HEALTH INSURANCE AND HEALTH CARE ACCESS IN CHINA A Thesis submitted to the Graduate School of Arts & Sciences at Georgetown University in partial fulfillment of the requirements for the degree of Master of Public Policy in the Georgetown Public Policy Institute By Verinda Jean Esther Fike, B.A Washington, DC April 14, 2008 HEALTH INSURANCE AND HEALTH CARE ACCESS IN CHINA Verinda Jean Esther Fike, B.A Thesis Advisor: Michael Clemens, Ph.D ABSTRACT The Chinese government has made recent efforts to expand health insurance to rural areas that have been primarily dependent upon private health care providers Because private providers not accept insurance, the insurance scheme may cause patients to shift usage from private to public providers, thus not necessarily resulting in an overall expansion of health care delivery This study uses a 2001 survey of 3,600 households in three Chinese provinces to analyze whether health insurance is actually expanding overall service delivery or is simply switching patients from private to public provider utilization By statistically comparing the provider choices of households with health insurance to those of uninsured households, this study finds evidence that health insurance expands overall health care utilization in China The findings relate only to service utilization and not address health outcomes ii TABLE OF CONTENTS INTRODUCTION LITERATURE REVIEW Historical Background of Health Care Policy in China Rural vs Urban Health Care Health Insurance The Quality of Private Health Care in China 12 CONCEPTUAL MODEL 13 DATA DESCRIPTION 16 ANALYSIS PLAN 19 Dependent Variables 19 Independent Variables 22 Interaction Terms 26 Discussion 28 DESCRIPTIVE STATISTICS 31 RESULTS 33 DISCUSSION 42 Interactions 46 Patient preferences: Demand vs Supply 51 CONCLUSION 56 REFERENCES 58 iii INTRODUCTION China is facing a health care crisis While total health care spending in China is increasing, the government’s share of this total spending has shrunk by more than half since 1980 This decrease in public health financing has been devastating for rural households where more than 90% of health care spending is now out-of-pocket Although 70% of the population lives in rural areas, public health expenditures in these areas constitutes only 30% of the national total (Lindelow and Wagstaff, 2003) In some parts of China, more than 60% of those in dire poverty have been driven there by these huge out-of-pocket medical expenses (Liu et al., 2003) The disparity between basic health conditions in urban and rural areas of China is enormous In 1999, infant mortality was 37 per 1,000 live births in rural areas, as compared with 11 per 1,000 in urban areas (Blumenthal and Hsaio, 2006) Perhaps most shocking is that in some poor rural areas, infant mortality has recently increased While broad outcomes of this kind depend on much more than health care provision, these numbers nevertheless suggest that access to quality medical care for rural residents remains limited and deficient In an effort to make health care more available at lower cost, the Chinese government in March of 2007 announced plans to expand its new cooperative health care system to cover all rural counties by 2010 (Xinhua, 2007) The expansion of the program is expected to cost US$1.3 billion this year and US$750 million for each subsequent year This program will allow rural residents to use a joint fund to pay for visits in public facilities, but the fund will cover only 40-60% of medical bills The joint fund will not be accepted at private health care facilities New Chinese health care policies generally fail to recognize the vast number of private health care facilities serving both rural and urban areas Private health care facilities are often more accessible and less costly than public facilities (Liu et al., 2006) Even if a particular treatment is more affordable at a public facility, the cost and hassle of transportation to a public provider that is farther away than a private facility may outweigh the advantage of having insurance However, urban and rural residents may experience this difficulty differently as urban residents have greater access to public facilities Previous research suggests that the quality of care in private facilities is better, worse, or the same as public facilities (Meng et al., 2000; Lim et al., 