Graph and download economic data for Value Added by Industry: Educational Services, Health Care, and Social Assistance: Educational Services as a Percentage of GDP (VAPGDPES) from Q1 2005 to Q1 2025 about value added, social assistance, health, education, private industries, percent, services, private, industry, GDP, and USA.

I discuss the health transition in the United States, bringing new data to bear on health indicators and investigating the changing relationship between health, income, and the environment. I argue that scientific advances played an outsize role and that health improvements were largest among the poor. Health improvements were not a precondition for modern economic growth. The gains to health are largest when the economy has moved from "brawn" to "brains" because this is when the wage returns to education are high, leading the healthy to obtain more education. More education may improve use of health knowledge, producing a virtuous cycle. (JEL H51, I10, J13, N31, N32)

In 2023, U.S. national health expenditure as a share of its gross domestic product (GDP) reached 17.6 percent, this was an increase on the previous year. The United States has the highest health spending based on GDP share among developed countries. Both public and private health spending in the U.S. is much higher than other developed countries. Why the U.S. pays so much moreWhile private health spending in Canada stays at around three percent and in Germany under two percent of the gross domestic product, it is nearly nine percent in the United States. Another reason for high costs can be found in physicians’ salaries, which are much higher in the U.S. than in other wealthy countries. A general practitioner in the U.S. earns nearly twice as much as the average physician in other high-income countries. Additionally, medicine spending per capita is also significantly higher in the United States. Finally, inflated health care administration costs are another of the predominant factors which make health care spending in the U.S. out of proportion. It is important to state that Americans do not pay more because they have a higher health care utilization, but mainly because of higher prices. Expected developmentsBy 2031, it is expected that health care spending in the U.S. will reach nearly one fifth of the nation’s gross domestic product. Or in dollar-terms, health care expenditures will accumulate to about seven trillion U.S. dollars in total.

Graph and download economic data for Economic Policy Uncertainty Index: Categorical Index: Health care (EPUHEALTHCARE) from Jan 1985 to Jun 2025 about healthcare, uncertainty, health, World, and indexes.

In 2024, the U.S. GDP increased from the previous year to about 29.18 trillion U.S. dollars. Gross domestic product (GDP) refers to the market value of all goods and services produced within a country. In 2024, the United States has the largest economy in the world. What is GDP? Gross domestic product is one of the most important indicators used to analyze the health of an economy. GDP is defined by the BEA as the market value of goods and services produced by labor and property in the United States, regardless of nationality. It is the primary measure of U.S. production. The OECD defines GDP as an aggregate measure of production equal to the sum of the gross values added of all resident, institutional units engaged in production (plus any taxes, and minus any subsidies, on products not included in the value of their outputs). GDP and national debt Although the United States had the highest Gross Domestic Product (GDP) in the world in 2022, this does not tell us much about the quality of life in any given country. GDP per capita at purchasing power parity (PPP) is an economic measurement that is thought to be a better method for comparing living standards across countries because it accounts for domestic inflation and variations in the cost of living. While the United States might have the largest economy, the country that ranked highest in terms of GDP at PPP was Luxembourg, amounting to around 141,333 international dollars per capita. Singapore, Ireland, and Qatar also ranked highly on the GDP PPP list, and the United States ranked 9th in 2022.

Note: Blueprint has been retired as of June 15, 2021. This dataset will be kept up for historical purposes, but will no longer be updated. California has a new blueprint for reducing COVID-19 in the state with revised criteria for loosening and tightening restrictions on activities. Every county in California is assigned to a tier based on its test positivity and adjusted case rate for tier assignment. Additionally, a new health equity metric took effect on October 6, 2020. In order to advance to the next less restrictive tier, each county will need to meet an equity metric or demonstrate targeted investments to eliminate disparities in levels of COVID-19 transmission, depending on its size. The California Health Equity Metric is designed to help guide counties in their continuing efforts to reduce COVID-19 cases in all communities and requires more intensive efforts to prevent and mitigate the spread of COVID-19 among Californians who have been disproportionately impacted by this pandemic. Please see https://www.cdph.ca.gov/Programs/CID/DCDC/Pages/COVID-19/COVID19CountyMonitoringOverview.aspx for more information. Also, in lieu of a Data Dictionary, please refer to the detailed explanation of the data columns in Appendix 1 of the above webpage. Because this data is in machine-readable format, the merged headers at the top of the source spreadsheet have not been included: The first 8 columns are under the header "County Status as of Tier Assignment" The next 3 columns are under the header "Current Data Week Tier and Metric Tiers for Data Week" The next 4 columns are under the header "Case Rate Adjustment Factors" The next column is under the header "Small County Considerations" The last 5 columns are under the header "Health Equity Framework Parameters"

