Among OECD member countries, the United States had the highest percentage of gross domestic product spent on health care as of 2023. The U.S. spent nearly ** percent of its GDP on health care services. Germany, France and Japan followed the U.S. with distinctly smaller percentages. The United States had both significantly higher private and public spending on health compared with other developed countries. Why compare OECD countries?OECD stands for Organization for Economic Co-operation and Development. It is an economic organization consisting of ** members, mostly high-income countries and committed to democratic principles and market economy. This makes OECD statistics more comparable than statistics of developed and undeveloped countries. Health economics is an important matter for the OECD, even more since increasing health costs and an aging population have become an issue for many developed countries. Health costs in the U.S.  A higher GDP share spent on health care does not automatically lead to a better functioning health system. In the case of the U.S., high spending is mainly because of higher costs and prices, not due to higher utilization. For example, physicians’ salaries are much higher in the U.S. than in other comparable countries. A doctor in the U.S. earns almost twice as much as the average physician in Germany. Pharmaceutical spending per capita is also distinctly higher in the United States. Furthermore, the U.S. also spends more on health administrative costs compare to other wealthy countries.

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.

In 2023, the United States had the highest per capita health expenditure among OECD countries. At that time, per capita health expenditure in the U.S. amounted over ****** U.S. dollars, significantly higher than in Switzerland, the country with the second-highest per capita health expenditure. Norway, Germany and Austria are also within the top five countries with the highest per capita health expenditure. The United States also spent the highest share of it’s gross domestic product on health care, with **** percent of its GDP spent on health care services. Health Expenditure in the U.S. The United States is the highest spending country worldwide when it comes to health care. In 2022, total health expenditure in the U.S. exceeded **** trillion dollars. Expenditure as a percentage of GDP is projected to increase to approximately ** percent by the year 2031. Distribution of Health Expenditure in the U.S. Health expenditure in the United States is spread out across multiple categories such as nursing home facilities, home health care, and prescription drugs. As of 2022, the majority of health expenditure in the United States was spent on hospital care, accounting for a bit less than *** third of all health spending. Hospital care was followed by spending on physician and clinical services which accounted for ** percent of overall health expenditure.

Spending on health represented six percent of Dominica's gross domestic product (GDP) in 2022, unchanged from the previous year. This figure was relatively stable during the period depicted, ranging from an equivalent of five to six percent of the country's GDP. In comparison, that year Cuba's healthcare spending corresponded to 12 percent of its GDP, the highest value reported among Latin American and Caribbean countries.

The total health expenditure accounted for 5.9 percent of the gross domestic product (GDP) of the Philippines in 2024. Since 2014, spending on human health has been stable, remaining between four and five percent of the GDP until 2021, when it reached 6.4 percent of the country's GDP. Health status of the aging population The Philippines was home to an estimated 113 million inhabitants in 2023. Along with the year-on-year increase in population came the need for broader healthcare services, especially for senior citizens who are at a higher risk of illnesses and diseases. In 2019, about seven percent of the aging population aged 60 years and older experienced a heart attack.  One of the leading health conditions diagnosed among the aging population was high blood pressure, arthritis, neuralgia, or rheumatism. Around 50 percent of women were diagnosed with high blood pressure, while 38.4 percent of men were diagnosed.    Health care protection With the Universal Health Care (UHC) Act enacted in 2019, more Filipinos can now have access to a government-subsidized public health insurance. The implementation of the program is under the Philippine Health Insurance Corporation (PhilHealth), alongside other related agencies. As of 2024, there were roughly 59 million registered members, the majority of whom were private employees.

