tiểu luận kinh tế lượng FACTORS AFFECTING HOUSEHOLDS’ HEALTH CARE EXPENDITURE IN COUNTRIES IN 2018

FOREIGN TRADE UNIVERSITY FACULTY OF INTERNATIONAL ECONOMICS =====000===== ECONOMETRICS ASSIGNMENT FACTORS AFFECTING HOUSEHOLDS’ HEALTH CARE EXPENDITURE IN COUNTRIES IN 2018 Group: Class: KTEE218.1 Lecturer: Ms Nguyen Thuy Quynh Ms Vu Thi Phuong Mai GROUP MEMBERS Phan Thi Minh Tu - 1810450005 Nguyen Minh Chau - 1814450017 Nguyen Khanh Ly - 1814450051 Nguyen Do Hue Nhi - 1814450060 Hanoi, September 2019 TABLE OF CONTENTS ABSTRACT INTRODUCTION SECTION 1: OVERVIEW OF THE TOPIC Definition Economic theories relating to health expenditure Related published research: Research hypotheses: 12 SECTION 2: MODEL SPECIFICATION 15 Methodology 15 Theoretical model specification 15 Describe the data 17 SECTION 3: ESTIMATED MODEL AND STATISTICAL INFERENCES 21 Estimated model 21 Hypothesis testing 23 Recommendations: 26 CONCLUSION 27 REFERENCES 28 APPENDIX 30 INDIVIDUAL ASSESSMENT 35 ABSTRACT The purpose of this report is to understanding more about Econometrics by running a regression model and discussing its result.The topic of our research team is health care expenditure, one of the issues that are close to our lives today We choose the health expenditure as a dependent variable and life expectancy, final consumption expenditure, out of pocket expenditure, GDP per capita as independent variables After collecting data from 158 countries in the world, we run the model and come up with the result as follows The result indicates that apart from life expectancy, other independent variables all have linear relationships with the dependent variable Their regression coefficients are statistically significant in the model However, the regression coefficient of life expectancy variable is not statistically significant in our model Therefore, the relationship between health expenditure and life expectancy is not inferred Overall, we can conclude that our model is statistically significant at 5% level of significance From the above results, we make some recommendations in order to give readers a closer look about this model in practice INTRODUCTION Econometrics is the quantitative application of statistical and mathematical models using data to develop theories or test existing hypotheses in economics and to forecast future trends from historical data It subjects real-world data to statistical trials and then compares and contrasts the results against the theory or theories being tested To understand more deeply about this method, we would like to deliver a econometrics report under the guidance of Ms Nguyen Thuy Quynh and Ms Vu Thi Phuong Mai Expenditure on health is growing faster than the rest of the global economy, accounting for 10% of global gross domestic product (GDP) World Health Organization (WHO) reveals a swift upward trajectory of global health spending, which is particularly noticeable in low- and middle-income countries where health spending is growing on average 6% annually compared with 4% in high-income countries Since this subject has become more and more noteworthy, as economics students, we decided to review the topic : “Factors Affecting Households' Health Care Expenditure in Countries in 2018” In the report, we used econometrics tool “STATA” to analyze the data we have researched on World Bank This essay aims at evaluating the impact of GDP per capita, life expectancy at birth, final consumption expenditure and out of pocket expenditure on health care expenditure of 158 random nations all over the world In the end, we are bound to achieve an objective look into the issue as well as apply appropriate measures to make progress in practicing health care tasks The report includes these contents: Abstract Introduction Section 1: Overview of the topic Section 2: Model Specification Section 3: Estimated model and statistical inferences Conclusion References Appendix Individual assessment SECTION 1: OVERVIEW OF THE TOPIC Definition Health care expenditure is the amount spent by individuals, groups, nations, or private or public organizations for medical care, prevention, promotion, rehabilitation, community health activities, health administration and regulation and capital formation with the predominant objective of improving health Economic theories relating to health expenditure 2.1 Demand theory for health care Preferences for Health and Health Care In general, health care is only valued to the extent that it improves health, so health is primitive in the description