OVERVIEW OF THE TOPIC
Definition
Health care expenditure refers to the financial resources allocated by individuals, groups, nations, or both private and public organizations for various health-related services This includes spending on medical care, prevention, health promotion, rehabilitation, community health initiatives, health administration, regulation, and capital investment, all aimed at enhancing overall health outcomes.
Economic theories relating to health expenditure
2.1 Demand theory for health care
Preferences for Health and Health Care
Health care is primarily valued for its ability to enhance health, making health a fundamental aspect of consumer preferences Shifts in consumer attitudes towards health care can significantly influence demand, as seen with the rising interest in cosmetic surgery driven by media and advertising Additionally, advancements in medical science have fostered a belief that most ailments can be treated, leading consumers to purchase greater amounts of medical services at various price points.
Health care is considered a normal good, meaning that as consumers' inflation-adjusted incomes increase, the demand for health care services rises, shifting the demand curve to the right Conversely, if the real median family income stays the same, there will be no effect on the demand curve for these services.
Changes in medical care prices significantly impact individual consumption, with demand decreasing as prices rise and increasing when prices fall However, this demand tends to be inelastic; sick consumers will prioritize purchasing healthcare services regardless of cost While a consumer's income ultimately limits their ability to afford healthcare, they are often willing to reduce spending on other products to ensure they receive necessary medical care.
Health care is not a singular service but a complex system that relies on various inputs, including doctors' time, hospital beds, X-rays, medications, and information The prices of these essential components significantly influence both the quantity of medical care individuals seek and the types of services provided Consequently, when governments or insurance companies regulate prices for these inputs, they must consider how changes in relative prices can alter the mix of services utilized in health care delivery.
As the population grows, so does the demand for healthcare services, particularly due to the rising number of individuals aged 65 and older, who typically experience more frequent and prolonged illnesses Additionally, increasing substance abuse, including alcohol, tobacco, and drugs, contributes to this heightened demand For instance, a rise in the percentage of babies born into drug-prone families will further shift the demand for healthcare services to the right.
2.2 Supply theory for health care
Economists often talk of output being produced using a production function that uses labor, capital, and intermediate inputs The production function of a hospital including:
● The labor in a hospital includes doctors, surgeons, orderlies, technicians, nurses, administrative staff, janitors, and many others
Hospital buildings are a vital component of a hospital's capital stock, which also includes a wide array of essential equipment This encompasses various medical tools and facilities, ranging from hospital beds to advanced diagnostic instruments like stethoscopes and x-ray machines.
● Intermediate inputs in a hospital include dressings for wounds, and pharmaceutical products, such as anesthetics used for operations
Furthermore, factors affecting the supply of health care include:
The health care industry comprises various sellers, including hospitals, nursing homes, private physicians, HMOs, drug companies, chiropractors, and psychologists To maintain quality and safety, the government and the American Medical Association (AMA) regulate and license nearly all aspects of this sector The AMA plays a crucial role in controlling the number of medical practitioners by enforcing stringent medical school accreditation and licensing standards.
The federal Food and Drug Administration (FDA) mandates testing that can postpone the launch of new medications Stricter limitations on the number of suppliers cause a leftward shift in the healthcare supply curve, while easing these restrictions results in a rightward shift.
The rising costs of healthcare resources lead to a leftward shift in the supply curve, primarily driven by technological advancements The extensive use of new diagnostic, surgical, and therapeutic equipment significantly contributes to increased healthcare expenditures Additionally, factors such as higher wages, salaries, and malpractice suit costs further affect the supply curve Consequently, if hospitals face increased prices for the inputs necessary for healthcare production, they can only supply the same quantities at elevated prices.
Related published research
A report titled "Determinants of Healthcare Expenditure in Economic Cooperation Organization (ECO) Countries: Evidence from Panel Cointegration Tests" published in The International Journal of Health Policy and Management highlights two opposing perspectives on the relationship between healthcare spending and production levels On one hand, it suggests that healthier workers demonstrate greater efficiency compared to their less healthy counterparts.
Increased focus on health expenditures is essential as they are viewed as "costs" that divert resources from other economic sectors, leading to a decline in production levels This has prompted health economists to analyze the determinants of these expenditures Notably, a study in the United States revealed that the percentage of GDP allocated to healthcare rose significantly from 9% in 1980 to 16% in 2008, highlighting the growing financial burden of health costs.