2002) The rising incomes of rural households may therefore influence households with health insurance to seek care at public or private facilities, depending on whether they believe one to have superior care over the other For more serious illnesses, however, households may choose public providers which are generally better equipped to handle these illnesses Although some policymakers believe that expanding health insurance coverage for rural households is considered effective in making health care more affordable and thereby expanding overall coverage, the results of any such effort depend crucially on patients’ decisions to utilize facilities where they are covered (public) versus those where they are not (private) This paper seeks to understand whether health insurance is expanding overall coverage or whether it is simply moving people from private health care provision to public provision Using data from a 2001 household survey of 3,600 households in three provinces in China, this study sets out to answer these questions This study finds that households with health insurance in large measure choose public providers This outcome is greater than the deterrent outcome of not choosing a private provider, suggesting that health insurance is increasing overall service delivery If redistribution is the goal of the Chinese health insurance program, then it appears this goal is being achieved through the scheme A policy to extend the program to other parts of China should therefore be considered If the goal of the health insurance program is to improve the overall health of the population, then more research is needed Expansion of service provision does not necessarily mean that health outcomes are improving, among other things because those being brought into the system could be those with the worst health and hopeless conditions One way to learn more about the relationship between health insurance and health outcomes would be to make insurance mandatory within a delimited group on a pilot basis and carefully track their health status over a number of years relative to a similar comparison group In order for either of these policy goals to be met, households should have access to health care facilities where their insurance may be used Therefore, the final recommendation of this study is to increase access to households by building more public facilities in rural areas and/or by allowing insurance to be accepted at private facilities Because rural areas are predominantly served by private providers, the latter option is likely to be the least costly LITERATURE REVIEW Historical Background of Health Care Policy in China China is a developing country struggling to meet the global demands of privatization while continuing to claim socialist status The many economic reforms the country has implemented have also created volatile social cleavages and challenges within China as urban coastal areas have benefited from the massive growth of the economy more rapidly than inland rural areas This division is particularly evident in the health care system where rural areas continue to be plagued chronic issues Not only is infant mortality over three times higher than in urban areas, but HIV/AIDS is spreading more rapidly among rural people Tens of thousands of peasants are estimated to have contracted the disease during blood collection at unsanitary rural clinics (Chan, 2001) While the health care system in urban areas is far from perfect, the system’s problems are intensified in the countryside China has undergone repeated health care reforms since the 1980s The massive changes within the health care system have shaped and been shaped by the extraordinarily rapid economic growth of the country China’s “growth-first” strategy during the 1980s under Deng Xiaoping emphasized economic growth at any cost (Meng et al., 2004) The health care reforms under this strategy included dramatically cutting public subsidies to hospitals and implementing new rules which allowed providers to charge patients more than average cost for certain services, such as prescription drugs and high technology diagnostic procedures—a similar tactic executed in the early years of U.S health care reform (Wang, 2004) This pricing scheme had two main consequences First, public hospitals experienced distorted incentives, viewing high