tiểu luận kinh tế lượng FACTORS AFFECTING HOUSEHOLDS’ HEALTH CARE EXPENDITURE IN COUNTRIES IN 2016

FOREIGN TRADE UNIVERSITY FACULTY OF INTERNATIONAL ECONOMICS -*** ECONOMETRICS REPORT FACTORS AFFECTING HOUSEHOLDS’ HEALTH CARE EXPENDITURE IN COUNTRIES IN 2016 Group: 18 Class: K57 - VJCC Lecturer: Dr Tu Thuy Anh Dr Chu Thi Mai Phuong GROUP MEMBERS Nguyễn Thị Hương Mai – 1815520201 Phan Đức Long - 1815520199 Đỗ Thị Thu Phương - 1815520215 Table of Contents ABSTRACT I, INTRODUCTION: .2 1, Basic concept: 2, Why we choose this topic? II, LITERATURE REVIEW III, THEORETICAL BACKGROUND 1, Overview of the study of factors affecting households’ health care expenditure in countries in 2016 2, Research model & research hypothesis 2.1 Research model 2.2 Research hypothesis IV DESCRIPTIVE STATISTIC OF DATA: Source of data Statistical description Correlation matrix between variables 10 V ECONOMETRIC MODEL .11 VI ROBUSTNESS CHECK 12 Multicollinearity 12 Normality 12 Heteroscedasticity 14 Testing an individual regression coefficient 16 Testing the overall significance .17 VII FINDINGS & DISCUSSION 18 VIII CONCLUSION .19 REFERENCES .20 ABSTRACT The purpose of this report is to understand 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 I INTRODUCTION: Basic concept: A healthy nation they say is a wealthy nation Healthcare is important to the society because people get ill, accidents and emergencies arise and the hospitals are needed to diagnose, treat and manage different types of ailments and diseases Many of people’s aspirations and desires cannot be met without longer, healthier, happy lives The healthcare industry is divided into several areas in order to meet the health needs of individuals and the population at large All over the world, the healthcare industry would continue to thrive and grow as long as man exists hence forming an enormous part of any country’s economy 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 Why we choose this topic? 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 2016” In the report, we used econometrics tool “GRETL” 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 II LITERATURE REVIEW Regarding to household's health care expenditure, in the past there are a number of research and articles which indicated that expenditure on health is growing rapidly A study of “Determinants of Health Care Expenditures and the Contribution of Associated Factors” in Korea during 2003-2010 showed 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 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 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 A detailed study from World Health Organization named “The determinants of health expenditure: A Country-level Panel Data Analysis” gave some key finding as well First factor affecting on household health expenditure is income In global literature, Musgrove, Zeramdini and Carrin used cross section data from 191 countries in 1997 and found that income elasticity of health expenditure was between 1.133 and 1.275 depending on the data included III THEORETICAL BACKGROUND Overview of the study of factors affecting households’ health care expenditure in countries in 2016 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 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 to prolong their life expectancy Life expectancy has been improving for many decades, and there is evidence that health among the elderly is also improving The aging process changes both the body and the mind Many aging changes are physiological in nature, as the body begins to degenerate and break down Declining health is a common issue with aging, with many illnesses and diseases plaguing the elderly population For this reason, the consumption for healthcare is important to the elderly Hence, when people are getting older, their spendings for healthcare are increasing The relationship between Life Expectancy and Healthcare Expenditure Research has also found a relationship between health care spending and Gross domestic products GDP is a monetary measure of the market value of all the final goods and services (including health care service) produced in a specific time period, often annually The growth of a country's GDP represents not only the economic development but also the improvement of other aspects of that country such as infrastructure, education, medical, etc Furthermore, the hypothesis outcome of a research team, whose members are Sojib Bin Zaman, Naznin Hossain, Varshil Mehta, Shuchita Sharmin and Shakeel Ahmed Ibne Mahmood, suggests that there is a positive relationship between GDP and Healthcare Expenditure Countries with high GDP are likely to spend more money on healthcare than countries with lower GDP In the report of WHO, the households with high Out of Pocket Expenditure have the higher spending on healthcare than the lower ones Out-of-pocket payments (OOPs) are defined as direct payments made by individuals to health care providers at the time of service use This excludes any prepayment for health services, for example in the form of taxes or specific insurance premiums or contributions and, where possible, net of any reimbursements to the individual who made the payments OOPs are part of the health financing landscape