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)

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

Nearly 10 percent of the world's economic resources are devoted to health care. But why do certain countries devote more resources to public health? Why are some countries better than others at achieving tangible health outcomes using the same level of economic resources? Surprisingly, political scientists and public health scholars have done only limited systematic research on these important questions. We address them by developing and testing an analytical framework of domestic and international political influences on public health. We use new data from the World Health Organization to examine cross-national variation first in the level of public expenditures on health, and then in the level of achievement of health outcomes. We measure these influences and their relative impact in terms of dollars and years of health, respectively. Dictatorship, severe income inequality, ethnic heterogeneity, and persistent international hostilities substantially depress the amount of public resources allocated to health care. Moreover, we analyze the extent to which, given the same level of resources allocated to public health, overall national health performance suffers further from unequal provision of services, rapid urbanization, and civil conflict.

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 September of 2025.

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;

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

Of the U.S. adults surveyed, most agreed to some extent that mental illness negatively affects the U.S. economy. This statistic shows the percentage of U.S. adults who agree or disagree with the statement "untreated mental illness has a significant negative impact on the U.S. economy" as of 2021.

Health economic models are crucial for health technology assessment (HTA) to evaluate the value of medical interventions. Open source models (OSMs), where source code and calculations are publicly accessible, enhance transparency, efficiency, credibility, and reproducibility. This study systematically reviews databases to map the landscape of available OSMs in health economics.

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.

This dataset is for the study on COVID-19 death that may vary across countries of different political regimes.

The global health economics and outcomes research services market is expected to grow at a CAGR of ~13% during the forecast period. Increase in R&D spending by the pharmaceutical and biotech companies, growing demand for real-world evidence, rising focus on value-based care models, and increasing awareness of the importance of health economics and outcomes research […]

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.

This folder contains the replication material for "Health and Economic Growth: Reconciling the Micro and Macro Evidence" by David E. Bloom, David Canning, Rainer Kotschy, Klaus Prettner, and Johannes Schünemann.

The global current health expenditure as a share of the GDP in was forecast to continuously increase between 2024 and 2029 by in total *** percentage points. After the seventh consecutive increasing year, the share is estimated to reach **** percent and therefore a new peak in 2029. According to Worldbank health spending includes expenditures with regards to healthcare services and goods. It is depicted here in relation to the total gross domestic product (GDP) of the country or region at hand.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to *** countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the current health expenditure as a share of the GDP in countries like North America and the Americas.

Kenya KE: Domestic General Government Health Expenditure: % of GDP data was reported at 1.728 % in 2015. This records a decrease from the previous number of 1.802 % for 2014. Kenya KE: Domestic General Government Health Expenditure: % of GDP data is updated yearly, averaging 1.793 % from Dec 2000 (Median) to 2015, with 16 observations. The data reached an all-time high of 2.200 % in 2002 and a record low of 1.188 % in 2008. Kenya KE: 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 Kenya – Table KE.World Bank.WDI: 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;

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.

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.

An information system based on data from the healthcare sector and related areas. The online portal gives researchers the opportunity to research various health topics including population, socio-economic factors, health insurance, health laws.

• Laws

• Population

  • Births
  • Deaths
  • Population Numbers
  • other

• Pregnancy, Births

• Terminations of Pregnancy

• Households, Families, Living Communities

• Social Conditions

  • Education
  • Habitation
  • Employment
  • Unemployment
  • Poverty and Disparity
  • Single parent
  • Income
  • Social Welfare
  • other

• Training of the Health Personnel

• Economy

• Research

• Private Health Insurance

• Statutory Health Insurance

  • Statutory Health Insurance: Income, Expenditure
  • Statutory Health Insurance: Members, Insured Persons
  • Statutory Health Insurance: Services
  • Statutory Health Insurance: Services (Pharmaceuticals)
  • Statutory Health Insurance: other

• Nursing Care Insurance

• Pension Insurance

  • Insured Persons
  • Rehabilitation Services
  • Pensions because of old age
  • Ppensions because of diminution of ability to wrk
  • Pensions because of death (widow/widower pensions)
  • other

• Accident Insurance, employers' liability insurance association

This database compiles five indicators regarding working conditions: fatal occupational injuries, non-fatal occupational injuries, forced labour, part-time employment and temporary employment.

Each of the five indicators is provided for 44 regions according to WIOD Release 2016 regional structure and is disaggregated by economic activity according to the International Standard Industrial Classification of All Economic Activities (ISIC) Rev. 3. Therefore, this dataset is suitable to be introduced as a social satellite account in a multi-regional input-output model.

Data has been compiled from official sources as ILOSTAT, EUROSTAT or OECD Data among others. Data coming from different databases have been homogenized, and in some cases estimation has been required. Main sources are specified in the dataset.

For further information about the generation process of the dataset, please check García-Alaminos Á, Monsalve F, Zafrilla J, Cadarso MA (2020) Unmasking social distant damage of developed regions’ lifestyle: A decoupling analysis of the indecent labour footprint. PLOS ONE 15(4): e0228649. https://doi.org/10.1371/journal.pone.0228649.

For any question, you can contact the author at angela.garcia@uclm.es.