HEALTH INSURANCE AND HEALTH CARE ACCESS IN CHINA

Transcription

1 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

2 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 do 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 do not address health outcomes. ii

3 TABLE OF CONTENTS INTRODUCTION...1 LITERATURE REVIEW...4 Historical Background of Health Care Policy in China... 4 Rural vs. Urban Health Care... 6 Health Insurance... 8 The Quality of Private Health Care in China CONCEPTUAL MODEL...13 DATA DESCRIPTION...16 ANALYSIS PLAN...19 Dependent Variables Independent Variables Interaction Terms Discussion DESCRIPTIVE STATISTICS...31 RESULTS...33 DISCUSSION...42 Interactions Patient preferences: Demand vs. Supply CONCLUSION...56 REFERENCES...58 iii

4 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 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 1

5 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 2

6 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 3

7 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 4

8 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 5

9 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 6

10 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). 7

11 Health Insurance There are signs that the Chinese government is changing its focus to address the particular needs of rural and urban residents in China. The President, Hu Jintao, has recently unveiled a new campaign entitled the Five Coordinations which seeks to coordinate development between five distinct pairs: urban and rural areas; the economy and social programs; the environment and human beings; domestic and international demands. This tall order may be only political propaganda, but the new policy does break from rigid traditional political thought in China and may foretell the emergence of a new softer thinking in China (Wong et al., 2005). One way the Chinese government plans to coordinate development in urban and rural areas is through its current plan of extending health care coverage to more than 80 percent of the country s rural counties, a plan announced by Premier Wen Jiabao in March, 2007 (Xinhua, 2007). Under this voluntary system, a farmer participant pays US$1.3 a year and the state, provincial, municipal and county governments supplement this amount with US$5.2. This program allows only rural residents to use this joint fund in public hospitals. Other developing countries have implemented similar voluntary and subsidized health insurance programs for rural residents. In 2003, Vietnam also introduced a program in which the poor were enrolled at taxpayer expense (Wagstaff, 2007). This new scheme resulted in patients switching from private providers to public providers. Studies have documented similar effects in other developing countries. For example, 8

12 Mexico and the Philippines have introduced voluntary tax-financed schemes where households, except for those in the poorest bracket, contribute according to their incomes (Knaul and Frenk, 2005; Obermann et al., 2006). Colombia has also introduced subsidized schemes within its social health insurance (Escobar and Panpolou, 2003). Data from Mexico suggests that implementing health insurance programs will increase utilization of public services, though the effect on private providers has not been explored. In Colombia, insurance coverage did not increase public hospital utilization but was shown to increase preventative and ambulatory care in public facilities. Wagstaff et al. (2007) analyze the impact of China s new insurance scheme which was implemented as a 2004 trial in a small population. The paper reports the insurance scheme had several limitations in that many services were not covered and high deductibles made health care very costly. As a result, the study finds having health insurance did not increase utilization of public facilities for the poorest bracket of those surveyed. This finding is consistent with the effects of a similar insurance scheme in Vietnam (Wagstaff, 2007). In contrast, households with higher incomes did increase their utilization of public facilities in both China and Vietnam. This finding may be explained by the moral hazard resulting from the insurance program and is consistent with findings of similar studies in Colombia, Mexico, and the Philippines. Patients seek more care as a result of being covered, thus increasing public utilization. Because private facilities outnumber public facilities in rural China, more research needs to be done on how the 9

13 insurance scheme and moral hazard affect overall utilization, including the area of private providers, especially for households in the lowest income quintile. The new insurance scheme in rural China has not been shown to reduce out-ofpocket spending (Wagstaff et al., 2007). This finding is consistent with experience in Vietnam, but remains at odds with the findings of similar studies in the Philippines, Colombia, and Mexico. However, while the effects are not always large, the insurance scheme in China is unique in that it may actually increase out-of-pocket spending. Lindelow and Wagner (2005), for example find that health shocks (serious illness and injury) may create a greater financial burden for those with insurance than those without insurance. Similarly, Shi and Chen (1998) confirm reports of significantly higher medical expenses for the insured than for the uninsured with the same health problems and treatment outcomes. Lindelow and Wagner suggest that moral hazard may be one factor explaining this increase. Because patients may be more likely to see a doctor when they have health insurance, out-of-pocket spending may also increase for those with health insurance. In addition, the increase may be attributable to physicians overcharging those with health insurance. Li and Li (2002) indicate that the main cause of differences in medical expenses between the insured and uninsured is the cost of drugs. Attributing the difference in cost to the effects of moral hazard either in the form of patient priceelasticity of demand or supplier-induced demand for those able to pay remains unclear (Eggleston et al., 2006). Several studies find that Chinese public health facilities burden patients with excessive drug prescriptions, high hospital expenditures, and 10

