The impact of health insurance on out of pocket payments in the mekong river delta

UNIVERSITY OF ECONOMICS ERASMUS UNVERSITY ROTTERDAM HO CHI MINH CITY INSTITUTE OF SOCIAL STUDIES VIETNAM THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS THE IMPACT OF HEALTH INSURANCE ON OUT-OF-POCKET PAYMENTS IN THE MEKONG RIVER DELTA BY TA THI HONG NGOC MASTER OF ARTS IN DEVELOPMENT ECONOMICS HO CHI MINH CITY - DECEMBER, 2017 UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES HO CHI MINH CITY THE HAGUE VIETNAM THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS THE IMPACT OF HEALTH INSURANCE ON OUT-OF-POCKET PAYMENTS IN THE MEKONG RIVER DELTA A thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS By TA THI HONG NGOC Academic Supervisor: DR TU VAN BINH HO CHI MINH CITY - DECEMBER, 2017 DECLARATION “I certify that this material is my own work, containing my independent research results, have not been published I assure that all sources of information in the thesis, including data sets, are clearly acknowledged I pledge to take responsibility for my research.” Signature Ta Thi Hong Ngoc Date: December, 2017 ACKNOWLEDGEMENT Firstly, I would like to express my appreciation to my supervisor Dr Tu Van Binh who provided me motivation, patience, and knowledge to complete my thesis I am very grateful for his sympathy and his kind encouragement to me last year when my mother was deeply sick His friendly guidance in all the time of research helped me overcome a hard time of writing this thesis Besides my supervisor, I am grateful to Dr Truong Dang Thuy and Dr Pham Khanh Nam who have provided the theoretical background and econometrics methodology, which strongly supported my thesis My sincere thanks to tutor Nguyen Van Dung who support me data and kindly encourage me during my thesis I would thank all lecturers, administrators and my VNP 22 classmate in the Vietnam – The Netherlands Program for giving me a loyal sympathy, for the memorable moments we were working together before deadlines of assignments and exams during the course Finally, I would like to give a special thanks to my family who has encouraged me not only in thesis period but also in my life ABSTRACT The study uses the Vietnam Household Living Standard Survey in the year of 2012 and 2014 in 13 provinces in the Mekong River Delta (MRD) to evaluate the impact of health insurance on out-of-pocket payments in the MRD Three models including Pool OLS, Random Effects and Fixed Effects are applied and the regression result shows that health insurance is statistically significant and has the negative relationship with out-of-pocket expenses per visit to outpatient service and inpatient service The study indicates that health insurance has a positive impact on reducing out-of-pocket expenses, meaning that people who have health insurance spend less than those who not have health insurance Heath insurance benefits the society by reducing the monetary cost of using the health services and therefore is potentially advantageous for poor and underprivileged people in approaching healthcare resources The policy implication insists that it is essential to increase health insurance coverage, especially for the poor and near poor In addition, policy makers could consider reducing or eliminating co-payments for the poor and policy beneficiaries such as ethnic minorities in the MRD Moreover, the authority needs to concern about awareness raising in the health insurance of people living in this area Key words: Health Insurance, Out-of-pocket expenses, Mekong River Delta JEL Classification: I13 TABLE OF CONTENTS DECLARATION i ACKNOWLEDGEMENT ii ABSTRACT iii TABLE OF CONTENTS iv LIST OF TABLES vi LIST OF FIGURES vii LIST OF ACRONYMS viii CHAPTER 1: INTRODUCTION 1.1 The practical problem 1.2 The research problem 1.3 Research Objectives 1.4 Contribution of the Study 1.5 Organization of the Study CHAPTER 2: LITERATURE REVIEW 2.1 Core concepts 2.1.1 Health insurance 2.1.2 Out-of-pocket payments 2.2 Theoretical background 2.3 Empirical studies about the impact of health insurance on out-of-pocket payments 2.3.1 Out-of-pocket payments definition and measurements 2.3.2 Estimation method 2.3.3 Control variables 10 2.3.4 Results 10 CHAPTER 3: RESEARCH METHODOLOGY AND DATA 13 3.1 Analytical framework 13 3.2 Research Methodology 13 3.2.1 Fixed Effects Estimation 14 3.2.2 Fixed Effects with Unbalanced Panels 15 3.2.3 Random Effects Models 15 3.2.4 Random Effects or Fixed Effects 16 iv 3.3 Model specification 17 3.4 Data 19 CHAPTER 4: FINDINGS AND DISCUSSION 21 4.1 Overview of the research