of consumers‟ preference Changes in consumer attitudes toward health care can also change demand For example, television, movies, magazines, and advertising may be responsible for changes in people's preferences for cosmetic surgery Moreover, medical science has improved so much that we believe there must be a cure for most ailments As a result, consumers are willing to buy larger quantities of medical services at each possible price Income and prices Health care is a normal good Rising inflation-adjusted incomes of consumers cause demand curve for health care services to shift to the right On the other hand, if real median family income remains unchanged, there is no influence on the demand curve For an individual with a particular health status, changes in the price of medical care will affect his demand for consumption If the prices increase, the demand will decrease and if the prices decrease, the demand will increase However, the demand for it is relatively inelastic If a consumer is sick and requires medical care, the consumer will purchase healthcare services at almost any price The consumers‟ ability to purchase healthcare is ultimately limited by the customers‟ income, but consumers are likely to trade off spending on many other products to purchase the medical care needed Multiple Goods We have spoken of health care as a single good or service, but many inputs are used in the production of health from health services Doctors‟ time, hospital beds, Xrays, drugs, and information are all used in the delivery of medical care The prices of these inputs will not only determine the overall level of medical care sought by individuals but also the mix of services through which it is provided This multiplicity of inputs means that if governments or insurance companies are involved in setting prices for components of medical care, they must be aware of the potential for the mix of inputs used to change in response to relative price changes Number of Buyers As the population increases, the demand for health care increases In addition to the total number of people, the distribution of older people in the population is important As more people move into the 65-and-older age group, the demand for health care services becomes greater because older people have more frequent and prolonged spells of illness An increase in substance abuse, involving alcohol, tobacco, or drugs, also increases the demand for health care For example, if the percentage of babies born into drug-prone families increases, the demand for health care will shift rightward 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 ● The hospital buildings are part of the hospital‟s capital stock In addition, hospitals contain an immense quantity of other capital goods, such as hospital beds and diagnostic tools—everything from stethoscopes to 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: Number of Sellers Sellers of health care include hospitals, nursing homes, physicians in private practice, HMOs, drug companies, chiropractors, psychologists, and a host of other suppliers To ensure the quality and safety of health care, virtually every facet of the industry is regulated and licensed by the government or controlled by the American Medical Association (AMA) The AMA limits the number of persons practicing medicine primarily through medical school accreditation and licensing requirements The federal Food and Drug Administration (FDA) required testing that delays the introduction of new drugs Tighter restrictions on the number of sellers shift the health care supply curve leftward, and reduced restrictions shift the supply curve rightward Resource Prices An increase in the costs of resources underlying the supply of health care shifts the supply curve leftward By far the single most important factor behind increasing health care expenditure has been technological change New diagnostic, surgical, and therapeutic equipment is used extensively in the healthcare industry, and the result is higher costs Wages, salaries, and other costs, such as the costs of malpractice suits, also influence the supply curve If hospitals, for example, are paying higher prices for inputs used to produce health care, the supply curve shifts to the left because the same quantities may be supplied only at higher prices Related published research: According to the report “ Determinants of Healthcare Expenditure in Economic Cooperation Organization (ECO) Countries: Evidence from Panel Cointegration Tests” from The