technology and prescription drugs as their only means to meet the demands of decreasing government subsidies and increasing budgets (Eggleston, 2006) This allowed physicians to over-stress the importance of specific services, often giving unnecessary treatment and overprescribing drugs Second, health care reform gave rise to the re-emergence of private hospitals and clinics, which began to flourish under this new system China has a history of “barefoot doctors” who provided services outside the public health realm in the countryside, but these doctors became virtually nonexistent during the Cultural Revolution of the 1960s (Blumenthal and Hsiao, 2005) The 1980 economic reforms, however, encouraged privatization, and many private facilities resurfaced in rural areas where barefoot doctors once provided services As public hospitals responded to budget cuts by seeing more patients, private facilities were often able to out-compete public ones, due to their lower infrastructure and staffing costs Despite the fact that these private doctors in China often have very little formal medical training, the government allowed these private facilities to exist, given the demand for health care services in rural areas (Wang, 2006) Although Chinese health care guidelines apply to both public and private service providers, government oversight of private health care facilities has been criticized as being too lax Because private providers are concentrated in rural areas, this lack of government oversight is particularly problematic in the countryside Rural vs Urban Health Care Health care reform has generated different effects in urban and rural areas and is partially to blame for the divergence in health outcomes in these areas As in many other developing countries, both urban and rural health care providers in China are primarily paid for by fee-for-service (FFS) which has been associated in OECD countries with producing higher health expenditures as a fraction of total GDP (Eggleston et al., 2006) In China, the FFS payment system when combined with a distorted fee schedule is widely acknowledged to spur cost escalation As income in rural areas is much lower than urban areas, the FFS system is shown to have a negative effect upon rural areas in many developing countries (Eggleston et al., 2006) While a vast number of private health care facilities serve both rural and urban areas in China, the majority of these providers are in rural areas Private health care facilities are often more accessible to rural communities in both cost and distance (Liu et al., 2006) The effect of distance on provider choice is well documented in other developing countries and is addressed in policy primarily through contracting For instance, in Colombia, the Philippines, and Thailand, contributions into a social health insurance fund are used to purchase services that members want, from providers they choose, and in close proximity to where they live (Hsaio and Shaw, 2007) The social health insurance fund within these countries allows for contracting with both public and private providers, holding them both accountable for quality and client satisfaction While some literature has explored how patients choose a medical provider in rural and urban areas, this area of research remains incomplete Yip et al (1998) were the first to quantify the factors which determine patient choice of provider for the rural population at village, township, and county levels in China They find that patient choice is determined by insurance status, income, disease pattern, education, and age This same study is restricted in its approach, however, as it does not include private facilities where insurance is not accepted In addition, the study was only conducted in one county, Shunyi County near Beijing, a relatively rich subpopulation of rural China, and its conclusions, say the authors, “may not be generalizable to the rest of China.” Liu et al (2006) find that although private services are not included in the social insurance benefit package, these services continue to be used by low-middle income groups in rural areas The study reports that patients within these groups choose private health care services on the basis of lower costs and higher quality of care This finding of utilization of private facilities by rural communities is not congruent with