The Economic Indicator Service (EIS) aims to deliver economic content to financial institutions on both buy and sell-side and service providers. This new service currently covers 34,351 recurring macro-economic indicators from 135 countries ( as of December 16, 2019 ) such as GDP data, unemployment releases, PMI numbers etc.

Economic Indicator Service gathers the major economic events from a variety of regions and countries around the globe and provides an Economic Events Data feed and Economic Calendar service to our clients. This service includes all previous historic data on economic indicators that are currently available on the database.

Depending on availability, information regarding economic indicators, including the details of the issuing agency as well as historical data series can be made accessible for the client. Key information about EIS: • Cloud-based service for Live Calendar – delivered via HTML/JavaScript application formats, which can then be embedded onto any website using iFrames • Alternatives methods available – such as API and JSON feed for the economic calendar that can be integrated into the company’s system • Live data – updated 24/5, immediately after the data has been released • Historical data – includes a feed of all previous economic indicators available We are currently adding additional indicators/countries from Africa as well as expanding our coverage of Indicators in G20. The calendar includes the following. • Recurring & Non-recurring indicators covering 136 countries across 21 regions. • Indicators showing high, medium, and low impact data. • Indicators showing actual, previous, and forecast data. • Indicators can be filtered across 16 subtypes. • News generation for selected high-impact data. • Indicator description and historical data up to the latest eight historical points with a chart.

1/ Historical labor force and jobs data revised. For details, see Hawaii DLIR http://www.hiwi.org/cgi/dataanalysis/?PAGEID=94 .
2/ Data from January 1999 have been revised and consist of domestic and international air arrivals. They are not comparable to Eastbound and Westbound series.
Source: Hawaii Department of Labor & Industrial Relations; Hawaii Department of Taxation; Hawaii Department of Business, Economic
Development and Tourism; county building departments; Honolulu Board of REALTORS® compiled by Harvey Shapiro, Title Guaranty of
Hawaii and Realtors® Association of Maui, Inc. Final tables compiled by Statistics and Data Support Branch, READ, DBEDT

National statistical systems are facing significant challenges. These challenges arise from increasing demands for high quality and trustworthy data to guide decision making, coupled with the rapidly changing landscape of the data revolution. To help create a mechanism for learning amongst national statistical systems, the World Bank has developed improved Statistical Performance Indicators (SPI) to monitor the statistical performance of countries. The SPI focuses on five key dimensions of a country’s statistical performance: (i) data use, (ii) data services, (iii) data products, (iv) data sources, and (v) data infrastructure. This will replace the Statistical Capacity Index (SCI) that the World Bank has regularly published since 2004.

The SPI focus on five key pillars of a country’s statistical performance: (i) data use, (ii) data services, (iii) data products, (iv) data sources, and (v) data infrastructure. The SPI are composed of more than 50 indicators and contain data for 186 countries. This set of countries covers 99 percent of the world population. The data extend from 2016-2023, with some indicators going back to 2004.

For more information, consult the academic article published in the journal Scientific Data. https://www.nature.com/articles/s41597-023-01971-0.

The Ancillary Data component of the Indicators of Coastal Water Quality Collection includes a 5 arc-minute (approximately 9 x 9 km at the equator) sequence grid, grid cell centroids that relate to the grid cells in the tabular "Indicators of Coastal Water Quality: Change in Chlorophyll-a Concentration 1998-2007" data set, and a country buffer data set that is divided by exclusive economic zones (EEZ). The data are produced by the Columbia University Center for International Earth Science Information Network (CIESIN).

Description

This dataset reflects theU.S. Chronic Disease Indicators from 2019 to 2022. CDC's Division of Population Health provides a cross-cutting set of 115 indicators developed by consensus among CDC, the Council of State and Territorial Epidemiologists, and the National Association of Chronic Disease Directors. These indicators allow states and territories to uniformly define, collect, and report chronic disease data that are important to public health practice in their area. In addition to providing access to state-specific indicator data, the CDI web site serves as a gateway to additional information and data resources.