International Journal of Health Policy and Management, there are two completely contradictory views about the relationship between healthcare spending and production levels First, healthy workers are more efficient than others They have more time for working and their time is not wasted for treatment Secondly, health expenditures are considered as “costs” These expenditures cause resources transfer from other sectors of economy to the health sector and are the reason why the level of production has diminished in countries Therefore, health economists pay more attention to health expenditures and study the determinants of health expenditures A research in the United States has shown that the share of GDP devoted to healthcare expenditures grew from 9% in 1980 to 16% in 2008 Meanwhile, in Iran, the health expenditures per capita increased from $80 in 1995 to $247 in 2005 in average exchange rates Long-term prediction also indicates that health expenditures continue to increase The findings of the study revealed a positive long-term relationship between the percentage of urbanisation and the health expenditures This is due to the fact that the individuals in urban regions have more access to healthcare providers, such as hospitals and clinics, and use more healthcare services leading to higher healthcare expenditures An study of Determinants of Health Care Expenditures and the Contribution of Associated Factors in Korea during 2003-2010 also show that health care expenditures have been drastically increasing every year Medical expenses covered by health insurance, which were about 13 trillion won in 2001, had jumped 2.6-fold by 2010, reaching around 34 trillion Korean won This was an average increase of over 11% annually in the first decade of the 21st century Such a trend raises concerns over the sustainability of health insurance finance following the increase in health care expenditures Medical costs can be explained by determinant factors that are produced by multiplying the volume of health services by the unit cost per service According to the fee-for-service system used in Korea, the unit cost is determined by multiplying relative value units per service by a conversion factor The conversion factor is determined through negotiation between the National Health Insurance Service and supplier groups every year The only mechanism by which insurers can control medical expenditures is this conversion factor Although the Korean government controlled the conversion factor in order to keep down medical expenses in health insurance for over ten years beginning 2001, under the current circumstances in which there is no mechanism to control the frequency or intensity of health services, health care expenditures continue to increase.To date, studies on determinant factors of health care expenditures have mostly used approaches focusing on the use of health services (based on the volume and price of health services) or an economic approach using demand and supply factors of health services Factors such as gross domestic product (GDP), population size, composition of the elderly population, number of physicians, number of medical institutions, and unit costs of services have been a major focus in such studies According to many previous studies, variables including GDP per capita and the proportion of the population aged 65 and over had a significant influence on national health care expenditure increases The overall Medicare Economic Index (MEI) and regional MEI were calculated using the Consumer Price Index and Producer Price Index of 16 cities and provinces The total rates of increase in health insurance medical expenses and annual rates of change in the number of health insurance beneficiaries and beneficiaries older than 65 years old were computed using National Health Insurance statistical yearbooks For example, the annual growth rate of the GDP per capita was produced by dividing the difference between the GDP per capita of year t and GDP per capita of year t-1 by the GDP per capita in year t-1 In another happenings, Baltagi and Moscone (Badi H Baltagi & Moscone 2010) present a negative long-term relationship was found between the health expenditures and ageing groups In case the proportion of the individuals below 15 and over 65 years old is more in a country, the country is considered healthy and, as a result, people consume less expensive healthcare compared to a country with unhealthy people Banins found that health expenditures increased when a country