topic 21 4.1.1 Background of health insurance in Vietnam and the MRD 21 4.1.2 Overview of out-of-pocket health expenditure in Vietnam 24 4.2 Descriptive statistics 26 4.3 Estimation results 33 CHAPTER 5: CONCLUSION AND POLICY IMPLICATIONS 40 5.1 Conclusion 40 5.2 Policy Implications 40 5.3 Limitation 41 REFERENCES 42 v LIST OF TABLES Table 1: Variable description and sources 18 Table 1: Descriptive statistics 26 Table 2: The correlation coefficient between the variables 27 Table 3: Number of observations and proportion of people having health insurance28 Table 4: Proportion of people having health insurance in 13 provinces in the MRD29 Table 5: Statistical coverage of health insurance by age 30 Table 6: Statistics on the share of health insurance participation by gender 30 Table 7: Statistics on the share of health insurance participation by marital status 31 Table 8: Statistics on health insurance participation by ethnics 31 Table 9: Statistics on health insurance coverage by level of education 32 Table 10: Statistics on health insurance coverage by rural, urban area 33 Table 11: The panel data regression result with Out-of-pocket expenses per visit to outpatient service (OOV) 34 Table 12: The panel data regression result with Out-of-pocket expenses per visit to inpatient service (OIV) 37 vi LIST OF FIGURES Figure Analytical framework 13 vii LIST OF ACRONYMS 2SLS Two-Stage Least Squares DID Difference-in-Difference FE Fixed Effects GLS Generalized Least Squares GSO General Statistics Office HI Health Insurance IV Instrumental Variables MRD Mekong River Delta OIV Out-of-pocket expenses per visit to inpatient service OLS Ordinary Least Squares OOP Out-of-pocket payments OOV Out-of-pocket expenses per visit to outpatient POLS Pooled Ordinary Least Squares PSM Propensity Score Matching RE Random Effects VHLSS Vietnam Household Living Standard Survey WHO World Health Organization viii Table 4: Proportion of people having health insurance in 13 provinces in the MRD Provinces in the Proportion of people having health Proportion of people having health MRD insurance in 2012 insurance in 2014 Long An 66% 81% Tien Giang 67% 72% Ben Tre 62% 71% Tra Vinh 65% 81% Vinh Long 68% 76% Dongs Thap 71% 76% An Giang 58% 65% Kien Giang 62% 66% Can Tho 63% 73% Hau Giang 63% 69% Soc Trang 50% 66% Bac Lieu 54% 69% Ca Mau 56% 64% Source: VHLSS 2012, 2014 According to the population structure, people are usually divided into three age groups: under working age (0-14 years old); in working age (15 - 59 years old); over working age (over 60 years old) The age structure shows the potential for population and labor development in the country /region, in which a specific strategy for population adjustment is appropriate to the state of the country/region From 2012 to 2014, in the MDR, working-age people account for about 70% of the population, and the proportion of workers with health insurance is not as high as the other two groups, the highest percentage is in the under working age group, following is the over working age group 29 Table 5: Statistical coverage of health insurance by age 2012 2014 Number Age Observ Populati of people ations on ratio having HI Proportion of people Observ ations having HI Number Popul of people ation having ratio HI Proportion of people having HI to 14 940 18% 817 87% 856 17% 783 91% 15 to 59 3,751 70% 2,346 63% 3,520 70% 2,514 71% From 60 674 12% 509 76% 677 13% 552 82% Source: VHLSS 2012, 2014 The statistical results of table 4.6, table 4.7, and table 4.8 show that there is a slight increase in the health insurance participation in 2014 compared to 2012, taking into account factors of gender, marital status and ethnicity In particular, the proportion of male having health insurance increases from 64% to 72%, and the proportion of female having health insurance increases from 62% to 71% In addition, the proportion of single person with health insurance rises from 59% to 68%, and the proportion of married person having health insurance also rises from 54% to 65% There is a similar trend for the proportion of ethnics having health insurance, which grows from 59% to 79%, and the proportion of Kinh with health insurance grows from 63% to 71% Table 6: Statistics on the share of health insurance participation by gender 2012 Gender Observati ons 2014 Number of Proportion of people people having having HI HI Observatio ns Number of Proportion of people people having having HI HI Male 1,634 1,040 64% 1,500 1,081 72% Female 2,117 1,306 62% 2,020 1,433 71% Source: VHLSS 2012, 2014 30 Table 7: Statistics on the share of health