Time series data for the data Current Account and Its Components - Current USD, TTM for the country United States. The Current Account and Its Components The current account is a component of a country's balance of payments that records the transactions of goods, services, income, and current transfers between residents of the country and the rest of the world. It consists of four main components:

a. Trade in Goods Balance

b. Trade in Services Balance

c. Primary Income Balance

d. Secondary Income Balance

1. Trade in Goods Balance Definition: This includes the export and import of physical items such as machinery, food, clothing, etc.

Credit Example: A German car manufacturer exports cars to the United States (value of exported cars).

Debit Example: A German electronics retailer imports smartphones from South Korea (value of imported smartphones).

1. Trade in Services Balance Definition: This includes the export and import of services such as tourism, financial services, consulting, transportation, etc.

Credit Example: A German IT company provides software development services to a client in Japan (value of exported services).

Debit Example: A German tourist books a hotel room in France (value of imported tourism services).

1. Primary Income Balance Definition: This includes earnings from the provision of factors of production such as labor, financial assets, land, and natural resources. It covers income from interest, profits, and dividends.

Credit Example: A German investor receives dividends from shares held in a U.S. company (value of received dividends).

Debit Example: Foreign investors receive interest payments on bonds issued by a German company (value of interest payments).

1. Secondary Income Balance Definition: This includes current transfers such as foreign aid, remittances, and other one-way payments that do not involve an exchange of goods or services.

Credit Example: Remittances sent by German residents working abroad to their families in Germany (value of received remittances).

Debit Example: Germany sends humanitarian aid to a developing country (value of sent aid). Current Account Balance (USD)The indicator "Current Account Balance (USD)" stands at -1.37 Trillion United States Dollars as of 3/31/2025, the lowest value at least since 6/30/2001, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes an decrease of -0.4138 Trillion United States Dollars compared to the value the year prior.The 1 year change is -0.4138 Trillion United States Dollars.The 3 year change is -0.4223 Trillion United States Dollars.The 5 year change is -0.9494 Trillion United States Dollars.The 10 year change is -0.9961 Trillion United States Dollars.The Serie's long term average value is -0.579 Trillion United States Dollars. It's latest available value, on 3/31/2025, is -0.795 Trillion United States Dollars lower, compared to it's long term average value.The Serie's change in United States Dollars from it's minimum value, on 3/31/2025, to it's latest available value, on 3/31/2025, is +0.0 Trillion.The Serie's change in United States Dollars from it's maximum value, on 6/30/2014, to it's latest available value, on 3/31/2025, is -1.04 Trillion.Trade in Services Balance (USD)The indicator "Trade in Services Balance (USD)" stands at 0.3089 Trillion United States Dollars as of 3/31/2025. Regarding the One-Year-Change of the series, the current value constitutes an increase of 0.0158 Trillion United States Dollars compared to the value the year prior.The 1 year change is 0.0158 Trillion United States Dollars.The 3 year change is 0.0674 Trillion United States Dollars.The 5 year change is 0.012 Trillion United States Dollars.The 10 year change is 0.0373 Trillion United States Dollars.The Serie's long term average value is 0.187 Trillion United States Dollars. It's latest available value, on 3/31/2025, is 0.122 Trillion United States Dollars higher, compared to it's long term average value.The Serie's change in United States Dollars from it's minimum value, on 12/31/2003, to it's latest available value, on 3/31/2025, is +0.2635 Trillion.The Serie's change in United States Dollars from it's maximum value, on 12/31/2024, to it's latest available value, on 3/31/2025, is -0.003 Trillion.Trade in Goods Balance (USD)The indicator "Trade in Goods Balance (USD)" stands at -1.40 Trillion United States Dollars as of 3/31/2025, the lowest value at least since 6/30/2001, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes an decrease of -0.3327 Trillion United States Dollars compared to the value the year prior.The 1 year change is -0.3327 Trillion United States Dollars.The 3 year change is -0.2509 Trillion United States Dollars.The 5 year change is -0.5657 Trillion United States Dollars.The 10 year change is -0.6467 Trillion United States Dollars.The ...

U.S. Transactions with Foreign-Residents in Long-Term Securities - Recent Net Foreign Purchases, by country. The data is collated by month.

External Debt in Mexico increased to 633750.90 USD Million in the second quarter of 2025 from 618661.90 USD Million in the first quarter of 2025. This dataset provides - Mexico External Debt - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Financial Security (Credit Union Dataset)

Geographic Distribution and Risk Factors

By Homeland Infrastructure Foundation [source]

About this dataset

This dataset contains credit union headquarters information from the National Credit Union Administration (NCUA) collected over the past two years. The data includes coordinates, addresses, names, telephone numbers, and other details for each credit union. It is a great tool for understanding the state of credit unions in the United States and their hierarchical structure.The NCUA has used this data to help measure financial health and performance across federal and state-chartered institutions in order to safeguard the savings of millions of account holders throughout the US. With this dataset you can explore regional trends within various regions while examining legal structures such as Federal or State-chartered boasting detailed analyses from underlying variables such as county FIPS codes alongside a variety useful features like latitude/longitude pairs and sourcedate values . These insights could then be applied to diverse financial products like loans, lending practices or investments furthering our understanding into how different finance divisions operate across countries. Through these resources we can take steps towards safer banking environments with sounder policies that are upto date with federal regulations while maintaining strong economic growth within our nation’s boundaries

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How to use the dataset

This dataset contains information about credit union headquarters, including address, contact information, and location. With this data you can explore how different financial variables affect the health of credit unions.