other studies which show primarily middle-high income groups choosing private facilities Private facilities offering lower costs than public facilities is also at odds with most other cases in developing countries (Wagstaff, 2007) However, if the Chinese goal is to “improve” the health of its citizens, then the results are inconclusive Because adverse selection and moral hazard cannot be completely captured in this model, the fact that public health care utilization increases more than private health care utilization decreases does not mean that the health of the population is improving For example, an increase in total utilization may be explained by taking into account those patients who take greater risks with their health and those who need more care more often Such moral hazard effects would not be captured in this model In addition, the increase in service utilization might not result in improved health outcomes due to adverse selection, as has been discussed above In addition to these central findings, the results in this analysis reveal other interesting trends The regressions also suggest that healthier households are in general more likely to visit private providers and sicker households are more likely to visit public providers This implies that moral hazard may be a problem that Chinese health insurance, like all health insurance schemes, will confront The next section further analyzes these effects through the inclusion of interaction terms Other overall findings suggest that low-income households, rural households, and households in Shanxi province are more likely to visit private health care providers The coefficient on patient perceptions that public physicians overcharge also often results in a statistically significant incentive for patients to choose private providers This analysis is clearer for private clinics where the number of 45 observations is much greater than for private hospitals The degrees to which these findings are influenced by health insurance are discussed in the next section Interactions Tables 11-13 present the results from regressions in which health insurance is interacted with some of the independent variables In Table 11, the results are shown for clinics Among the variables interacted with health insurance, only the coefficients on the two highest income levels are statistically significant The results are also mixed, revealing that the relationship between insurance and provider choice is different in these groups than in the other three income quintiles The coefficient on the ‘overcharging’ response is also statistically significant and consistent with findings above The coefficient on this interaction term is positive for both public and private providers for two of the four questions Patient perceptions on public physicians overcharging may be biased as households asked this question are people who are visiting clinics Because they are visiting clinics, these households may be in need of more time or money in general, so it is understandable that they think physicians overcharge Healthier households who not visit clinics would not be asked this question and may have different perceptions on public physicians overcharging patients Column reveals that the coefficient on the interaction term for urban and insurance is statistically significant and negative for public clinics This finding is interesting because those in urban areas are generally more likely to choose public 46 facilities as are those with health insurance For one of the questions in the dataset, it appears that health insurance particularly affects health care provision among people in urban areas to dissuade them from choosing a public provider, more so than in rural areas This interaction term could also describe the negative degree to which the correlation between living in an urban area and choosing a public provider depends on whether the household has health insurance Table 12 analyzes the interaction terms for hospital choice Column reveals that the coefficient on the interaction term for insurance and one of the highest income groups is statistically significant In this income group, then, the association between insurance and public hospital choice may differ from that seen in other income groups, all else equal The very large coefficient for quintile 4, however, suggests