14 unnecessary medical treatments (Liu and Mills, 1999; Zhang et al., 2003; Zhan et al., 2004). Meng et al. (2000) report that both public and private facilities in China overcharge patients equally. The effect of moral hazard on patients choice of provider remains little-researched. Another potential problem of the new insurance scheme is adverse selection. Wang et al. (2006) find that adverse selection may cause the new voluntary insurance schemes in China to be financially unstable. They find that the sickest patients are enrolled in the rural voluntary insurance scheme, but the healthiest patients have opted out of the program. While the effects on the private sector are not included in their research, this finding may support the notion that those with less severe illnesses choose private facilities while those with more serious conditions choose public facilities. As a result, the health care insurance reforms which China continues to choose may be unsustainable as those who enroll in the insurance scheme are likely to be the most costly patients. Since the study by Wang et al. is limited to a very small segment of the rural population for a trial, more research needs to be done to support this finding. In addition to difference in price, the perceived difference in quality between public and private clinics may also lead some patients to choose one facility over another. Because the Chinese government s goal in expanding insurance is to increase the overall access to care, there needs to be more research on whether this goal is being achieved. This paper seeks to fill this gap and find whether overall service delivery is 11

15 increasing as a result of insurance or whether it is only moving patients from private to public health care facilities. The Quality of Private Health Care in China Due to the informal training of medical teams working in the private sector, some rural counties have issued a ban on private health care facilities, stating that the quality is too low or the cost is too high. Zhang and Qiao (2002) find that in China s rural township health centers, just 41% of health workers had graduated from high school, while 30% had only a primary school education. Despite this lack of education, however, the quality of private health care providers has surprisingly not been shown to be dramatically inferior to public health care providers. Meng et al. (2000) report that although the quality of care in private facilities is poor, there is no difference between public and private clinics. In addition, work by Lim et al. (2002) find that patient satisfaction within private facilities is often higher than within public facilities. Most studies confirm that banning private clinics is not a useful policy option, but rather policies that seek to improve quality and access to both public and private facilities should be pursued (Lindelow and Wagner, 2006) As there appears to be little difference in quality between private and public providers, this paper seeks to understand how health insurance may affect public versus private provider utilization. Cost is clearly an incentive for patients to move from private to public providers, but the goal of China s health insurance is to expand overall coverage. This area of research has been largely under-researched. Such an 12

16 analysis will fill the gap between studies on the quality of private providers and studies on insurance policy in China. A better understanding of the effects of insurance on patient behavior may aid policymakers to find methods that will more effectively reach rural and low-income households. CONCEPTUAL MODEL To analyze whether health insurance is accomplishing its goal of expanding access to health care, this paper compares the statistical outcome of health insurance on both public and private providers. Therefore, the dependent variable in this analysis is provider choice which represents the decision made by household respondents to choose a public or private health care provider. The main independent variable of interest is health insurance. If patients are simply switching from private to public providers but using the same amount of health care services, then I expect the degree to which health insurance is negatively associated with private provider choice to be equal in magnitude to the degree to which insurance is positively associated with public provider choice. In this case, health care is not being created; it is simply transferring money from people with mostly good health to people with mostly poor health. On the other hand, if the degree to which health insurance is positively associated with public provider usage exceeds the degree to which it is negatively associated with private provider usage, then health insurance may be producing a net expansion of total health service provision. 13

