obs]

Sampling procedure

The indicators in the 2017 Global Findex database are drawn from survey data covering almost 150,000 people in 144 economies-representing more than 97 percent of the world's population (see Table A.1 of the Global Findex Database 2017 Report). The survey was carried out over the 2017 calendar year by Gallup, Inc., as part of its Gallup World Poll, which since 2005 has annually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 150 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. Interview procedure Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or where this is the customary methodology. In most economies the fieldwork is completed in two to four weeks.

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 handheld 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 economies where telephone interviewing is employed, random digit dialing or a nationally representative list of phone numbers is used. In most economies where cell phone penetration is high, a dual sampling frame is used. Random selection of respondents is achieved by using either the latest birthday or household enumeration method. At least three attempts are made to reach a person in each household, spread over different days and times of day.

The sample size was 1000.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup Inc. also provided valuable input. The questionnaire was piloted in multiple countries, using focus groups, cognitive interviews, and field testing. The questionnaire is available in more than 140 languages upon request.

Questions on cash on delivery, saving using an informal savings club or person outside the family, domestic remittances, and agricultural payments are only asked in developing economies and few other selected countries. The question on mobile money accounts was only asked in economies that were part of the Mobile Money for the Unbanked (MMU) database of the GSMA at the time the interviews were being held.

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, and Jake Hess. 2018. The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution. Washington, DC: World Bank

Abstract

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems.

By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

Geographic coverage

National Coverage

Analysis unit

Individual

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Sample survey data [ssd]

Frequency of data collection

Triennial

Sampling procedure

As in the first edition, the indicators in the 2014 Global Findex are drawn from survey data covering almost 150,000 people in more than 140 economies-representing more than 97 percent of the world's population. The survey was carried out over the 2014 calendar year by Gallup, Inc. as part of its Gallup World Poll, which since 2005 has continually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 140 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. The set of indicators will be collected again in 2017.

Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or is the customary methodology. In most economies the fieldwork is completed in two to four weeks. 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 by means of the Kish grid. In economies where cultural restrictions dictate gender matching, respondents are randomly selected through the Kish grid from among all eligible adults of the interviewer's gender.

In economies where telephone interviewing is employed, random digit dialing or a nationally representative list of phone numbers is used. In most economies where cell phone penetration is high, a dual sampling frame is used. Random selection of respondents is achieved by using either the latest birthday or Kish grid method. At least three attempts are made to reach a person in each household, spread over different days and times of day.

The sample size in Colombia was 1,000 individuals.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup Inc. also provided valuable input. The questionnaire was piloted in multiple countries, using focus groups, cognitive interviews, and field testing. The questionnaire is available in 142 languages upon request.

Questions on cash withdrawals, saving using an informal savings club or person outside the family, domestic remittances, school fees, and agricultural payments are only asked in developing economies and few other selected countries. The question on mobile money accounts was only asked in economies that were part of the Mobile Money for the Unbanked (MMU) database of the GSMA at the time the interviews were being held.

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 Asli Demirguc-Kunt, Leora Klapper, Dorothe Singer, and Peter Van Oudheusden, “The Global Findex Database 2014: Measuring Financial Inclusion around the World.” Policy Research Working Paper 7255, World Bank, Washington, D.C.