Research Ideas

• Developing mapping and heat-mapping applications to visually identify credit union saturation across the country.
• Establishing patterns in credit union success and failure based on geographical areas, types of services offered, or other factors such as telephone access.
• Targeting marketing campaigns with an emphasis on local outreach and developing a better understanding of the demographic composition, interests, or financial needs of customers in specific areas accessible to particular credit unions

Acknowledgements

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

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: Credit_Union_Headquarters.csv.crdownload | Column name | Description | |:---------------|:--------------------------------------------------------------------------------------------------------------------------------| | X | X coordinate of the credit union headquarters. (Numeric) | | Y | Y coordinate of the credit union headquarters. (Numeric) | | CU_NUMBER | The unique number assigned to the credit union. (String) | | NAME | The name of the credit union. (String) | | ADDRESS | The street address of the credit union headquarters. (String) | | ADDRESS2 | The secondary address of the credit union headquarters (if applicable). (String) | | CITY | The city where the credit union headquarters is located. (String) | | STATE | The state where the credit union headquarters is located. (String) | |...

(Banking And Systemic Crises)

prepared by (Mohamed Abd Al-mgyd)

https://github.com/1145267383/Systemic-And-Banking-Crises

• Web APP > https://crises.herokuapp.com/

Dataset

A)20160923_global_crisis_data:

https://www.hbs.edu/behavioral-finance-and-financial-stability/data/Pages/global.aspx

This data was collected over many years by Carmen Reinhart (with her coauthors Ken Rogoff, Christoph Trebesch, and Vincent Reinhart). This data contains the banking crises of 70 countries, from 1800 AD to 2016 AD, with a total of 15,190 records and 16 variables. But the data stabilized after cleaning and adjusting to 8642 records and 17 variables.

B)Label_Country: This data contains a description of the country whether it's Developing or Developed .

Variable: Description:

1-Case: ID Number for Country.

2-Cc3: ID String for Country.

3-Country : Name Country.

4-Year: The date from 1800 to 2016.

5-Banking_Crisis: Banking problems can often be traced to a decrease the value of banks' assets.

A) due to a collapse in real estate prices or When the bank asset values decrease substantially . B) if a government stops paying its obligations, this can trigger a sharp decline in value of bonds.

6-Systemic_Crisis : when many banks in a country are in serious solvency or liquidity problems at the same time—either:

A) because there are all hits by the same outside shock. B) or because failure in one bank or a group of banks spreads to other banks in the system.

7-Gold_Standard: The Country have crisis in Gold Standard.

8-Exch_Usd: Exch local currency in USD, Except exch USD currency in GBP.

9-Domestic_Debt_In_Default: The Country have domestic debt in default.

10-Sovereign_External_Debt_1: Default and Restructurings, -Does not include defaults on WWI debt to United States and United Kingdom and post-1975 defaults on Official External Creditors.

11-Sovereign_External_Debt_2: Default and Restructurings, -Does not include defaults on WWI debt to United States and United Kingdom but includes post-1975 defaults on Official External Creditors.

12-Gdp_Weighted_Default:GDP Weighted Default for country.

13-Inflation: Annual percentages of average consumer prices.

14-Independence: Independence for country.

15-Currency_Crises: The Country have crisis in Currency.

16-Inflation_Crises: The Country have crisis in Inflation.

17-Level_Country: The description of the country whether it's Developing or Developed.

Time series data for the data Current Account and Its Components - Current USD, TTM for the country Switzerland. The Current Account and Its Components The current account is a component of a country's balance of payments that records the transactions of goods, services, income, and current transfers between residents of the country and the rest of the world. It consists of four main components:

a. Trade in Goods Balance

b. Trade in Services Balance

c. Primary Income Balance

d. Secondary Income Balance

1. Trade in Goods Balance Definition: This includes the export and import of physical items such as machinery, food, clothing, etc.

Credit Example: A German car manufacturer exports cars to the United States (value of exported cars).

Debit Example: A German electronics retailer imports smartphones from South Korea (value of imported smartphones).

1. Trade in Services Balance Definition: This includes the export and import of services such as tourism, financial services, consulting, transportation, etc.

Credit Example: A German IT company provides software development services to a client in Japan (value of exported services).

Debit Example: A German tourist books a hotel room in France (value of imported tourism services).

1. Primary Income Balance Definition: This includes earnings from the provision of factors of production such as labor, financial assets, land, and natural resources. It covers income from interest, profits, and dividends.

Credit Example: A German investor receives dividends from shares held in a U.S. company (value of received dividends).

Debit Example: Foreign investors receive interest payments on bonds issued by a German company (value of interest payments).

1. Secondary Income Balance Definition: This includes current transfers such as foreign aid, remittances, and other one-way payments that do not involve an exchange of goods or services.

Credit Example: Remittances sent by German residents working abroad to their families in Germany (value of received remittances).