in all countries relying on user fees and co payments to mobilize revenue, rationalize the use of health services, contain health system costs or improve health system efficiency and service quality According to WHO, Health expenditure share, or the percentage of the household expenditure spent on health care, is a necessary spending for members of the households In the national accounts expenditure on goods and services that are used for the direct satisfaction of individual needs (individual consumption) or collective needs of members of the community (collective consumption) is recorded in the use of income account under the transaction final consumption expenditure (FCE) The most important part of final consumption expenditure is household final consumption expenditure (including healthcare expense) Research model & research hypothesis 2.1 Research model 2.1.1 Methodology • Method of collecting data 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 2016 in 158 countries The data was taken from the highly accurate source which is World Bank • Method used to analyze the data and derive the model The team used multiple linear regression model in combination with OLS (Ordinary Least Square) estimation method to analyze the relationship between health expenditure and other factors including GDP per capita, life expectancy at birth, final consumption expenditure and out of pocket expenditure During the course of the project, the team used the knowledge of econometrics with the main support of GRETL software, Microsoft Excel, Microsoft Word for synthesis and completion of this project 2.1.2 Theoretical model specification • Determine the model type From the reference of previous researches, the team decided to use population linear regression function to carry out the project The population regression function consists of dependent variable and independent variables HE = ₀ + ₁.LIFE + ₂.GDP + + Where: ₃.FCON + ₄.OOP ₀: intercept term : partial regression coefficients u: disturbance • Explain the variables Table 1: Explain the variables and expected sign Variables Meaning Unit Expected sign of regression coefficient HE Health care expenditure Current US$ LIFE Life expectancy Years + GDP Gross domestic product per capita Current US$ + FCON Final consumption Current US$ + Current US$ + expenditure OOP Out of pocket expenditure Source: The research group self-synthesis Theoretically, all independent variables have a positive relationship with dependent variable - That life expectancy is higher leads to the increase in the elderly population It is well understood that ageing population require more health services which could result in higher health expenditure - 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 provider, without a third-party (insurer, or State) is known as “Out of Pocket Expenditure‟ Higher out of pocket expenses will lead to higher health expenditure 2.2 Research hypothesis After studying related theories and referring to domestic and foreign studies, our research team searched and synthesized following hypotheses to study the factors affecting the households ‟healthcare expenditure” of countries in the world Table 2: Hypotheses of the factors affecting the households Variables Hypothesis Expected sign of regression coefficient LE GDP Life expectancy has a positive effect on healthcare expenditure The higher life expectancy is, the more spending people spend on their healthcare + GDP has a positive effect on healthcare expenditure The higher GDP is, the more spending people spend on their healthcare + FCON Final Consumption Expenditure has a positive effect on healthcare expenditure The higher Final Consumption Expenditure is, the more spending people spend on their healthcare + OOP 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 + Source: The research group self-synthesis IV DESCRIPTIVE STATISTIC OF DATA: Source of data Source of data used for each variable is in this below table: Table 3: Source of data used Variable Type of variable Short - form Year Source of data Health expenditure Dependent HE 2016 World bank Life expectancy Independent LIFE 2016 World bank Gross Domestic Product Independent GDP 2016 World bank Final consumption expenditure Independent FCON 2016 World bank Out of pocket expenditure Independent OOP 2016 World bank Source: https://data.worldbank.org/indicator/SH.XPD.CHEX.GD.ZS Statistical description The typical data representing the variables are listed in the table below: Variable Table 4: The typical data representing the variables Number of observation Mean Std.Dev Max Min LIFE 158 69.78425 9.151229 82.84268 45.1 GDP 158 13164.41 18743.24 104965.3 234.2356 FCON 158 79.45112 19.26681 154.0412 12.17321 OOP 158 204.6979 304.3439 2332.798 1.773446 HE 158 1006.304 1733.355 8021.81 12.85765 Source: The research group self-synthesis The standard deviation of variable LIFE is 9.151229 It can be seen that the data are relatively high standard deviation, high level of dispersion, showing the relatively high difference among countries Developed countries like Japan, Switzerland, Italy, etc usually have a high average life expectancy meanwhile that in Africa or some part in Asia are of the low average longevity As population in rich countries