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.

Money Supply M0 in the United States increased to 5748600 USD Million in June from 5648700 USD Million in May of 2025. This dataset provides - United States Money Supply M0 - actual values, historical data, forecast, chart, statistics, economic calendar 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)

High accuracy points-of-interest (POI) business listing data for all places in the USA that consumers spend money. Dataset includes geometry point data and accurate name, address and category data.SafeGraph Places is a points-of-interest (POI) dataset with business listing, building footprint, visitor insights, & foot-traffic data for every place people spend money in the U.S.The complete SafeGraph Places dataset has ~ 5.4 million points-of-interest in the USA and is updated monthly (to reflect store openings & closings).Here, for free on this listing, SafeGraph offers a subset of attributes from SafeGraph Places: POI business listing information and POI locations (building centroids).Columns in this dataset:safegraph_place_idparent_safegraph_place_idlocation_namesafegraph_brand_idsbrandstop_categorystreet_addresscitystatezip_codeNAICS codeGeometry Point data. Latitude and longitude of building centroid.For data definitions and complete documentation visit SafeGraph Developer and Data Scientist Docs.For statistics on the dataset, see SafeGraph Places Summary Statistics.Data is available as a hosted Feature Service to easily integrate with all ESRI products in the ArcGIS ecosystem.Want More? Want this POI data for use outside of ArcGIS Online? Want POI data for Canada? Want POI building footprints (Geometry)?Want more detailed category information (Core Places)?Want phone numbers or operating hours (Core Places)?Want POI visitor insights & foot-traffic data (Places Patterns)?To see more, preview & download all SafeGraph Places, Patterns, & Geometry data from SafeGraph’s Data Bar.Or drop us a line! Your data needs are our data delights. Contact: support-esri@safegraph.com

The Child Care and Development Fund (CCDF) provides federal money to states and territories to provide assistance to low-income families, 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 CCDF policy information that supports the needs of a variety of audiences through (1) analytic data files, (2) a project website and search tool, and (3) an annual report (Book of Tables). These resources are made available to researchers, administrators, and policymakers with the goal of addressing important questions concerning the effects of child care subsidy policies and practices on the children and families served. A description of the data files, project website and search tool, and Book of Tables is provided below: 1. Detailed, longitudinal analytic data files provide CCDF policy information for all 50 States, the District of Columbia, and the United States Territories and outlying areas that capture the policies actually in effect at a point in time, rather than proposals or legislation. They capture changes throughout each year, allowing users to access the policies in place at any point in time between October 2009 and the most recent data release. 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 CCDF Plans submitted by states and territories to the federal government. The caseworker manuals also provide ongoing detail for periods in between CCDF Plan 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. Most variables have 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 project website and search tool provide access to a point-and-click user interface. Users can select from the full set of public data to create custom tables. The website also provides access to the full range of reports and products released under the CCDF Policies Database project. The project website and search tool and the data files provide a more detailed set of information than what the Book of Tables provides, including a wider selection of variables and policies over time. 3. The annual Book of Tables provides key policy information for October 1 of each year. The report presents policy variations across the states and territories and is available on the project website. The Book of Tables summarizes a subset of the information available in the full database and data files, and includes information about eligibility requirements for families; application, redetermination, priority, and waiting list policies; family co-payments; and provider policies and reimbursement rates. In many cases, a variable in the Book of Tables will correspond to a single variable in the data files. Usuall

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.

A comprehensive dataset of average residential, commercial, and combined electricity rates in cents per kWh for all 50 U.S. states.

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, Territories, and Tribes 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 four datasets (Datasets 33-37) and they present key aspects of the differences in CCDF funded programs across all states, territories, and tribes as of October 1, 2013. The Book of Tables includes variables that are calculated us

The Grant Programs Directorate strategically and effectively administers and manages FEMA grants to ensure critical and measurable results for customers and stakeholders. The grants represented in this dataset are Preparedness (Non-Disaster or ND) Grants and Assistance to Firefighters Grants (AFG).rnrnND Grants and AFG are awarded and managed differently within the Grants Program Directorate (GPD) and should be treated with discretion.rnrnThe only measure in this dataset is Award Amount. It is an additive measure that can be applied across multiple dimensions to create various views of the data.rnrnAFG awards are assigned to individual Fire Departments. ND Grants are typically assigned to state agencies; however, exceptions do exist such as Port Security Grant Program which is assigned to port areas and not States. It is important to know that when looking at Award Amount by State it does not mean the State actually received that money. In addition, some grant programs may have pass-through requirements where the recipient State is required to sub-grant a minimum amount of the award and only retain a portion of the award.rnrnGrants guidance is described in the Funding Opportunity Announcement (FOA). Each grant program has its own grant guidance containing eligibility requirements, program objectives, and funding restrictions which are published annually. FOAs are public documents and may be found online at www.fema.gov/grants.rnrnFor more information on grants, visit https://www.fema.gov/grants/preparedness and https://www.fema.gov/grants/preparedness/firefighters rnrnIf you have media inquiries about this dataset, please email the FEMA News Desk at FEMA-News-Desk@dhs.gov or call (202) 646-3272. For inquiries about FEMA's data and Open Government program, please email the OpenFEMA team at OpenFEMA@fema.dhs.gov.