The research on life expectancy in countries takes the spotlight in the notebook's machine learning model. Substantial data analysis and predictive algorithms are used to uncover the reasons causing differences in longevity among countries. With the aid of strong statistical tools, valuable insights into the complex link between healthcare, socioeconomic factors, and life expectancy are sought |Description|Column| |:------:|:--------:| |Country under study|Country| |year|Year| |Status of the country's development|Status| |Population of country|Population| |Percentage of people finally one year old who were immunized against hepatitis B|Hepatitis B| |The number of reported measles cases per 1000 people|Measles| |Percentage of 1-year-olds immunized against polio|Polio| |Percentage of people finally one year old who were immunized against diphtheria|Diphtheria| |The number of deaths caused by AIDS of the last 4-year-olds who were born alive per 1000 people|HIV/AIDS| |The number of infant deaths per 1000 people|infant deaths| |he number of deaths of people under 5 years old per 1000 people|under-five deaths| |The ratio of government medical-health expenses to total government expenses in percentage|Total expenditure| |Gross domestic product|GDP| |The average body mass index of the entire population of the country|BMI| |Prevalence of thinness among people 19 years old in percentage|thinness 1-19 years| |Liters of alcohol consumption among people over 15 years old|Alcohol| |The number of years that people study|Schooling| |Country life expectancy|Life expectancy [target variable]|

This file provides bilingual Chinese-English transcripts of nine focus group discussions (FGDs) carried out in three Chinese cities in June and July 2012. The focus groups were commissioned by the authors from the Research Center for Contemporary China (RCCC) at Peking University as part of the ESRC project ‘Performance evaluations, trust and utilization of health care in China: understanding relationships between attitudes and health-related behaviour’. Local residents over the age of 30 took part in the discussions, which were moderated by a senior researcher from RCCC. The FGDs dealt with five main issues: how people know about changes in the health care system changes; how people make decisions to see a doctor when they are unwell; health care system evaluations; trust in doctors and the health care system; and what kind of a system people would like. The FGDs use a series of fictional scenarios (vignettes) to elicit responses concerning what influences people’s decisions about going to a doctor when they are unwell.This interdisciplinary project establishes a new collaboration among UK researchers and a leading Chinese social research team, to conduct the first major study of Chinese people's attitudes towards their health care. The project's core theoretical contribution is to understanding the relationships between attitudes and health-related behaviours, focussing particularly on how people evaluate their health system, their trust in doctors and the health system, and their utilization of preventive and curative health services. Previous quantitative research on health in China has examined the influence on utilization of age and gender, incomes, insurance protection, distance to health service providers and perceived health care needs. Yet work done in other countries has shown that attitudes, including performance evaluations and trust, can impact on people's decisions about when and where to use health services. At the same time, qualitative studies in China have suggested that people are often critical of performance and that there is a crisis of trust in doctors and the health care system. Our project is the first systematic study of these attitudes and how they influence utilization. The three cities chosen for focus group discussions, Chifeng, Yueyang and Shaoxing, represented respectively a city below the national average, close to the average and above the average in terms of GDP per capita. Two stratifications were used to select participants (see Focus Group Participant Profiles for details): Stratification One: of the general population by location and individual circumstances. This stratification was used in Chifeng and Shaoxing; all participants were local residents. In Chifeng, two discussions was conducted in the city itself and one discussion in a rural area under the city’s jurisdiction. In Shaoxing, one discussion was conducted in the city itself and one in a rural village within the city’s jurisdiction. Stratification Two: of patients by individual circumstances. This stratification was used in Yueyang. The participants in each of the four focus groups were screened by asking whether they had had contact with the health care system during the last two weeks in connection with an injury or illness; and what type of medical insurance they possessed. The initial intention was to stratify patients according to whether they reported suffering acute or chronic conditions. However, the difficulty of recruiting participants prevented this. The stratification of patients was thus according to their type of insurance. Nearly all participants on the first day of discussions (#4 and #5) had medical insurance equivalent to Urban Employees Basic Medical Insurance, whilst participants on the second day of discussions (#6 and #7) did not have this level of insurance. Most of these were members of the Rural Cooperative Medical Scheme, which gives them only limited entitlements to reimbursement of medical expenses in Yueyang.