insurance participation by marital status 2012 Marital status Observati ons 2014 Number of Proportion of people people having having HI HI Observatio ns Number of Proportion of people people having having HI HI Single 447 263 59% 413 280 68% Married 2,448 1,324 54% 2,335 1,524 65% Source: VHLSS 2012, 2014 Table 8: Statistics on health insurance participation by ethnics 2012 Ethnics Observati ons 2014 Number of Proportion of people people having having HI HI Observatio ns Number of Proportion of people people having having HI HI Kinh 3,487 2,190 63% 3,262 2,309 71% Ethnics 264 156 59% 258 205 79% Source: VHLSS 2012, 2014 In terms of education, the statistical results in table 4.9 show that the higher the level of education, the more likely they tend to participate into health insurance The percentage of health insurance at each level is slightly increasing from 2012 to 2014 For example in 2014, the percentage of people who have no qualification education having health insurance is 73% while the proportion of people who have college level having health insurance is 81%, and the proportion of people who have university level having health insurance is 95% Moreover, the proportion of people with undergraduate educational level or higher participating into health insurance is nearly 100% in 2014 31 Table 9: Statistics on health insurance coverage by level of education 2012 Level of education Observati ons No 2014 Number of Proportion of people people having having HI HI Observati ons Number of Proportion of people people having having HI HI 1,328 852 64% 1,164 847 73% 996 511 51% 957 590 62% 412 202 49% 417 257 62% 153 85 56% 148 109 74% 56 39 70% 44 29 66% 12 10 83% 20 17 85% 65 57 88% 51 45 88% 1 100% 57% College 26 25 96% 27 22 81% University 76 66 87% 101 96 95% MA/MSc 67% 2 100% PhD 1 100% 1 100% qualification Primary Lower secondary Higher secondary Elementary vocational school Middle level vocational school Professional school Vocational college Source: VHLSS 2012, 2014 Statistical results for 2012 show that the proportion of people having health insurance in urban areas is higher than rural areas, but this gap is narrowed in 2014 In detail, the proportion of people having health insurance in rural areas is 61% and this is lower than the proportion of people having health insurance in urban areas (66%) in 2012 Although the proportion of people having health insurance in rural areas is still lower than the proportion of people having health insurance in urban areas in 2014, the difference between them is small, just 1% (around 71%, and 72%) 32 Table 10: Statistics on health insurance coverage by rural, urban area 2012 Area Observati ons 2014 Number of Proportion of people people having having HI HI Observatio ns Number of Proportion of people people having having HI HI Rural 2,856 1,751 61% 2,699 1,919 71% Urban 895 595 66% 821 595 72% Source: VHLSS 2012, 2014 4.3 Estimation results This section applies the panel data regression with Pooled regression, Random effects and Fixed effects Table 4.11 presents the Pooled OLS, Random effects and Fixed effects estimation results with Out-of-pocket expenses per visit to outpatient service (OOV) Moreover, Table 4.12 presents the Pooled OLS, Random effects and Fixed effects estimation results with Out-of-pocket expenses per visit to inpatient service (OIV) 33 Table 11: The panel data regression result with Out-of-pocket expenses per visit to outpatient service (OOV) OOV Pooled OLS Random Effects Fixed Effects 6129 (.6176) 6533 (.6426) 6.2247 (3.9502) 5.4179 (4.7961) 4.6967 (4.9411) 6.7168 (25.0539) income 0524*** (.0039) 0519*** (.0039) 0077 (.0138) gender -35.9193** (18.0325) -33.9898** (18.8579) 44.5556 (111.0827) ethnics -48.3027 (38.4031) -59.9813 (39.8897) 95.7444 (253.1317) 68.5719** (28.9002) 59.7705** (29.7964) -270.1789* (139.2839) -13.1836 (21.3353) -6.1614 (22.3602) 383.3863 (401.5995) -79.2827*** (18.8414) -68.6985*** (19.0868) 80.2046 (55.2792) 71.8459** (29.2440) 64.0432** (29.2184) 69.2324 (73.9716) 113.9226*** (30.7991) 115.9169*** (31.9647) 0.0466 11.8657 (203.8644) 0.0000 0.2497 age education marital_status urban HI inpatient _cons R-squared 0.0468 Overall significance of the whole model (p>F or p> chi2) 0.0000 Breusch-Pagan test (p-value) 0.0002 0.0000 Hausman test (p-value) Notes: Standard deviation is expressed in parentheses Symbol: ***, ** and * respectively represent significance level of 1%, 5% and 10% Table 4.11 reports the results of all three models with Out-of-pocket expenses per visit to outpatient service (OOV) Overall, Pooled OLS and Random effect