Debit Example: Germany sends humanitarian aid to a developing country (value of sent aid). Trade in Goods Balance (USD)The indicator "Trade in Goods Balance (USD)" stands at 138.92 Billion United States Dollars as of 3/31/2025, the highest value at least since 6/30/2001, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes an increase of 14.88 Billion United States Dollars compared to the value the year prior.The 1 year change is 14.88 Billion United States Dollars.The 3 year change is 9.65 Billion United States Dollars.The 5 year change is 66.90 Billion United States Dollars.The 10 year change is 74.98 Billion United States Dollars.The Serie's long term average value is 52.34 Billion United States Dollars. It's latest available value, on 3/31/2025, is 86.59 Billion United States Dollars higher, compared to it's long term average value.The Serie's change in United States Dollars from it's minimum value, on 9/30/2001, to it's latest available value, on 3/31/2025, is +139.21 Billion.The Serie's change in United States Dollars from it's maximum value, on 3/31/2025, to it's latest available value, on 3/31/2025, is 0.0 Billion.Secondary Income Balance (USD)The indicator "Secondary Income Balance (USD)" stands at -15.35 Billion United States Dollars as of 3/31/2025, the lowest value since 12/31/2021. Regarding the One-Year-Change of the series, the current value constitutes an decrease of -2.90 Billion United States Dollars compared to the value the year prior.The 1 year change is -2.90 Billion United States Dollars.The 3 year change is -0.5885 Billion United States Dollars.The 5 year change is -1.90 Billion United States Dollars.The 10 year change is 2.40 Billion United States Dollars.The Serie's long term average value is -9.48 Billion United States Dollars. It's latest available value, on 3/31/2025, is -5.87 Billion United States Dollars lower, compared to it's long term average value.The Serie's change in United States Dollars from it's minimum value, on 12/31/2014, to it's latest available value, on 3/31/2025, is +2.75 Billion.The Serie's change in United States Dollars from it's maximum value, on 3/31/2001, to it's latest available value, on 3/31/2025, is -12.72 Billion.Primary Income Balance (USD)The indicator "Primary Income Balance (USD)" stands at -30.25 Billion United States Dollars as of 3/31/2025, the lowest value since 3/31/2024. Regarding the One-Year-Change of the series, the current value constitutes an decrease of -7.49 Billion United States Dollars compared to the value the year prior.The 1 year change is -7.49 Billion United States Dollars.The 3 year change is 5.02 Billion United States Dollars.The 5 year change is -18.51 Billion United States Dollars.The 10 year change is -32.49 Billion United States Dollars.The Serie's long term average value is -0.618 Billion United ...