File Description: "Life Expectancy Data.csv" This dataset contains 2,938 entries and 22 columns, covering life expectancy and related health indicators for multiple nations from 2000 to 2015. It includes country-wise data and other economic, social, and health metrics. Column Description: 1. Country – Name of the country. 2. Year – Data year (ranging from 2000 to 2015). 3. Status – Economic classification (Developing/Developed). 4. Life expectancy – Average lifespan in years. 5. Adult Mortality – Probability of death between ages 15-60 per 1,000 individuals. 6. Infant Deaths – Number of infant deaths per 1,000 live births. 7. Alcohol – Per capita alcohol consumption. 8. Percentage Expenditure – Government health expenditure as a percentage of GDP. 9. Hepatitis B – Immunization coverage percentage. 10. Measles – Number of reported measles cases. 11. BMI – Average Body Mass Index. 12. Under-Five Deaths – Mortality rate for children under five. 13. Polio & Diphtheria – Immunization rates. 14. HIV/AIDS – Deaths due to HIV/AIDS per 1,000 individuals. 15. GDP – Gross Domestic Product per capita. 16. Population – Total population of the country. 17. Thinness (1-19 years, 5-9 years) – Percentage of underweight children. 18. Income Composition of Resources– Human development index proxy. 19. Schooling– Average number of years of schooling. Missing Data: Some columns (like Hepatitis B, GDP, Population, Total Expenditure) contain missing values. Further File Information: Total Countries: 193 Years Covered: 2000–2015 Total Entries: 2,938 Missing Data Overview: Some columns have missing values, notably: Hepatitis B (553 missing) GDP (448 missing) Population (652 missing) Total expenditure (226 missing) Income Composition of Resources (167 missing) Schooling (163 missing) Summary Statistics: Life Expectancy:

Range: 36.3 to 89 years Mean: 69.2 years Adult Mortality:

Mean: 165 per 1,000 Max: 723 per 1,000 GDP per Capita:

Mean: $7,483 Max: $119,172 Population:

Mean: ~12.75 million Max: 1.29 billion Education:

Schooling Average: 12 years Max: 20.7 years

Futuristic Scope of this data: For comparative analysis of the 2000–2015 life expectancy dataset with new datasets on the same parametres , you can perform several statistical tests and analytical methods based on different research questions. Below are some key tests and approaches:

1. Trend Analysis (Time-Series) Objective: Identify trends in life expectancy and related indicators over time. Methods: Moving Averages: Smooth fluctuations to detect trends. Linear/Polynomial Regression: Check whether life expectancy follows an increasing or decreasing trend. Time-Series Decomposition: Separate data into trend, seasonality, and residuals.
2. Descriptive Statistics & Comparative Summary Objective: Compare summary statistics between years or groups. Tests/Methods: Mean, Median, Standard Deviation: Compare distributions of life expectancy, GDP, or schooling. Boxplots & Histograms: Show variations over different years or between developing vs. developed countries. Coefficient of Variation (CV): Compare variability in life expectancy across regions.
3. Correlation & Regression Analysis Objective: Examine relationships between variables. Methods: Pearson/Spearman Correlation: Check relationships between life expectancy and GDP, health expenditure, etc. Multiple Linear Regression: Predict life expectancy based on GDP, immunization, and schooling. Multicollinearity (VIF Test): Ensure independent variables are not highly correlated.
4. Hypothesis Testing (Comparative Analysis) Test Objective When to Use? t-Test (Independent Samples) Compare life expectancy between developed & developing nations Two groups (e.g., 2000 vs. 2015, or developed vs. developing) Paired t-Test Compare life expectancy in the same country over two time periods Before/after comparison (e.g., 2000 vs. 2015 for the same country) ANOVA (One-Way) Compare life expectancy across multiple groups More than two groups (e.g., continents or income groups) Chi-Square Test Test if categorical distributions (e.g., immunization coverage) differ over time Categorical variables (e.g., immunization rates vs. year)
5. Clustering & Classification (Machine Learning) Objective: Group countries based on life expectancy patterns. Methods: K-Means Clustering: Identify groups with similar life expectancy trends. Hierarchical Clustering: Create a country similarity tree. Decision Trees/Random Forest: Classify countries based on development status using life expectancy factors.
6. Forecasting Future Trends Objective: Predict life expectancy in future years using historical data. Methods: ARIMA (AutoRegressive Integrated Moving Average): Time-series forecasting. Exponential Smoothing: Forecast gradual trends. Machine Learning (LSTM, XGBoost): Predict based on multiple indicators.
7. Comparative Regional Analysis O...

