Health insurance and public health care utilization in vietnam

UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES HO CHI MINH CITY VIETNAM THE HAGUE THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS HEALTH INSURANCE AND PUBLIC HEALTH CARE UTILIZATION IN VIETNAM BY TRAN THE HUNG MASTER OF ARTS IN DEVELOPMENT ECONOMICS HO CHI MINH CITY, DECEMBER 2014 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 HEALTH INSURANCE AND PUBLIC HEALTH CARE UTILIZATION IN VIETNAM A thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS By TRAN THE HUNG Academic Supervisor: Dr TRUONG DANG THUY ABSTRACT Vietnam is in the process of improving health system To achieve this goal, the Vietnam Government attempts to expend the coverage of public health insurance which is an effective tool in low and middle income countries to finance health care provision (WHO, 2000) Although the insurance coverage increases significantly over the last ten years, the private expenditure on health is still high It only reduces 6%, particularly from 69.1% of total expenditure on health in 2000 to 62.9% in 2010 (WHO, 2013) This comes up with a question that whether health insurance improves access to care? To answer this question, this study will assess the impact of health insurance on health care utilization, particularly public health services through two purposes: medical examination and treatment A binary probit model is used to estimate the impact of health insurance on public health care utilization Then we investigate determinants of insurance enrollment to increase the number of insurance participators if insurance affects positively significant on health care use Data are obtained from Vietnam Household Living Standard Surveys (VHLSS) in 2010 The empirical results indicate that insurance has a positively significant effect on public health care utilization In other words, we can conclude that health insurance actually improve access to care Moreover, the results of insurance participation show that insurance enrollment is affected strongly by income and interaction terms of frequency of illness It is also remarked that demand for insurance is different between five income quintiles Finally, household’s characteristics including household’s size, income and illness ratio affect significantly to insurance enrollment ACKNOWLEDGEMENT This thesis is not only the result of my own effort, it also consists direct and indirect supports of other individuals and organizations I would like to express my deep gratitude to them My academic supervisor, Dr Truong Dang Thuy, is the person that I would like to thank firstly Without his comments and supports, I would not finish my thesis in time and as good as this Furthermore, I would also like to acknowledge the Scientific Committee, the lecturers and staffs of Vietnam-Netherlands Programme for the knowledge and guidance during the period of studying and writing thesis Last but not least, I am grateful to my family for create favorable conditions to help me learn better Finally, I would like to thank my friends, especially “HLNTTV Group” for their supports in the whole time of studying HCMC, December 2014 Trần Thế Hùng TABLE OF CONTENTS LIST OF FIGURES iii LIST OF TABLES iii CHAPTER 1: INTRODUCTION 1.1 Problem statement 1.2 Research objectives 1.3 Research question 1.4 Research scope and data 1.5 The structure of this study CHAPTER 2: LITERATURE REVIEW 2.1 Relationship between health utilization and insurance 2.1.1 2.1.2 Health care usage theory Theory of relationship between health insurance and health utilization 11 2.3 Empirical reviews of relationship between health insurance and Health utilization: 14 2.4 Theory of insurance participation: 23 2.5 Empirical reviews of insurance participation 24 CHAPTER 3: RESEARCH METHODOLOGY 30 3.1 An overview of Vietnam health system and health care use 30 3.1.1 3.1.2 3.2 Provider network 30 Access and utilization of medical examination and treatment services 32 Overview of health insurance .34 3.3 Methodology and data 36 3.3.1 Methodology 36 3.3.2 Data 38 3.4 Measurement of variables and expected sign 39 CHAPTER 4: RESULTS 45 4.1 Descriptive statistic 45 4.2 Empirical results 50 4.2.1 Impact of health insurance on public health care use .50 4.2.1.1 Medical examination 50 4.2.1.2 Treatment 52 4.2.2 Determinants of insurance participation 54 CHAPTER 5: CONCLUSIONS AND POLICY IMPLICATIONS 63 5.1 Conclusion remarks and policy implication 63 5.2 Limitation and further research 67 REFERENCES 69 APPENDIX 75 LIST OF FIGURES Figure 1: Initial behavioral model of health services utilization Figure 2: Modeling the effect