Meanwhile, in Iran, the health expenditures per capita increased from $80 in 1995 to
In 2005, the average health expenditure was $247, and long-term forecasts suggest a continued rise in these costs The study's findings highlight a positive long-term correlation between urbanization and health expenditures This relationship arises from the increased access to healthcare providers, such as hospitals and clinics, in urban areas, resulting in greater utilization of healthcare services and consequently higher healthcare spending.
A study examining the determinants of health care expenditures in Korea from 2003 to 2010 reveals a significant annual increase in medical expenses, with health insurance costs soaring from approximately 13 trillion won in 2001 to around 34 trillion won by 2010, averaging over 11% growth annually This trend raises concerns regarding the sustainability of health insurance financing The rising medical costs are attributed to the volume of health services multiplied by their unit costs, which are determined through a fee-for-service system negotiated annually between the National Health Insurance Service and supplier groups Despite government efforts to control costs by managing the conversion factor for over a decade, the lack of mechanisms to regulate the frequency and intensity of health services has led to continued expenditure growth Previous studies have highlighted key factors influencing health care spending, such as GDP, population size, the elderly demographic, and the number of healthcare providers, with GDP per capita and the proportion of seniors significantly affecting national health care expenditures Additionally, the Medicare Economic Index (MEI) was calculated using price indices from various regions, while changes in health insurance beneficiary numbers were analyzed using data from National Health Insurance statistical yearbooks.
In another happenings, Baltagi and Moscone (Badi H Baltagi & Moscone
A study conducted in 2010 revealed a negative long-term relationship between health expenditures and aging populations Countries with a higher proportion of individuals under 15 and over 65 years old are deemed healthier, resulting in lower healthcare costs compared to those with less healthy populations Additionally, Banins found that while health expenditures rise as life expectancy increases, they begin to decline after reaching a certain peak.
A comprehensive study by the World Health Organization titled "The Determinants of Health Expenditure: A Country-Level Panel Data Analysis" highlights income as a primary factor influencing household health expenditure Research by Musgrove, Zeramdini, and Carrin (1997) analyzed data from 191 countries, revealing an income elasticity of health expenditure ranging from 1.133 to 1.275 Similarly, Gaag and Stimac's 2004 study of 175 countries found an income elasticity of 1.09, with variations by region—0.830 in the Middle East and 1.197 in OECD countries Further, Murthy and Okunade (2001) examined 44 African countries, identifying an income elasticity between 1.089 and 1.121 Additionally, Schieber and Maeda's 1994 research estimated a global income elasticity of 1.13, noting that public spending exhibited higher income elasticity compared to private spending.
In a study examining health system characteristics, researchers analyzed service provision and health financing differences in OECD countries and Eastern European and Central Asian (ECA) nations, focusing on the disparities in health expenditure between tax-based and social-insurance-based systems (Wagstaff & Bank, 2009; Wagstaff & Moreno-Serra, 2009).
The OECD study indicates that countries with social health insurance mechanisms have higher per capita health expenditures Similarly, the ECA study reveals that government health spending per capita is greater in nations utilizing social health insurance compared to those relying solely on general taxation While health-specific official development aid (ODA) does not significantly impact total health expenditure, it demonstrates an elasticity of 0.138 in relation to public health spending, highlighting its role in enhancing health funding.
A study by Lu et al (2010) revealed that health Official Development Assistance (ODA) directed towards the non-government sector positively influenced overall government health spending Conversely, when health ODA was funneled through the government sector, a negative correlation emerged Additionally, research from Eastern and Central Asia indicated that transitioning provider payment mechanisms from hospital budgets to fee-for-service or patient-based payments led to increases in both public and private health expenditures.
The age structure of a population significantly influences epidemiological needs and is often considered a covariate in health assessments Key indicators include the proportion of young individuals (under 15 years) and older adults (over 65 or 75 years) relative to the active or total population However, these demographic variables tend to show insignificance in regression models that analyze per-capita health spending, as noted in various studies (Leu 1986; Hitiris & Posnett 1992; L Di Matteo & R Di Matteo).
Epidemiological need is often included as a covariate using various proxies in health expenditure studies For instance, Lu et al utilized HIV seroprevalence as a proxy and discovered no significant correlation with general government health expenditure as a percentage of GDP Similarly, research by Murthy and Okunade indicated that maternal mortality rates did not have a relationship with health expenditure in African nations.
Research hypotheses
Our research team analyzed relevant theories and reviewed both domestic and international studies to formulate hypotheses regarding the factors influencing household healthcare expenditures across various countries.