Yemen YE: Current Health Expenditure: % of GDP data was reported at 5.983 % in 2015. This records an increase from the previous number of 5.637 % for 2014. Yemen YE: Current Health Expenditure: % of GDP data is updated yearly, averaging 5.022 % from Dec 2000 (Median) to 2015, with 16 observations. The data reached an all-time high of 5.983 % in 2015 and a record low of 4.139 % in 2000. Yemen YE: Current Health Expenditure: % of GDP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Yemen – Table YE.World Bank: Health Statistics. Level of current health expenditure expressed as a percentage of GDP. Estimates of current health expenditures include healthcare goods and services consumed during each year. This indicator does not include capital health expenditures such as buildings, machinery, IT and stocks of vaccines for emergency or outbreaks.; ; World Health Organization Global Health Expenditure database (http://apps.who.int/nha/database).; Weighted Average;

Georgia Term Loans: GEL: Economy: Health Care and Social Services data was reported at 107,788.871 GEL th in Jun 2018. This records a decrease from the previous number of 112,366.927 GEL th for May 2018. Georgia Term Loans: GEL: Economy: Health Care and Social Services data is updated monthly, averaging 16,687.426 GEL th from Oct 2003 (Median) to Jun 2018, with 177 observations. The data reached an all-time high of 193,121.833 GEL th in Jul 2015 and a record low of 213.259 GEL th in Oct 2003. Georgia Term Loans: GEL: Economy: Health Care and Social Services data remains active status in CEIC and is reported by National Bank of Georgia . The data is categorized under Global Database’s Georgia – Table GE.KB003: Loans: by Industry.

Dataset Description

This dataset contains the actual and predicted federal funds target rate for the United States from 1990 to 2023. The federal funds target rate is the interest rate at which depository institutions lend their excess reserves to each other overnight. It is set by the Federal Open Market Committee (FOMC) and is a key tool used by the Federal Reserve to influence the economy.

The dataset includes the following five columns:

Release Date: The date on which the data was released by the Federal Reserve. Time: The time of day at which the data was released. Actual: The actual federal funds target rate. Predicted: The predicted federal funds target rate. Forecast: The forecast federal funds target rate.

Data Usage

This dataset can be used for a variety of purposes, including: - Analyzing trends in the federal funds target rate over time. - Forecasting the future path of the federal funds target rate. - Assessing the effectiveness of monetary policy. - Data Quality

The data for this dataset is of high quality. The Federal Reserve is a reputable source of data and the data is updated regularly.

Data Limitations

The data for this dataset is limited to the United States. Additionally, the data does not include information on the factors that influenced the Federal Open Market Committee's decision to set the federal funds target rate.

Global Data: GDP, Life Expectancy & More

This dataset comprises 204 entries and 38 attributes, providing a comprehensive analysis of key economic and social indicators across various countries. It includes a diverse range of metrics, allowing for in-depth exploration of global trends related to GDP, education, health, and environmental factors.