of insurance programme on the use of health services 21 Figure 1: Proportion of seeking care in 2010 33 Figure 2: Timeline and roadmap of universal health insurance coverage 34 Figure 3: Trend in health insurance coverage from 1993-2010 36 LIST OF TABLES Table 1: Measurement of variables 40 Table 1: Descriptive statistics of using public health care services by purpose 45 Table 2: Descriptive statistics of insurance participation 45 Table 3: Descriptive statistics of continuous independent variables 46 Table 4: Public health care use and insurance enrollment by gender 47 Table 5: Public health care use and insurance enrollment by employment status .47 Table 6: Public health care use and insurance enrollment by area (rural) 48 Table 7: Public health care use and insurance enrollment by minor ethnic people .49 Table 8: Results of impact of health insurance on medical examination 50 Table 9: Results of impact of health insurance on medical treatment 52 Table 10: Results of insurance participation (household level) 54 Table 11: Results of insurance participation (individual level) 58 Table 12: Results of insurance participation by different income quintile 61 iii Tran The Hung Master’s Thesis VNP19-2014 CHAPTER 1: INTRODUCTION 1.1 Problem statement After “Doi Moi” program in 1986, Vietnam has experienced rapid and continuous economic growth with GDP per capita increases from 140 USD in 1992 to 1,168 USD in 2010 Moreover, Vietnam’s poverty headcount drops from 60% to 20.7% in the past twenty years (Work Bank, 2013) When people become more affluent, they will have higher demand for care (McPake et al 2002; Folland et al 2004) Therefore, the rate of healthcare usage increases significantly from 2002 to in 2010 Typically, percentage of people having health treatment in 2002 is 18.9%, and then they rise to 40.9% of total population in 2010 Over the period of 2002-2010, healthcare utilization in Vietnam increases dramatically It suggests that people pay more attention to their health As for 2010, the percentage of people having health treatment is about 40.9% Of which, the rates of inpatient and outpatient are 8.1% and 37.1% respectively There are two main kinds of health care services that people use in Vietnam, including public and private health care services The percentage of people using public health care services is nearly seventy percent; particularly, the ratio of inpatient hospitalized in public health services is around 90.1% of total inpatient and 57.2% is the percentage of outpatient using public health care services in 2010 In the last ten years, Vietnam households still have to concern with a burden of health care expenditure The amount of money that people have to spend in health care is much more than Government spending; private expenditure on health accounts for around 62.9% of total expenditure on health while general Government expenditure on health is around 37.1 in 2010 compared to Thailand with 25% of private expenditure and 75% of Government expenditure on health (World Health Organization 2013) The major element that makes the large proportion of private expenditure is households’ out-of-pocket payment Out-of-pocket expenditure is about 93% of private expenditure on health in Vietnam 2010 (WHO, 2013) An increase in out-of-pocket payment on health may lead households to sell their assets to be able to pay the treatment fees Most of households, especially poor households, have to pay such a substantial share of their income for health service As the result, they are pushed into poverty (World Health Organization, 2004) Health risk is probably the greatest threat to people’ lives because it impacts on their direct expenditure and it also reduces their health affecting to labor supply and productivity leading to income poverty (Asfaw, 2003) This author suggests that health insurance is an effective tool to deal with health risk for the poor In addition, health insurance is as a part of income protection because it reduces financial burden of treatment at low income levels (Jutting, 2003) Health insurance is also a tool in order to create an equitable access to health services throughout the population at lowincome countries (WHO, 2000) Ensor (1995) discusses that voluntary health insurance plays an important role in reforming overall health care system by making health service provision more efficient Recognizing the important role of health insurance, many authors study the relationship between health insurance and financial risk protection or health, especially, impact of health insurance on health utilization Saksena et al (2010) state that health