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: The Database on Ideology, Money in Politics, and Elections (DIME) is intended as a general resource for the study of campaign finance and ideology in American politics. The database was developed as part of the project on Ideology in the Political Marketplace, which is an on-going effort to perform a comprehensive ideological mapping of political elites, interest groups, and donors using the common-space CFscore scaling methodology (Bonica 2014). Constructing the database required a large-scale effort to compile, clean, and process data on contribution records, candidate characteristics, and election outcomes from various sources. The resulting database contains over 130 million political contributions made by individuals and organizations to local, state, and federal elections spanning a period from 1979 to 2014. A corresponding database of candidates and committees provides additional information on state and federal elections. The DIME+ data repository on congressional activity extends DIME to cover detailed data on legislative voting, lawmaking, and political rhetoric. (See http://dx.doi.org/10.7910/DVN/BO7WOW for details.) The DIME data is available for download as a standalone SQLite database. The SQLite database is stored on disk and can be accessed using a SQLite client or queried directly from R using the RSQLite package. SQLite is particularly well-suited for tasks that require searching through the database for specific individuals or contribution records. (Click here to download.) Overview: The database is intended to make data on campaign finance and elections (1) more centralized and accessible, (2) easier to work with, and (3) more versatile in terms of the types of questions that can be addressed. A list of the main value-added features of the database is below: Data processing: Names, addresses, and occupation and employer titles have been cleaned and standardized. Unique identifiers: Entity resolution techniques were used to assign unique identifiers for all individual and institutional donors included in the database. The contributor IDs make it possible to track giving by individuals across election cycles and levels of government. Geocoding: Each record has been geocoded and placed into congressional districts. The geocoding scheme relies on the contributor IDs to assign a complete set of consistent geo-coordinates to donors that report their full address in some records but not in others. This is accomplished by combining information on self-reported address across records. The geocoding scheme further takes into account donors with multiple addresses. Geocoding was performed using the Data Science Toolkit maintained by Pete Warden and hosted at http://www.datasciencetoolkit.org/. Shape files for congressional districts are from Census.gov (http://www.census.gov/rdo/data). Ideological measures: The common-space CFscores allow for direct distance comparisons of the ideal points of a wide range of political actors from state and federal politics spanning a 35 year period. In total, the database includes ideal point estimates for 70,871 candidates and 12,271 political committees as recipients and 14.7 million individuals and 1.7 million organizations as donors. Corresponding data on candidates, committees, and elections: The recipient database includes information on voting records, fundraising statistics, election outcomes, gender, and other candidate characteristics. All candidates are assigned unique identifiers that make it possible to track candidates if they campaign for different offices. The recipient IDs can also be used to match against the database of contribution records. The database also includes entries for PACs, super PACs, party committees, leadership PACs, 527s, state ballot campaigns, and other committees that engage in fundraising activities. Identifying sets of important political actors: Contribution records have been matched onto other publicly available databases of important political actors. Examples include: Fortune 500 directors and CEOs: (Data) (Paper) Federal court judges: (Data) (Paper} State supreme court justices: (Data) (Paper} Executives appointees to federal agencies: (Data) (Paper) Medical professionals: (Data) (Paper)

Money Supply M0 in the United States decreased to 5648600 USD Million in May from 5732900 USD Million in April of 2025. This dataset provides - United States Money Supply M0 - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The market for contemporary authors’ archives in the United States began when research libraries needed to cheaply provide sources for the swelling number of students and faculty following World War II. Soon, the demand for contemporary authors’ archives developed into a multimillion-dollar trade. Writers and their families enjoyed their new opportunity to make money, as did the book dealers and literary agents with the foresight to pivot their businesses to serve living authors. For a while, library directors and curators across the American Midwest and West relished their new-found opportunity increase their prestige by building collections that could compete on equal footing against British and Ivy League holdings. But as the twentieth century progressed, and public interest around celebrity writers grew more frenzied, even the most well-funded institutions found acquiring contemporary literary archives had become cost prohibitive. Researchers began to question how papers came to be housed in locales disconnected from authors’ professional and personal lives. Placing Papers: The American Literary Archives Market is the first book to chart how the market for writers’ papers became overheated to explore what happens when tourists, rather than scholars, become the designated audience for literary archives.