Key Features:

• GDP: Gross Domestic Product (in current US dollars), representing the total economic output of a country.
• Sex Ratio: The ratio of males to females in the population, highlighting demographic trends.
• Life Expectancy: Average lifespan for males and females, an essential indicator of healthcare quality.
• Education Enrollment Rates: Data on primary, secondary, and post-secondary education enrollment for males and females, reflecting educational attainment.
• Unemployment Rate: Percentage of the labor force that is unemployed, indicating economic health.
• Homicide Rate: Number of homicides per 100,000 population, providing insight into safety and crime levels.
• Urban Population Growth: Rate of growth in urban populations, illustrating migration trends.
• CO2 Emissions: Carbon dioxide emissions per capita, an important measure of environmental impact.
• Forested Area: Percentage of land covered by forests, indicating biodiversity and environmental health.
• Tourist Numbers: Total number of international visitors, which can reflect a country's tourism potential.

Applications and Uses:

1. Research and Analysis: Ideal for researchers studying the correlation between economic performance and social indicators. This dataset can help identify trends and patterns relevant to global development.

2. Policy Development: Policymakers can utilize this data to inform decisions on education, healthcare, and environmental policies, aiming to improve national outcomes.

3. Machine Learning and Data Science: Data scientists can apply machine learning techniques to predict economic trends, analyze social impacts, or classify countries based on various indicators.

4. Educational Purposes: Suitable for students and educators in fields like economics, sociology, and environmental science for practical data analysis exercises.

5. Visualization Projects: Perfect for creating compelling visualizations that illustrate relationships between different metrics, aiding in public understanding and engagement.

By leveraging this dataset, users can uncover insights into how different factors influence a country's development, making it a valuable resource for diverse applications across various fields.

Developing a modern low-carbon economy while protecting health is not only a current trend but also an urgent problem that needs to be solved. The growth of the national low-carbon economy is closely related to various sectors; however, it remains unclear how the development of low-carbon economies in these sectors impacts the national economy and the health of residents. Using panel data on carbon emissions and resident health in 28 province-level regions in China, this study employs unit root tests, co-integration tests, and regression analysis to empirically examine the relationship between carbon emissions, low-carbon economic development, health, and GDP in industry, construction, and transportation. The results show that: First, China’s carbon emissions can promote economic development. Second, low-carbon economic development can enhance resident health while improving GDP. Third, low-carbon economic development has a significant positive effect on GDP and resident health in the industrial and transportation sector, but not in the construction sector, and the level of industrial development and carbon emission sources are significant factors contributing to the inconsistency. Our findings complement existing insights into the coupling effect of carbon emissions and economic development across sectors. They can assist policymakers in tailoring low-carbon policies to specific sectors, formulating strategies to optimize energy consumption structures, improving green technology levels, and aiding enterprises in gradually reducing carbon emissions without sacrificing economic benefits, thus achieving low-carbon economic development.

JO: Domestic General Government Health Expenditure: % of GDP data was reported at 3.593 % in 2015. This records a decrease from the previous number of 4.780 % for 2014. JO: Domestic General Government Health Expenditure: % of GDP data is updated yearly, averaging 4.580 % from Dec 2000 (Median) to 2015, with 16 observations. The data reached an all-time high of 6.374 % in 2009 and a record low of 3.552 % in 2004. JO: Domestic General Government Health Expenditure: % of GDP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Jordan – Table JO.World Bank: Health Statistics. Public expenditure on health from domestic sources as a share of the economy as measured by GDP.; ; World Health Organization Global Health Expenditure database (http://apps.who.int/nha/database).; Weighted Average;

United States - Economic Policy Uncertainty : Categorical : Health care was 594.53679 Index in March of 2025, according to the United States Federal Reserve. Historically, United States - Economic Policy Uncertainty : Categorical : Health care reached a record high of 1030.68062 in April of 2020 and a record low of 6.85732 in December of 1985. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Economic Policy Uncertainty : Categorical : Health care - last updated from the United States Federal Reserve on July of 2025.

SHE: All Japan: EI: OU: Health and Medical (HM) data was reported at 601.000 JPY in May 2018. This records an increase from the previous number of 573.000 JPY for Apr 2018. SHE: All Japan: EI: OU: Health and Medical (HM) data is updated monthly, averaging 412.000 JPY from Jan 2015 (Median) to May 2018, with 41 observations. The data reached an all-time high of 601.000 JPY in May 2018 and a record low of 367.000 JPY in Nov 2015. SHE: All Japan: EI: OU: Health and Medical (HM) data remains active status in CEIC and is reported by Statistical Bureau. The data is categorized under Global Database’s Japan – Table JP.H070: Survey of Household Economy: All Japan.