insurance has statistically significant positive impact on health care utilization of health services when people are needed For the poor, health insurance is an effective tool which increases health care usage when they are sick (Jutting, 2003) Health insurance does not only rise health care utilization, but it also increases the usage of physician services and preventive services and so it improves health (Freeman et al, 2008) Health utilization is affected by many determinants including demographic factors; social structures, characteristics of family and community (Anderson, 1995) The author argues that demographic variables such as age, gender, education have low mutability, so they cannot be altered to change utilization; and cultural backgrounds (ie, ethnicity, region) are not changeable to promote health care usage (Anderson & Newman, 2005) while personal/family and community’s characteristics which include an important factor: health insurance are quite mutable and strongly associated with health utilization For example, the impact of health insurance on health care use has been demonstrated dramatically by The Rand Health Insurance Study such as the studies of Manning et al (1987) and Jutting (2003) As a result, we can conclude that increasing insurance participation is a good choice to accelerate health utilization; and it is necessary for policy makers to adopt how the impact of insurance on health care utilization is and then assess what are determinants of insurance participation so as to create favorable conditions for people to join health insurance scheme, specially, for the poor who not have enough resources to use health services In this situation, the study will examine the effect of health insurance to health care utilization at public health care services with different purposes including health test and treatment In other word, we will hypothesize whether health insurance improves access to health care since many studies use health care utilization as a proxy for access such as Fox (1972); Aday & Anderson (1974; 1995) After measuring the impact of health insurance on health care usage, if the effect is positively significant meaning that health insurance actually improves access to health care, we then investigate determinants affecting to insurance enrollment Then, the results are used to recommend policy implications to improve insurance participation including: Ensor, T (1995) Introducing health insurance in Vietnam Health Policy and Planning 10:154–63 Escobar, M., Griffin, C., and Shaw, R (2010) Impact of health insurance in low- and middle-income countries Communications Development Incorporated, Washington, DC Folland, S., Goodman, AC., Stano, M 2004.The economics of health and health care Upper Sadle River, NJ: Pearson Prentice Hall Fox, P D Access to medical care for the poor: A federal perspective Med Care 10:272 May-June 1972 Freeman, JD., Kadiyala, S., Bell, JF., Martin, DP (2008) The causal effect of health insurance on utilization and outcomes in adults: a systematic review of US studies Medical care 46(10):1023-32 doi: 10.1097/MLR.0b013e318185c913 Gertler, P., and van der Gaag, J (1990) The willingness to pay for medical care Baltimore: Johns Hopkins University Press Jütting, J (2003) Do Community-based Health Insurance Schemes Improve Poor People’s Access to Health Care? Evidence From Rural Senegal World Development Vol 32, No 2, pp 273–288 doi:10.1016/j.worlddev.2003.10.001 Kirigia, J.M., Sambo, L.G., Nganda, B., Mwabu, G.M., Chatora, R., and Mwase, T (2005) Determinants of health insurance ownership among South African women BMC Health Services Research2005, 5:17 doi:10.1186/1472-6963-517 Lee, S.-J., Kwon, S.I., Chung, S.Y (2010) Determinants of household demand for insurance: the case of Korea The Geneva Papers, Vol 35, p S82–S91 Liberman, S and Wagstaff, A (2009) Health Financing and Delivery in Vietnam The World Bank Manning, W.G., Newhouse, J.P., Duan, N., Keeler, E.B., and Leibowitz, A (1987) Health Insurance and the Demand for Medical Care: Evidence from a Randomized Experiment The American Economic Review, Vol 77, No 3, pp 251-277 Matsushima, M and Yamada, H (2013) Public Health Insurance in Vietnam towards Universal Coverage: Identifying the challenges, issues, and problems in its design and organizational practices McPake, B., Kumaranayake, L., Normand, C (2002) Health economics: an international perspective London and New York: Routledge Mechanic, D (1978) Medical Sociology: A comprehensive text(2nd ed.) New York, NY: Free Press Mendoza-Sassi, R., Béria, J.U and Sevilla, J (n.d) Factors Associated with Health Services Utilization A Population -based Study Assessing the Characteristics of People that Visit Doctors in Southern Brazil Moore, Wilbert E (1969) Social Structure and Behavior Pp 283–322 in Gardner Lindzey and Elliot Aronson (eds.), The Handbook of Social Psychology Volume 4, 2nd edition Reading, Massachusetts: Addison-Wesley Nolan, R L., Schwartz, J.L, and Simonian, K.