Life expectancy and health among the elderly have significantly improved over the decades, despite the natural physiological changes that occur with aging As the body begins to degenerate, declining health becomes a common concern, leading to various illnesses and diseases in the elderly population Consequently, healthcare consumption becomes increasingly vital for older individuals, resulting in higher healthcare expenditures as they age.
Figure 1 The relationship between Life Expectancy and Healthcare Expenditure
Life expectancy has a positive effect on healthcare expenditure The higher life expectancy is, the more spending people spend on their healthcare
Gross Domestic Product (GDP) measures the market value of all final goods and services produced within a specific period, typically annually The growth of GDP not only indicates economic development but also reflects improvements in infrastructure, education, and healthcare Research conducted by Sojib Bin Zaman, Naznin Hossain, Varshil Mehta, Shuchita Sharmin, and Shakeel Ahmed Ibne Mahmood reveals a positive correlation between GDP and healthcare expenditure, suggesting that countries with higher GDP tend to invest more in healthcare compared to those with lower GDP.
Therefore, our team hypothesized that:
GDP has a positive effect on healthcare expenditure The higher GDP is, the more spending people spend on their healthcare
In national accounts, expenditure on goods and services for individual and collective needs is categorized under final consumption expenditure (FCE) in the use of income account A significant component of FCE is household final consumption expenditure, which includes healthcare costs The World Health Organization (WHO) emphasizes that health expenditure share, representing the portion of household spending allocated to healthcare, is essential for family members However, prior research has not clearly established the connection between Final Consumption Expenditure and Healthcare Expenditure.
We decided to examine this determinant and hypothesize that:
Final Consumption Expenditure has a positive effect on healthcare expenditure The higher Final Consumption Expenditure is, the more spending people spend on their healthcare
Out-of-pocket payments (OOPs) refer to the direct payments individuals make to healthcare providers when receiving services These payments do not include any prepayments, such as taxes or insurance premiums, and are calculated net of any reimbursements received by the individual.
Out-of-pocket (OOP) payments play a crucial role in the health financing landscape across nations, as they help generate revenue, optimize the utilization of health services, manage healthcare costs, and enhance the overall efficiency and quality of health systems.
In the report of WHO, the households with high Out of Pocket Expenditure have the higher spending on healthcare than the lower ones
Therefore, our team hypothesized that:
Out of Pocket Expenditure has a positive effect on healthcare expenditure
The higher Out of Pocket Expenditure is, the more spending people spend on their healthcare
Group‟s hypotheses are tested through estimated model and statistical inferences in section 3.
MODEL SPECIFICATION
Methodology
The collected data is in the form of secondary information and cross - section data, showing the factors which affect households‟ health care expenditure based on
158 observations in 2018 in 158 countries The data was taken from the highly accurate source which is World Bank
1.2 Method used to analyze the data and derive the model
The team employed a multiple linear regression model alongside the Ordinary Least Squares (OLS) estimation method to examine the correlation between health expenditure and various factors, such as GDP per capita, life expectancy at birth, final consumption expenditure, and out-of-pocket expenditure.
Throughout the project, the team leveraged econometric expertise, utilizing STATA software alongside Microsoft Excel and Word to effectively synthesize and complete their work.
Theoretical model specification
Based on previous research references, the team opted to implement a population linear regression function for their project, which includes one dependent variable and four independent variables.
Variables Meaning Unit Expected sign of regression coefficient
LIFE Life expectancy at birth
GDP Gross domestic product per capita
OOP Out of pocket expenditure
Theoretically, all independent variables have a positive relationship with dependent variable
The rise in life expectancy has resulted in a growing elderly population, which significantly increases the demand for healthcare services This shift in demographics is expected to lead to higher health expenditures as the aging population requires more medical attention and resources.
- GDP is the most effective factor in determining the health expenditures
Countries with good economic infrastructure have more knowledge about the benefits of healthcare and, consequently, they use healthcare more than other countries
- As the concern of good health among people is rising, they demand for more health goods and services Therefore, higher consumption expenditure may consist of higher health expenditure
- The expenses that the patient or the family pays directly to the health care
Expenditure‟ Higher out of pocket expenses will lead to higher health expenditure.
Describe the data
Source of data used for each variable is in this below table:
Short-form Year Source of
Life expectancy Independent LIFE 2018 World bank
The typical data representing the variables are listed in the table below:
Mean Std Dev Max Min
The standard deviation of the variable LIFE is 9.151229, indicating a significant level of dispersion in life expectancy across different countries Developed nations such as Japan, Switzerland, and Italy typically exhibit high average life expectancies, while many regions in Africa and parts of Asia report considerably lower averages As the population in wealthier countries ages, there is an anticipated increase in health expenditure.