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.

The Child Care and Development Fund (CCDF) provides federal money to states and territories to provide assistance to low-income families, 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 CCDF policy information that supports the needs of a variety of audiences through (1) analytic data files, (2) a project website and search tool, and (3) an annual report (Book of Tables). These resources are made available to researchers, administrators, and policymakers with the goal of addressing important questions concerning the effects of child care subsidy policies and practices on the children and families served. A description of the data files, project website and search tool, and Book of Tables is provided below: 1. Detailed, longitudinal analytic data files provide CCDF policy information for all 50 States, the District of Columbia, and the United States Territories and outlying areas that capture the policies actually in effect at a point in time, rather than proposals or legislation. They capture changes throughout each year, allowing users to access the policies in place at any point in time between October 2009 and the most recent data release. 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 CCDF Plans submitted by states and territories to the federal government. The caseworker manuals also provide ongoing detail for periods in between CCDF Plan 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. Most variables have 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 project website and search tool provide access to a point-and-click user interface. Users can select from the full set of public data to create custom tables. The website also provides access to the full range of reports and products released under the CCDF Policies Database project. The project website and search tool and the data files provide a more detailed set of information than what the Book of Tables provides, including a wider selection of variables and policies over time. 3. The annual Book of Tables provides key policy information for October 1 of each year. The report presents policy variations across the states and territories and is available on the project website. The Book of Tables summarizes a subset of the information available in the full database and data files, and includes information about eligibility requirements for families; application, redetermination, priority, and waiting list policies; family co-payments; and provider policies and reimbursement rates. In many cases, a variable in the Book of Tables will correspond to a single variable in the data files. Usuall

Small Business Administration Loan Guarantee (SBA)

1.5 Million Loan Guarantees Across 7(a) and 504 Programs

By Noah Brod [source]

About this dataset

The Small Business Administration (SBA) Loan Guarantee Data provides a comprehensive look at loans that were approved by the SBA from January 1, 1990 to December 31, 1999. This dataset offers insight into roughly 1.5 million approved loans, including details such as the loan amount, interest rate, project county, and more.

This data was collected as part of an FOIA request and is updated quarterly for up-to-date information. It should be noted that the SBA is not a direct lender but rather a guarantor of the loan which is made by either a bank or non-bank lender.

The dataset includes detailed fields such as AsOfDate, Program Type, Gross Approval Amounts and Initial Interest Rates; Fanchise Codes and County Information; Delivery Method and Status Reports; Congressional Districts involved in financing these loans; Jobs Supported as part of each loan; Billing Information related to ChargeOff Dates and Amounts; SBADistrict Office associated with individual loan approvals ;and more!

This unique pool of data can offer invaluable insights into the mechanisms behind small business lending throughout the nineties in America – showing how much has changed since then but also how some trends remain consistent over time. The Small Business Administration Loan Guarantee Data can help shine light on important topics such as demographic disparities among borrowers or regional differences between approving offices - increasing our understanding of American business practices overall!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

Research Ideas

• Using NaicsCode, initialize a visual representation of the most popular types of businesses that receive SBA loan ensures to get a better sense of which industries are the biggest uses for this financing program.
• Calculating Loan Status data over a period of time to analyse trends in terms of loan defaults and how much money is disbursed vs charged off.
• Examining GrossApproval and SBAGuarterNeedApproval data to determine which zipcodes or states have received more funding from the SBA and apply this information in aid targeting certain areas as part of govermental stimulus packages during tough economic times

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: 7a_504_FOIA%20Data%20Dictionary.csv

File: FOIA%20-%207(a)(FY1991-FY1999).csv | Column name | Description | |:--------------------------|:-------------------------------------------------------------| | AsOfDate | Date the data was last updated. (Date) | | Program | Type of loan program. (String) | | BorrName | Name of the borrower. (String) | | BorrStreet | Street address of the borrower. (String) | | BorrCity | City of the borrower. (String) | | BorrState | State of the borrower. (String) | | BorrZip | Zip code of the borrower. (String) | | BankName | Name of the bank. (String) | | BankStreet | Street address of the bank. (String) | | BankCity | City of the bank. (String) | | BankState | State of the bank. (String) | | BankZip | Zip code of the bank. (String) | | GrossApproval | Total amount of the loan approved. (Number) | | SBAGuaranteedApproval | Amount of the loan guaranteed by the SBA. (Number) | | ApprovalDate | Date the loan was approved. (Date) | | ApprovalFiscalYear | Fiscal year the loan was approved. (Number) | | FirstDisbursementDate | Date the loan was disbursed. (Date) | | DeliveryMethod | Method of delivery for the loan. (String) | | subpgmdesc | Description of the loan program. (String) ...