that this may arise simply from a few influential observations in a small sample and not much should be made of this result Columns and reveal that the coefficient on the interaction term for insurance and Guangdong is statistically significant and positively correlated with choosing a public hospital Therefore, those in Guangdong province are particularly persuaded to visit public facilities on the condition of having insurance compared to those in Shanxi province In analyzing the results of the interactions for overall public and private facilities, there are few statistically significant relationships These results are shown in Table 13 Column reveals the coefficient on the interaction term between insurance and opinions that public physicians overcharge is positively 47 associated with going to a private facility This is not in any way definitive but may help explain instances where those with health insurance continue to seek care at private facilities because they trust their doctor more at a private facility than at a public facility Column finds the coefficient for the interaction with income quintile four is statistically significant and negatively correlated with going to a public facility Again, higher income groups may be less persuaded by insurance effects to visit a public facility than lower income groups 48 TABLE 11: Odds ratio partial correlation between clinic choice and variables interacted with health insurance Variable Description Clinic visited within last 12 mo Clinic on last visit Private Public Private Public Health Insurance 0.70 2.67* 0.49 3.57*** (-0.78) (1.76) (-1.28) (2.20 Urban 0.47*** 2.52*** 0.18*** 1.69** (-4.43) (4.95) (-8.25) (1.70) Guangdong Province 1.70*** 0.14*** 2.69*** 0.66 (3.39) (-11.24) (5.25) (-1.51) Sichuan Province 2.31*** 0.14*** 2.63*** 0.52*** (5.91) (-5.94) (5.43) (-2.93) Physician Overcharging Patient 1.12 0.84 1.44*** 0.82 (1.03) (-1.50) (2.83) (-1.04) General health status good and very good 1.01 0.57 1.93*** 0.87 (0.05) (-4.88) (5.21) (-0.70) Income Quintile (2000-4999) 1.00 1.09 0.79 1.57 (-0.00) (0.05) (-1.28) (1.62) Income Quintile (5000-9999) 1.00 0.89 1.00 0.95 (-0.00) (-0.67) (0.02) (-0.15) Income Quintile (10000-19999) 1.00 1.24 0.88 1.34 (0.01) (1.01) (-0.53) (0.88) Income Quintile (20000 and above) 1.56** 1.34 0.83 0.63 (1.98) (1.19) (-0.68) (-1.04) Age 1.00 1.00 1.00 1.00 (-1.08) (0.03) (1.06) (-0.37) Male 0.94 0.75*** 1.18 0.90 (-0.60) (-2.87) (1.43) (-0.65) Primary education 0.87 1.10 1.00 0.97 (-0.85) (0.54) (0.00) (-0.14) Middle school education 0.98 1.05 1.04 0.58** (-0.15) (0.28) (0.20) (-2.00) Junior College and higher education 0.69 1.88*** 0.57* 0.83 (-1.56) (2.57) (-1.84) (-0.51) Married 1.23* 0.93 1.10 0.86 (1.61) (-0.53) (0.58) (-0.74) Occupation as government officer 1.16 0.71 1.28 0.67 (0.40) (-0.96) (0.48) (-0.75) Occupation as managerial/executives 1.71*** 0.66* 1.76** 0.57 (2.57) (-1.89) (1.98) (-1.57) Occupation as clerks/service personnel 1.78** 0.51** 2.75*** 0.35** (2.32) (-2.54) (2.98) (-2.18) Occupation as self-employed 1.41 0.92 1.36 0.91 (1.59) (-0.35) (1.06) (-0.24) Occupation as farmer 1.71*** 0.78 1.55* 1.00 (2.74) (-1.17) (1.76) (0.01) Occupation as student/part-time/others 1.78** 0.44*** 3.23*** 0.74 (2.32) (-3.02) (3.64) (-0.68) Retired 1.13 0.95 1.25 0.79 (0.54) (-0.20) (0.77) (-0.70) Sichuan Province* insurance 1.10 0.84 0.95 1.76 (0.32) (-0.58) (-0.15) (1.41) Guangdong* insurance 1.47 0.95 0.83 0.65 (1.24) (-0.15) (-0.46) (-0.95) Urban* insurance 0.66 0.85 1.30 0.33*** (-1.43) (-0.54) (0.78) (-2.55) Income Quintile 2* insurance 0.87 0.40 1.33 0.34 (-0.25) (-1.46) (0.46) (-1.50) Income Quintile 3* insurance 1.41 0.41 1.72 1.07 (0.72) (-1.60) (0.98) (0.11) Income Quintile 4* insurance 1.46 0.34* 1.79 0.57 (0.78) (-1.89) (1.04) (-0.86) Income Quintile 5* insurance 0.42* 0.63 0.60 2.28 (-1.71) (-0.79) (-0.83 (1.15) General health status good and very good 1.27 1.08 1.00 0.71 (1.41) (0.37) (0.01) (-1.02) Public doctors tend to over-prescribe their 1.58** 1.51** 1.11 0.98 patients (2.09) (1.96) (0.37) (-0.07) Pseudo r-squared 0.09 0.18 0.20 0.04 N 2500 2515 2206 2206 TABLE 12: Odds ratio partial correlation between hospital choice and variables interacted with health insurance Variable Description Clinic visited in last 12 mo Clinic on last visit Private Public Private Public Health Insurance 