Money Supply M2 in the United States increased to 21942 USD Billion in May from 21862.40 USD Billion in April of 2025. This dataset provides - United States Money Supply M2 - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset contains expenditures made by from surplus funds accounts of Washington State Candidates for the last 10 years as reported to the PDC on forms C3, C4, Schedule C and their electronic filing equivalents. A surplus funds account uses the same value for the filer id field in the data set except the surplus account has a "*" in it. This can be used to correlate to the same candidate in other data sets. For surplus accounts, the number of years is determined by the year of the election, not necessarily the year the expenditure was reported. This dataset is a best-effort by the PDC to provide a complete set of records as described herewith and may contain incomplete or incorrect information. The PDC provides access to the original reports for the purpose of record verification. Descriptions attached to this dataset do not constitute legal definitions; please consult RCW 42.17A and WAC Title 390 for legal definitions and additional information regarding political finance disclosure requirements. CONDITION OF RELEASE: This publication constitutes a list of individuals prepared by the Washington State Public Disclosure Commission and may not be used for commercial purposes. This list is provided on the condition and with the understanding that the persons receiving it agree to this statutorily imposed limitation on its use. See RCW 42.56.070(9) and AGO 1975 No. 15.

The number of hospitals in the United States was forecast to continuously decrease between 2024 and 2029 by in total 13 hospitals (-0.23 percent). According to this forecast, in 2029, the number of hospitals will have decreased for the twelfth consecutive year to 5,548 hospitals. Depicted is the number of hospitals in the country or region at hand. As the OECD states, the rules according to which an institution can be registered as a hospital vary across countries.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of hospitals in countries like Canada and Mexico.

The number of hospital beds in the United States was forecast to continuously increase between 2024 and 2029 by in total 16.6 thousand beds (+1.75 percent). After the fifteenth consecutive increasing year, the number of hospital beds is estimated to reach 967.9 thousand beds and therefore a new peak in 2029. Notably, the number of hospital beds of was continuously increasing over the past years.Depicted is the estimated total number of hospital beds in the country or region at hand.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of hospital beds in countries like Mexico and Canada.

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 United States was 1,021 individuals.

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.