Jurisdictional Unit, 2022-05-21. For use with WFDSS, IFTDSS, IRWIN, and InFORM.This is a feature service which provides Identify and Copy Feature capabilities. If fast-drawing at coarse zoom levels is a requirement, consider using the tile (map) service layer located at https://nifc.maps.arcgis.com/home/item.html?id=3b2c5daad00742cd9f9b676c09d03d13.OverviewThe Jurisdictional Agencies dataset is developed as a national land management geospatial layer, focused on representing wildland fire jurisdictional responsibility, for interagency wildland fire applications, including WFDSS (Wildland Fire Decision Support System), IFTDSS (Interagency Fuels Treatment Decision Support System), IRWIN (Interagency Reporting of Wildland Fire Information), and InFORM (Interagency Fire Occurrence Reporting Modules). It is intended to provide federal wildland fire jurisdictional boundaries on a national scale. The agency and unit names are an indication of the primary manager name and unit name, respectively, recognizing that:There may be multiple owner names.Jurisdiction may be held jointly by agencies at different levels of government (ie State and Local), especially on private lands, Some owner names may be blocked for security reasons.Some jurisdictions may not allow the distribution of owner names. Private ownerships are shown in this layer with JurisdictionalUnitIdentifier=null,JurisdictionalUnitAgency=null, JurisdictionalUnitKind=null, and LandownerKind="Private", LandownerCategory="Private". All land inside the US country boundary is covered by a polygon.Jurisdiction for privately owned land varies widely depending on state, county, or local laws and ordinances, fire workload, and other factors, and is not available in a national dataset in most cases.For publicly held lands the agency name is the surface managing agency, such as Bureau of Land Management, United States Forest Service, etc. The unit name refers to the descriptive name of the polygon (i.e. Northern California District, Boise National Forest, etc.).These data are used to automatically populate fields on the WFDSS Incident Information page.This data layer implements the NWCG Jurisdictional Unit Polygon Geospatial Data Layer Standard.Relevant NWCG Definitions and StandardsUnit2. A generic term that represents an organizational entity that only has meaning when it is contextualized by a descriptor, e.g. jurisdictional.Definition Extension: When referring to an organizational entity, a unit refers to the smallest area or lowest level. Higher levels of an organization (region, agency, department, etc) can be derived from a unit based on organization hierarchy.Unit, JurisdictionalThe governmental entity having overall land and resource management responsibility for a specific geographical area as provided by law.Definition Extension: 1) Ultimately responsible for the fire report to account for statistical fire occurrence; 2) Responsible for setting fire management objectives; 3) Jurisdiction cannot be re-assigned by agreement; 4) The nature and extent of the incident determines jurisdiction (for example, Wildfire vs. All Hazard); 5) Responsible for signing a Delegation of Authority to the Incident Commander.See also: Unit, Protecting; LandownerUnit IdentifierThis data standard specifies the standard format and rules for Unit Identifier, a code used within the wildland fire community to uniquely identify a particular government organizational unit.Landowner Kind & CategoryThis data standard provides a two-tier classification (kind and category) of landownership. Attribute Fields JurisdictionalAgencyKind Describes the type of unit Jurisdiction using the NWCG Landowner Kind data standard. There are two valid values: Federal, and Other. A value may not be populated for all polygons.JurisdictionalAgencyCategoryDescribes the type of unit Jurisdiction using the NWCG Landowner Category data standard. Valid values include: ANCSA, BIA, BLM, BOR, DOD, DOE, NPS, USFS, USFWS, Foreign, Tribal, City, County, OtherLoc (other local, not in the standard), State. A value may not be populated for all polygons.JurisdictionalUnitNameThe name of the Jurisdictional Unit. Where an NWCG Unit ID exists for a polygon, this is the name used in the Name field from the NWCG Unit ID database. Where no NWCG Unit ID exists, this is the “Unit Name” or other specific, descriptive unit name field from the source dataset. A value is populated for all polygons.JurisdictionalUnitIDWhere it could be determined, this is the NWCG Standard Unit Identifier (Unit ID). Where it is unknown, the value is ‘Null’. Null Unit IDs can occur because a unit may not have a Unit ID, or because one could not be reliably determined from the source data. Not every land ownership has an NWCG Unit ID. Unit ID assignment rules are available from the Unit ID standard, linked above.LandownerKindThe landowner category value associated with the polygon. May be inferred from jurisdictional agency, or by lack of a jurisdictional agency. A value is populated for all polygons. There are three valid values: Federal, Private, or Other.LandownerCategoryThe landowner kind value associated with the polygon. May be inferred from jurisdictional agency, or by lack of a jurisdictional agency. A value is populated for all polygons. Valid values include: ANCSA, BIA, BLM, BOR, DOD, DOE, NPS, USFS, USFWS, Foreign, Tribal, City, County, OtherLoc (other local, not in the standard), State, Private.DataSourceThe database from which the polygon originated. Be as specific as possible, identify the geodatabase name and feature class in which the polygon originated.SecondaryDataSourceIf the Data Source is an aggregation from other sources, use this field to specify the source that supplied data to the aggregation. For example, if Data Source is "PAD-US 2.1", then for a USDA Forest Service polygon, the Secondary Data Source would be "USDA FS Automated Lands Program (ALP)". For a BLM polygon in the same dataset, Secondary Source would be "Surface Management Agency (SMA)."SourceUniqueIDIdentifier (GUID or ObjectID) in the data source. Used to trace the polygon back to its authoritative source.MapMethod:Controlled vocabulary to define how the geospatial feature was derived. Map method may help define data quality. MapMethod will be Mixed Method by default for this layer as the data are from mixed sources. Valid Values include: GPS-Driven; GPS-Flight; GPS-Walked; GPS-Walked/Driven; GPS-Unknown Travel Method; Hand Sketch; Digitized-Image; DigitizedTopo; Digitized-Other; Image Interpretation; Infrared Image; Modeled; Mixed Methods; Remote Sensing Derived; Survey/GCDB/Cadastral; Vector; Phone/Tablet; OtherDateCurrentThe last edit, update, of this GIS record. Date should follow the assigned NWCG Date Time data standard, using 24 hour clock, YYYY-MM-DDhh.mm.ssZ, ISO8601 Standard.CommentsAdditional information describing the feature. GeometryIDPrimary key for linking geospatial objects with other database systems. Required for every feature. This field may be renamed for each standard to fit the feature.JurisdictionalUnitID_sansUSNWCG Unit ID with the "US" characters removed from the beginning. Provided for backwards compatibility.JoinMethodAdditional information on how the polygon was matched information in the NWCG Unit ID database.LocalNameLocalName for the polygon provided from PADUS or other source.LegendJurisdictionalAgencyJurisdictional Agency but smaller landholding agencies, or agencies of indeterminate status are grouped for more intuitive use in a map legend or summary table.LegendLandownerAgencyLandowner Agency but smaller landholding agencies, or agencies of indeterminate status are grouped for more intuitive use in a map legend or summary table.DataSourceYearYear that the source data for the polygon were acquired.Data InputThis dataset is based on an aggregation of 4 spatial data sources: Protected Areas Database US (PAD-US 2.1), data from Bureau of Indian Affairs regional offices, the BLM Alaska Fire Service/State of Alaska, and Census Block-Group Geometry. NWCG Unit ID and Agency Kind/Category data are tabular and sourced from UnitIDActive.txt, in the WFMI Unit ID application (https://wfmi.nifc.gov/unit_id/Publish.html). Areas of with unknown Landowner Kind/Category and Jurisdictional Agency Kind/Category are assigned LandownerKind and LandownerCategory values of "Private" by use of the non-water polygons from the Census Block-Group geometry.PAD-US 2.1:This dataset is based in large part on the USGS Protected Areas Database of the United States - PAD-US 2.`. PAD-US is a compilation of authoritative protected areas data between agencies and organizations that ultimately results in a comprehensive and accurate inventory of protected areas for the United States to meet a variety of needs (e.g. conservation, recreation, public health, transportation, energy siting, ecological, or watershed assessments and planning). Extensive documentation on PAD-US processes and data sources is available.How these data were aggregated:Boundaries, and their descriptors, available in spatial databases (i.e. shapefiles or geodatabase feature classes) from land management agencies are the desired and primary data sources in PAD-US. If these authoritative sources are unavailable, or the agency recommends another source, data may be incorporated by other aggregators such as non-governmental organizations. Data sources are tracked for each record in the PAD-US geodatabase (see below).BIA and Tribal Data:BIA and Tribal land management data are not available in PAD-US. As such, data were aggregated from BIA regional offices. These data date from 2012 and were substantially updated in 2022. Indian Trust Land affiliated with Tribes, Reservations, or BIA Agencies: These data are not considered the system of record and are not intended to be used as such. The Bureau of Indian Affairs (BIA), Branch of Wildland Fire Management (BWFM) is not the originator of these data. The

Time series data for the data Current Account and Its Components - Current USD, TTM for the country Russian Federation. The Current Account and Its Components The current account is a component of a country's balance of payments that records the transactions of goods, services, income, and current transfers between residents of the country and the rest of the world. It consists of four main components:

a. Trade in Goods Balance

b. Trade in Services Balance

c. Primary Income Balance

d. Secondary Income Balance

1. Trade in Goods Balance Definition: This includes the export and import of physical items such as machinery, food, clothing, etc.