Tutkimuksessa kartoitetaan 15 - 74-vuotiaiden suomalaisten rahapelaamiseen liittyviä mielipiteitä ja rahapelaamista. Siinä pyritään selvittämään mitä mieltä suomalaiset ovat rahapelaamisesta ja nykyisestä rahapelipolitiikasta, mitä rahapelejä pelataan ja kuinka usein, sekä missä määrin rahapelaamisesta aiheutuu haittoja. Aluksi esitettiin rahapelaamista koskevia väitteitä, joilla kartoitettiin rahapelaamiseen liittyviä asenteita yleensä. Seuraavat mielipidekysymykset käsittelivät rahapelien mainontaa, rahapeliautomaattien sijoittelua, valtion ohjausta rahapelaamisessa sekä pelaamisen aiheuttamia sosiaalisia, terveydellisiä ja taloudellisia ongelmia. Seuraavaksi kysyttiin, mitä rahapelejä ja kuinka usein vastaajilla oli tapana pelata. Haluttiin myös tietää, kuinka paljon vastaajat keskimäärin käyttivät rahaa rahapeleihin yhden viikon aikana, mikä oli suurin heidän saamansa rahapelivoitto viimeisen 12 kuukauden aikana ja minkä ikäisenä he olivat aloittaneet rahapelaamisen. Kysymykset käsittelivät myös rahapeliongelmia. Ensin kysyttiin, millaisia ongelmia vastaajalle oli mahdollisesti aiheutunut rahapelaamisesta. Tämän jälkeen kysymykset käsittelivät vastaajan läheisten rahapelaamista sekä heille pelaamisesta mahdollisesti aiheutuneita haittoja. Kysymykset käsittelivät myös vastaajan hyvinvointia ja elämäntapoja. Hyvinvointikysymykset tarkastelivat yleistä terveydentilaa, psyykkistä kuormittuneisuutta, yksinäisyyttä, tupakointitottumuksia ja alkoholin käyttöä. Lopuksi kysyttiin ovatko vastaajat pelanneet video-, konsoli-, tietokone- tai mobiilipelejä, siis pelejä, joita pelataan tietokoneella, pelikonsolilla, älypuhelimella tai tabletilla ilman rahapanosta. Lisäksi vastaajilta tiedusteltiin em. pelien pelaamisen ongelmallisuutta. Taustamuuttujina olivat sukupuoli, ikä, siviilisääty, lasten lukumäärä sekä erilaisia paikkamuuttujia. The survey charted Finnish gambling habits, frequency of gambling, amount of money gambled as well as views on problem gambling and gambling policy and regulation. The term gambling is used here as an umbrella term for lotteries, slot machines, betting, bookmaking, the pools, roulette wheels, and card and dice tables as well as online variations of all of these. First perceptions on gambling were studied. The respondents were asked to what extent they agreed with statements relating to gambling, such as "people should have the right to gamble whenever they want" and "gambling is detrimental to family life." The respondents were asked whether they thought gambling was a problem in Finland, whether the problems associated with gambling had increased or decreased and if the government monopoly and the age limit of 18 were effective ways of limiting problem gambling. The next section of the survey focused on the respondents' experiences of gambling. The respondents were presented with a list of various games (e.g. lotto games and scratchcards of Veikkaus, the National Lottery of Finland, games of chance in a casino and slot machines of Finland's Slot Machine Association, RAY) and asked whether they had played them during the previous 12 months. Other questions charted online gambling in the previous 12 months, gambling websites visited, and frequency of gambling activities. The respondents were asked to estimate the average weekly sum spent on gambling, their largest win in the previous 12 months, and at what age they had gambled for the first time. The respondents' relationship to gambling was examined. They were asked how often they returned another day to try to win back the money they had lost, whether they had claimed to be winning at gambling even though they were actually losing money, whether they had gambled more than they had intended to, and whether other people had criticised them for gambling. Some questions explored whether the respondents had felt guilty while gambling, whether they had wanted to stop betting money or gambling but could not do so, and whether they had hidden their gambling habits from family members. Some questions covered arguments with the people the respondents lived with over how the respondents handled money and whether those arguments had centred on their gambling. Regarding gambling by family members, relatives and friends, the respondents were asked whether any people close to them had problems with gambling, what kind of harm these gambling problems had caused, and how much concern the problems had caused the respondents. The final section pertained to health, well-being and non-gambling games. The respondents were asked to assess their current health status and were asked how often they had felt nervous, calm, despondent and happy in the previous four weeks. Smoking and alcohol use were charted. Finally, the respondents were asked whether they played video games or mobile games, how many hours they had played them in the previous week and month, and whether they felt they might have a problem with these kinds of games. Background variables included the respondent's age, gender, marital status, region, municipality type, education, monthly net income, economic activity and occupational status as well as the number of underage children.

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.

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