reached higher life expectancy and started to decrease after achieving its peak 10 SECTION 3: ESTIMATED MODEL AND STATISTICAL INFERENCES Estimated model 1.1 Regression Function (Population + Sample) The Stochastic specification of population regression function is set up: The Stochastic specification of sample regression function is set up: ̂ ̂ ̂ ̂ Source SS df MS Model 430324878 107581220 Residual 41384831 153 270489.091 Total 471709709 157 3004520.44 ̂ ̂ Number of obs =158 F (4,153) =397.73 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] LIFE -4.244299 5.873232 -0.72 0.471 -15.8474 7.3588 GDP 0587388 0044777 13.12 0.000 0498926 067585 FCON 10.04638 2.380952 4.22 0.000 5.342595 4.75017 OOP 2.287889 2625289 8.71 0.000 1.76924 2.806539 _cons -737.2944 459.4156 -1.60 0.111 -1644.911 170.3227 21 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 then the expected mean value of the dependent variable is the intercept term { => ̂ = -737.2944 Slope term  ̂ = -4.244299: When LIFE increases by unit and other independent variables unchanged, the expected value of he will decrease by -4.244299 unit  ̂ =0.0587388: When GDP increases by unit and other independent variables unchanged, the expected value of he will increase by 0.0587388 unit   ̂ =10.04638: When FCON increases by unit and other independent variables unchanged, the expected value of he will increase by 10.04638 unit ̂ =2.287889: When OOP increases by 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 22 1.3 The coefficient of determination R2: = 0.9123 In the results, we can see that is high, which mean that the model is fit The estimated model explains 91.23% of the total variation in the value of HE in this sample In other words, LIFE, FCON, GDP, OOP jointly explain 91.23% of the total variation in the value of HE in this sample 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 , we reject With = 0.05 , the results are clear: Variables P>|t| Decision GDP 0.00 Reject LIFE 0.471 Do not reject FCON 0.00 Reject OOP 0.00 Reject or not If (P>|t|) < ̂ Because the p-value of coefficient of LIFE ( > 0.05 (level of significance), is not statistically significant at 5% level of significance we can conclude that ̂ 23 Other variables all have p-value < 0.05 which means that GDP, FCON, OOP will actually affect the value of HE statistically significant at 5% level of significance ̂̂̂ are 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 - - ̂ is not significant in the estimated model which means that LIFE actually not affect the value of HE We tried to research and found that the relationship between LIFE and HE is not linear There may exist other trends that we have not considered in our hypotheses A research show that elderly persons in better health had a longer life expectancy than those in poorer health but had similar health care expenditures until death The expected health expenditures for healthier elderly persons, despite their greater longevity, were similar to those for less healthy persons Health-promotion efforts aimed at persons under 65 years of age may improve the health and longevity of the elderly without increasing health expenditures This meant that in spite of spending money for medical service, people also have another choice to maintain fitness such as exercising, taking part in activities like yoga, dancing,… so the impact of life expectancy on HE is somehow not exactly defined ̂ is significant in the estimated model Because ̂ = 0.0587388, the relationship between GDP and HE is a positive relationship This means that every change in value of GDP will lead to a change in value of HE in the same direction The result is consistent with our prediction It is clearly because when GDP per capita increases, people will want to spend more to improve their living standard (house, food, … including health care) Besides, as gross domestic product increases, government will invest more on public services, especially health services, improving access to new health care technologies and treatments Therefore, it is reasonable to expect that the sign expectation of this variable is (+) 24 ̂ ̂ ̂ ̂ - - is significant in the estimated model Because = 10.04638 , the relationship between FCON and HE is a positive relationship This means that every change in value of FCON will lead to a change in value of HE in the same direction The result is consistent with our prediction The total consumption comprises of many elements and health expenditure is one of those Therefore, it could be easily