Credit Example: A German car manufacturer exports cars to the United States (value of exported cars).

Debit Example: A German electronics retailer imports smartphones from South Korea (value of imported smartphones).

1. Trade in Services Balance Definition: This includes the export and import of services such as tourism, financial services, consulting, transportation, etc.

Credit Example: A German IT company provides software development services to a client in Japan (value of exported services).

Debit Example: A German tourist books a hotel room in France (value of imported tourism services).

1. Primary Income Balance Definition: This includes earnings from the provision of factors of production such as labor, financial assets, land, and natural resources. It covers income from interest, profits, and dividends.

Credit Example: A German investor receives dividends from shares held in a U.S. company (value of received dividends).

Debit Example: Foreign investors receive interest payments on bonds issued by a German company (value of interest payments).

1. Secondary Income Balance Definition: This includes current transfers such as foreign aid, remittances, and other one-way payments that do not involve an exchange of goods or services.

Credit Example: Remittances sent by German residents working abroad to their families in Germany (value of received remittances).

Debit Example: Germany sends humanitarian aid to a developing country (value of sent aid). Trade in Goods Balance (USD)The indicator "Trade in Goods Balance (USD)" stands at 125.52 Billion United States Dollars as of 3/31/2025, the lowest value since 3/31/2024. Regarding the One-Year-Change of the series, the current value constitutes an decrease of -5.57 Billion United States Dollars compared to the value the year prior.The 1 year change is -5.57 Billion United States Dollars.The 3 year change is -119.74 Billion United States Dollars.The 5 year change is -25.87 Billion United States Dollars.The 10 year change is -62.30 Billion United States Dollars.The Serie's long term average value is 139.34 Billion United States Dollars. It's latest available value, on 3/31/2025, is -13.82 Billion United States Dollars lower, compared to it's long term average value.The Serie's change in United States Dollars from it's minimum value, on 6/30/2002, to it's latest available value, on 3/31/2025, is +85.62 Billion.The Serie's change in United States Dollars from it's maximum value, on 9/30/2022, to it's latest available value, on 3/31/2025, is -197.02 Billion.Secondary Income Balance (USD)The indicator "Secondary Income Balance (USD)" stands at -3.24 Billion United States Dollars as of 3/31/2025. Regarding the One-Year-Change of the series, the current value constitutes an increase of 4.61 Billion United States Dollars compared to the value the year prior.The 1 year change is 4.61 Billion United States Dollars.The 3 year change is 1.19 Billion United States Dollars.The 5 year change is 5.37 Billion United States Dollars.The 10 year change is 4.04 Billion United States Dollars.The Serie's long term average value is -5.45 Billion United States Dollars. It's latest available value, on 3/31/2025, is 2.21 Billion United States Dollars higher, compared to it's long term average value.The Serie's change in United States Dollars from it's minimum value, on 12/31/2019, to it's latest available value, on 3/31/2025, is +6.98 Billion.The Serie's change in United States Dollars from it's maximum value, on 3/31/2001, to it's latest available value, on 3/31/2025, is -3.07 Billion.Primary Income Balance (USD)The indicator "Primary Income Balance (USD)" stands at -29.02 Billion United States Dollars as of 3/31/2025, the lowest value since 12/31/2023. Regarding the One-Year-Change of the series, the current value constitutes an decrease of -5.37 Billion United States Dollars compared to the value the year prior.The 1 year change is -5.37 Billion United States Dollars.The 3 year change is 19.99 Billion United States Dollars.The 5 year change is 19.20 Billion United States Dollars.The 10 year change is 31.14 Billion United States Dollars.The Serie's long term average value is -37.25 Billion United States Dollars. It's latest available value, on 3/31/2025, is 8.23 ...

TThe ERS International Macroeconomic Data Set provides historical and projected data for 181 countries that account for more than 99 percent of the world economy. These data and projections are assembled explicitly to serve as underlying assumptions for the annual USDA agricultural supply and demand projections, which provide a 10-year outlook on U.S. and global agriculture. The macroeconomic projections describe the long-term, 10-year scenario that is used as a benchmark for analyzing the impacts of alternative scenarios and macroeconomic shocks.

Explore the International Macroeconomic Data Set 2015 for annual growth rates, consumer price indices, real GDP per capita, exchange rates, and more. Get detailed projections and forecasts for countries worldwide.