17 This paper seeks to explore these issues using the dataset by Lim et al. (2002). These authors find that households with health insurance are more likely to choose private clinics than those households without insurance. This pattern is particularly interesting because social insurance is not accepted at private clinics in China. Therefore, this paper first attempts to replicate Lim et al. s finding of a positive correlation between having insurance and seeking care at private facilities. As this finding is counterintuitive and contrary to previous studies, this paper then analyzes the various independent variables which may explain this phenomenon. I hypothesize that the correlation arises from systematic differences in the traits of households with and without insurance, rather than from the insurance itself. Interacting these independent variables with health insurance reveals what types of people react more strongly to insurance in their decisions. The characteristics I posit to explain as the pattern in provider choice include household income, health status, the particular Chinese province, the view that public physicians overcharge patients, and the location of the household (rural or urban). Households with higher incomes might be better able to afford private clinics fees, but they are also better able to afford transportation to distant public clinics. The relationship between income and provider choice might therefore be more complex. Household income could also explain potential moral hazard effects generated from the insurance scheme. Evidence suggests that those insured often have higher out-of-pocket costs than the uninsured due to either increased utilization or to the tendency of providers to given unnecessary care to the insured (Wagstaff et al., 2007). 14

18 If higher out-of-pocket spending is attributable to an increase in demand among the insured, then it is likely that that the insured will seek care at public providers. This finding, however, is not consistent with the findings of Lim et al., who find the insured are more likely to visit private facilities than public facilities. If the higher cost is attributable to public providers giving more unnecessary care to the insured, then there may be a tendency of those with insurance to switch to private providers to minimize this risk (Lim et al., 2002). Evidence of higher cost for the same care at public facilities compared to private facilities would also be expected. Households with health insurance may also make the decision to visit a public or private facility on the basis of their health status. Patients may visit private facilities for minor health care issues and public facilities for more major issues. Evidence of this adverse selection has been documented as being a potential problem for insurance schemes in rural China where the severely ill and more costly patients enroll while less costly patients opt out of the voluntary insurance scheme (Wang et al., 2006). Patients with chronic illnesses may also choose one facility over the other. If adverse selection is a problem, then those with health insurance who choose to visit a private facility may do so only for minor health concerns. Age, education, marital status, occupation, and gender could also affect choice of health care facility. More educated people may be more health-conscious and have more income, which may also influence provider choice. Elderly patients who suffer from more serious illnesses may also be more likely to utilize health insurance. 15

19 We must interpret any correlation between these traits and provider choice, however, bearing in mind that some such correlations can arise either because provider choice itself causes the characteristic in question, or because something associated with provider choice does, or because something about provider choice causes people to make different decisions about insurance. Choosing providers who offer only provide lower-quality care, for example, could extend treatment and thereby decrease household income from missed workdays. Quality or availability of care, certainly related to provider choice, could even influence whether a person chooses to live in a rural or urban area; those in rural communities might move to urban areas where health care is more readily available. The cost of care may also be affected by provider choice as clinics seek to become more competitive. Such caveats are needed in the interpretation of this and all other retrospective evaluation. DATA DESCRIPTION The dataset in this paper is taken from Lim et al. (2002), whose work was sponsored by the United Nations Development Program and the World Health Organization. The study had cooperation and support from the Ministry of Health in China, and the Health Bureaus of Guangdong, Shanxi, and Sichuan Provinces where the questionnaire surveys were administered. The study s questionnaire was administered by interviewers to 3,600 households in three provinces, involving both rural and urban areas, between January and December The study also included a self-administered questionnaire survey 16

20 of 720 medical practitioners and 24 focus group sessions for patients and practitioners in the three provinces. Interviews with health officials, health care managers, and health care investors were also conducted during this time. This paper, however, will only focus on the interviewer-administered questionnaire surveys given to the 3,600 households. The household questionnaires were administered to heads of household or the equivalent, defined as any person living in the house, male or female, aged 18 years or older, and a Chinese permanent resident. Respondents were excluded if they were: below age 18; mentally or cognitively impaired; too sick or weak to answer questions; unable to answer questions because of other reasons; or unable to use or understand Mandarin. Participation from households was strictly voluntary. Invitations were sent via mail followed by home visits where participants were guaranteed full confidentiality. The questionnaire was designed in English, translated into Chinese, and then translated back into English. The questionnaire was pre-tested with a random sample of ten households in Beijing and further refined before final printing. The surveys were administered by medical students from the three provincial universities who had received training by a WHO consultant at Peking University. A pilot study involving 50 households was conducted by the medical students who were also required to pass an exam before officially administering the surveys. University teaching staff members were deployed as supervisors. For quality control, supervisors reinvestigated 5% of all household surveys. 17