Acknowledgements

https://catalog.data.gov/

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There are two datasets related to the County Level Prevention Agenda Tracking Indicators posted on this site. Each dataset consists of county level data for 70 health tracking indicators and sub-indicators for the Prevention Agenda 2019-2024: New York State’s Health Improvement Plan. A health tracking indicator is a metric through which progress on a certain area of health improvement can be assessed. The indicators are organized by the Priority Area of the Prevention Agenda as well as the Focus Area under each Priority Area. The data sets also include indicators about major cross-cutting health outcomes and about health disparities. Each dataset includes tracking indicators for the five Priority Areas of the Prevention Agenda 2019-2024. The most recent year dataset includes the most recent county level data for all indicators. The trend dataset includes the most recent county level data and historical data, where available. Each dataset also includes the Prevention Agenda 2024 state objectives for the indicators. Sub-indicators are included in these datasets to measure health disparities among socioeconomic groups.

Graph and download economic data for Composite Leading Indicators: Composite Leading Indicator (CLI) Normalized for Germany (DEULOLITONOSTSAM) from Jan 1961 to Jan 2024 about leading indicator and Germany.

The Indicators chosen represent the best proxies we could find for the complex disparity themes we set out to measure. The following criteria were used to determining the indicators included in each of the topics in the final framework: 1. Data is available, high quality, and from a reliable source. 2. We will be able to calculate change over time (i.e., data is updated and accessible on an annual basis and changes from year to year can be meaningfully interpreted). 3. There is a strong causal model for why this Indicator matters (i.e., we understand the context behind the Indicator and how disparities affect people). 4. The data accurately represents the impact of inequity on people’s lives (e.g., not measuring quantity when what matters is quality).

These indicators are presented by Public Health — Seattle & King County, in conjunction with the King County Hospitals for a Healthier Community (HHC). The data offer a comprehensive overview of demographics, health, and health behaviors among King County residents.

Users can search by key word or topic area to filter the table of contents displayed below. After clicking on an indicator, a summary tab will open and users can click on additional tabs to explore data analyzed by demographic characteristics, see how rates have changed over time, and view data for cities/neighborhoods. Most indicators are interactive and users can hover over maps or charts to find more information.

The data presented on this website may be reproduced without permission. Please use the following citation when reproducing: "Retrieved (date) from Public Health – Seattle & King County, Community Health Indicators. www.kingcounty.gov/chi"

The report covers the most recently available economic, financial, social, and environmental indicators for the 50 regional members of Asian Development Bank. It discusses trends in development progress and the challenges of achieving inclusive and sustainable economic growth across Asia and the Pacific. This 56th edition also explores how harmonized environmental-economic data can serve as a public good for guiding cooperation toward resilient, sustainable development.

US County Demographics

Social, Health, and Economic Indicators

By Danny [source]

About this dataset

This dataset contains US county-level demographic data from 2016, giving insight into the health and economic conditions of counties in the United States. Aggregated and filtered from various sources such as the US Census Small Area Income and Poverty Estimates (SAIPE) Program, American Community Survey, CDC National Center for Health Statistics, and more, this comprehensive dataset provides information on population as well as desert population for each county. Additionally, data is split between metropolitan and nonmetropolitan areas according to the Office of Management and Budget's 2013 classification scheme. Valuable information pertaining to infant mortality rates and total population are also included in this detailed set of data. Use this dataset to gain a better understanding of one of our nation's most essential regions

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How to use the dataset

• Look at the information within the 'About this Dataset' section to have an understanding of what data sources were used to create this dataset as well as any transformations that may have been done while creating it.
• Familiarize yourself with the columns provided in the data set to understand what information is available for each county such as total population (totpop), parental education level (educationLvl), median household income (medianIncome), etc.,
• Use a combination of filtering and sorting techniques to narrow down results and focus in on more specific county demographics that you are looking for such as total households living below poverty line by state or median household income per capita between two counties etc.,
• Keep in mind any additional transformations/simplifications/aggregations done during step 2 when using your data for analysis. For example, if certain variables were pivoted during step two from being rows into columns because it was easier to work with multiple years of income levels by having them all consolidated into one column then be aware that some states may not appear in all records due to those transformations being applied differently between regions which could result in missing values or other inconsistencies when doing downstream analysis on your selected variables.
• Utilize resources such as Wikipedia and government census estimates if you need more detailed information surrounding these demographic characteristics beyond what's available within our current dataset – these can be helpful when conducting further research outside of solely relying on our provided spreadsheet values alone!