Annual growth rates, Consumer price indices (CPI), Real GDP per capita, Real exchange rates, Population, GDP deflator, Real gross domestic product (GDP), Real GDP shares, GDP, projections, Forecast, Real Estate, Per capita, Deflator, share, Exchange Rates, CPI

Afghanistan, Albania, Algeria, Angola, Antigua and Barbuda, Argentina, Armenia, Australia, Austria, Azerbaijan, Bahamas, Bahrain, Bangladesh, Barbados, Belarus, Belgium, Belize, Benin, Bhutan, Bolivia, Bosnia and Herzegovina, Botswana, Brazil, Brunei, Bulgaria, Burkina Faso, Burundi, Côte d'Ivoire, Cabo Verde, Cambodia, Cameroon, Canada, Central African Republic, Chad, Chile, China, Colombia, Congo, Costa Rica, Croatia, Cuba, Cyprus, Denmark, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Estonia, Eswatini, Ethiopia, Fiji, Finland, France, Gabon, Gambia, Georgia, Germany, Ghana, Greece, Grenada, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, Hungary, Iceland, India, Indonesia, Iran, Iraq, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kazakhstan, Kenya, Kuwait, Kyrgyzstan, Laos, Latvia, Lebanon, Lesotho, Liberia, Libya, Lithuania, Luxembourg, Madagascar, Malawi, Malaysia, Maldives, Mali, Malta, Mauritania, Mauritius, Mexico, Moldova, Mongolia, Morocco, Mozambique, Myanmar, Namibia, Nepal, Netherlands, New Zealand, Nicaragua, Niger, Nigeria, Norway, Oman, Pakistan, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Rwanda, Samoa, Saudi Arabia, Senegal, Serbia, Seychelles, Sierra Leone, Singapore, Slovakia, Slovenia, Solomon Islands, South Africa, Spain, Sri Lanka, Sudan, Suriname, Sweden, Switzerland, Syria, Tajikistan, Tanzania, Thailand, Togo, Tonga, Trinidad and Tobago, Tunisia, Turkey, Turkmenistan, Uganda, Ukraine, United Arab Emirates, United Kingdom, Uruguay, Uzbekistan, Vanuatu, Venezuela, Vietnam, Yemen, Zambia, Zimbabwe, WORLD Follow data.kapsarc.org for timely data to advance energy economics research. Notes:

Developed countries/1 Australia, New Zealand, Japan, Other Western Europe, European Union 27, North America

Developed countries less USA/2 Australia, New Zealand, Japan, Other Western Europe, European Union 27, Canada

Developing countries/3 Africa, Middle East, Other Oceania, Asia less Japan, Latin America;

Low-income developing countries/4 Haiti, Afghanistan, Nepal, Benin, Burkina Faso, Burundi, Central African Republic, Chad, Democratic Republic of Congo, Eritrea, Ethiopia, Gambia, Guinea, Guinea-Bissau, Liberia, Madagascar, Malawi, Mali, Mozambique, Niger, Rwanda, Senegal, Sierra Leone, Somalia, Tanzania, Togo, Uganda, Zimbabwe;

Emerging markets/5 Mexico, Brazil, Chile, Czech Republic, Hungary, Poland, Slovakia, Russia, China, India, Korea, Taiwan, Indonesia, Malaysia, Philippines, Thailand, Vietnam, Singapore

BRIICs/5 Brazil, Russia, India, Indonesia, China; Former Centrally Planned Economies

Former centrally planned economies/7 Cyprus, Malta, Recently acceded countries, Other Central Europe, Former Soviet Union

USMCA/8 Canada, Mexico, United States

Europe and Central Asia/9 Europe, Former Soviet Union

Middle East and North Africa/10 Middle East and North Africa

Other Southeast Asia outlook/11 Malaysia, Philippines, Thailand, Vietnam

Other South America outlook/12 Chile, Colombia, Peru, Bolivia, Paraguay, Uruguay

Indicator Source

Real gross domestic product (GDP) World Bank World Development Indicators, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by the Economic Research Service all converted to a 2015 base year.

Real GDP per capita U.S. Department of Agriculture, Economic Research Service, Macroeconomic Data Set, GDP table and Population table.

GDP deflator World Bank World Development Indicators, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by the Economic Research Service, all converted to a 2015 base year.

Real GDP shares U.S. Department of Agriculture, Economic Research Service, Macroeconomic Data Set, GDP table.

Real exchange rates U.S. Department of Agriculture, Economic Research Service, Macroeconomic Data Set, CPI table, and Nominal XR and Trade Weights tables developed by the Economic Research Service.

Consumer price indices (CPI) International Financial Statistics International Monetary Fund, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by the Economic Research Service, all converted to a 2015 base year.

Population Department of Commerce, Bureau of the Census, U.S. Department of Agriculture, Economic Research Service, International Data Base.

United States US: Share of Tariff Lines with Specific Rates: Primary Products data was reported at 2.760 % in 2016. This records a decrease from the previous number of 2.785 % for 2015. United States US: Share of Tariff Lines with Specific Rates: Primary Products data is updated yearly, averaging 2.713 % from Dec 1989 (Median) to 2016, with 27 observations. The data reached an all-time high of 3.650 % in 1993 and a record low of 0.023 % in 1997. United States US: Share of Tariff Lines with Specific Rates: Primary Products data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Trade Tariffs. Share of tariff lines with specific rates is the share of lines in the tariff schedule that are set on a per unit basis or that combine ad valorem and per unit rates. It shows the extent to which countries use tariffs based on physical quantities or other, non-ad valorem measures. Primary products are commodities classified in SITC revision 3 sections 0-4 plus division 68 (nonferrous metals).; ; World Bank staff estimates using the World Integrated Trade Solution system, based on data from United Nations Conference on Trade and Development's Trade Analysis and Information System (TRAINS) database.; ;

Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretability. We also formatted the data into a standard data format.

Each Project Tycho dataset contains case counts for a specific condition (e.g. measles) and for a specific country (e.g. The United States). Case counts are reported per time interval. In addition to case counts, datasets include information about these counts (attributes), such as the location, age group, subpopulation, diagnostic certainty, place of acquisition, and the source from which we extracted case counts. One dataset can include many series of case count time intervals, such as "US measles cases as reported by CDC", or "US measles cases reported by WHO", or "US measles cases that originated abroad", etc.