models are statistically significant with p-value being smaller than 1% However, Fixed effect model is not statistically significant In order to test Pooled OLS and Random effect models which one is better, the Breush-Pagan test was used, resulting in the Random model being better than the Pool OLS model in explaining the impact of health insurance on out-of-pocket payments in the MRD 34 Because the Fixed effect model does not have the overall significance, the Fixed effect model is not used in estimating the impact of health insurance on out-of-pocket payments in the MRD At the same time, Hausman test also failed Therefore, we analyze the results from the Random effect model (the best of the three models mentioned above) At the same time, the study also compared the results from the Random Effect model with the Pool OLS model for further insights As for the Random Effect regression, health insurance is statistically significant and has the negative relationship with out-of-pocket expenses per visit to outpatient service The coefficient of health insurance is -68.6985, which means that people who have health insurance spend less than those who not have health insurance 68,6985 VND for out-ofpocket expenses per visit to outpatient service The result of health insurance is similar to the Pool OLS model Heath insurance benefits the society by reducing the monetary cost of using the health services and therefore is potentially advantageous for poor and underprivileged people in approaching health care resources In the public health sector, the quality and quantity of services, as well as the accessibility to public health facilities, could be increased through additional resources supplied by health insurance In addition, health insurance helps to reduce the incident of large out-of-pocket expenses caused by catastrophic illnesses (Sepehri et al., 2006) The findings are consistent with previous studies by Jowett et al (2003), Wagstaff and Pradhan (2005), Sepehri et al (2006), Sepehri et al (2011), Fan et al (2012), Aji et al (2013), Van Minh et al (2013), Alkenbrack and Lindelow (2015) In addition, income is statistically significant and has the positive relationship with out-of-pocket expenses per visit to outpatient service The coefficient of income is 0.0519, which means that as people are richer they have the tendency to pay more for outpatient service expense When the income increases 1,000,000 VNDs, out-of-pocket expenses per visit to outpatient service increases by 51,900 VNDs The result of income is similar in the Pool OLS model In this study, income is elastic with out-of-pocket payments, because when people have higher incomes, they tend to use faster diagnostic services and more expensive foreign drugs The finding of income in this study is consistent with previous studies such as Jowett et al (2003) Moreover, gender is statistically significant and has the negative relationship with outof-pocket expenses per visit to outpatient service The coefficient of gender is -33.9898, which means that men spend less than women 33,9898 VNDs for out-of-pocket expenses per visit to outpatient service The result of gender is similar to the Pool OLS model 35 Marital status is statistically significant and has the positive relationship with out-ofpocket expenses per visit to outpatient service The coefficient marital_status is 59.7705, which means married people spend more than single people for out-of-pocket expenses 59,7705 VND per visit to outpatient service The result of marital status is similar in the Pool OLS model Married people tend to care for each other more than single people because married people tend to choose high-quality health care (medicines and medicines) and higher price Therefore, married people will have higher OOV than single people, and the result is similar to Sepehri (2006) fixed effect model Inpatient is statistically significant and has the positive relationship with out-of-pocket expenses per visit to outpatient service The coefficient of inpatient 64.0432 means that outpatients who receive more inpatient treatment will spend more than those who only go outpatient without inpatient treatment in the past 12 months with the amount being 64,0432 VNDs per visit The result of inpatient is similar in the Pool OLS model The findings are consistent with previous studies by Nguyen (2012) 36 Table 12: The panel data regression result with Out-of-pocket expenses per visit to inpatient service (OIV) OIV Pooled OLS Random effects Fixed effects 31.2549* (16.6654) 31.5719* (16.7605) -1204.132 (942.9025) education 