USER NOTE: This database no longer contains the most up-to-date information. Some errors and missing data from the previous years have been fixed in the most recent data release in the CCDF Policies Database Series. The most recent release is a cumulative file which includes the most accurate version of this and all past years' data. Please do not use this study's data unless you are attempting to replicate the analysis of someone who specifically used this version of the CCDF Policies Database. For any other type of analysis, please use the most recent release in the CCDF Policies Database Series. The Child Care and Development Fund (CCDF) provides federal money to States and Territories to provide assistance to low-income families receiving or in transition from temporary public assistance, to obtain quality child care so they can work, attend training, or receive education. Within the broad federal parameters, states and territories set the detailed policies. Those details determine whether a particular family will or will not be eligible for subsidies, how much the family will have to pay for the care, how families apply for and retain subsidies, the maximum amounts that child care providers will be reimbursed, and the administrative procedures that providers must follow. Thus, while CCDF is a single program from the perspective of federal law, it is in practice a different program in every state and territory. The CCDF Policies Database project is a comprehensive, up-to-date database of inter-related sources of CCDF policy information that support the needs of a variety of audiences through (1) Analytic Data Files and (2) a Book of Tables. These are made available to researchers, administrators, and policymakers with the goal of addressing important questions concerning the effects of alternative child care subsidy policies and practices on the children and families served, specifically parental employment and self-sufficiency, the availability and quality of care, and children's development. A description of the Data Files and Book of Tables is provided below: 1. Detailed, longitudinal Analytic Data Files of CCDF policy information for all 50 States, the District of Columbia, and United States Territories that capture the policies actually in effect at a point in time, rather than proposals or legislation. They focus on the policies in place at the start of each fiscal year, but also capture changes during that fiscal year. The data are organized into 32 categories with each category of variables separated into its own dataset. The categories span five general areas of policy including: Eligibility Requirements for Families and Children (Datasets 1-5) Family Application, Terms of Authorization, and Redetermination (Datasets 6-13) Family Payments (Datasets 14-18) Policies for Providers, Including Maximum Reimbursement Rates (Datasets 19-27) Overall Administrative and Quality Information Plans (Datasets 28-32) The information in the Data Files is based primarily on the documents that caseworkers use as they work with families and providers (often termed "caseworker manuals"). The caseworker manuals generally provide much more detailed information on eligibility, family payments, and provider-related policies than the documents submitted by states and territories to the federal government. The caseworker manuals also provide ongoing detail for periods in between submission dates. Each dataset contains a series of variables designed to capture the intricacies of the rules covered in the category. The variables include a mix of categorical, numeric, and text variables. Every variable has a corresponding notes field to capture additional details related to that particular variable. In addition, each category has an additional notes field to capture any information regarding the rules that is not already outlined in the category's variables. 2. The Book of Tables is available as seven datasets (Datasets 33-39) and they present key aspects of the differences in CCDF funded programs across all states and territories as of October 1, 2015. The Book of Tables includes variables that are calculated using several variables from the Data Files (Datasets 1-32). The Book of Tables summarizes a subset of the information available in the Data Files, and includes information about eligibility requirements for families; application,

This data set shows the last C4 submitted for a surplus account. C4s are submitted for a specific time period and contain a start date and end date. This dataset shows the last C4 reporting period filed by the candidate and therefore shows the latest balance for the surplus account.

This dataset is a best-effort by the PDC to provide a complete set of records as described herewith and may contain incomplete or incorrect information. The PDC provides access to the original reports for the purpose of record verification.

Descriptions attached to this dataset do not constitute legal definitions; please consult RCW 42.17A and WAC Title 390 for legal definitions and additional information regarding political finance disclosure requirements.

CONDITION OF RELEASE: This publication and or referenced documents constitutes a list of individuals prepared by the Washington State Public Disclosure Commission and may not be used for commercial purposes. This list is provided on the condition and with the understanding that the persons receiving it agree to this statutorily imposed limitation on its use. See RCW 42.56.070(9) and AGO 1975 No. 15.