9.61e-07*** 0.67 1.78e-07*** 0.74 (-5.60) (-0.56) (-9.64) (-0.67) Urban 5.12** 2.15** 10.33*** 2.36*** (2.10) (2.42) (4.16) (4.54) Guangdong Province 10.21** 0.64 14.31*** 0.32*** (2.02) (-1.58) (3.97) (-6.28) Sichuan Province 0.55 0.63** 0.80 0.66*** (-0.41) (-2.03) (-0.29) (-2.95) Physician Overcharging Patient 0.86 0.89 0.46** 0.90 (-0.63) (-0.61) (-2.42) (-0.90) General health status good and very good 0.87 0.55*** 0.73 0.64*** (-0.21) (-2.85) (-1.05) (-3.74) Quintile (2000-4999) 1.61 1.0/ 0.60 1.07 (0.47) (-0.01) (-0.55) (0.35) Quintile (5000-9999) 0.43 0.66 2.07 0.99 (-0.78) (-1.36) (1.11) (-0.02) Quintile (10000-19999) 0.39 0.75 0.78 1.07 (-0.91) (-0.86) (-0.35) (0.30) Quintile (20000 and above) 0.23 1.49 0.82 1.36 (-1.39) (1.06) (-0.30) (1.20) Age 1.01 1.01 0.97** 1.00 (0.41) (1.67) (-2.16) (0.57) Male 1.82 0.91 1.15 0.87 (1.37) (-0.67) (0.57) (-1.32) Primary education 0.60** 0.76 0.30** 1.16 (-2.16) (-1.01) (-2.09) (0.85) Middle school education 0.80* 1.02 0.30** 1.37* (-1.72) (0.08) (-2.20) (1.74) Junior College and higher education 0.08** 0.95 0.33* 1.70** (-2.19) (-0.16) (-1.79) (2.15) Married 1.28 1.17 1.20 1.05 (0.47) (0.85) (0.61) (0.37) Occupation as government officer 1.75 0.52 1.09 N/A (1.44) (-0.77) (0.24) Occupation as managerial/executives 3.32 0.80 0.90 0.82 (1.29) (-0.75) (-0.25) (-0.91) Occupation as clerks/service personnel 1.72 0.62 0.58 0.87 (0.05) (-1.35) (-0.92) (-0.50) Occupation as self-employed 1.18 0.56 0.87 0.86 (0.16) (-1.63) (-0.31) (-0.64) Occupation as farmer 0.34 0.97 0.82 0.71 (-0.94) (-0.08) (0.31) (-1.56) Occupation as student/part-time/others 5.03 1.09 0.68 0.52** (1.66) (0.24) (-0.70) (-2.33) Retired 1.02 0.76 0.97 (0.07) (-0.40) (-0.13) N/A Sichuan Province 1.43 1.05 (0.96) N/A (0.18) N/A Guangdong 0.31 2.25** 0.76 1.92** (-0.72) (2.09) (-0.25) (2.09) Urban 0.11 0.95 1.36 (-1.49) (-0.13) N/A (1.06) Income Quintile 2.39 1.45 N/A (1.13) N/A (0.70) Income Quintile 3.12 1.96 0.70 N/A (1.56) (0.46) (-0.78) Income Quintile 2.67e+07*** 1.44 2.71 0.82 (13.86) (0.49) (0.72) (-0.42) Income Quintile 0.42* 1.19 1.29 1.00 (-1.71) (0.23) (0.20) (-0.00) General health status good and very good 0.71 1.32 1.58 1.33 (-0.42) (0.96) (0.95) (1.33) Public doctors tend to over-prescribe their 1.30 0.88 1.12 1.11 patients (0.31) (-0.47) (0.22) (0.52) Pseudo r-squared 0.22 0.08 0.28 0.12 N 1918 2515 2036 2206 50 TABLE 13: Odds ratio partial correlation between overall provider choice and variables interacted with insurance Variable Description Clinic visited in last 12 mo Clinic on last visit Private Public Private Public 0.72 2.76* 0.6j 1.85 Health Insurance (-0.72) (1.74) (-1.05) (1.19) Urban 0.51*** 2.88*** 0.39*** 3.08*** (-3.94) (5.65) (-5.53) (5.69) Guangdong Province 1.78*** 0.18*** 3.01*** 0.27*** (3.67) (-9.90) (7.24) (-7.22) Sichuan Province 2.29*** 0.48*** 1.52*** 0.47*** (5.86) (-4.88) (3.14) (-4.95) Public Physician Overcharging Patient 1.11 0.88 1.13 0.82 (1.02) (-1.06) (1.17) (-1.58) General health status good and very good 1.01 0.49 0.90 0.60*** (0.10) (-6.16) (-1.04) (-4.16) Quintile (2000-4999) 1.00 1.12 0.89 1.31 (-0.02) (0.67) (-0.76) (1.45) Quintile (5000-9999) 0.99 0.88 0.97 1.00 (-0.07) (-0.72) (-0.22) (-0.02) Quintile (10000-19999) 0.95 1.24 0.94 1.27 (-0.26) (1.01) (-0.31) (1.07) Quintile (20000 and above) 1.44 1.46 1.30 1.14 (1.62) (1.54) (1.22) (0.50) Age 0.99 1.00 1.00 1.00 (-1.18) (0.40) (0.64) (0.51) Male 0.97 0.76*** 0.96 0.82* (-0.32) (-2.68) (-0.44) (-1.82) Primary education 0.85 1.06 0.81 1.16 (-0.99) (0.31) (-1.33) (0.83) Middle school education 0.94 1.08 0.87 1.11 (-0.38) (0.42) (-0.89) (0.54) Junior College and higher education 0.66* 1.83** 0.69 1.71 (-1.79) (2.35) (-1.51) (1.97) Married 1.22 1.06 1.15 0.98 (1.61) (0.43) (1.10) (-0.16) Occupation as government officer 1.13 1.12 1.32 1.00 (0.35) (0.28) (0.65) (0.00) Occupation as managerial/executives 1.86*** 0.67* 1.90*** 0.59** (2.96) (-1.78) (2.89) (-2.16) Occupation as clerks/service personnel 1.35 0.51** 1.90** 0.53 (1.15) (-2.45) (2.36) (-2.10) Occupation as self-employed 1.44* 0.80 1.61** 0.76 (1.69) (-0.94) (2.10) (-1.09) Occupation as farmer 1.77*** 0.78 1.82*** 0.65* (2.92) (-1.17) (2.98) (-1.90) Occupation as student/part-time/others 1.90*** 0.50*** 2.28*** 0.41*** (2.60) (-2.58) (3.31) (-3.00) Retired 1.11 1.17 1.13 0.93 (0.48) (0.60) (0.50) (-0.28) Sichuan Province 