● The standard deviation of variable GDP is 18743.24 We can realize the high standard deviation result in large gap in the average income between countries
The variation in regional development significantly influences income levels, which in turn positively affects spending habits As income increases, so does expenditure, particularly in areas such as health care.
The average value of FCON stands at approximately $79.45, with a standard deviation of 19.27, indicating significant variability in healthcare spending A substantial portion of private sector healthcare expenditure is attributed to household final consumption, which includes costs for medicine, medical devices, and treatment services The high standard deviation reflects the differing healthcare needs and consumption patterns across regions, influenced by varying economic conditions and pricing.
The standard deviation of out-of-pocket (OOP) health expenditures is 304.3439, with Switzerland leading at 2332.798, followed by developed nations like Norway and Australia In contrast, Mozambique has the lowest OOP expenditure at 1.773446, with similar trends observed in other African countries such as Malawi and Congo This disparity highlights the substantial private costs that citizens incur, known as out-of-pocket health expenditures, which are prevalent in many developing nations The significant impact of healthcare spending has prompted numerous countries worldwide to initiate healthcare reforms.
Before conducting the regression analysis, it is essential to examine the correlation between variables to formulate hypotheses regarding the relationship between the dependent and independent variables The identified correlations among the variables are as follows:
HE GDP LIFE FCON OOP
From the above table, we could see that the correlation between GDP, LIFE, OOP and HE is extremely strong
The correlation coefficient between the HE and GDP is 0.9190 (a strong positive correlation), showing a positive relationship
The correlation coefficient between the HE and LIFE is 0.5711 (a relatively strong positive correlation), showing a positive relationship
The correlation coefficient between the HE and FCON is -0.1831 (a weak negative correlation), indicating a inverse relationship
The correlation coefficient between HE and OOP is 0.9006 (a strong positive correlation), indicating a positive relationship
The high correlations among the independent variables suggest potential multicollinearity in our model, which may lead to misleading signs in the correlation coefficients To better understand the relationships between the variables, we will conduct a model analysis in the subsequent section.
ESTIMATED MODEL AND STATISTICAL INFERENCES
Estimated model
The Stochastic specification of population regression function is set up:
The Stochastic specification of sample regression function is set up: ̂ ̂ ̂ ̂ ̂ ̂
F (4,153) 97.73 Prob > F= 0.0000 R-squared = 0.9123 Adj R-squared = 0.9100 Root MSE = 520.09
HE Coef Std Err t P>|t| [95% Conf Interval]
The results showed in the above table led to the equation as follows: ̂
1.2 Interpretation of the intercept term and slope terms in the regression equation
We could tell the meaning of the regression coefficients:
Intercept term: ̂ = -737.2944: If the independent variables equal to 0 then the expected mean value of the dependent variable is the intercept term
̂ = -4.244299: When LIFE increases by 1 unit and other independent variables unchanged, the expected value of he will decrease by -4.244299 unit
̂ =0.0587388: When GDP increases by 1 unit and other independent variables unchanged, the expected value of he will increase by 0.0587388 unit
̂ 04638: When FCON increases by 1 unit and other independent variables unchanged, the expected value of he will increase by 10.04638 unit
̂ =2.287889: When OOP increases by 1 unit and other independent variables unchanged, the expected value of he will increase by 2.287889 unit
However, we can only know that whether those independent variables actually affect the dependent variable or not by testing the significance of regression coefficients as below
The results indicate a strong model fit, with the estimated model accounting for 91.23% of the total variation in health expenditure (HE) within the sample This demonstrates that the variables LIFE, FCON, GDP, and OOP collectively explain a significant portion of the variation in HE.
Hypothesis testing
2.1 Testing the significance of regression coefficients
Firstly, we established the hypothesis:
Secondly, we use the way of comparing p-value of each estimated coefficients with (level of significance) in order to decide whether to reject or not If (P>|t|) <
, we reject With = 0.05 , the results are clear:
Because the p-value of coefficient of LIFE ( ̂ > 0.05 (level of significance), we can conclude that ̂ is not statistically significant at 5% level of significance
Other variables all have p-value < 0.05 which means that GDP, FCON, OOP will actually affect the value of HE ̂ ̂ ̂ are statistically significant at 5% level of significance
CONCLUSION: To clarify how the independent variables impact the dependent variable, we would like to show mechanism of each variable on the total health expenditure
The relationship between LIFE and health expenditures (HE) is not significant, suggesting that LIFE does not directly influence HE values Research indicates that while healthier elderly individuals tend to have longer life expectancies, their health care expenditures remain comparable to those in poorer health This implies that health-promotion initiatives targeting individuals under 65 could enhance the health and longevity of the elderly without increasing overall health costs Therefore, despite investing in medical services, individuals have alternative options for maintaining fitness, such as exercising and participating in activities like yoga and dancing, which complicates the understanding of how life expectancy impacts health expenditures.