Depending on the intended use of a dataset, we recommend a few data processing steps before analysis: - Analyze missing data: Project Tycho datasets do not include time intervals for which no case count was reported (for many datasets, time series of case counts are incomplete, due to incompleteness of source documents) and users will need to add time intervals for which no count value is available. Project Tycho datasets do include time intervals for which a case count value of zero was reported. - Separate cumulative from non-cumulative time interval series. Case count time series in Project Tycho datasets can be "cumulative" or "fixed-intervals". Cumulative case count time series consist of overlapping case count intervals starting on the same date, but ending on different dates. For example, each interval in a cumulative count time series can start on January 1st, but end on January 7th, 14th, 21st, etc. It is common practice among public health agencies to report cases for cumulative time intervals. Case count series with fixed time intervals consist of mutually exclusive time intervals that all start and end on different dates and all have identical length (day, week, month, year). Given the different nature of these two types of case count data, we indicated this with an attribute for each count value, named "PartOfCumulativeCountSeries".

Midyear population estimates and projections for all countries and areas of the world with a population of 5,000 or more // Source: U.S. Census Bureau, Population Division, International Programs Center// Note: Total population available from 1950 to 2100 for 227 countries and areas. Other demographic variables available from base year to 2100. Base year varies by country and therefore data are not available for all years for all countries. See methodologyhttps://www.census.gov/programs-surveys/international-programs/about/idb.html

This dataset presents information on historical central government revenues for 31 countries in Europe and the Americas for the period from 1800 (or independence) to 2012. The countries included are: Argentina, Australia, Austria, Belgium, Bolivia, Brazil, Canada, Chile, Colombia, Denmark, Ecuador, Finland, France, Germany (West Germany between 1949 and 1990), Ireland, Italy, Japan, Mexico, New Zealand, Norway, Paraguay, Peru, Portugal, Spain, Sweden, Switzerland, the Netherlands, the United Kingdom, the United States, Uruguay, and Venezuela. In other words, the dataset includes all South American, North American, and Western European countries with a population of more than one million, plus Australia, New Zealand, Japan, and Mexico. The dataset contains information on the public finances of central governments. To make such information comparable cross-nationally we have chosen to normalize nominal revenue figures in two ways: (i) as a share of the total budget, and (ii) as a share of total gross domestic product. The total tax revenue of the central state is disaggregated guided by the Government Finance Statistics Manual 2001 of the International Monetary Fund (IMF) which provides a classification of types of revenue, and describes in detail the contents of each classification category. Given the paucity of detailed historical data and the needs of our project, we combined some subcategories. First, we are interested in total tax revenue (centaxtot), as well as the shares of total revenue coming from direct (centaxdirectsh) and indirect (centaxindirectsh) taxes. Further, we measure two sub-categories of direct taxation, namely taxes on property (centaxpropertysh) and income (centaxincomesh). For indirect taxes, we separate excises (centaxexcisesh), consumption (centaxconssh), and customs(centaxcustomssh).

For a more detailed description of the dataset and the coding process, see the codebook available in the .zip-file.

Purpose:

This dataset presents information on historical central government revenues for 31 countries in Europe and the Americas for the period from 1800 (or independence) to 2012. The countries included are: Argentina, Australia, Austria, Belgium, Bolivia, Brazil, Canada, Chile, Colombia, Denmark, Ecuador, Finland, France, Germany (West Germany between 1949 and 1990), Ireland, Italy, Japan, Mexico, New Zealand, Norway, Paraguay, Peru, Portugal, Spain, Sweden, Switzerland, the Netherlands, the United Kingdom, the United States, Uruguay, and Venezuela. In other words, the dataset includes all South American, North American, and Western European countries with a population of more than one million, plus Australia, New Zealand, Japan, and Mexico. The dataset contains information on the public finances of central governments. To make such information comparable cross-nationally we have chosen to normalize nominal revenue figures in two ways: (i) as a share of the total budget, and (ii) as a share of total gross domestic product. The total tax revenue of the central state is disaggregated guided by the Government Finance Statistics Manual 2001 of the International Monetary Fund (IMF) which provides a classification of types of revenue, and describes in detail the contents of each classification category. Given the paucity of detailed historical data and the needs of our project, we combined some subcategories. First, we are interested in total tax revenue (centaxtot), as well as the shares of total revenue coming from direct (centaxdirectsh) and indirect (centaxindirectsh) taxes. Further, we measure two sub-categories of direct taxation, namely taxes on property (centaxpropertysh) and income (centaxincomesh). For indirect taxes, we separate excises (centaxexcisesh), consumption (centaxconssh), and customs(centaxcustomssh).

This dataset is about books. It has 32 rows and is filtered where the book subjects is Developing countries-Foreign relations-United States. It features 9 columns including author, publication date, language, and book publisher.

Gross Purchases by Foreigners: New Zealand: US Corp Stocks data was reported at 395.000 USD mn in May 2018. This records an increase from the previous number of 333.000 USD mn for Apr 2018. Gross Purchases by Foreigners: New Zealand: US Corp Stocks data is updated monthly, averaging 152.000 USD mn from Jan 2001 (Median) to May 2018, with 209 observations. The data reached an all-time high of 580.000 USD mn in Aug 2014 and a record low of 18.000 USD mn in Sep 2001. Gross Purchases by Foreigners: New Zealand: US Corp Stocks data remains active status in CEIC and is reported by US Department of Treasury. The data is categorized under Global Database’s USA – Table US.Z043: Foreign Purchases and Sales in Long Term Securities: Other Countries.