understood that when total consumption increases, the expenditure for health also increase even by a small value Therefore, it is reasonable to expect that the sign expectation of this variable is (+) is significant in the estimated model Because = 2.287889 , the relationship between OOP and HE is a positive relationship This means that every change in value of OOP will lead to a change in value of HE in the same direction The result is consistent with our prediction Out-of-pocket costs include deductibles, coinsurance, and co-payments for covered services plus all costs for services that aren't covered In poor resource-settings in particular, where health care providers tend to be inadequately paid, user fees constitute a major source of revenue for health workers They primarily serve to sustain the provision of health services, creating perverse financial incentives Therefore, it is reasonable to expect that the sign expectation of this variable is (+) 2.2 Testing the overall significance Firstly, we established the hypothesis: { Secondly , we could see in the above table of level significance) In this case, we must reject = 0.00 < 0.05 ( 5% Conclusion: The estimated model is statistically significant at 5% level of significance 25 Recommendations: It can be seen that health care expenditure is a concern these days The study of the factors that influence health expenditure is becoming increasingly important and urgent There are factors in the model that need more concern for improvement so the team would lịke to give some recommendations for these factors Firstly, GDP plays an important role on how much can be spent on health For low GDP countries where health expenditure is often lower than the minimum required to provide very basic services, great effort is needed to make more resources available for heath from both public and private sources Countries with high health expenditure may need to find ways to increase the value they are getting for their money Secondly, the rising out-of-pocket health expenditures all over the globe are a cause of worry for all the policy makers and economists Although there is no magic bullet, available information illustrates that countries can succeed with well-designed policies and strategies to reduce out of pocket expenditure and its negative impacts 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 These strategies need political support, decision-making and proper preparation User fee abolition and exemption can have a large impact on both demand and supply of health services They likely increase the demand for services which subsequently affects the workload of health workers 26 CONCLUSION This essay is completed under the contribution of members with knowledge gained from the research and study of Econometrics By doing this essay, we can better understand the process of running the econometric model, analyzing, verifying the fit of the model and the relationship between variables in the model In addition, we can apply the knowledge learned and through the econometric model analysis to draw useful conclusions about socio-economic problems Within the scope of the essay, the team examined the effect of life expectancy at birth, GDP per capita, final consumption expenditure and out of pocket expenditure on health care expenditure of 158 countries in 2018 According to the model, GDP per capita, final consumption expenditure and out of pocket expenditure are statistically significant in the model We would like to thank Ms Nguyen Thuy Quynh and Ms Vu Thi Phuong Mai for their guidance and suggestions to help us understand the problem and analyze in the right direction In the process, due to our limited understanding and collecting data, it is inevitable that there are some mistakes in the assignment In addition, the selected variables are not necessarily the best ones that affect health care expenditure These factors are correlated with some of the variables studied but they may not be completely accurate Therefore, we hope this essay will contribute as a review and an analysis of some factors that affect health expenditure for everyone to consult and learn more about the model as well as the issue 27 REFERENCES Anthony J Culyer, Joseph P Newhouse, 2000, Handbook of Health Economics Volume 1A Business Theory and Practice, On the examination of out-of-pocket health expenditures in India, Pakistan, Sri Lanka, Maldives, Bhutan, Bangladesh and Nepal https://btp.press.vgtu.lt/article/12943/ Danuvas Sagarik, 2016, Determinants