The aim of the project was to identify and compile the best available historical time series for Germany, and to complement or update them at reasonable expense. Time series were only to be included, if data for the entire period from 1834 to 2012 was at least theoretically available. An integral aspect of the concept of our project is the combination of data with critical commentaries of the time series by established expert scientists. The following themes are covered (authors in parentheses): 1. Environment, Climate, and Nature (Paul Erker) 2. Population, Households, Families (Georg Fertig/Franz Rothenbacher) 3. Migration (Jochen Oltmer) 4. Education and Science (Volker Müller-Benedict) 5. Health Service (Reinhard Spree) 6. Social Policy (Marcel Boldorf) 7. Public Finance and Taxation (Mark Spoerer) 8. Political Participation (Marc Debus) 9. Crime and Justice (Dietrich Oberwittler) 10. Work, Income, and Standard of Living (Toni Pierenkemper) 11. Culture, Tourism, and Sports (Heike Wolter/Bernd Wedemeyer-Kolwe) 12. Religion (Thomas Großbölting/Markus Goldbeck) 13. National Accounts (Rainer Metz) 14. Prices (Rainer Metz) 15. Money and Credit (Richard Tilly) 16. Transport and Communication (Christopher Kopper) 17. Agriculture (Michael Kopsidis) 18. Business, Industry, and Craft (Alfred Reckendrees) 19. Building and Housing (Günther Schulz) 20. Trade (Markus Lampe/ Nikolaus Wolf) 21. Balance of Payments (Nikolaus Wolf) 22. International Comparisons (Herman de Jong/Joerg Baten) Basically, the structure of a dataset is guided by the tables in the print publication by the Federal Agency. The print publication allows for four to eight tables for each of the 22 chapters, which means the data record is correspondingly made up of 120 tables in total. The inner structure of the dataset is a consequence of a German idiosyncrasy: the numerous territorial changes. To account for this idiosyncrasy, we decided on a four-fold data structure. Four territorial units with their respective data, are therefore differentiated in each table in separate columns: A German Confederation/Custom Union/German Reich (1834-1945).B German Federal Republic (1949-1989).C German Democratic Republic (1949-1989).D Germany since the reunification (since 1990). Years in parentheses should be considered a guideline only. It is possible that series for the territory of the old Federal Republic or the new federal states are continued after 1990, or that all-German data from before 1990 were available or were reconstructed.All time series are identified by a distinct ID consisting of an “x” and a four-digit number (for numbers under 1000 with leading zeros). The time series that exclusively contain GDR data were identified with a “c” prefix instead of the “x”.For the four territorial units, the time series are arranged in four blocks side by side within the XLSX files. That means: first all time series for the territory and the period of the Custom Union and German Reich, the next columns contain side by side all time series for the territory of the German Federal Republic / the old federal states, then – if available – those for the territory of the German Democratic Republic / the new federal states, and finally for the reunified Germany. There is at most one row for each year. Dates can be missing if no data for the respective year are available in either of the table’s time series, but no date will appear twice. The four territorial units and the resultant time periods cause a “stepwise” appearance of the data tables.

If you find anything missing, unclear, incomprehensible, improvable, etc., please contact me (kontakt@deutschland-in-daten.de). Further reading:Rahlf, Thomas, The German Time Series Dataset 1834-2012, in: Journal of Economics and Statistics 236/1 (2016), pp. 129-143. [DOI: 10.1515/jbnst-2015-1005] Open Access: Rahlf, Thomas, Voraussetzungen für eine Historische Statistik von Deutschland (19./20. Jh.), in: Vierteljahrschrift für Sozial- und Wirtschaftsgeschichte 101/3 (2014), S. 322-352. [PDF] Rahlf, Thomas (Hrsg.), Dokumentation zum Zeitreihendatensatz für Deutschland, 1834-2012, Version 01 (= Historical Social Research Transition 26v01), Köln 2015. http://dx.doi.org/10.12759/hsr.trans.26.v01.2015Rahlf, Thomas (Hrsg.), Deutschland in Daten. Zeitreihen zur Historischen Statistik, Bonn: Bundeszentrale für Politische Bildung, 2015. [EconStor]

introduction:

"Kickstarter is an American public-benefit corporation2 based in Brooklyn, New York, that maintains a global crowdfunding platform focused on creativity and merchandising.3 The company's stated mission is to "help bring creative projects to life".4 Kickstarter has reportedly received more than $1.9 billion in pledges from 9.4 million backers to fund 257,000 creative projects, such as films, music, stage shows, comics, journalism, video games, technology and food-related projects.[5]

People who back Kickstarter projects are offered tangible rewards or experiences in exchange for their pledges.[6] This model traces its roots to subscription model of arts patronage, where artists would go directly to their audiences to fund their work"Wikipedia

So, what if you can predict if a project will be or not to be able to get the money from their backers?

Content

The datastet contains the blurbs or short description of 215513 projects runned along 2017, all written in english and all labeled with "successful" or "failed", if they get the money or not, respectively. From those texts you can train linguistics models for description, and even embeddings relative to the case.