Graph and download economic data for Per Capita Personal Consumption Expenditures: Services: Health Care for Florida (FLPCEPCHLTHCARE) from 1997 to 2023 about healthcare, health, PCE, per capita, FL, consumption expenditures, consumption, personal, services, and USA.

As urbanization speeds up, the concept of healthy cities is receiving more focus. This article compares Chongzuo and Nanning in Guangxi with Beijing to assess the development gaps in cities in Guangxi. An indicator system for healthy cities was designed from six dimensions—healthy economy, healthy population, healthy healthcare, healthy environment, healthy facilities, and healthy transportation—and 26 secondary indicators, which were selected from 2005 to 2022, and an improved factor analysis was used to synthesize a healthy city index (HCI). The number of factors was determined by combining characteristic roots and the variance contribution rate, and the HCI was weighted using the entropy-weighted Topsis method. A comprehensive evaluation of the urban health status of these cities was conducted. The results showed that extracting six common factors had the greatest effect, with a cumulative variance contribution rate of 93.83%. Chongzuo city scored higher in the field of healthcare. The healthy environment score of Nanning was relatively high, which may be related to continuous increases in green measures. In terms of the healthy economy dimension, Beijing was far ahead. However, in recent years, the healthy economy level in Chongzuo has increased, and the GDP growth rate has ranked among the highest in Guangxi. In addition, the growth rate of healthy facilities in Nanning was relatively fast and has been greater than that in Chongzuo in recent years, which indicates that the Nanning Municipal Government believes urban construction and municipal supporting facilities are highly important. In terms of healthy transportation, Chongzuo and Nanning scored higher than Beijing. This may be because the transportation in these two cities is convenient and the traffic density is more balanced than that in Beijing, thereby reducing traffic congestion. Chongzuo had the highest score for a healthy population, and a steadily growing population provides the city with stable human resources, which helps promote urban economic and social development. Finally, relevant policy recommendations were put forwards to enhance the health level of the cities.

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, Climate Change, External Debt, Traedde.

The Health Information Technology for Economic and Clinical Health (HITECH) Act was passed as part of the American Recovery and Reinvestment Act (ARRA) to invest in the U.S. health IT infrastructure. The Office of the National Coordinator for Health IT (ONC) received over $2 billion of these HITECH funds, which was granted to health and community organizations across the U.S. This crosswalk provides geographic data for the service areas of two of these HITECH programs: the Health IT Regional Extension Centers (REC) Program and the Beacon Communities Program. This data can be linked to program financial and performance data to map and visualize program data. You can access the data in multiple formats below.

Updated weekly Public Health — Seattle & King County is monitoring changes in key economic, social, and other health indicators resulting from strategies to slow the spread of COVID-19. The metrics below were selected based on studies from previous outbreaks, which have linked strategies such as social distancing, school closures, and business closures to specific outcomes. Individual indicators in the grid below are updated daily, weekly, or monthly, depending on the source of data. Additional data will be added over time.

Poland PL: Domestic General Government Health Expenditure: % of GDP data was reported at 4.436 % in 2015. This records an increase from the previous number of 4.411 % for 2014. Poland PL: Domestic General Government Health Expenditure: % of GDP data is updated yearly, averaging 4.436 % from Dec 2013 (Median) to 2015, with 3 observations. The data reached an all-time high of 4.506 % in 2013 and a record low of 4.411 % in 2014. Poland PL: Domestic General Government Health Expenditure: % of GDP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Poland – Table PL.World Bank: Health Statistics. Public expenditure on health from domestic sources as a share of the economy as measured by GDP.; ; World Health Organization Global Health Expenditure database (http://apps.who.int/nha/database).; Weighted Average;