21 The three provinces where the questionnaires were administered Guangdong, Shanxi, and Sichuan are geographically disparate and were purposefully chosen, based on their different stages of economic development and on the availability of suitable research collaborators in the provinces. Selection of households within these provinces was determined by the use of multistage cluster sampling. The sampling was conducted as follows. First, the health bureaus of the three provinces were asked to suggest urban cities and rural counties that they thought were typical of the province and which were also known to have private medical providers. Additional criteria included the support from the local government and health department officials necessary to conduct such a study. The sample size of 600 households for each city or county was based on the goal of achieving a 95% confidence level for estimated mean values of responses. After the counties and cities were chosen within the three provinces, two urban districts and two rural townships were then randomly selected within each county and city. In addition, within these districts and townships, two urban residential committees and two rural village committees were randomly selected. Finally, the households within these committees constituted the cluster of households to be studied. The target sample population was 150 households from the total households under the jurisdiction of each committee. A total of 180 households were randomly chosen, with the last 30 households designated as reserve households for replacements if necessary. 18

22 ANALYSIS PLAN Dependent Variables To analyze the effects of insurance on households choosing public or private facilities, I employ a logit regression model. I first create indicator variables for use as dependent variables and attempt to establish the simple correlations between insurance coverage and public or proviate facility choice. The definition of a private provider in this dataset is based upon ownership. All non-governmental institutions are defined as private. Public provider is defined as a governmental institution. Clinics and hospitals are analyzed both separately and combined. I employ the model separately on several dependent variables which describe choosing a public or private provider. To check for robustness, I include a dependent variable of patient preference to be seen by a private doctor over a public one. Testing these different dependent variables is necessary to find common trends and bring about more robust results. Because only 5% percent of the observations sought treatment at private hospitals whereas 53% sought treatment at public hospitals, I separate hospitals from clinics. I then compare these trends among the other dependent variables in the dataset which are reported within Table 6 and discussed in more detail below. For the creation of the first set of dependent dummy variables, household answers to the following question are used: Where did you see the doctor on your last visit? Based on respondent answers, the following six indicator variables are created: 19

23 1) Private clinic on last visit: If the respondent answered private clinic, the dependent variable takes a value of 1 (otherwise, 0). 2) Public clinic on last visit: If the respondent answered public clinic, the dependent variable takes a value of 1 (otherwise, 0). 3) Private hospital on last visit: If the respondent answered private hospital, the dependent variable takes a value of 1 (otherwise, 0). 4) Public hospital on last visit: If the respondent answered public hospital, the dependent variable takes a value of 1 (otherwise, 0). 5) Private facility on last visit: This variable combines both private clinics and private hospitals to measure overall health insurance effects on private facility utilization. If the respondent answered either private clinic or private hospital, the dependent variable takes a value of 1 (otherwise, 0). 6) Public facility on last visit: This variable combines both public clinics and public hospitals to measure overall health insurance effects on public facility utilization. If the respondent answered either public clinic or public hospital, the dependent variable takes a value of 1 (otherwise, 0). For the creation of the second set of six dependent dummy variables, household answers to the following questions are used 1) Private clinic in the last 12 months: If the respondent answered yes to the question, In the last 12 months, have you been admitted to a private clinic? the dependent variable takes a value of 1 (otherwise, 0). 20

24 2) Public clinic in the last 12 months: If the respondent answered yes to the question, In the last 12 months, have you been admitted to a public clinic? the dependent variable takes a value of 1 (otherwise, 0). 3) Private hospital in the last 12 months: If the respondent answered yes to the question, In the last 12 months, have you been admitted to a private hospital? the dependent variable takes a value of 1 (otherwise, 0). 4) Public hospital in the last 12 months: If the respondent answered yes to the question, In the last 12 months, have you been admitted to a public hospital? the dependent variable takes a value of 1 (otherwise, 0). 5) Private facility in the last 12 months: This dependent variable combines private clinics and private hospitals. If a respondent answered yes to the questions in 1 or 3 above, then the dependent variable takes a value of 1 (otherwise, 0). 6) Public facility in the last 12 months: This dependent variable combines public clinics and public hospitals. If a respondent answered yes to the questions in 2 or 4 above, then the dependent variable takes a value of 1 (otherwise, 0). After finding the results for these models, I then interact the variable of having insurance with the other independent variables as is further discussed in Tables I employ these models on the above dependent variables to find whether particular groups are more sensitive to the effects of health insurance in health care utilization over other groups. 21