All this data were colected from the webrobots.io, who did the web scrapping and have a lot more of data. Then cleaned and tidied, keeping just the two columns we are interested by now and the projects with blurbs or descriptions in english and with final state of "successful" or "failed".

Acknowledgements

This dataset wouldn't be here without the help of webrobots and the incredible help of the tidyverse ecosystem packages.

Photo by Pablo Rebolledo on Unsplash.

Inspiration

Can you create a model with an accuracy upper to 0.67? How about the quality and utility of the embeddings generated from this data? If you get a major accuracy, please, contact me.

The average number of hospital beds available per 1,000 people in the United States was forecast to continuously decrease between 2024 and 2029 by in total 0.1 beds (-3.7 percent). After the eighth consecutive decreasing year, the number of available beds per 1,000 people is estimated to reach 2.63 beds and therefore a new minimum in 2029. Depicted is the number of hospital beds per capita in the country or region at hand. As defined by World Bank this includes inpatient beds in general, specialized, public and private hospitals as well as rehabilitation centers.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the average number of hospital beds available per 1,000 people in countries like Canada and Mexico.

Indicators of compliance and efficiency in combatting money laundering, collected by EUROSTAT, are plagued with shortcomings. In this paper, I have carried out a forensic analysis on a 2003–2010 dataset of indicators of compliance and efficiency in combatting money laundering, that European Union member states self-reported to EUROSTAT, and on the basis of which, their efforts were evaluated. I used Benford’s law to detect any anomalous statistical patterns and found that statistical anomalies were also consistent with strategic manipulation. According to Benford’s law, if we pick a random sample of numbers representing natural processes, and look at the distribution of the first digits of these numbers, we see that, contrary to popular belief, digit 1 occurs most often, then digit 2, and so on, with digit 9 occurring in less than 5% of the sample. Without prior knowledge of Benford’s law, since people are not intuitively good at creating truly random numbers, deviations thereof can capture strategic alterations. In order to eliminate other sources of deviation, I have compared deviations in situations where incentives and opportunities for manipulation existed and in situations where they did not. While my results are not a conclusive proof of strategic manipulation, they signal that countries that faced incentives and opportunities to misinform the international community about their efforts to combat money laundering may have manipulated these indicators. Finally, my analysis points to the high potential for disruption that the manipulation of national statistics has, and calls for the acknowledgment that strategic manipulation can be an unintended consequence of the international community’s pressure on countries to put combatting money laundering on the top of their national agenda.

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 states of Adamawa, Borno, and Yobe because of security concerns. These states represent 7% of the population.

Analysis unit

Individuals

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.

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

This collection consists of a massive array of economic time series data pertaining to the United States, United Kingdom, Germany, and France, measuring production, construction, prices, income, employment, inventories, sales, interest rates, money supply, and a variety of other factors. These data were collected by the National Bureau of Economic Research (NBER) during the past five decades, and constitute a research resource of major importance to economists as well as political scientists, sociologists, historians and other scholars. Under a grant from the National Science Foundation, the Consortium and the National Bureau of Economic Research converted this collection (which existed heretofore only on handwritten sheets stored in New York) into fully accessible, readily usable, and completely documented machine-readable form. The NBER collection--now containing an estimated 1.6 million entries--is divided into 16 major categories: I. Production of Commodities II. Construction III. Transportation and Public Utilities IV. Prices V. Stocks of Commodities VI. Distribution of Commodities VII. Foreign Trade VIII. Income and Employment IX. Financial Status of Business X. Savings and Investment XI. Security Markets XII. Volume of Transactions XIII. Interest Rates XIV. Money and Banking XV. Government Finance XVI. Indexes of Leading, Coincident and Lagging Indicators Data from all categories are currently available from ICPSR as twenty-four OSIRIS datasets. The economic variables of the datasets are usually observations on the entire nation or large subsets of the nation. Frequently, however, and especially in the United States, separate regional and metropolitan data are included in other variables. This makes cross-sectional analysis possible in many cases. The time span of variables in these files may be as short as one year or as long as 160 years. Chronologically, most data fall within the first half of the twentieth century. Many series, however, extend into the 19th century, and a few reach into the 18th. The oldest series, covering brick production in England and Wales, begins in 1785, and the most recent United States data extend to 1968. Data in the NBER collected were reported at annual, quarterly, or monthly intervals. Most of the data are monthly observations, and practically all monthly variables contain annual values as well. Infrequently, a variable may contain monthly, quarterly, and annual data. Next to monthly series in number are annual series, which contain only annual values. Quarterly series, of which there are relatively few, contain, like the monthly series, implied annual values. Most of the quarterly and monthly data is presented in both original and seasonally-adjusted form. Additional information on the content and characteristics of each series is available from the Center for International Business Cycle Research, Rutgers University, Newark, N.J. 07102.