262.3021* (149.6637) 265.2384* (149.9677) 2063.938 (2390.216) income 1.1063*** (.1727) 1.1057*** (.1730) 1.8605 (1.1388) Gender 951.8434* (570.1164) 982.8141* (574.0077) 31689.68 (19433.44) ethnics -1731.56* (1021.6) -1719.303* (1024.962) 367.7337 (9437.341) marital_status 197.8142 (902.5846) 208.9497 (904.5386) -11533.34 (12555.62) Urban -165.066 (663.6119) -214.3361 (668.0978) -1754.77*** (601.9519) -1773.252*** (602.2379) -1358.1 (3560.741) 317.3054 (582.7754) 1133.184 (1033.184) 317.4488 (583.921) 1120.766 (1034.632) 0.0902 -1659.89 (4648.285) 54132.62 (40496.21) 0.0000 0.8168 Age HI outpatient _cons R-squared Overall significance of the whole model (p>F or p> chi2) Breusch-Pagan test (p-value) 0.0902 0.0000 0.0003 0.4940 Hausman test (p-value) Notes: Standard deviation is expressed in parentheses Symbol: ***, ** and * respectively represent significance level of 1%, 5% and 10% The results of three models with Out-of-pocket expenses per visit to inpatient service (OIV) are presented in table 4.12 Overall, Pooled OLS and Random effect models are statistically significant with p-value being smaller than 1% Moreover, Fixed effect model is not statistically significant In order to test Pooled OLS and Random effect models which one is better, the Breusch-Pagan test was used, resulting in Pool OLS model that is better than the Random effect model in explaining the impact of health insurance on out-of-pocket payments in the MRD 37 Because the Fixed effect model does not have the overall significance, the Fixed effect model is not used in estimating the impact of health insurance on out-of-pocket payments in the MRD At the same time, Hausman test also failed Therefore, we analyze the results from the Pool OLS model (the best of the three models mentioned above) At the same time, the study also compares the results from the Pool OLS model with the Random effect model for further insights As for the Pool OLS regression, the estimation result of health insurance with OIV is the same meaning with OOV result Both in OOV and OIV regression, health insurance is statistically significant and has the negative relationship with out-of-pocket expenses per visit to outpatient service and inpatient service The findings are consistent with previous studies by Aji et al (2013), Alkenbrack and Lindelow (2015), Fan et al (2012), Jowett et al (2003), Van Minh et al (2013), Wagstaff and Pradhan (2005), Sepehri et al (2006), Sepehri et al (2011) Additionally, age is statistically significant and has the positive relationship with outof-pocket expenses per visit to inpatient service The coefficient of age is 31.2549, which means that as people are older they have the tendency to pay more for out-of-pocket expenses per visit to inpatient service, when the age increases year, out-of-pocket expenses per visit to inpatient service increases by 31,2495 VNDs The result of age is similar to the Random effect model The higher the age, the greater the risk of illness Older people who have more time than those in working age, take care of health more than young people For that reason, as the age increases, medical expenses also increase This model also does not control the moral hazard factor of the age factor, suggesting that when individuals age is high, the risk of illness should increase the demand for health insurance The type and severity of illness might be reflected by the positive relation between age and out-of-pocket spending While diseases experienced by younger people tend to be cheaper to treat, the treatment for diseases suffered by older people tends to be more expensive The findings are consistent with previous studies by Sepehri et al (2011) Education is statistically significant and has the positive relationship with out-ofpocket expenses per visit to inpatient service The coefficient of education 262.3021 which means high educated people tend to pay more for out-of-pocket expenses per visit to inpatient service The result of education is similar to the Random effect model This result is similar to that of Aji (2013), Nguyen (2012), Sepehri (2006) Interestingly, the estimation result of income with OIV is the same meaning with OOV result Income is statistically significant and has the positive relationship with out-of- 38 pocket expenses per visit to outpatient service and inpatient service both in OOV and OIV regression The result of income and health insurance is similar to the Random effect model Another finding is the estimate results of gender, the result in the OIV regression is opposite of the OOV regression We found