25 To check for robustness of these results, I include a final dependent variable on patient preference using a question from the dataset, which asks respondents their degree of agreement with the statement, When I m sick, I prefer to be seen by a private doctor than a public doctor. This dependent variable takes a value of 1 if the respondent agrees or strongly agrees and 0 otherwise. Understanding patient preferences is important to compare with actual patient behavior, as patients may not necessarily be going to the facility of their preference because a particular facility is not easily accessible. Therefore, demand for a particular facility may be higher than the supply available and using patient preference as a metric for demand is useful in interpreting the results. Comparing the results of these regressions among the various dependent variables adds robustness and confirmation of particular trends on the effects of health insurance. Independent Variables The independent variables throughout this paper are described below. Health Insurance This is an indicator variable for health insurance (X1) where 1= household has health insurance (insurance here includes all forms of insurance: state health insurance, co-operated medical services, and company health insurance as 22

26 each of these insurance programs are not accepted at private facilities). X1 takes the value of 0 if the household does not have health insurance. 1 Urban/Rural and Province The location of the household and the category of the household being either urban or rural may affect a household s decision to visit a public or private facility. From the dataset, I measure this effect using the variables: X2= Urban/rural, where 1 = urban, else = 0. The definition of being urban or rural is designated by the health bureaus within the three Chinese provinces. X3= Guangdong Province, where 1= lives in Guangdong province, else = 0 X4= Sichuan Province, where 1=lives in Sichuan province, else = 0. The omitted base group for X3 and X4 is Shanxi Province. Physician overcharging patient To measure potential supply-side moral hazard effects of physicians overcharging patients with health insurance in public facilities, I would ideally like to know the exact amount that patients are charged for services in public facilities and compare this amount with what patients are charged in private facilities. As this information is unattainable within the dataset, a proxy on patient opinions of being overcharged is employed. This variable (X5) is taken from a question in the dataset which states: Doctors in public practice tend to over prescribe medicines for patients in the purpose of earning money. Variable X5 takes the value 1 State health insurance is defined as government-issued health insurance outside of rural areas. Cooperated Medical Services is the social health insurance issued by the government particularly for rural areas. Company Health Insurance is private health insurance issued from an employer. 23

27 of 1 if the household reported that they strongly agree or agree with the statement. Variable X5 takes the value of 0 for any other response. Health Status To analyze the potential effects of adverse selection, including the patient s health status in the model is necessary. Patients with health insurance may make the decision to visit a public or private provider on the basis of how sick they are, possibly choosing private facilities for minor health care issues and public facilities for more major issues. Patients who suffer from chronic illnesses may also choose one facility over the other. To measure this effect, I use a question from the survey which asks respondents to give information on their present health status. From this information, I create the variable X6 which will take the value of 1 if the respondent reports their present health status as being either fair, good, or very good. Variable X6 will take the value of 0 for any other answer. Income Households may make the decision to choose a private facility on the basis of their income. From the dataset, I measure this effect using the variable: X7= Annual combined household income where income is yuan (USD$ ) X8= Annual combined household income where income is yuan (USD$696-1,390) X9= Annual combined household income where income is yuan (USD$1,391-2,780) 24

28 X10= Annual combined household income where income is yuan and above (USD$2,781+) Annual household income less than 2000 yuan is omitted as a variable and included in the baseline group. Demographics Additional demographic factors that could influence provider choice are also incorporated in the model. These variables include: X11= Age X12= Male where male=1, female=0 X13= Education where primary school is the highest level of education completed=1, else = 0 X14= Education where junior and senior middle school is the highest level of education completed = 1, else =0 X15= Education where junior college or above is the highest level of education completed =1, else = 0 The omitted baseline group for Education is No education. X16= Marital status where married =1, else =0 X17= Occupation where government officer = 1, else=0 X18= Occupation where Manager/executive=1, else = 0 X19= Occupation where Clerk/serviceman=1, else = 0 X20=Occupation where self-employed=1, else=0 X21=Occupation where farmer=1, else=0 25