of Health Expenditures in ASEAN Region: Theory and Evidence Economics Online, 2019, Healthcare as a merit good https://www.economicsonline.co.uk/Market_failures/Healthcare.html https://journals.sagepub.com/doi/full/10.1177/0976399615624054 Harvard University, 2017, The Economics of Health Care https://mronline.org/wp-content/uploads/2018/03/economics_of_healthcare.pdf Health Affairs, Out-Of-Pocket Medical Spending For Care Of Chronic Conditions https://www.healthaffairs.org/doi/full/10.1377/hlthaff.20.6.267 The Journal of Medical Research and Innovation, An Association of Total Health Expenditure with GDP and Life Expectancy https://jmrionline.com/jmri/article/view/72 The National Center for Biotechnology Information NCBI, Determinants of Healthcare Expenditure in Economic Cooperation Organization (ECO) Countries: Evidence from Panel Cointegration Tests https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3937933/ The National Center for Biotechnology Information NCBI, Determinants of Health Care Expenditures and the Contribution of Associated Factors: 16 Cities and Provinces in Korea, 2003-2010 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3859851/ 28 10 The National Center for Biotechnology Information NCBI, Estimating health expenditure shares from household surveys https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3699797/ 11 The New England Journal of Medicine, Health, Life Expectancy, and Health Care Spending among the Elderly https://www.nejm.org/doi/full/10.1056/NEJMsa020614 12 OECD/European Union 2018, Health at a Glance: Europe 2018 https://www.oecd-ilibrary.org/docserver/health_glance_eur-2018-30- en.pdf? expires=1569561579&id=id&accname=guest&checksum=1FFCDF101DB530 8D84DEC06C156ED496 13 School of Business and Economics, Universiti Malaysia Sabah, What are the determinants of health care expenditure? Empirical results from Asian countries https://core.ac.uk/download/pdf/148366622.pdf 14 Wikipedia, Health Economics https://en.wikipedia.org/wiki/Health_economics#Healthcare_demand 15 WHO, The determinants of health expenditure https://www.who.int/health_financing/documents/report_en_11_deter-he.pdf?ua=1 16 WHO, Out-of-pocket payments, user fees and catastrophic expenditure https://www.who.int/health_financing/topics/financial-protection/out-of-pocketpayments/en/ 29 APPENDIX 30 Country Afghanistan Albania Algeria Australia Brunei Darussalam Cambodia China Indonesia Japan Korea, Rep Lao PDR Malaysia Mongolia Myanmar New Zealand Philippines Singapore Thailand Timor-Leste Tonga Vanuatu Vietnam Bangladesh Bhutan India Nepal Pakistan Sri Lanka Armenia Austria Azerbaijan Belarus Belgium Bosnia and Herzegovina Bulgaria Croatia Cyprus Czech Republic Denmark Estonia Finland France Georgia LIFE GDP FCON OOP HE 61.028 76.562 74.938 81.69512195 74.74 66.56 74.409 69.205 82.84268293 80.11707317 64.306 74.493 67.38 63.525 80.70243902 69.823 81.54146341 74.184 67.186 70.059 69.123 74.837 69.881 68.384 66.693 67.611 65.264 75.439 73.331 80.5804878 70.938 70.40487805 80.18292683 543.3030419 4094.358816 4480.785777 52022.1256 35269.55311 785.5022829 4550.453596 3122.362815 44507.67639 22086.95292 1140.599205 9040.566251 2643.292914 979.05163 33692.01083 2124.05677 47236.96023 5076.342992 3656.952175 3553.220614 2966.857116 1317.890706 781.1535936 2312.860096 1357.563701 592.4010975 988.7541283 2799.648876 3218.372707 46858.04327 5842.805784 6181.399916 44380.17663 114.5424053 89.26672756 51.54727329 74.17836154 36.86463349 87.63652929 48.28651788 65.22309079 77.24200811 64.7922245 86.48979807 60.69763365 67.9051122 67.30270163 77.62320949 81.27218113 46.02940668 67.97631713 38.49503815 116.0349656 79.45871127 72.55453169 79.18997615 66.57617557 65.73246978 88.54859901 90.03183293 76.90676661 95.06807217 73.99446307 50.24396562 69.90415036 75.51120856 36.01279563 97.56730498 65.96736711 978.1374557 66.37942672 28.18929637 76.59527976 52.53594097 591.5842801 477.0266197 12.58282166 101.1440636 27.80502925 12.17689042 384.5995923 50.37061065 617.6331642 24.94385569 5.94813765 20.51698555 8.86267813 29.25773118 13.56128539 11.50495038 29.49660985 16.84531655 18.70229984 57.88955263 105.2628185 892.1551365 216.1369279 99.67877137 774.3813449 45.5877498 203.2085885 228.3995436 4952.776147 803.4938122 54.30473225 187.7334619 92.18704117 4060.190083 1378.393657 34.99106906 292.8906935 88.91862143 15.33668661 3216.223625 91.84031697 1502.198522 172.057683 51.39423986 177.7108903 100.099884 78.18682568 20.17736083 69.80133315 45.25077162 29.97179738 