is now becoming older, the spending on healthy expenditure will increase ● 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 This is understandable due to the variance in development of each region This featured marked a lot because income and spending have positive relation, the increasing of the former will lead to the rise of the latter, including the spending on health expenditure ● The mean value of FCON is relatively high, about $79.45112 US and standard deviation is 19.26681 Most private sector healthcare expenditure was from household final consumption expenditure, for example, the expenses for medicine, medical device as well as the cost for treatment The standard deviation is high because the need and consumption for health expenditure in each region changes depending on situation, because of the variation in price ● The standard deviation of variable OOP is 304.3439, reach the top with 2332.798 in Switzerland, followed by developed countries such as Norway, Australia, etc and get the lowest value of 1.773446 in Mozambique, followed by mostly African countries like Malawi, Congo, etc.This resulted from the fact that This private cost borne by citizens is also referred to as the out-of-pocket health expenditure Out of pocket payments are those which are made at the point of service In most of the developing countries around the world, total spending on health care is dominated by huge amount of private out-of-pocket health expenditures The tremendous significance of health care imparts huge importance to this issue due to which healthcare reforms are being initiated in many countries all over the world Correlation matrix between variables Before running the regression model, we consider the correlation between variables Thus, we can establish some hypothesis about the relationship between dependent variable and independent variables The correlation between variables are as follows: Table 5: Correlation matrix between variables HE GDP LIFE FCON OOP HE 1000 GDP 0.919 1.0000 LIFE 0.5711 0.6108 1.0000 FCON -0.1831 -0.3551 -0.2810 1.0000 OOP 0.9006 0.8394 0.5899 -0.1880 1.0000 Source: The research group self-synthesis 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 correlations between independent variables are relatively high Therefore, there could be multi-collinear in our model Multi-collinear can cause wrong sign in the 10 correlation coefficient We will run the model to clarify the relationship between variables in the following section V Econometric model The regression result is given as below: PRF: HE = ₀ + ₁.LIFE + ₂.GDP + ₃.FCON + ₄.OOP + u SRF: HE = -737,284 - 4,24430LIFE + 0,058GDP + 10,046FCON + 2,289OOP + e Meaning of Regressors: • ₀ = -737,284 means when LIFE, GDP, FCON, OOP equal to 0, HE value equal to -737,284 • ₁ = -4,24430 means when GDP, FCON, OOP are constant, if LIFE increases by 1, HE decreases by -4,24430 • ₂ = 0,058 means when LIFE, FCON, OOP are constant, if GDP increases by HE increases by 0,058 • ₃ = 10,046 means when LIFE, GDP, OOP are constant, if FCON increases by 1, HE increases by 10,046 • ₄ = 2,289 means when LIFE, GDP, FCON are constant, if OOP increases by 1, HE increases by 2,289 • The expected sign of ₁ is different from the result ((+) in Table and (-) in the result) The expected signs of the rest of regressors is the same as the result (+) 11 VI ROBUSTNESS CHECK Multicollinearity In statistics, multicollinearity (also collinearity) is a phenomenon in which one predictor variable in a multiple regression model can be linearly predicted from the others with a substantial degree of accuracy In this situation the coefficient estimates of the multiple regression may change erratically in response to small changes in the model or the data Multicollinearity does not reduce the predictive power or reliability of the model as a whole, at least within the sample data set; it only affects calculations regarding individual predictors That is, a multivariate regression model with collinear predictors can indicate how well the entire bundle of predictors predicts the outcome variable, but it may not give valid results about any individual predictor, or about which predictors are redundant with respect to others Detection: VIF stands for Variance Inflation Factor During regression analysis, VIF assesses whether factors are correlated to each other (multicollinearity), which could affect p-values and the model isn’t going to be as reliable If a VIF is greater than 10, you have high multicollinearity and the variation will seem larger and the factor will appear to be more influential than it is If VIF is closer to 1, then the model is much stronger, as the factors are not impacted by correlation with other factors Our multicollinearity check is given as below: Our model has VIF < 10, so multicollinearity is not our problem Normality In statistics, sometimes It is needed to assess the normality of a given set of data For many statistical processes, it is prerequisite to make the assessment of the normality of the data, since it is an important assumption in parametric testing There are various normality tests are available for the determination of normality of a data In statistics, the normality tests are used in order to determine whether a given set of data is well- 12 defined by a normal distribution They are also used to measure how likely a set of data to be normally distributed for a random variable Detection: The Jarque-Bera test: JB= N6.