that gender is statistically significant and has the positive relationship with out-of-pocket expenses per visit to inpatient service The coefficient of gender is 951.8434, which means that men spend more than women 951,8434 VNDs for out-of-pocket expenses per visit to inpatient service The result of gender is similar in the Random effect model Because of living habits in the MRD, men are less likely to seek medical care than women, so when they are inpatient, men's illness is a relatively serious and costly treatment for inpatient care Finally, ethnics has the negative relationship with out-of-pocket expenses per visit to inpatient service The coefficient of ethnics is -1731.56, which means Ethnic minority people spend less than Kinh people 1,731,560 VND for out-of-pocket expenses per visit to inpatient service The result of ethnics is similar in the Random effect model Kinh people have higher incomes than ethnic ones, hence they tend to pay higher costs than ethnic ones 39 CHAPTER 5: CONCLUSION AND POLICY IMPLICATIONS 5.1 Conclusion The study aims to estimate the impact of health insurance on out-of-pocket payments in the MRD, using the Vietnam Household Living Standard Survey in the year of 2012 and 2014 in 13 provinces in the MRD This study employs three models including Pool OLS, Random Effects and Fixed Effects The estimation result shows that health insurance is statistically significant and has the negative relationship with out-of-pocket expenses per visit to outpatient service and inpatient service, which means that people who have health insurance spend less than those who not have health insurance The study implies that health insurance has a positive impact on reducing out-of-pocket expenses Heath insurance benefits the society by reducing the monetary cost of using the health services and therefore is potentially advantageous for poor and underprivileged people in approaching health care resources 5.2 Policy Implications As a result of regression in Chapter 4, health insurance has an important role in reducing the out-of-pocket health costs of people living in the MRD, where is identified as the poor region comparing to the nationwide Hence in order to make the health insurance more effective in this area, we need to increase health insurance coverage In particular, policy makers might consider increasing the overall budget for insurance coverage for the poor and near poor In addition, policy makers could consider reducing or eliminating co-payments for the poor and policy beneficiaries such as ethnic minorities in the MRD For cases where medical costs are high, we could implement a supportive insurance policy and gradually change the patient's drug habits, leading patients to take lower-cost generic drugs, this contributes to partially offset the increase in out-of-pocket spending Another issue we need to concern about is raising the propaganda and awareness of people in the MRD about health insurance Therefore, in order to achieve the target, functional agencies in the MRD need to strengthen communication and popularize knowledge about the rights and how to use health insurance: strengthening information, education, and communication on health insurance for both medical facilities and beneficiaries are also required; encouraging people to buy insurance for the whole household; enhancing adherence 40 to compulsory insurance coverage in the compulsory insurance sector, especially the formal workforce 5.3 Limitation Although this study has answered all the research questions about the impact of health insurance on out-of-pocket payments, it also has a few remaining drawbacks This study has the limitation that only panel data regression was applied Hence, possible future studies can use such method as difference-in-differences, propensity score matching to estimate the impact of health insurance on out-of-pocket payments in the MRD 41 REFERENCES Aji, 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Living Standard Survey in the year of 2012 and 2014 in 13 provinces in the Mekong River Delta (MRD) to evaluate the impact of health insurance on out -of- pocket payments in the MRD Three models including... about the impact of health insurance on out -of- pocket payments 2.3.1 Out -of- pocket payments definition and measurements Out -of- pocket payments have been used as the main variable in a number of studies... dissemination to help people understand the meaning of health insurance operation 2.1.2 Out -of- pocket payments The concept of out -of- pocket payments is a widely used concept in health economics