The estimated model indicates a significant positive relationship between GDP and health expenditure (HE), with a coefficient of 0.0587388 This suggests that any increase in GDP per capita leads to a corresponding increase in HE, aligning with our predictions As GDP rises, individuals are likely to allocate more resources towards enhancing their living standards, including healthcare Additionally, a growing GDP allows governments to invest more in public services, particularly in health services, thereby improving access to advanced healthcare technologies and treatments.
The estimated model reveals a significant positive relationship between FCON and HE, with a coefficient of 10.04638 This indicates that any change in FCON will correspondingly affect HE in the same direction This finding aligns with our initial predictions, as total consumption includes various components, one of which is health expenditure Consequently, it is evident that an increase in total consumption will also lead to a rise in health expenditure, even if the increase is minimal.
Therefore, it is reasonable to expect that the sign expectation of this variable is (+)
The estimated model indicates a significant positive relationship between out-of-pocket (OOP) costs and health expenditure (HE), as evidenced by the coefficient of 2.287889 This suggests that any change in OOP costs will correspondingly affect HE in the same direction, aligning with our initial predictions Out-of-pocket costs encompass deductibles, coinsurance, and co-payments for covered services, as well as expenses for non-covered services In resource-limited settings, where healthcare providers often receive inadequate compensation, user fees become a crucial revenue source for health workers, which in turn sustains health service delivery but also creates negative financial incentives Thus, it is reasonable to anticipate a positive expectation for this variable.
Firstly, we established the hypothesis:
Secondly , we could see in the above table = 0.00 < 0.05 ( 5% of level significance) In this case, we must reject Conclusion: The estimated model is statistically significant at 5% level of significance.
Recommendations
Health care expenditure has emerged as a significant concern in today's society, making the examination of its influencing factors increasingly vital This article highlights two key factors within the model that require focused attention for enhancement The team aims to provide targeted recommendations to address these critical areas of health expenditure.
Gross Domestic Product (GDP) significantly influences healthcare spending, with low GDP countries often struggling to allocate sufficient funds for even basic health services These nations must exert considerable effort to enhance resource availability from both public and private sectors Conversely, countries with higher health expenditures should focus on optimizing the value derived from their healthcare investments.
Rising out-of-pocket health expenditures worldwide pose significant concerns for policymakers and economists While there is no one-size-fits-all solution, evidence shows that countries can effectively implement well-designed policies and strategies to mitigate these costs and their adverse effects.
The main strategies that countries use include:
● abolish user fees and charges in public health facilities;
● target and exempt specific population groups such as the poor and vulnerable, pregnant women and children from official payments; and
● target and exempt a range of health services such as maternal and child care from official payments and deliver them free of charge
Effective implementation of these strategies requires strong political backing, informed decision-making, and thorough preparation Abolishing user fees and providing exemptions can significantly influence both the demand and supply of healthcare services, likely leading to an increase in service demand that ultimately impacts the workload of healthcare workers.
This essay showcases the collaborative efforts of members who have gained insights from research in Econometrics Through this work, we enhance our understanding of econometric modeling, including the analysis and validation of model fit and variable relationships Furthermore, we apply our acquired knowledge to analyze econometric models, enabling us to draw valuable conclusions regarding socio-economic issues.
In a comprehensive analysis of 158 countries in 2018, the study explored the impact of life expectancy at birth, GDP per capita, final consumption expenditure, and out-of-pocket expenditure on healthcare expenditure The findings indicate that GDP per capita, final consumption expenditure, and out-of-pocket expenditure are statistically significant factors influencing healthcare spending.
We extend our gratitude to Ms Nguyen Thuy Quynh and Ms Vu Thi Phuong Mai for their invaluable guidance and suggestions, which helped us gain a clearer understanding of the issues at hand Acknowledging our limited knowledge and challenges in data collection, we recognize that some errors may have occurred in our assignment Furthermore, the variables we selected may not represent the most significant factors influencing health care expenditure.