(1969) Social class differences in utilization of pediatric services in a prepaid direct service medical care program Am J Pub Health 57:34 Nyman, J.A (2001) The Theory of Demand for Health Insurance Saksena, P., Antunes, A.F., Xu, K., Musango, L., and Carrin, G (2010) Impact of mutual health insurance on access to health care and financial risk protection in Rwanda World Health Report Background Paper, No Showers, V.E and Shotick, J.A (1994) ‘The effects of household characteristics on demand for insurance: A Tobit analysis’,The Journal of Risk and Insurance61(3): 492–502 Shuval, J T Social Functions of Medical Practice San Francisco: Jossey-Bass, 1970 Stoeckle, J., I K Zola, and G E Davidson (1963) On going to see the doctor: The contributions of the patient to the decision to seek medical aid J Chron Dis 16:975 The Milbank Memorial Fund Quarterly: Health and Society, Vol 51, No 1, 1973 (pp 95–124) Tompa, E (2002) The Impact of Health on Productivity: Empirical Evidence and Policy Implications THE REVIEW OF ECONOMIC PERFORMANCE AND SOCIAL PROGRESS Tran T, Hoang P, Inke M, Nguyen P 2011 A HEALTH FINANCING REVIEW OF VIET NAM WITH A FOCUS ON SOCIAL HEALTH INSURANCE Bottlenecks in institutional design and organizational practice of health financing and options to accelerate progress towards universal coverage Hanoi: World Health Organization Zborowski, M Cultural components in responses to pain J Soc Issues 8:16, 1952 Waters, H (1999) Measuring the impact of health insurance with a correction for selection bias a case study of Ecuador Health Economics and Econometrics, 8, 473–483 Weinberger, K and Jütting, J (2001): Women’s participation in local organizations: conditions and constraints In: World Development, Vol 29, no 8, pp 1391 1404 WHO (2000): World Health Report 2000—Health Systems: Measuring Performance Geneva: WHO Yip, W.; Berman, P (2001): Targeted health insurance in a lowincome country and its impact on access and equity in access: Egypt’s school health insurance In: Health Economics 10, pp 207 – 220 WHO Health services delivery profile Vietnam, 2012 Retrieved from http://www.wpro.who.int/health_services/service_delivery_profile_vietnam.pdf Work Bank (data) Out-of-pocket health expenditure (% of private expenditure on health) Retrieved from http://data.worldbank.org/indicator/SH.XPD.OOPC.ZS World Health Oganization (2004) The impact of health expenditure on households and options for alternative financing Technical paper World Health Organization (2013) World Health Statistics 2013 Geneva: WHO Retrieved from http://www.who.int/gho/publications/world_health_statistics/EN_WHS2013_Fu ll.pdf Yip, W., & Berman, P (2001) Targeted health insurance in a low income country and its impact on access and equity in access: Egypt’s school health insurance Health Economics, 10, 207–220 APPENDIX Endogeneity test: When assessing the impact of insurance on health care use, Water (1999) and Jutting (2001, 2003) experience the problem of endogeneity which is caused by the choice of enrollment or not enrollment of individuals It means that potential bias leads to potential endogeneity To measure this problem, the Hausman test is employed Firstly, the two equations of impact of insurance on medical examination and treatment are run and we get the results Secondly, we run the regressions of insurance participation at both household and individual level These functions are the two reduction forms of examination and treatment regressions Then, residuals of insurance regressions are collected Thirdly, we add residuals in the equations of examination and treatment and run them again There is a hypothesis that: H0: σ = 0: insurance is exogenous, not need IV H1: σ # 0: insurance is endogenous, need IV Where: σ is coefficient of residuals Variable Medical examination Coef Medical treatment Coef insu 0.275*** income (0.033) 2.88E-05 (0.000)*** 0.437*** (0.025) -4.71E-05 Freuency of illness outpatient (0.000)*** -0.049*** 0.001 (0.006) (0.003) -0.880*** sex age edu employed ethnic -0.054** (0.035) 0.134*** (0.027) (0.021) 0.004*** 0.009*** (0.001) (0.001) 0.028*** 0.0001 (0.004) (0.003) 0.105** -0.127*** (0.047) (0.047) 0.380*** 0.061 (0.048) (0.048) rural -0.073** Residual (0.031) 0.125 (0.131) Constant -1.639*** 0.089*** (0.025) -0.105 (0.106) 0.157* (0.095) (0.110) (*), (**), (***) are the significant level at 0.1, 0.05 and 0.01 level, respectively The residual coefficients in two examination and treatment model are insignificant meaning that we fail to reject the null-hypothesis Hence, insurance is exogenous variable and so we not need instrument variables Impact of insurance on medical examination probit medicalexamination insu income Freuencyofill sex age edu employed ethnic rural Iteration Iteration Iteration Iteration Iteration 0: 1: 2: 3: 4: log log log log log likelihood likelihood likelihood likelihood likelihood = = = = = -6054.8298 -5782.3234 -5772.1167 -5772.0754 -5772.0754 Probit regression Number of obs LR chi2(9) Prob > chi2 Pseudo R2 Log likelihood = -5772.0754 medicalexamination Coef insu income Freuencyofill sex age edu employed ethnic rural _cons 2839031 000034 -.0493998 -.0515702 003419 027846 0730233 4122208 -.0800468 -1.544819 Std Err .0314383 9.89e-06 0056897 0265881 0006251 0035852 0330509 0346011 0306577 0499374 z 9.03 3.44 -8.68 -1.94 5.47 7.77 2.21 11.91 -2.61 -30.94 P>|z| = = = = 15213 565.51 0.0000 0.0467 [95% Conf Interval] 2222852 3455209 0000146 0000534 -.0605514 -.0382481 -.103682 0005415 0021939 0046441 0208191 0348729 0082447 1378019 3444038 4800378 -.1401348 -.0199589 -1.642695 -1.446944 0.000 0.001 0.000 0.052 0.000 0.000 0.027 0.000 0.009 0.000 dprobit medicalexamination insu income Freuencyofill sex age edu employed ethnic rural Iteration Iteration Iteration Iteration Iteration 0: 1: 2: 3: 4: log log log log log likelihood likelihood likelihood likelihood likelihood = = = = = -6054.8298 -5782.3234 -5772.2883 -5772.0755 -5772.0754 Probit regression, reporting marginal effects Log likelihood = -5772.0754 medica~n insu* income Freuen~l sex* age edu employed* ethnic* rural* dF/dx 0548966 6.96e-06 -.0101098 -.0105155 0006997 0056987 014853 0979422 -.0167203 Std Err .0057139 2.03e-06 0011418 0054006 0001277 0007312 0066793 0093111 0065316 z 9.03 3.44 -8.68 -1.94 5.47 7.77 2.21 11.91 -2.61 Number of obs LR chi2(9) Prob > chi2 Pseudo R2 P>|z| x-bar 0.000 0.001 0.000 0.052 0.000 0.000 0.027 0.000 0.009 673832 430.915 2.54716 438178 34.7329 5.74778 573391 167357 722014 [ = 15213 = 565.51 = 0.0000 = 0.0467 95% C.I ] 043698 066096 3.0e-06 000011 -.012348 -.007872 -.0211 000069 000449 00095 004266 007132 001762 027944 079693 116192 -.029522 -.003919 Impact of insurance on medical treatment (model1): probit medicaltreatment insu income Freuencyofill outpatient sex age edu employed ethnic rural Iteration 0: log likelihood = -10533.598 Iteration 1: log likelihood = -9803.4255 Iteration 2: log likelihood = -9802.2721 Iteration 3: log likelihood -9802.272 = Probit regression Log likelihood = Number of obs LR chi2(10) Prob > chi2 Pseudo R2 -9802.272 medicaltreatment Coef insu income Freuencyofill outpatient sex age edu employed ethnic rural _cons 4298644 -.0000525 0010199 -.8806745 1317429 0088849 0001106 -.097572 0335361 0945309 0754695 Std Err .0243213 0000101 0027192 0351626 0213125 0005045 0028781 0270573 0298727 0246729 0490864 z 17.67 -5.21 0.38 -25.05 6.18 17.61 0.04 -3.61 1.12 3.83 1.54 P>|z| 0.000 0.000 0.708 0.000 0.000 0.000 0.969 0.000 0.262 0.000 0.124 = = = = 15213 1462.65 0.0000 0.0694 [95% Conf Interval] 3821954 4775333 -.0000723 -.0000328 -.0043097 0063494 -.949592 -.811757 0899712 1735145 0078961 0098737 -.0055304 0057515 -.1506032 -.0445407 -.0250133 0920854 0461729 1428889 -.020738 1716769 Impact of insurance on medical treatment (model2): add income square probit medicaltreatment insu income incomesquare Freuencyofill outpatient sex age edu employed ethnic rural Iteration Iteration Iteration Iteration Iteration Iteration 0: 1: 2: 3: 4: 5: log log log log log log likelihood likelihood likelihood likelihood likelihood likelihood = = = = = = -10533.598 -9803.224 -9800.4646 -9799.9544 -9799.9507 -9799.9507 Probit regression Number of obs LR chi2(11) 1467.29 Prob > chi2 Pseudo R2 Log likelihood = -9799.9507 medicaltreatment Coef insu income incomesquare Freuencyofill outpatient sex age edu employed ethnic rural _cons 4269947 -.0000204 -5.35e-09 0009589 -.8800975 1310105 0089745 -.0004414 -.1080244 0369301 0958837 0762323 Std Err .0243634 0000189 2.87e-09 0027189 035158 0213181 0005065 002891 0275311 0299159 0246874 0490885 z 17.53 -1.08 -1.87 0.35 -25.03 6.15 17.72 -0.15 -3.92 1.23 3.88 1.55 P>|z| 0.000 0.281 0.062 0.724 0.000 0.000 0.000 0.879 0.000 0.217 0.000 0.120 = = 15213 = = 0.0000 0.0696 [95% Conf Interval] 3792432 4747461 -.0000574 0000166 -1.10e-08 2.72e-10 -.00437 0062878 -.9490059 -.8111891 0892278 1727931 0079818 0099672 -.0061076 0052248 -.1619843 -.0540645 -.021704 0955642 0474973 1442702 -.0199794 1724439 Note: failure and successes completely determined dprobit medicaltreatment insu income incomesquare Freuencyofill outpatient sex age edu employed ethnic rural Iteration Iteration Iteration Iteration Iteration Iteration 0: 1: 2: 3: 4: 5: log log log log log log likelihood likelihood likelihood likelihood likelihood likelihood = = = = = = -10533.598 -9809.2752 -9800.8015 -9800.0063 -9799.9509 -9799.9507 Probit regression, reporting marginal effects Log likelihood = -9799.9507 medica~t dF/dx Std Err insu* income income~e Freuen~l outpat~t* sex* age edu employed* ethnic* rural* 167936 -8.12e-06 -2.13e-09 0003822 -.3271258 0521979 0035771 -.0001759 -.0430493 0147255 0381605 0093585 7.53e-06 1.14e-09 0010837 0111207 008484 0002019 0011523 0109638 0119317 0098044 obs P pred P 480773 4831648 (at x-bar) z 17.53 -1.08 -1.87 0.35 -25.03 6.15 17.72 -0.15 -3.92 1.23 3.88 Number of obs LR chi2(11) Prob > chi2 Pseudo R2 P>|z| x-bar 0.000 0.281 0.062 0.724 0.000 0.000 0.000 0.879 0.000 0.217 0.000 673832 430.915 1.8e+06 2.54716 875304 438178 34.7329 5.74778 573391 167357 722014 [ = 15213 =1467.29 = 0.0000 = 0.0696 95% C.I ] 149594 186278 -.000023 6.6e-06 -4.4e-09 1.1e-10 -.001742 002506 -.348922 -.30533 03557 068826 003181 003973 -.002434 002083 -.064538 -.021561 -.00866 038111 018944 057377 Determinant of insurance participation at household level Model 1: without interaction terms probit HHinsu sexofhead ageofhead eduofhead heademployed hhurban hhethnic hhsize hhincome illnessratio Iteration Iteration Iteration Iteration Iteration 0: 1: 2: 3: 4: log log log log log likelihood likelihood likelihood likelihood likelihood = = = = = -6510.528 -5530.4362 -5522.0991 -5522.0841 -5522.0841 Probit regression Number of obs LR chi2(9) Prob > chi2 Pseudo R2 Log likelihood = -5522.0841 HHinsu Coef sexofhead ageofhead eduofhead heademployed hhurban hhethnic hhsize hhincome illnessratio _cons -.0693656 0213812 0415358 -.23235 -.0493032 1.484827 -.0780423 8.21e-06 009201 -1.18263 Std Err .0344976 0011692 004345 0475959 0336643 0425191 0099414 4.56e-07 0091828 1018855 z -2.01 18.29 9.56 -4.88 -1.46 34.92 -7.85 18.00 1.00 -11.61 P>|z| 0.044 0.000 0.000 0.000 0.143 0.000 0.000 0.000 0.316 0.000 = = = = [95% Conf Interval] -.1369797 0190896 0330197 -.3256362 -.115284 1.401491 -.097527 7.31e-06 -.0087969 -1.382322 9402 1976.89 0.0000 0.1518 -.0017516 0236728 050052 -.1390637 0166775 1.568163 -.0585575 9.10e-06 0271989 -.9829386 Model 2: with interaction terms: probit HHinsu sexofhead ageofhead eduofhead heademployed hhurban hhethnic hhsize hhincome illnessratio ille > thnic illincome Iteration Iteration Iteration Iteration Iteration 0: 1: 2: 3: 4: log log log log log likelihood likelihood likelihood likelihood likelihood = -6510.528 = -5514.4971 = -5504.389 = -5504.3634 = -5504.3634 Probit regression Number of obs LR chi2(11) Prob > chi2 Pseudo R2 Log likelihood = -5504.3634 HHinsu Coef sexofhead ageofhead eduofhead heademployed hhurban hhethnic hhsize hhincome illnessratio illethnic illincome _cons -.0668004 021569 0411665 -.2295853 -.047807 1.344686 -.0818005 9.91e-06 0158009 0784519 -9.06e-07 -1.194784 Std Err .0345339 0011725 0043525 0476387 0337076 0569152 0099793 6.40e-07 0127026 0219405 2.38e-07 1031806 z -1.93 18.40 9.46 -4.82 -1.42 23.63 -8.20 15.49 1.24 3.58 -3.80 -11.58 P>|z| 0.053 0.000 0.000 0.000 0.156 0.000 0.000 0.000 0.214 0.000 0.000 0.000 = = = = [95% Conf Interval] -.1344855 0192709 0326358 -.3229553 -.1138727 1.233134 -.1013596 8.66e-06 -.0090957 0354492 -1.37e-06 -1.397015 9402 2012.33 0.0000 0.1545 0008848 0238671 0496973 -.1362152 0182588 1.45623 -.0622413 0000112 0406975 1214545 -4.39e-07 -.9925541 dprobit HHinsu sexofhead ageofhead eduofhead heademployed hhurban hhethnic hhsize hhincome illnessratio ill > ethnic illincsquare Iteration Iteration Iteration Iteration 0: 1: 2: 3: log log log log likelihood likelihood likelihood likelihood = = = = -6510.528 -5529.1177 -5501.0447 -5500.9435 Probit regression, reporting marginal effects Log likelihood = -5500.9435 HHinsu sexofh~d* ageofh~d eduofh~d headem~d* hhurban* hhethnic* hhsize hhincome illnes~o illeth~c illincs~ dF/dx -.0249608 0085526 016286 -.0906379 -.020446 4431367 -.0324708 3.62e-06 -.0011324 0358075 -2.57e-13 Std Err .0137003 000466 0017319 018392 0134361 0134217 0039666 1.96e-07 004186 0085097 5.14e-14 z -1.82 18.36 9.40 -4.84 -1.52 23.62 -8.19 18.39 -0.27 4.21 -5.00 Number of obs LR chi2(11) Prob > chi2 Pseudo R2 P>|z| x-bar 0.069 0.000 0.000 0.000 0.128 0.000 0.000 0.000 0.787 0.000 0.000 752393 48.3454 7.14178 86471 282068 178366 3.93661 27015.7 