1.02 1.01 0.76 1.11 (0.09) (0.04) (-0.93) (0.33) Guangdong 1.45 1.26 1.33 1.01 (1.21) (0.66) (0.84) (0.02) Urban 0.63 0.97 0.97 0.95 (-1.63) (-0.09) (-0.10) (-0.18) Income Quintile 0.82 0.75 1.17 0.77 (-0.37) (-0.41) (0.29) (-0.43) Income Quintile 1.45 0.46 1.95 5.69 (0.79) (-1.38) (1.40) (-1.09) Income Quintile 1.65 0.28** 1.61 0.53 (1.05) (-2.16) (0.97) (-1.21) Income Quintile 0.48 0.59 0.45 1.76 (-1.44) (-0.87) (-1.50) (1.00) General health status good and very good 1.23 0.95 1.26 0.99 (1.01) (0.24) (1.06) (-0.04) Public doctors tend to over-prescribe their 1.5* 1.34 1.05 1.01 patients (1.99) (1.29) (0.23) (0.05) Pseudo r-squared 0.09 0.20 0.10 0.16 N 2500 2514 3491 2206 51 Patient preferences: Demand vs Supply Patient preference for a public or private health care provider may not necessarily equate with the actual choices that patients make The effect of health insurance may be constrained by the fact that some people not have a public facility nearby to switch to If they did have a public facility nearby, they may be likely to switch To address this issue, I analyze the correlation between health insurance and those who answered sithat they “prefer to be seen by a private physician” which is a more direct measure of demand that does not depend on supply The results in Table 14 reveal that those with health insurance are 26% less likely to prefer a private physician This correlation is consistent with the actual likelihood of not choosing a private provider, which was 20-25%, as reported in Table 10 In addition, households who believe that public physicians tend to over-prescribe medication are more likely to prefer private physicians This finding adds robustness to the findings in this analysis and suggests that having health insurance deters demand for private providers by roughly the same amount that it deters usage of private providers Other statistically significant findings include the coefficient on good health status Those who are healthy are more likely to prefer private physicians than those with poor health status This correlation further reveals that moral hazard may indeed be a risk in interpreting results 52 In addition, households from Guangdong and Sichuan provinces are more likely to prefer private physicians than those in the poorer province of Shaanxi Those in urban areas are less likely to prefer private physicians than those in rural areas The coefficients on these regional results of patient preference are statistically significant and similar to findings of actual patient choice reported earlier in this study, further adding robustness to the results The coefficients on the middle income brackets are statistically significant, suggesting that these households are more likely to prefer private physicians than those in the lowest income bracket The results are not statistically significant for the higher income groups Other statistically significant relationships among the coefficients include middle school and college groups which are less likely to prefer private physicians Patients who are married are more likely to prefer private physicians Additional interaction terms are included in the model where patient preference is the dependent variable The results are reported in Table 15 Only the coefficients on the interaction terms of “urban” and “income quintile 4” are statistically significant The coefficient on the interaction term of having health insurance and being in an urban area is statistically significant This finding is not surprising as previous results reveal that households in urban areas are more likely to utilize public facilities as are households with health insurance The coefficient on the interaction term for insurance and the fourth quintile income group is also statistically significant, suggesting that richer households with health insurance are less likely to prefer private physicians 53 TABLE 14: Odds ratio partial correlation between patient preference for private physician and health insurance with additional independent variables Variable Description Health Insurance Location Urban Guangdong Province Sichuan Province Physician Overcharging Patient Agree and strongly agree that public doctors tend to over-prescribe their patients Health Status General health status good and very good Income (in Chinese Yuan) Quintile (2000-4999) Quintile (5000-9999) Quintile (10000-19999) Quintile (20000 and above) Demographics Age Male Primary education Middle school education Junior College and higher education Married Occupation as government officer Occupation as managerial/executives Occupation as clerks/service personnel Occupation as self-employed Occupation as farmer Occupation as student/part-time/others Retired Pseudo r-squared N 0.74** (-2.48) 0.45*** (-5.45) 3.63*** (9.82) 1.42*** (3.25) 1.58*** (5.04) 1.29*** (2.96) 1.26* (1.66) 1.36** (2.17) 1.27 (1.40) 0.96 (-0.20) 1.00 (1.06) 1.02 (0.18) 0.86 (-1.04) 0.59*** (-3.44) 0.32*** (-4.70) 1.40*** (2.68) 0.56 (-1.37) 0.95 (-0.27) 1.11 (0.44) 0.83 (-0.93) 0.92 (-0.51) 1.15 (0.59) 0.87 (-0.68) 0.10 3397 TABLE 15: Odds ratio partial correlation between patient preference for private physician and variables interacted with health insurance Health Insurance Urban Guangdong Province Sichuan Province Public Physician Overcharging Patient General health status good and very good Income Quintile Income Quintile Income Quintile Income Quintile Age Male Primary education Middle school education Junior College and higher education Married Occupation as government officer Occupation as managerial/executives Occupation as clerks/service personnel Occupation as self-employed Occupation as farmer Occupation as student/part-time/others Retired Sichuan Province Guangdong Urban Income Quintile Income Quintile Income Quintile Income Quintile General health status good and very good Public doctors tend to over-prescribe their patients Pseudo r-squared N 1.79 (1.44) 0.53*** (-3.98) 4.13*** (9.55) 1.47*** (3.14) 1.63*** (4.81) 1.24** (2.23) 1.35** (2.03) 1.38** (2.11) 1.40* (1.86) 1.02 (0.10) 1.00 (1.17) 1.01 (0.14) 0.88 (-0.90) 0.61*** (-3.17) 0.37*** (-4.05) 1.40*** (2.70) 0.64 (-1.03) 1.03 (0.15) 1.24 (0.86) 0.84 (-0.82) 1.03 (0.15) 1.17 (0.67) 0.90 (-0.54) 1.15 (0.50) 0.66 (-1.27) 0.58** (-2.01) 0.60 (-1.12) 0.73 (-0.76) 0.47* (-1.75) 0.67 (-0.86) 1.15 (0.68) 0.87 (-0.63) 0.11 3397 CONCLUSION Overall, this study finds evidence that health insurance is both expanding overall health care coverage and redistributing the costs of health care If the policy goal of the Chinese government is to achieve these two results, then evidence from this analysis suggests that the current health insurance scheme is working Expanding health insurance to other parts of the population would be an effective policy tool to further carry out this goal If the policy goal of the Chinese government is to improve the overall health of the nation’s population, more research using additional information is required to know if that goal is being met, and it cannot be assessed based on the analysis presented here The potential effects of moral hazard and adverse selection cannot be completely controlled for in the analysis Because previous studies suggest that both moral hazard and adverse selection are likely to be systemic in China’s health insurance schemes, this concern is a real one (Wang et al., 2006) One way to better account for the effects of adverse selection is to eliminate the risk by conducting a pilot mandatory insurance program rather than the voluntary one now in place Tracking the health outcomes of this type of program would reveal whether the insurance scheme was actually improving the health of the sample If it were improving the health of the sample, then extending the program to the rest of the country would be a good policy consideration Such a policy would ensure that each household is enrolled in the program and limit potential abuses of the sickest 56 households to take advantage of the health insurance scheme Failure to make the plan mandatory could potentially result in an unsustainable scheme in the long-run A final policy recommendation includes increasing access to providers Both the policy goal of redistribution and the policy goal of improving the overall health of the population can only be achieved if households have access to health care The dataset used in this study asked households that had access to health care providers, but there could be an existing unmet need not represented in this study Expanding access could be in the form of building more public facilities, particularly in rural areas which are predominantly serviced by private 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