This essay aims to provide a comprehensive review and analysis of various factors influencing health expenditure, acknowledging that while these factors are related to certain variables, they may not be entirely precise It serves as a valuable resource for readers seeking to deepen their understanding of the model and the broader issue at hand.
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Country LIFE GDP FCON OOP HE
Afghanistan 61.028 543.3030419 114.5424053 36.01279563 45.5877498 Albania 76.562 4094.358816 89.26672756 97.56730498 203.2085885 Algeria 74.938 4480.785777 51.54727329 65.96736711 228.3995436 Australia 81.69512195 52022.1256 74.17836154 978.1374557 4952.776147 Brunei Darussalam 74.74 35269.55311 36.86463349 66.37942672 803.4938122 Cambodia 66.56 785.5022829 87.63652929 28.18929637 54.30473225 China 74.409 4550.453596 48.28651788 76.59527976 187.7334619 Indonesia 69.205 3122.362815 65.22309079 52.53594097 92.18704117 Japan 82.84268293 44507.67639 77.24200811 591.5842801 4060.190083 Korea, Rep 80.11707317 22086.95292 64.7922245 477.0266197 1378.393657 Lao PDR 64.306 1140.599205 86.48979807 12.58282166 34.99106906 Malaysia 74.493 9040.566251 60.69763365 101.1440636 292.8906935 Mongolia 67.38 2643.292914 67.9051122 27.80502925 88.91862143 Myanmar 63.525 979.05163 67.30270163 12.17689042 15.33668661 New Zealand 80.70243902 33692.01083 77.62320949 384.5995923 3216.223625 Philippines 69.823 2124.05677 81.27218113 50.37061065 91.84031697 Singapore 81.54146341 47236.96023 46.02940668 617.6331642 1502.198522 Thailand 74.184 5076.342992 67.97631713 24.94385569 172.057683 Timor-Leste 67.186 3656.952175 38.49503815 5.94813765 51.39423986 Tonga 70.059 3553.220614 116.0349656 20.51698555 177.7108903 Vanuatu 69.123 2966.857116 79.45871127 8.86267813 100.099884 Vietnam 74.837 1317.890706 72.55453169 29.25773118 78.18682568 Bangladesh 69.881 781.1535936 79.18997615 13.56128539 20.17736083 Bhutan 68.384 2312.860096 66.57617557 11.50495038 69.80133315 India 66.693 1357.563701 65.73246978 29.49660985 45.25077162 Nepal 67.611 592.4010975 88.54859901 16.84531655 29.97179738 Pakistan 65.264 988.7541283 90.03183293 18.70229984 26.5686252 Sri Lanka 75.439 2799.648876 76.90676661 57.88955263 108.6149672 Armenia 73.331 3218.372707 95.06807217 105.2628185 169.421455 Austria 80.5804878 46858.04327 73.99446307 892.1551365 4796.113589 Azerbaijan 70.938 5842.805784 50.24396562 216.1369279 287.676229 Belarus 70.40487805 6181.399916 69.90415036 99.67877137 341.7747082 Belgium 80.18292683 44380.17663 75.51120856 774.3813449 4389.942997 Bosnia and
Herzegovina 76.031 4635.517779 107.8818636 125.080665 415.8838784 Bulgaria 73.51219512 6843.26695 80.28173912 209.1286458 484.7522866 Croatia 76.47560976 13937.14227 78.99653073 157.2054589 1126.3736 Cyprus 79.43 30818.47993 83.434139 802.1905212 1958.802127 Czech Republic 77.42439024 19808.07109 69.78534064 209.5366084 1373.931749 Denmark 79.1 58041.39844 74.97806642 864.8024562 6011.537838 Estonia 75.42926829 14638.60482 72.36849826 203.1092588 926.4502423 Finland 79.87073171 46202.41516 77.09941208 818.4784019 4095.699421 France 81.66341463 40638.334 79.34494702 463.8013838 4576.263887 Georgia 71.46 3073.524753 96.22752214 190.9285244 262.526721