BackgroundSince 2015, a major economic crisis in Brazil has led to increasing poverty and the implementation of long-term fiscal austerity measures that will substantially reduce expenditure on social welfare programmes as a percentage of the country’s GDP over the next 20 years. The Bolsa Família Programme (BFP)—one of the largest conditional cash transfer programmes in the world—and the nationwide primary healthcare strategy (Estratégia Saúde da Família [ESF]) are affected by fiscal austerity, despite being among the policy interventions with the strongest estimated impact on child mortality in the country. We investigated how reduced coverage of the BFP and ESF—compared to an alternative scenario where the level of social protection under these programmes is maintained—may affect the under-five mortality rate (U5MR) and socioeconomic inequalities in child health in the country until 2030, the end date of the Sustainable Development Goals.Methods and findingsWe developed and validated a microsimulation model, creating a synthetic cohort of all 5,507 Brazilian municipalities for the period 2017–2030. This model was based on the longitudinal dataset and effect estimates from a previously published study that evaluated the effects of poverty, the BFP, and the ESF on child health. We forecast the economic crisis and the effect of reductions in BFP and ESF coverage due to current fiscal austerity on the U5MR, and compared this scenario with a scenario where these programmes maintain the levels of social protection by increasing or decreasing with the size of Brazil’s vulnerable populations (policy response scenarios). We used fixed effects multivariate regression models including BFP and ESF coverage and accounting for secular trends, demographic and socioeconomic changes, and programme duration effects. With the maintenance of the levels of social protection provided by the BFP and ESF, in the most likely economic crisis scenario the U5MR is expected to be 8.57% (95% CI: 6.88%–10.24%) lower in 2030 than under fiscal austerity—a cumulative 19,732 (95% CI: 10,207–29,285) averted under-five deaths between 2017 and 2030. U5MRs from diarrhoea, malnutrition, and lower respiratory tract infections are projected to be 39.3% (95% CI: 36.9%–41.8%), 35.8% (95% CI: 31.5%–39.9%), and 8.5% (95% CI: 4.1%–12.0%) lower, respectively, in 2030 under the maintenance of BFP and ESF coverage, with 123,549 fewer under-five hospitalisations from all causes over the study period. Reduced coverage of the BFP and ESF will also disproportionately affect U5MR in the most vulnerable areas, with the U5MR in the poorest quintile of municipalities expected to be 11.0% (95% CI: 8.0%–13.8%) lower in 2030 under the maintenance of BFP and ESF levels of social protection than under fiscal austerity, compared to no difference in the richest quintile. Declines in health inequalities over the last decade will also stop under a fiscal austerity scenario: the U5MR concentration index is expected to remain stable over the period 2017–2030, compared to a 13.3% (95% CI: 5.6%–21.8%) reduction under the maintenance of BFP and ESF levels of protection. Limitations of our analysis are the ecological nature of the study, uncertainty around future macroeconomic scenarios, and potential changes in other factors affecting child health. A wide range of sensitivity analyses were conducted to minimise these limitations.ConclusionsThe implementation of fiscal austerity measures in Brazil can be responsible for substantively higher childhood morbidity and mortality than expected under maintenance of social protection—threatening attainment of Sustainable Development Goals for child health and reducing inequality.