26.5686252 108.6149672 169.421455 4796.113589 287.676229 341.7747082 4389.942997 76.031 73.51219512 76.47560976 79.43 77.42439024 79.1 75.42926829 79.87073171 81.66341463 71.46 4635.517779 6843.26695 13937.14227 30818.47993 19808.07109 58041.39844 14638.60482 46202.41516 40638.334 3073.524753 107.8818636 80.28173912 78.99653073 83.434139 69.78534064 74.97806642 72.36849826 77.09941208 79.34494702 96.22752214 125.080665 209.1286458 157.2054589 802.1905212 209.5366084 864.8024562 203.1092588 818.4784019 463.8013838 190.9285244 415.8838784 484.7522866 1126.3736 1958.802127 1373.931749 6011.537838 926.4502423 4095.699421 4576.263887 262.526721 31 Germany Greece Hungary Iceland Ireland Italy Kazakhstan Kyrgyz Republic Latvia Lithuania Luxembourg Moldova Netherlands North Macedonia Norway Poland Portugal Romania Russian Federation Serbia Slovak Republic Slovenia Spain Sweden Switzerland Tajikistan Turkey Turkmenistan Ukraine United Kingdom Uzbekistan Canada United States Bahrain Egypt, Arab Rep Iran, Islamic Rep Iraq Israel Jordan Kuwait Lebanon Morocco Oman Qatar 79.98780488 80.38780488 74.20731707 81.89756098 80.74390244 82.03658537 68.29536585 69.3 73.48292683 73.26829268 80.63170732 69.616 80.70243902 74.662 80.99756098 76.24634146 79.02682927 73.45853659 68.84121951 74.33658537 75.11219512 79.42195122 81.62682927 81.45121951 82.24634146 68.736 74.507 66.657 70.26536585 80.40243902 69.672 81.24634146 78.54146341 76.057 70.349 73.905 68.567 81.60243902 73.428 74.358 78.36 74.382 75.682 79.108 41785.55691 26917.75898 13092.23376 43024.92384 48715.17686 35849.3732 9070.488253 880.0377751 11326.21947 11984.86857 104965.3061 1958.133697 50950.03434 4542.899717 87770.26684 12599.53358 22538.65408 8209.919456 10674.9972 5735.422857 16600.61359 23437.47202 30736.62785 52132.91853 74605.72102 749.552711 10672.38925 4439.200382 2965.142365 39079.84261 1377.08214 47450.31847 48466.82338 20722.13729 2644.817039 6603.212269 4657.280426 30659.12775 3656.453675 38577.38166 7756.744069 2839.925168 19281.16563 67403.1603 75.17693387 91.57644654 74.03401607 76.21837933 66.37136017 81.43147974 56.19282688 102.7226115 82.15574627 83.87758421 49.22970668 108.4268389 71.6931039 93.83301014 63.32526133 80.68481379 86.47764885 79.29523499 70.21296539 94.61984538 77.43908427 76.32515291 77.75601678 71.54193853 65.45750195 122.266474 78.02883716 12.17320546 83.15989497 86.41646498 75.69567371 78.66437724 84.68472751 54.11147278 85.73678104 53.72654862 63.361826 79.4231364 91.17822045 46.02241294 99.23331309 76.70718714 50.08123692 30.15401227 639.4947495 723.955093 269.5292815 668.5015451 707.5024231 660.55478 68.22687049 26.48114622 256.2378194 222.1520126 762.5360146 80.17191647 515.2380191 115.3432261 1182.579915 191.8941344 543.4315575 92.70017939 200.4302744 198.4997684 295.3315416 254.7220789 576.0892946 751.7023515 2332.797752 29.82425407 90.99419333 157.4749289 84.68289655 324.9039383 33.62727253 765.8587497 971.1667772 226.6355197 69.78984542 261.8541422 37.97239174 526.2650818 67.45763489 147.7998173 287.3779699 93.79866385 55.0625045 235.5657823 4596.645775 2573.822453 983.1107045 3675.762185 5128.45058 3214.546282 246.1910625 61.4532889 689.0771916 805.2219626 7451.574721 198.2889469 5249.393482 305.3469358 7859.516365 809.1963297 2213.097918 472.2075179 567.3776823 545.3565525 1295.278933 2015.180276 2778.444191 4437.118939 8021.809438 42.34383803 539.3288059 221.7985704 207.3542017 3309.332813 72.27025516 4984.501291 7957.729296 796.3171415 111.4353496 440.8525072 145.4638422 2218.186431 308.6578992 1061.365617 659.3505459 168.6640117 529.0764244 1257.945977 32 Saudi Arabia Tunisia United Arab Emirates Argentina Bahamas, The Barbados Belize Bolivia Brazil Chile Colombia Costa Rica Cuba Dominican Republic Ecuador El Salvador Guatemala Guyana Haiti Honduras Jamaica Mexico Nicaragua Panama Paraguay Peru Suriname Angola Benin Botswana Burkina Faso Burundi Cabo Verde Cameroon Central African Republic Chad Comoros Congo, Dem Rep Congo, Rep Cote d'Ivoire Equatorial Guinea Eritrea 73.917 75.041 19262.54768 4141.976353 52.48394948 79.15787137 133.1015505 102.5726004 702.5630532 243.6066389 76.332 75.278 72.144 78.364 72.057 68.007 73.619 78.779 75.424 78.769 78.338 33893.30351 10385.96443 28443.40766 16056.01653 4331.434517 1955.461557 11286.24302 12808.03459 6326.549469 8141.913599 5730.354775 58.75118864 79.39783265 74.19946591 87.4975165 86.17101266 76.12969561 79.239687 70.43350479 80.47312487 82.15780114 85.07803756 243.887232 175.6376724 471.1333806 417.9761577 