Germany 79.98780488 41785.55691 75.17693387 639.4947495 4596.645775 Greece 80.38780488 26917.75898 91.57644654 723.955093 2573.822453 Hungary 74.20731707 13092.23376 74.03401607 269.5292815 983.1107045 Iceland 81.89756098 43024.92384 76.21837933 668.5015451 3675.762185 Ireland 80.74390244 48715.17686 66.37136017 707.5024231 5128.45058 Italy 82.03658537 35849.3732 81.43147974 660.55478 3214.546282 Kazakhstan 68.29536585 9070.488253 56.19282688 68.22687049 246.1910625 Kyrgyz Republic 69.3 880.0377751 102.7226115 26.48114622 61.4532889 Latvia 73.48292683 11326.21947 82.15574627 256.2378194 689.0771916 Lithuania 73.26829268 11984.86857 83.87758421 222.1520126 805.2219626 Luxembourg 80.63170732 104965.3061 49.22970668 762.5360146 7451.574721 Moldova 69.616 1958.133697 108.4268389 80.17191647 198.2889469 Netherlands 80.70243902 50950.03434 71.6931039 515.2380191 5249.393482 North Macedonia 74.662 4542.899717 93.83301014 115.3432261 305.3469358 Norway 80.99756098 87770.26684 63.32526133 1182.579915 7859.516365 Poland 76.24634146 12599.53358 80.68481379 191.8941344 809.1963297 Portugal 79.02682927 22538.65408 86.47764885 543.4315575 2213.097918 Romania 73.45853659 8209.919456 79.29523499 92.70017939 472.2075179 Russian Federation 68.84121951 10674.9972 70.21296539 200.4302744 567.3776823 Serbia 74.33658537 5735.422857 94.61984538 198.4997684 545.3565525 Slovak Republic 75.11219512 16600.61359 77.43908427 295.3315416 1295.278933 Slovenia 79.42195122 23437.47202 76.32515291 254.7220789 2015.180276 Spain 81.62682927 30736.62785 77.75601678 576.0892946 2778.444191 Sweden 81.45121951 52132.91853 71.54193853 751.7023515 4437.118939 Switzerland 82.24634146 74605.72102 65.45750195 2332.797752 8021.809438 Tajikistan 68.736 749.552711 122.266474 29.82425407 42.34383803 Turkey 74.507 10672.38925 78.02883716 90.99419333 539.3288059 Turkmenistan 66.657 4439.200382 12.17320546 157.4749289 221.7985704 Ukraine 70.26536585 2965.142365 83.15989497 84.68289655 207.3542017 United Kingdom 80.40243902 39079.84261 86.41646498 324.9039383 3309.332813 Uzbekistan 69.672 1377.08214 75.69567371 33.62727253 72.27025516 Canada 81.24634146 47450.31847 78.66437724 765.8587497 4984.501291 United States 78.54146341 48466.82338 84.68472751 971.1667772 7957.729296 Bahrain 76.057 20722.13729 54.11147278 226.6355197 796.3171415 Egypt, Arab Rep 70.349 2644.817039 85.73678104 69.78984542 111.4353496 Iran, Islamic Rep 73.905 6603.212269 53.72654862 261.8541422 440.8525072 Iraq 68.567 4657.280426 63.361826 37.97239174 145.4638422 Israel 81.60243902 30659.12775 79.4231364 526.2650818 2218.186431 Jordan 73.428 3656.453675 91.17822045 67.45763489 308.6578992 Kuwait 74.358 38577.38166 46.02241294 147.7998173 1061.365617 Lebanon 78.36 7756.744069 99.23331309 287.3779699 659.3505459 Morocco 74.382 2839.925168 76.70718714 93.79866385 168.6640117 Oman 75.682 19281.16563 50.08123692 55.0625045 529.0764244
Saudi Arabia 73.917 19262.54768 52.48394948 133.1015505 702.5630532 Tunisia 75.041 4141.976353 79.15787137 102.5726004 243.6066389 United Arab
Emirates 76.332 33893.30351 58.75118864 243.887232 1359.02545 Argentina 75.278 10385.96443 79.39783265 175.6376724 890.9648311 Bahamas, The 72.144 28443.40766 74.19946591 471.1333806 1657.280951 Barbados 78.364 16056.01653 87.4975165 417.9761577 1097.623883 Belize 72.057 4331.434517 86.17101266 60.04203876 250.41443 Bolivia 68.007 1955.461557 76.12969561 32.21240382 100.9812816 Brazil 73.619 11286.24302 79.239687 477.8381821 1090.782516 Chile 78.779 12808.03459 70.43350479 300.3038439 871.2634399 Colombia 75.424 6326.549469 80.47312487 81.60277815 391.0363598 Costa Rica 78.769 8141.913599 82.15780114 168.869511 665.251421 Cuba 78.338 5730.354775 85.07803756 55.70432887 606.6446217 Dominican