IntroductionUnder-five mortality rate (U5MR) and maternal mortality rate (MMR) are important indicators for evaluating the quality of perinatal health and child health services in a country or region, and are research priorities for promoting maternal and infant safety and maternal and child health. This paper aimed to analysis and predict the trends of U5MR and MMR in China, to explore the impact of social health services and economic factors on U5MR and MMR, and to provide a basis for relevant departments to formulate relevant policies and measures.MethodsThe JoinPoint regression model was established to conduct time trend analysis and describe the trend of neonatal mortality rate (NMR), infant mortality rate (IMR), U5MR and MMR in China from 1991 to 2020. The linear mixed effect model was used to assess the fixed effects of maternal health care services and socioeconomic factors on U5MR and MMR were explored, with year as a random effect to minimize the effect of collinearity. Auto regressive integrated moving average models (ARIMA) were built to predict U5MR and MMR from 2021 to 2025.ResultsThe NMR, IMR, U5MR and MMR from 1991 to 2020 in China among national, urban and rural areas showed continuous downward trends. The NMR, IMR, U5MR and MMR were significantly negatively correlated with gross domestic product (GDP), the proportion of the total health expenditure (THE) to GDP, system management rate, prenatal care rate, post-natal visit rate and hospital delivery rate. The predicted values of national U5MR from 2021 to 2025 were 7.3 ‰, 7.2 ‰, 7.1 ‰, 7.1 ‰ and 7.2 ‰ and the predicted values of national MMR were 13.8/100000, 12.1/100000, 10.6/100000, 9.6/100000 and 8.3/100000.ConclusionChina has made great achievements in reducing the U5MR and MMR. It is necessary for achieving the goals of Healthy China 2030 by promoting the equalization of basic public health services and further optimizing the allocation of government health resources. China’s experience in reducing U5MR and MMR can be used as a reference for developing countries to realize the SDGs.

In 2020, there were 29 hospitals in Estonia. Since 2000, the number of hospitals in Estonia has fallen significantly from 68 hospitals to only 29 in 2020. The number of hospitals remained the same from 2013 to 2018, although there was a break in series, so the source may have changed certain standards resulting in this value drop. Healthcare workers in Estonia Although the number of healthcare institutions in Estonia has decreased, the number of personnel working within them has remained stable. The number of physicians in Estonia in the period 2000 to 2020 has held steady, not changing by more than three hundred employees. In addition, the number of practicing nurses experienced more fluctuations over the time period, yet amounted to over eight thousand in 2018, which was an increase on the preceding five years. Spending on health Estonia’s expenditure on healthcare was 8.1 percent of GDP in 2020, this share has generally been increasing since 2002. In comparison to other European countries, Estonia ranked rather low on the share of GDP spent on health expenditures. Germany had the highest share of expenditure on health, spending 12.8 percent of its GDP in this year.

Vietnam’s real gross domestic product (GDP) has been experiencing positive growth for the past five years since 2019, and is projected to continue to do so through 2030. In 2023, Vietnam’s real GDP increased by around five percent compared to the previous year. Learning from real GDP Real gross domestic product (GDP) is a measure that reflects the value of all goods and services an economy produces within a given year. It is expressed in base-year prices, and is thus an inflation-adjusted way to compare a country’s economic output through the years. The GDP growth rate is a significant indicator of a country’s economic health, as it reacts to the economy’s expansions and contractions. Vietnam’s optimistic future As indicated by the positive growth rate of its real GDP, Vietnam’s economy is expanding due to growth in exports, domestic demand, and the manufacturing sector. As the economy expands, so does the total expenditure of Vietnamese consumers. The average monthly income per capita in Vietnam increased to almost 3.8 percent in 2018, and is spent on fast moving consumer goods from popular brands like Vinamilk and P/S.

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.