60.04203876 32.21240382 477.8381821 300.3038439 81.60277815 168.869511 55.70432887 1359.02545 890.9648311 1657.280951 1097.623883 250.41443 100.9812816 1090.782516 871.2634399 391.0363598 665.251421 606.6446217 72.046 75.089 71.21 71.481 68.059 60.511 73.317 74.038 75.065 72.428 76.792 72.653 74.41 70.465 55.35 59.318 60.211 57.096 57.228 71.062 55.101 5568.046676 4633.590358 2983.229771 2825.519029 3033.247725 665.6274195 1891.157395 4696.841894 9271.398233 1503.867544 8082.028459 4355.934939 5082.354757 8255.796859 3587.883798 757.6959074 6434.815657 575.4464527 234.2356469 3378.25486 1285.261726 84.26846572 76.47601371 102.9723134 96.56600653 102.4504261 123.9998202 96.04418516 98.04325366 77.12073754 94.58995992 69.97686844 73.29864274 72.25997159 49.25373134 52.84009578 90.28735299 68.9027413 83.48248966 102.9396209 81.45038847 81.57391567 131.5983463 157.6196091 80.78348954 102.4251837 59.86890072 18.20071566 78.02124963 58.01062464 225.0551102 44.12315895 164.3737329 92.32947136 94.46833811 98.49396504 18.69452215 13.96442478 19.60616562 10.23046381 7.37104277 41.62655839 42.29906742 299.656102 331.6893003 240.2416627 171.7151027 140.5747592 54.6409318 166.7753354 234.5828712 518.5691457 99.24694269 579.5228266 200.9926578 238.6962984 460.8165775 96.64370053 30.98648958 380.966423 33.91081732 26.13816583 148.2326743 58.96177642 47.312 50.89 61.862 56.909 60.093 52.964 55.622 62.193 487.9453833 891.6988174 1315.214806 334.0215726 2809.694957 1211.930285 17272.00977 667.7441778 89.92638533 72.22044078 102.0973625 79.74395305 49.05508063 79.25711596 35.09505911 109.2570922 7.75762298 24.65903756 52.82506598 4.76371959 23.37399422 43.56358626 180.514535 8.75616676 16.70606813 36.48620983 67.15080266 12.8576483 55.44844074 74.97267639 262.5166205 16.61465149 33 Eswatini Gabon Gambia, The Ghana Guinea Guinea-Bissau Kenya Lesotho Liberia Madagascar Malawi Mali Mauritania Mauritius Mozambique Namibia Niger Nigeria Rwanda Senegal Seychelles Sierra Leone South Africa Sudan Tanzania Togo Uganda Zambia Zimbabwe 46.601 61.4 59.637 61.03 56.861 54.628 60.959 45.1 59.6 63.388 55.564 55.251 62.527 72.96731707 52.31 56.665 57.333 50.896 63.433 64.284 73.19756098 49.382 57.669 62.764 58.584 57.468 57.099 55.655 50.64 4168.505062 8840.730664 530.7876934 1298.436952 672.4244026 557.6321326 951.6879611 1183.42153 513.4456986 412.7309341 478.6685897 709.5819646 1241.428756 8000.376432 431.5154715 5324.61704 347.3430407 2292.445156 576.049201 1278.977754 10804.68447 401.8350014 7328.615629 1489.876911 743.4037847 533.508792 622.4988457 1489.45907 948.3318545 102.17752 44.12956417 97.61201898 100.6831939 92.5387597 109.5330057 91.32382661 131.2839521 154.0411461 97.29959706 89.2502244 88.28521184 71.23808394 87.00922531 96.4157274 88.77453257 86.94093153 76.00683355 94.81003518 93.1802905 77.5651431 86.60371048 79.24821925 77.3976549 76.40813765 91.53333417 85.88965905 63.97142854 105.0790991 33.69320967 55.27434023 6.77362085 26.86365527 12.68115238 15.48446649 17.85660655 20.62023296 22.72284118 8.01486318 3.63110235 21.69558213 25.74955231 192.0660728 1.77344596 46.84775757 13.08014141 58.98108304 5.76473387 24.44663635 15.58831297 27.96467514 45.89809081 66.87764311 11.5156529 19.30288128 20.76445911 13.31663609 30.83472143 312.726667 216.7141215 32.28450882 80.46975657 19.50076044 34.64383589 59.16981655 93.27350523 44.57002365 21.95881931 33.22835282 31.36370581 40.0749762 367.1208105 21.20860339 491.5041103 22.67416641 75.8629144 52.81532112 48.74603613 380.4055072 43.68096064 539.5676148 109.0750019 36.10598419 30.8381707 62.66848192 54.42088116 84.53079248 Figure Testing Data 34 INDIVIDUAL ASSESSMENT Evaluator Phan Thi Phan Thi Nguyen Do Nguyen Nguyen Minh Minh Tu - Hue Nhi - Khanh Ly - Chau - 1810450005 1814450060 1814450051 1814450017 _ 10 10 10 10 _ 10 10 10 10 _ 10 10 10 10 _ 10 10 10 10 Minh Tu Nguyen Do Hue Nhi Nguyen Khanh Ly Nguyen Minh Chau Average Score 35 ... sustainability of health insurance finance following the increase in health care expenditures Medical costs can be explained by determinant factors that are produced by multiplying the volume of health. .. relating to health expenditure 2.1 Demand theory for health care Preferences for Health and Health Care In general, health care is only valued to the extent that it improves health, so health. .. intensity of health services, health care expenditures continue to increase.To date, studies on determinant factors of health care expenditures have mostly used approaches focusing on the use of health