Research Ideas

• Creating a US county-level heat map of infant mortality rates, offering insight into which areas are most at risk for poor health outcomes.
• Generating predictive models from the population data to anticipate and prepare for future population trends in different states or regions.
• Developing an interactive web-based tool for school districts to explore potential impacts of student mobility on their area's population stability and diversity

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: Food Desert.csv | Column name | Description | |:--------------------|:----------------------------------------------------------------------------------| | year | The year the data was collected. (Integer) | | fips | The Federal Information Processing Standard (FIPS) code for the county. (Integer) | | state_fips | The FIPS code for the state. (Integer) | | county_fips | The FIPS code for the county. (Integer)...

The international investment position (IIP) is a statistical statement that shows at a point in time the value and composition of: -financial assets of residents of an economy that are claims on non-residents and gold bullion held as reserve assets, and -liabilities of residents of an economy to non-residents. The difference between an economy’s external financial assets and liabilities is the economy’s net IIP, which may be positive or negative. Respectively the net international investment position (NIIP) provides an aggregate view of the net financial position (assets minus liabilities) of a country vis-à-vis the rest of the world. It allows for a stock-flow analysis of external position of the country. The indicator is expressed in percent of GDP. The indicator is based on the Eurostat data from the Balance of payment statistics, i.e. the same data source used for the current account balance. Definitions are based on the IMF Sixth Balance of Payments Manual (BPM6).

Graph and download economic data for Leading Indicators OECD: Component Series: Stocks: Normalised for Japan (JPNLOCOSKNOSTSAM) from Jan 1960 to Nov 2023 about leading indicator, stocks, and Japan.

Graph and download economic data for Leading Indicators OECD: Reference series: Gross Domestic Product (GDP): Normalised for Greece (GRCLORSGPNOSTSAM) from Feb 1960 to May 2022 about leading indicator, Greece, and GDP.

Data for School year-end 1994 through year-end 2020. State oral health surveys are the data sources for these indicators. States periodically conduct independent screening surveys of a probability sample designed to be representative of all third-grade students in the state. Some states also conduct surveys of students in other grades in school, or of Head Start program enrollees. This surveillance activity is voluntary. States submit their data to the Association of State and Territorial Dental Directors (ASTDD), where the survey design and data collected are reviewed for quality and against the criteria for inclusion in NOHSS, before being sent to CDC for inclusion in Oral Health Data. For more information, see: http://www.cdc.gov/oralhealthdata/overview/childIndicators/

Key indicators - annual data

To fully implement and monitor progress on the Sustainable Development Goals, decision makers everywhere need data and statistics that are accurate, timely, sufficiently disaggregated, relevant, accessible and easy to use. The Open SDG Data Hub promotes the exploration, analysis, and use of authoritative SDG data sources for evidence-based decision-making and advocacy. Its goal is to enable data providers, managers and users to discover, understand, and communicate patterns and interrelationships in the wealth of SDG data and statistics that are now available.The global Sustainable Development Goal indicators API gives programmatic access to the global indicators database using the OpenAPI specification. The database, maintained by the Statistics Division, released on 20 June 2018 contains over 1 million observations. However, this is not the number of unique observations, as several indicators and their data are repeated. For the complete list of the indicators that are repeated in the indicator framework please see https://unstats.un.org/sdgs/indicators/indicators-list/ .

Financial Performance Indicator: Northeast: Paraíba data was reported at 98.010 % in 2022. This records a decrease from the previous number of 106.570 % for 2021. Financial Performance Indicator: Northeast: Paraíba data is updated yearly, averaging 95.100 % from Dec 2012 (Median) to 2022, with 11 observations. The data reached an all-time high of 106.730 % in 2018 and a record low of 84.250 % in 2015. Financial Performance Indicator: Northeast: Paraíba data remains active status in CEIC and is reported by Ministry of Cities. The data is categorized under Brazil Premium Database’s Environmental, Social and Governance Sector – Table BR.EVB018: Economic, Financial and Administrative Indicators: Financial Performance.

Contains data from the World Bank's data portal covering the following topics which also exist as individual datasets on HDX: Agriculture and Rural Development, Aid Effectiveness, Economy and Growth, Education, Energy and Mining, Environment, Financial Sector, Health, Infrastructure, Social Protection and Labor, Poverty, Private Sector, Public Sector, Science and Technology, Social Development, Urban Development, Gender, Millenium development goals, Climate Change, External Debt, Trade.

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.