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 parts of 9 of 10 states because of security concerns. It excludes the majority of Unity State and Upper Nile State as well as all of Jonglei State except Bor South County. The excluded areas represent 44% 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

In March 2024 Bitcoin BTC reached a new all-time high with prices exceeding 73000 USD marking a milestone for the cryptocurrency market This surge was due to the approval of Bitcoin exchange-traded funds ETFs in the United States allowing investors to access Bitcoin without directly holding it This development increased Bitcoin’s credibility and brought fresh demand from institutional investors echoing previous price surges in 2021 when Tesla announced its 15 billion investment in Bitcoin and Coinbase was listed on the Nasdaq By the end of 2022 Bitcoin prices dropped sharply to 15000 USD following the collapse of cryptocurrency exchange FTX and its bankruptcy which caused a loss of confidence in the market By August 2024 Bitcoin rebounded to approximately 64178 USD but remained volatile due to inflation and interest rate hikes Unlike fiat currency like the US dollar Bitcoin’s supply is finite with 21 million coins as its maximum supply By September 2024 over 92 percent of Bitcoin had been mined Bitcoin’s value is tied to its scarcity and its mining process is regulated through halving events which cut the reward for mining every four years making it harder and more energy-intensive to mine The next halving event in 2024 will reduce the reward to 3125 BTC from its current 625 BTC The final Bitcoin is expected to be mined around 2140 The energy required to mine Bitcoin has led to criticisms about its environmental impact with estimates in 2021 suggesting that one Bitcoin transaction used as much energy as Argentina Bitcoin’s future price is difficult to predict due to the influence of large holders known as whales who own about 92 percent of all Bitcoin These whales can cause dramatic market swings by making large trades and many retail investors still dominate the market While institutional interest has grown it remains a small fraction compared to retail Bitcoin is vulnerable to external factors like regulatory changes and economic crises leading some to believe it is in a speculative bubble However others argue that Bitcoin is still in its early stages of adoption and will grow further as more institutions and governments recognize its potential as a hedge against inflation and a store of value 2024 has also seen the rise of Bitcoin Layer 2 technologies like the Lightning Network which improve scalability by enabling faster and cheaper transactions These innovations are crucial for Bitcoin’s wider adoption especially for day-to-day use and cross-border remittances At the same time central bank digital currencies CBDCs are gaining traction as several governments including China and the European Union have accelerated the development of their own state-controlled digital currencies while Bitcoin remains decentralized offering financial sovereignty for those who prefer independence from government control The rise of CBDCs is expected to increase interest in Bitcoin as a hedge against these centralized currencies Bitcoin’s journey in 2024 highlights its growing institutional acceptance alongside its inherent market volatility While the approval of Bitcoin ETFs has significantly boosted interest the market remains sensitive to events like exchange collapses and regulatory decisions With the limited supply of Bitcoin and improvements in its transaction efficiency it is expected to remain a key player in the financial world for years to come Whether Bitcoin is currently in a speculative bubble or on a sustainable path to greater adoption will ultimately be revealed over time.

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