1.59903 342081 1.4e+10 [ = 9402 =2019.17 = 0.0000 = 0.1551 95% C.I -.051813 007639 012891 -.126686 -.04678 416831 -.040245 3.2e-06 -.009337 019129 -3.6e-13 ] 001891 009466 01968 -.0545 005888 469443 -.024696 4.0e-06 Determinants of insurance participation at individual levels Model 1: without interaction terms: probit insu sex edu age18 age60 employed rural ethnic income Freuencyofill Iteration 0: log likelihood = -9605.9734 Iteration 1: log likelihood = -8023.9419 Iteration 2: log likelihood = -7999.2193 Iteration 3: log likelihood -7999.157 = Iteration 4: log likelihood = -7999.157 Number of obs LR chi2(9) Prob > chi2 Pseudo R2 Probit regression Log likelihood = -7999.157 insu Coef sex edu age18 age60 employed rural ethnic income Freuencyofill _cons 0065661 0253275 -1.039464 -.406014 -.4364399 -.1560826 1.069391 0002102 0010274 1.121867 Std Err .0235122 0033145 0406613 0400317 0335655 0268353 0374634 0000117 0028319 036558 z 0.28 7.64 -25.56 -10.14 -13.00 -5.82 28.54 17.98 0.36 30.69 P>|z| 0.780 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.717 0.000 = = = = 15213 3213.63 0.0000 0.1673 [95% Conf Interval] -.039517 0188312 -1.119159 -.4844746 -.5022271 -.2086788 9959644 0001873 -.0045231 1.050215 0526493 0318238 -.9597695 -.3275534 -.3706528 -.1034864 1.142818 0002331 0065778 1.193519 Model2: with interaction terms: probit insu sex edu age18 age60 employed rural ethnic income > illness ethnicillness incillness Iteration Iteration Iteration Iteration Iteration 0: 1: 2: 3: 4: log log log log log likelihood likelihood likelihood likelihood likelihood = = = = = -9605.9734 -8006.2373 -7978.7526 -7978.6709 -7978.6709 Probit regression Number of obs LR chi2(14) Prob > chi2 Pseudo R2 Log likelihood = -7978.6709 insu Coef sex edu age18 age60 employed rural ethnic income Freuencyofill sexillness employedillness ruralillness ethnicillness incillness _cons 0080415 0259089 -1.037153 -.401359 -.4864237 -.1459069 1.210905 0002196 0016567 -.0005709 0168675 -.0043423 -.0619322 -3.48e-06 1.11966 Freuencyofill sexillness employedillness rural Std Err .027933 0033233 040783 0401704 0380121 0315957 0443227 0000143 0061314 0058281 0062096 0061582 0099862 3.55e-06 0391652 z 0.29 7.80 -25.43 -9.99 -12.80 -4.62 27.32 15.36 0.27 -0.10 2.72 -0.71 -6.20 -0.98 28.59 P>|z| 0.773 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.787 0.922 0.007 0.481 0.000 0.327 0.000 = = = = 15213 3254.61 0.0000 0.1694 [95% Conf Interval] -.0467062 0193954 -1.117086 -.4800915 -.5609261 -.2078334 1.124034 0001916 -.0103606 -.0119938 004697 -.0164121 -.0815047 -.0000105 1.042897 0627892 0324224 -.9572201 -.3226265 -.4119212 -.0839804 1.297776 0002476 0136739 010852 029038 0077276 -.0423596 3.48e-06 1.196422 dprobit insu sex edu age18 age60 employed rural ethnic income rura > lillness ethnicillness incillness Iteration Iteration Iteration Iteration Iteration 0: 1: 2: 3: 4: log log log log log likelihood likelihood likelihood likelihood likelihood = = = = = -9605.9734 -8038.8685 -7979.0605 -7978.6709 -7978.6709 Probit regression, reporting marginal effects Log likelihood = -7978.6709 insu sex* edu age18* age60* employed* rural* ethnic* income Freuen~l sexill~s employ~s rurali~s ethnic~s incill~s dF/dx 0027406 0088324 -.336284 -.1455639 -.1614061 -.048791 3018119 0000749 0005648 -.0001946 0057502 -.0014803 -.0211128 -1.19e-06 Std Err .0095171 0011323 0120706 0151786 0121845 0103517 0071656 4.87e-06 0020902 0019868 0021164 0020993 0034012 1.21e-06 Freuencyofill sexillness employedillness z 0.29 7.80 -25.43 -9.99 -12.80 -4.62 27.32 15.36 0.27 -0.10 2.72 -0.71 -6.20 -0.98 Number of obs LR chi2(14) Prob > chi2 Pseudo R2 P>|z| x-bar 0.773 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.787 0.922 0.007 0.481 0.000 0.327 438178 5.74778 535792 158812 573391 722014 167357 430.915 2.54716 1.08815 1.3595 1.77204 327549 997.754 [ = 15213 =3254.61 = 0.0000 = 0.1694 95% C.I -.015913 006613 -.359942 -.175313 -.185287 -.06908 287768 000065 -.003532 -.004089 001602 -.005595 -.027779 -3.6e-06 ] 021394 011052 -.31262 -.11581 -.13752 -.02850 315856 000084 Model 3: use income quintiles instead of income probit insu sex edu age18 age60 headage employed rural ethnic Freuencyofill incquantile1 incquantile2 incqua > ntile3 incquantile4 incquantile5 Iteration Iteration Iteration Iteration Iteration 0: 1: 2: 3: 4: log log log log log likelihood likelihood likelihood likelihood likelihood = = = = = -9605.9734 -7973.2189 -7950.3693 -7950.3073 -7950.3073 Probit regression Number of obs LR chi2(14) Prob > chi2 Pseudo R2 Log likelihood = -7950.3073 insu Coef sex edu age18 age60 headage employed rural ethnic Freuencyofill incquantile1 