(S2+K24) Where S and K are the sample Skewness and Kurtosis statistics The JB test has an asymptotic chi-square distribution with two degrees of freedom * Hypotheses: H0: The error term is normal distributed H1: The error term is not normal distributed * Significance level: α = 0.05 13 p-value = 0,00000 < 0,05 => At 5% level of significant, we have enough evidence to reject H0 => The data is not normally distributed However, according to Central Limit Theorem, in some situations, when independent random variables are added, their properly normalized sum tends toward a normal distribution (informally a "bell curve") even if the original variables themselves are not normally distributed Therefore, in our situation (n = 158, k=5), because of the large number of observations, it is still possible to conduct a significant test as usual Heteroscedasticity In statistics, a collection of random variables is heteroscedastic, if there are sub populations that have different variabilities from others Here “variability” could be quantified by the variance or any other measure of statistical dispersion Thus heteroscedasticity is the absence of homoscedasticity The existence of heteroscedasticity is a major concern in the application of regression analysis, including the analysis of variance, as it can invalidate statistical tests of significance that assume that the modelling errors are uncorrelated and uniform—hence that their variances not vary with the effects being modeled For instance, while the ordinary least squares estimator is still unbiased in the presence of heteroscedasticity, it is inefficient because the true variance and covariance are underestimated Similarly, in testing for differences between sub-populations using a location test, some standard tests assume that variances within groups are equal 14 Detection: White test H0: Var(ui) = σ2 for all i H1: Var(ui) ≠ σ2 for all i p-value = 0,000 < 0,05 => At 5% level of significant, we have enough evidence to reject H0 => Heteroscedasticity problem hasn’t been solved Fix with Robust standard errors Result: Heteros does not affect statistical inferrence 15 Thus, we have the following model after remedying defects: HE = -737,294 - 4.24430*LIFE + 0.0587388*GDP + 10.0464*FCON + 2.28789*OOP + e Testing an individual regression coefficient Firstly, we established the hypothesis: H0: βj = H1: βj ≠ 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 Ho With = 0.05, the results are clear: Table 6: Comparing p-value of each estimated coefficients and decision Variables P>|t| Decision GDP 0.00 Reject H0 LIFE 0.471 Do not reject H0 FCON 0.00 Reject H0 OOP 0.00 Reject H0 Source: The research group self-synthesis Because the p-value of coefficient of LIFE > 0.05 (level of significance), we can conclude that β1 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 ₂, ₃, ₄ 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 ₁ 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 16 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 ₂ is significant in the estimated model Because ₂ = 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 (+) ₃ is significant in the estimated model Because ₃ = 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 (+) ₄ is significant in the estimated model Because ₄ = 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 copayments 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 (+) Testing the overall significance Firstly, we established the hypothesis: H0 : R = H1: R2≠ 17 Secondly, we could see in the above table p - value = 0.00 < 0.05 (5% of level F - test significance) In this case, we have enough evidence to reject H => The estimated model is statistically significant at 5% level of significance VII FINDINGS & DISCUSSION 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 like 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; ● 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 18 VIII CONCLUSION This 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 2016 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 Tu Thuy Anh and Ms Chu Thi Mai Phuong 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 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Empirical results from Asian countries (https://core.ac.uk/download/pdf/148366622.pdf) 14 WHO, The determinants of health expenditure (https://www.who.int/health_financing/documents/report_en_11_deter-he.pdf?ua=1) 15 WHO, Out-of-pocket payments, user fees and catastrophic expenditure (https://www.who.int/health_financing/topics/financial-protection/out-of-pocketpayments/en/) 21 ... and 1.275 depending on the data included III THEORETICAL BACKGROUND Overview of the study of factors affecting households’ health care expenditure in countries in 2016 Health care expenditure is... 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... expenditure on health is growing rapidly A study of “Determinants of Health Care Expenditures and the Contribution of Associated Factors in Korea during 2003-2010 showed that health care expenditures