The statistic shows global gross domestic product (GDP) from 1985 to 2024, with projections up until 2030. In 2020, global GDP amounted to about 85.76 trillion U.S. dollars, two and a half trillion lower than in 2019. Gross domestic product Gross domestic product, also known as GDP, is the accumulated value of all finished goods and services produced in a country, often measured annually. GDP is significant in determining the economic health, growth and productivity in the country, and is a stat often used when comparing several countries at a time, most likely in order to determine which country has seen the most progress. Until 2020, Global GDP had experienced a growth every year since 2010. However, a strong growth rate does not necessarily lead to all positive outcomes and often has a negative effect on inflation rates. A severe growth in GDP leads to lower unemployment, however lower unemployment often leads to higher inflation rates due to demand increasing at a much higher rate than supply and as a result prices rise accordingly. In terms of unemployment, growth had been fairly stagnant since the economic downturn of 2007-2009, but it remains to be seen what the total impact of the coronavirus pandemic will be on total employment.

In 2024, Myanmar had the highest crude death rate among the Southeast Asian countries, with *** deaths per thousand population. That year, Singapore had the lowest crude death rate, with *** deaths per thousand population.Factors that influence the death rateThe death rate, also called mortality rate, is generally influenced by various factors such as the social environment, diseases, health facilities and services as well as the food supply of the respective countries. Myanmar’s government spent five percent of its public budget on health in 2016. In 2020, health expenditure per capita in Myanmar amounted to around ** U.S. dollars. The Maldives had the lowest crude death rate in the Asia-Pacific region in 2024. There, health expenditure accounted for ***** percent of the country’s GDP. Furthermore, the share of undernourished people was at around ***** percent in Myanmar in 2020. Within Southeast Asia, Myanmar has also been one of the poorest countries. In 2020, the country’s GDP per capita was estimated at **** thousand U.S. dollars, the lowest across the Asia-Pacific region.

The statistic shows the growth rate of Australia’s real GDP from 2020 to 2024, with projections up until 2030. In 2024, GDP in Australia grew by about 1.04 percent on the previous year.The recession-proof land down underGDP is one of the primary indicators used to gauge the state and health of a country’s economy. It is the total market value of all final goods and services that have been produced within a country in a given period of time, usually a year. GDP figures allow us to understand a country’s economy in a clear way. Real GDP, in a similar vein, is also a very useful indicator; this is a measurement that takes prices changes (inflation and deflation) into account, therefore acting as a key indicator for economic growth.The gross domestic product (GDP) growth rate in Australia has, for sometime, been able to get a steady foothold in the somewhat shaky post-recession world, shaky, but far from catastrophic. The annual growth rate between the 2008 and 2009 financial years, for example, a time at which the world was brought to its proverbial knees, saw growth rates down under reach to 2.49 and 1.37 percent respectively on the previous years, whereas the GDP growth rate in the United States plummeted well into the minus zone. Australia, like all other capitalist nations, is at the mercy of international markets, and when the world economy takes a hit, it would be foolish to suggest it could emerge fully unscathed. However, Australia has earned some much deserved praise and attention owing to the fact that it has managed to remain recession-free for the past twenty years. This could be thanks to its abundance of raw materials, the Australian mining boom, the fact the recession came at a time of high commodity prices and, maybe most importantly, that just under a third of its exports go to China.

Singapore led the Index of Economic Freedom in 2024, with an index score of 83.5 out of 100. Switzerland, Ireland, Taiwan, and Luxembourg rounded out the top five. Economic Freedom Index In order to calculate the Economic Freedom Index, the source takes 12 different factors into account, including the rule of law, government size, regulatory efficiency, and open markets. All 12 factors are rated on a scale of zero to 100 and are weighted equally. Every country is rated within the Index in order to provide insight into the health and freedom of the global economy. Singapore's economy Singapore is one of the four so-called Asian Tigers, a term used to describe four countries in Asia that saw a booming economic development from the 1950s to the early 1990. Today, the City-State is known for its many skyscrapers, and its economy continue to boom. It has one of the lowest tax-rates in the Asia-Pacific region, and continues to be open towards foreign direct investment (FDI). Moreover, Singapore has one of the highest trade-to-GDP ratios worldwide, underlining its export-oriented economy. Finally, its geographic location has given it a strategic position as a center connecting other countries in the region with the outside world. However, the economic boom has come at a cost, with the city now ranked among the world's most expensive.