incquantile2 incquantile3 incquantile4 incquantile5 _cons 0005857 023689 -1.073708 -.5385234 0062948 -.4506463 -.1468926 1.108758 0007969 -.3994713 -.3279556 -.0862281 1550989 7879552 1.249718 Std Err .0235883 0033292 0410371 0454551 0009648 0340769 0269283 037881 0028324 1093903 1337478 1312615 1139034 1289344 1228454 z 0.02 7.12 -26.16 -11.85 6.52 -13.22 -5.45 29.27 0.28 -3.65 -2.45 -0.66 1.36 6.11 10.17 P>|z| 0.980 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.778 0.000 0.014 0.511 0.173 0.000 0.000 Probit regression, reporting marginal effects Log likelihood = -7950.3073 insu sex* edu age18* age60* headage employed* rural* ethnic* Freuen~l incqua~1* incqua~2* incqua~3* incqua~4* incqua~5* dF/dx 0002 0080883 -.3477078 -.1982489 0021493 -.150143 -.0491951 2855163 0002721 -.1253242 -.1203359 -.0300971 0509867 2042064 Std Err .0080536 001136 0120671 0174361 0003295 0110176 0088342 0066453 0009671 0310532 0517825 046774 0359318 0224059 z 0.02 7.12 -26.16 -11.85 6.52 -13.22 -5.45 29.27 0.28 -3.65 -2.45 -0.66 1.36 6.11 = = = = [95% Conf Interval] -.0456466 0171639 -1.15414 -.6276137 0044039 -.5174357 -.1996711 1.034513 -.0047546 -.6138724 -.5900965 -.3434959 -.0681476 5352483 1.008946 15213 3311.33 0.0000 0.1724 0468179 0302141 -.99327 -.449433 0081858 -.383856 -.094114 11.18300 0063484 -.185070 -.065814 1710396 3783455 1.04066 1.49049 Number of obs LR chi2(14) Prob > chi2 Pseudo R2 P>|z| x-bar 0.980 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.778 0.000 0.014 0.511 0.173 0.000 438178 5.74778 535792 158812 48.4762 573391 722014 167357 2.54716 826004 017551 019589 092224 034839 [ = 15213 =3311.33 = 0.0000 = 0.1724 95% C.I -.015585 005862 -.371359 -.232423 001504 -.171737 -.06651 272492 -.001623 -.186187 -.221828 -.121772 -.019438 160292 ] 015985 010315 -.324057 -.164075 002795 -.128549 -.03188 298541 002168 -.064461 -.018844 061578 121412 248121 Endogenous test: probit medicalexamination insu income Freuencyofill sex age edu employed ethnic rural residual Iteration Iteration Iteration Iteration Iteration 0: 1: 2: 3: 4: log log log log log likelihood likelihood likelihood likelihood likelihood = = = = = -6054.8298 -5781.8893 -5771.6553 -5771.6139 -5771.6139 Probit regression Number of obs LR chi2(10) Prob > chi2 Pseudo R2 Log likelihood = -5771.6139 medicalexamination Coef insu income Freuencyofill sex age edu employed ethnic rural residual _cons 2747485 0000288 -.0494908 -.0544686 0035038 0277283 1054931 3799268 -.0734022 1254046 -1.639481 Std Err .0328396 0000113 0056938 0267595 0006306 0035882 0472899 0482148 0314227 130512 1104905 z P>|z| 8.37 2.54 -8.69 -2.04 5.56 7.73 2.23 7.88 -2.34 0.96 -14.84 0.000 0.011 0.000 0.042 0.000 0.000 0.026 0.000 0.019 0.337 0.000 = = = = 15213 566.43 0.0000 0.0468 [95% Conf Interval] 210384 6.59e-06 -.0606504 -.1069164 0022679 0206957 0128066 2854276 -.1349896 -.1303942 -1.856038 339113 0000509 -.0383313 -.0020209 0047397 034761 1981796 474426 -.0118148 3812035 -1.422923 probit medicaltreatment insu income Freuencyofill outpatient sex age edu employed ethnic rural residual Iteration Iteration Iteration Iteration 0: 1: 2: 3: log log log log likelihood likelihood likelihood likelihood = = = = -10533.598 -9802.9574 -9801.7769 -9801.7769 Probit regression Number of obs LR chi2(11) Prob > chi2 Pseudo R2 Log likelihood = -9801.7769 medicaltreatment Coef insu income Freuencyofill outpatient sex age edu employed ethnic rural residual _cons 436911 -.0000471 0010069 -.8802317 1341335 0088046 0001388 -.1267942 0607318 0892582 -.1054288 156508 Std Err .0253352 0000114 0027192 0351613 0214482 0005109 0028778 0399121 0404814 0252358 105933 0950766 z 17.25 -4.14 0.37 -25.03 6.25 17.23 0.05 -3.18 1.50 3.54 -1.00 1.65 P>|z| 0.000 0.000 0.711 0.000 0.000 0.000 0.962 0.001 0.134 0.000 0.320 0.100 = = = = 15213 1463.64 0.0000 0.0695 [95% Conf Interval] 387255 486567 -.0000695 -.0000248 -.0043227 0063365 -.9491465 -.8113169 0920958 1761711 0078033 0098059 -.0055016 0057791 -.2050204 -.048568 -.0186103 1400739 039797 1387195 -.3130536 102196 -.0298387 3428547 ... Public health care use and insurance enrollment by employment status .47 Table 6: Public health care use and insurance enrollment by area (rural) 48 Table 7: Public health care use and insurance. .. estimate the impact of health insurance on public health care utilization Then we investigate determinants of insurance enrollment to increase the number of insurance participators if insurance affects... ECONOMICS INSTITUTE OF SOCIAL STUDIES HO CHI MINH CITY THE HAGUE VIETNAM THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS HEALTH INSURANCE AND PUBLIC HEALTH CARE UTILIZATION