El Salvador 71.21 2983.229771 102.9723134 80.78348954 240.2416627 Guatemala 71.481 2825.519029 96.56600653 102.4251837 171.7151027 Guyana 68.059 3033.247725 102.4504261 59.86890072 140.5747592 Haiti 60.511 665.6274195 123.9998202 18.20071566 54.6409318 Honduras 73.317 1891.157395 96.04418516 78.02124963 166.7753354 Jamaica 74.038 4696.841894 98.04325366 58.01062464 234.5828712 Mexico 75.065 9271.398233 77.12073754 225.0551102 518.5691457 Nicaragua 72.428 1503.867544 94.58995992 44.12315895 99.24694269 Panama 76.792 8082.028459 69.97686844 164.3737329 579.5228266 Paraguay 72.653 4355.934939 73.29864274 92.32947136 200.9926578 Peru 74.41 5082.354757 72.25997159 94.46833811 238.6962984 Suriname 70.465 8255.796859 49.25373134 98.49396504 460.8165775 Angola 55.35 3587.883798 52.84009578 18.69452215 96.64370053 Benin 59.318 757.6959074 90.28735299 13.96442478 30.98648958 Botswana 60.211 6434.815657 68.9027413 19.60616562 380.966423 Burkina Faso 57.096 575.4464527 83.48248966 10.23046381 33.91081732 Burundi 57.228 234.2356469 102.9396209 7.37104277 26.13816583 Cabo Verde 71.062 3378.25486 81.45038847 41.62655839 148.2326743 Cameroon 55.101 1285.261726 81.57391567 42.29906742 58.96177642 Central African
Republic 47.312 487.9453833 89.92638533 7.75762298 16.70606813 Chad 50.89 891.6988174 72.22044078 24.65903756 36.48620983 Comoros 61.862 1315.214806 102.0973625 52.82506598 67.15080266 Congo, Dem Rep 56.909 334.0215726 79.74395305 4.76371959 12.8576483 Congo, Rep 60.093 2809.694957 49.05508063 23.37399422 55.44844074 Cote d'Ivoire 52.964 1211.930285 79.25711596 43.56358626 74.97267639 Equatorial Guinea 55.622 17272.00977 35.09505911 180.514535 262.5166205 Eritrea 62.193 667.7441778 109.2570922 8.75616676 16.61465149
Eswatini 46.601 4168.505062 102.17752 33.69320967 312.726667 Gabon 61.4 8840.730664 44.12956417 55.27434023 216.7141215 Gambia, The 59.637 530.7876934 97.61201898 6.77362085 32.28450882 Ghana 61.03 1298.436952 100.6831939 26.86365527 80.46975657 Guinea 56.861 672.4244026 92.5387597 12.68115238 19.50076044 Guinea-Bissau 54.628 557.6321326 109.5330057 15.48446649 34.64383589 Kenya 60.959 951.6879611 91.32382661 17.85660655 59.16981655 Lesotho 45.1 1183.42153 131.2839521 20.62023296 93.27350523 Liberia 59.6 513.4456986 154.0411461 22.72284118 44.57002365 Madagascar 63.388 412.7309341 97.29959706 8.01486318 21.95881931 Malawi 55.564 478.6685897 89.2502244 3.63110235 33.22835282 Mali 55.251 709.5819646 88.28521184 21.69558213 31.36370581 Mauritania 62.527 1241.428756 71.23808394 25.74955231 40.0749762 Mauritius 72.96731707 8000.376432 87.00922531 192.0660728 367.1208105 Mozambique 52.31 431.5154715 96.4157274 1.77344596 21.20860339 Namibia 56.665 5324.61704 88.77453257 46.84775757 491.5041103 Niger 57.333 347.3430407 86.94093153 13.08014141 22.67416641 Nigeria 50.896 2292.445156 76.00683355 58.98108304 75.8629144 Rwanda 63.433 576.049201 94.81003518 5.76473387 52.81532112 Senegal 64.284 1278.977754 93.1802905 24.44663635 48.74603613 Seychelles 73.19756098 10804.68447 77.5651431 15.58831297 380.4055072 Sierra Leone 49.382 401.8350014 86.60371048 27.96467514 43.68096064 South Africa 57.669 7328.615629 79.24821925 45.89809081 539.5676148 Sudan 62.764 1489.876911 77.3976549 66.87764311 109.0750019 Tanzania 58.584 743.4037847 76.40813765 11.5156529 36.10598419 Togo 57.468 533.508792 91.53333417 19.30288128 30.8381707 Uganda 57.099 622.4988457 85.88965905 20.76445911 62.66848192 Zambia 55.655 1489.45907 63.97142854 13.31663609 54.42088116 Zimbabwe 50.64 948.3318545 105.0790991 30.83472143 84.53079248