In 2024, Sudan was ranked as the most miserable country in the world, with a misery index score of 374.8. Argentina ranked second with an index score of 195.9. Quality of life around the worldThe misery index was created by the economist Arthur Okun in the 1960s. The index is calculated by adding the unemployment rate, the lending rate and the inflation rate minus percent change of GDP per capita. Another famous tool used for the comparison of development of countries around the world is the Human Development Index, which takes into account such factors as life expectancy at birth, literacy rate, education level and gross national income (GNI) per capita. Better economic conditions correlate with higher quality of life Economic conditions affect the life expectancy, which is much higher in the wealthiest regions. With a life expectancy of 85 years, Liechtenstein led the ranking of countries with the highest life expectancy in 2023. On the other hand, Nigeria was the country with the lowest life expectancy, where men were expected to live 55 years as of 2024. The Global Liveability Index ranks the quality of life in cities around the world, basing on political, social, economic and environmental aspects, such as personal safety and health, education and transport services and other public services. In 2024, Vienna was ranked as the city with the highest quality of life worldwide.

As of 2024, Mumbai had a gross domestic product of *** billion U.S. dollars, the highest among other major cities in India. It was followed by Delhi with a GDP of around *** billion U.S. dollars. India’s megacities also boast the highest GDP among other cities in the country. What drives the GDP of India’s megacities? Mumbai is the financial capital of the country, and its GDP growth is primarily fueled by the financial services sector, port-based trade, and the Hindi film industry or Bollywood. Delhi in addition to being the political hub hosts a significant services sector. The satellite cities of Noida and Gurugram amplify the city's economic status. The southern cities of Bengaluru and Chennai have emerged as IT and manufacturing hubs respectively. Hyderabad is a significant player in the pharma and IT industries. Lastly, the western city of Ahmedabad, in addition to its strategic location and ports, is powered by the textile, chemicals, and machinery sectors. Does GDP equal to quality of life? Cities propelling economic growth and generating a major share of GDP is a global phenomenon, as in the case of Tokyo, Shanghai, New York, and others. However, the GDP, which measures the market value of all final goods and services produced in a region, does not always translate to a rise in quality of life. Five of India’s megacities featured in the Global Livability Index, with low ranks among global peers. The Index was based on indicators such as healthcare, political stability, environment and culture, infrastructure, and others.

The statistic shows the life expectancy at birth in India from 2013 to 2023. The average life expectancy at birth in India in 2023 was 72 years. Standard of living in India India is one of the so-called BRIC countries, an acronym which stands for Brazil, Russia, India and China, the four states considered the major emerging market countries. They are all in a similar advanced economic state and are expected to advance even further. India is also among the twenty leading countries with the largest gross domestic product / GDP, and the twenty countries with the largest proportion of global gross domestic product / GDP based on Purchasing Power Parity (PPP). Its unemployment rate has been stable over the past few years; India is also among the leading import and export countries worldwide. This alone should put India in a relatively comfortable position economically speaking, however, parts of the population of India are struggling with poverty and health problems. When looking at a comparison of the median age of the population in selected countries – i.e. one half of the population is older and the other half is younger –, it can be seen that the median age of the Indian population is about twenty years less than that of the Germans or Japanese. In fact, the median age in India is significantly lower than the median age of the population of the other emerging BRIC countries – Russia, China and Brazil. Additionally, the total population of India has been steadily increasing. Regarding life expectancy, India is neither among the countries with the highest, nor among those with the lowest life expectancy at birth. The majority of the Indian population is aged between 15 and 64 years, with only about 5 percent being older than 64.