Series Name: Total assistance for development by recipient countries (millions of current United States dollars)Series Code: DC_TRF_TOTLRelease Version: 2020.Q2.G.03 This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 10.b.1: Total resource flows for development, by recipient and donor countries and type of flow (e.g. official development assistance, foreign direct investment and other flows)Target 10.b: Encourage official development assistance and financial flows, including foreign direct investment, to States where the need is greatest, in particular least developed countries, African countries, small island developing States and landlocked developing countries, in accordance with their national plans and programmesGoal 10: Reduce inequality within and among countriesFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

About this dataset

Context

The dataset tabulates the Country Life Acres population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Country Life Acres across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2022, the population of Country Life Acres was 72, a 0.00% decrease year-by-year from 2021. Previously, in 2021, Country Life Acres population was 72, a decline of 1.37% compared to a population of 73 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Country Life Acres decreased by 9. In this period, the peak population was 84 in the year 2001. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Country Life Acres is shown in this column.
• Year on Year Change: This column displays the change in Country Life Acres population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Country Life Acres Population by Year. You can refer the same here

Techsalerator’s Business Funding Data for North America is an extensive and insightful resource designed for businesses, investors, and financial analysts who need a deep understanding of the Asian funding landscape. This dataset meticulously captures and categorizes critical information about the funding activities of companies across the continent, providing valuable insights into the financial health and investment trends within various sectors.

What the Dataset Includes: Funding Rounds: Detailed records of funding rounds for companies in North America, including the size of the round, the date it occurred, and the stages of investment (Seed, Series A, Series B, etc.).

Investment Sources: Information on the sources of investment, such as venture capital firms, private equity investors, angel investors, and corporate investors.

Financial Milestones: Key financial achievements and benchmarks reached by companies, including valuation increases, revenue milestones, and profitability metrics.

Sector-Specific Data: Insights into how different sectors are performing, with data segmented by industry verticals such as technology, healthcare, finance, and consumer goods.

Geographic Breakdown: An overview of funding trends and activities specific to each North America country, allowing users to identify regional patterns and opportunities.

EU Countries Included in the Dataset: Antigua and Barbuda Bahamas Barbados Belize Canada Costa Rica Cuba Dominica Dominican Republic El Salvador Grenada Guatemala Haiti Honduras Jamaica Mexico Nicaragua Panama Saint Kitts and Nevis Saint Lucia Saint Vincent and the Grenadines Trinidad and Tobago United States

Benefits of the Dataset: Informed Decision-Making: Investors and analysts can use the data to make well-informed investment decisions by understanding funding trends and financial health across different regions and sectors. Strategic Planning: Businesses can leverage the insights to identify potential investors, benchmark against industry peers, and plan their funding strategies effectively. Market Analysis: The dataset helps in analyzing market dynamics, identifying emerging sectors, and spotting investment opportunities across North America. Techsalerator’s Business Funding Data for North America is a vital tool for anyone involved in the financial and investment sectors, offering a granular view of the funding landscape and enabling more strategic and data-driven decisions.

This description provides a more detailed view of what the dataset offers and highlights the relevance and benefits for various stakeholders.

This database compiles currrent standardized statistics on sovereign debt issuances for the Latin American and Caribbean (LAC) region and contains biannual data starting in 2006 through December 2023. Sovereign debt data is classified by legislation, creditor, currency, and maturity, among other areas, for 26 LAC countries. The availability of valid, comparable, and standardized public debt data is essential for the implementation of sound policies. As such, at the core of the LAC Debt Group initiative is the development of a standardized sovereign debt database to help debt managers, policymakers, and other actors of financial markets analyze the evolution and composition of public debt in the region and conduct cross-country comparisons. The data are provided by LAC public debt offices in response to a questionnaire specifically designed to allow comparability of data. The questionnaire, whose response is non-compulsory, is intended to compile current standardized statistics for objective and homogeneous definitions of public debt.

The Global Welfare Dataset (GLOW) is a cross-national panel dataset that aims at facilitating comparative social policy research on the Global North and Global South. The database includes 381 variables on 61 countries from years between 1989 and 2015. The database has four main categories of data: welfare, development, economy and politics.The data is the result of an original data compilation assembled by using information from several international and domestic sources. Missing data was supplemented by domestic sources where available. We sourced data primarily from these international databases:Atlas of Social Protection Indicators of Resilience and Equity – ASPIRE (World Bank)Government Finance Statistics (International Monetary Fund)Social Expenditure Database – SOCX (Organisation for Economic Co-operation and Development)Social Protection Statistics – ESPROSS (Eurostat)Social Security Inquiry (International Labour Organization)Social Security Programs Throughout the World (Social Security Administration)Statistics on Income and Living Conditions – EU-SILC (European Union)World Development Indicators (World Bank)However, much of the welfare data from these sources are not compatible between all country cases. We conducted an extensive review of the compatibility of the data and computed compatible figures where possible. Since the heart of this database is the provision of social assistance across a global sample, we applied the ASPIRE methodology in order to build comparable indicators across European and Emerging Market economies. Specifically, we constructed indicators of average per capita transfers and coverage rates for social assistance programs for all the country cases not included in the World Bank’s ASPIRE dataset (Austria, Belgium, Bulgaria, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Luxembourg, Netherlands, Norway, Poland, Portugal, Romania, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, and United Kingdom.)For details, please see:https://glow.ku.edu.tr/about