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 1005.

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

Over the years, we have developed distinct competencies in numerous areas so that our clients can rely on the Business Information Reports they purchase from us. Operating on the globe, we are able to provide local/regional intelligence with the most up-to-date and accurate information.

We have local presence in each country we provide information to, through our own offices or via our global network of partners that extends to more than 227 countries worldwide.

Our International Credit Reports include, among other, data on
• Shareholders & Directors • Secretary • Registered Number & Registered Address • Date of registration • Capital • Charges • Company Activities • Shareholding and/or Director Relationships • Detrimental Data • Payment records • Financial statements • Credit Scoring Assessment

Our Due Diligence Report, include: • Relationship Checks • Global KYC Screening • Negative & Local language media checks • Site & Reputation Check • Legal cases relating to the primary subject and its related entities • General media information • Passport/ID authentication • Official Documents and Certificates

Our KYC Reports investigate the subject entity against the following Global lists, amongst other categories: • Sanction Lists • Enforcement Lists • Arms Trafficking • Drug Trafficking • Fraud • Money Laundering • Terrorism • Adverse Media • Political Exposed Persons • State Owned Entities

Our local knowledge and understanding of languages, laws, customs, culture economy and commercial parameters in every country, provide us the advantage of having reliable and relevant products and services no matter where your target company is located.

Graph and download economic data for Personal Saving Rate (PSAVERT) from Jan 1959 to Jun 2025 about savings, personal, rate, and USA.

In this research we hypothesize that aggregate PAC behavior is conditional in nature. PACs in a specific issue sector donate more to a certain political party’s candidates the more that political party controls the legislature. However, the more active the legislature is on a specific set of issues the more people/groups/PACs are mobilized in response to the issue. Thus, a conditional relationship emerges where aggregate PAC donations to a political party are a function of party control, agenda activity, and an interaction of the two. We test this conditional theory using data from the Institute on Money in State Politics database on PAC donations to state legislative candidates divided into issue sectors. Our results provide support for our hypotheses that aggregate PAC donations to a political party’s candidates are conditional on the level of agenda activity on the issues that concern the PACs.

This feature layer, utilizing data from the Homeland Infrastructure Foundation-Level Data (HIFLD) organization, portrays U.S. District Courts. There are 94 district or trial courts in the U.S. There is at least one district court in each state, and the District of Columbia. Each district includes a U.S. bankruptcy court as a unit of the district court. Four territories of the United States have U.S. district courts that hear federal cases, including bankruptcy cases: Puerto Rico, the Virgin Islands, Guam, and the Northern Mariana Islands.There are also two special trial courts. The Court of International Trade addresses cases involving international trade and customs laws. The U.S. Court of Federal Claims deals with most claims for money damages against the U.S. government.North Carolina Eastern District CourtData currency: see US District Court JurisdictionsData Download from: US District Court JurisdictionsFor more information, please visit: Court Role and StructureFor feedback please contact: ArcGIScomNationalMaps@esri.comThumbnail image courtesy of Tim Evanson

UniCourt’s PACER API provides you with a real-time interface and bulk access to the entire PACER database of civil and criminal federal court data from U.S. District Courts, Bankruptcy Courts, Courts of Appeal, and more.

Our PACER API fully integrates with PACER data so you can streamline pulling the court data you need to automate your internal workflows while saving money on outrageous fees.

Leave behind PACER’s outdated search tools for a modern case search with the precision you need.

Search Smarter and Curb Costs

• With UniCourt’s PACER API you can download the court data you need and lower your PACER costs by pulling data smarter. • When you search for court cases using our API for PACER, your search results show (1) which cases are already available in UniCourt, (2) when they were added to our database and last updated, and (3) the UniCourt Case IDs for each case so you can easily pull any additional data you need. • Don’t pay for PACER data when you don’t have to and stop wasting time logging into PACER everyday when there’s a smarter way to search.

Bulk Access to PACER Data and Documents

• Get the complete historical data set you need for criminal and civil PACER data seamlessly integrated with all your internal applications and client facing solutions. • Leverage UniCourt's extensive free repository of case metadata, docket entries, and court documents to get bulk API access to PACER data without breaking your budget. • Get bulk court data from PACER that has been normalized with our artificial intelligence and enriched with other public data sets like attorney bar data, Secretary of State data, and judicial data.

Track PACER Litigation at Scale

• Combine the power of UniCourt’s PACER API with our Court Data API to track your litigation at scale. • Automatically track PACER cases with ease and receive alerts when new docket updates are available so you never miss a federal court filing. • Save money on outrageous PACER fees by leveraging the sophisticated algorithms we’ve developed to intelligently track court cases in bulk without incurring over-the-top fees.

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