29 X22=Occupation where student/part time/others= 1, else = 0 X23=Occupation where retired = 1, else = 0 The omitted baseline group for occupation is Unemployed. Thus the model I use for the results in Tables 7-10 is: Y= β0 + β1x1 + β2x2 + β3x3 + β4x4 + β5x5 + β6x6 + β7x7 + β8x8 + β9x9 + β10x10 + β11x11 + β12x12 + β13x13 + β14x14 + β15x15 + β16x16 + β17x17 + β18x28 + β19x29 + β20x20 + β21x21 + β22x22 + β23x23 Interaction Terms In order to determine whether the relationship between insurance and provider choice is contingent on certain patient characteristics, the indicator variable for insured is interacted with the following variables: urban/rural, province, physician overcharging the patient, convenience of location, health status, and income. Tables present the findings from regressions which include these interacted variables. By interacting these independent variables with health insurance, this study is able to estimate whether the correlation between insurance and provider choice depends on another independent variable. Some groups may be more sensitive to health care coverage than other groups in determining whether to go to a public or private health care facility. For example, if provider choice is regressed on the independent variables of insurance, urban, and insurance x urban, then the coefficient on the interaction term will describe the degree to which the correlation between insurance and provider choice depends on whether or not the household lives 26

30 in an urban area. This interaction term could also describe the degree to which the correlation between living in an urban area and choosing a private provider depends on whether or not the household has health insurance. If the covariate on the interaction term has a large positive sign in the public regressions and a small negative sign in the private regressions, then insurance particularly affects health care provision among people in urban areas. Similarly, if provider choice is regressed on insurance, Sichuan province, Guangdong province, insurance x Sichuan province, and insurance x Guangdong province, then the value of the coefficient on the two interaction terms expresses the degree to which the relationship between provider choice and insurance depends upon the province the household lives in, or the degree to which the relationship between provider choice and province depends on insurance. Comparing the coefficients of the interaction terms between the public and private regressions reveals whether particular provinces are more or less inclined to visit public or private facilities. If provider choice is regressed on insurance, physician overcharging the patient in public facilities, and insurance x physician overcharging the patient in public facilities, then the coefficient on the interaction term describes the degree to which the correlation between insurance and provider choice depends on whether or not the household believes that public facilities overcharge patients, or vice versa. Comparing the coefficients on the interaction terms between the public and private regressions reveals whether those who believe public facilities overcharge patients are more or less inclined to visit public or private facilities. 27

31 Having health insurance is also expected to have different effects on choosing a private provider among households in different income brackets, good and poor health statuses, and households with high or low satisfaction with the convenience of the location of the provider. Therefore, interaction terms of insurance and these independent variables are also included in the model. Comparing the coefficients of the interaction terms between the public and private regressions reveals whether these particular segments of the sample are particularly sensitive to the expansion or health care coverage or switching from public to private clinics. Thus, the model I use to generate the results presented in Tables can be expressed as: Y= β0 + β1x1 + β2x2 + β3x3 + β4x4 + β5x5 + β6x6 + β7x7 + β8x8 + β9x9 + β10x10 + β11x11 + β12x12 + β13x13 + β14x14 + β15x15 + β16x16 + β17x17 + β18x28 + β19x29 + β20x20 + β21x21 + β22x22 + β23x23 + β24(x1*x2) + β25(x1*x3) + β26(x1*x4) + β27(x1*x5) + β28(x1*x6) + β29(x1*x7) + β30(x1*x8) + β31(x1*x9) + β32(x1*x10) + β33(x1*x11) + β34(x1*x12) + β35(x1*x13) Discussion Any estimate of the true partial correlation between the regressors and provider choice will be biased if important variables correlated with both of them are omitted from the regression model. An important additional variable that may influence choice in provider is the cost of treatment, but costs are not observed in the survey data in a 28

32 usable form. The variable, cost of last treatment, does not include information on the cost of the type of treatment and therefore remains difficult to compare across public and private facilities. Questions regarding a respondent s last treatment include asking the cost and diagnosis or symptoms. Because many respondents described symptoms, determining the manner in which physicians treated the patient is difficult and comparing costs would result in poor estimates. Additionally, the location of the provider to the household may also lead to omitted variable bias. Finding a good estimate for this variable, however, is difficult as questions on health care generally are only asked to households who have access to a health care provider. Therefore, households that are most inconvenienced by the location of a health care provider will not be observed. In the dataset used in this study, a measure of the convenience of the provider s location is included, but this variable cannot be employed in the model as it is perfectly correlated with the other independent variables and cannot give accurate results. In addition, the actual cost of transportation to a public facility or to a private facility is not measured in the dataset but could affect the utilization of health insurance. Another potential omitted independent variable may be family relations to the private medical practitioner where families in rural areas continue to see a family member in the private village clinic rather than use their insurance at a public clinic, resulting in an upward bias on the coefficient of the dependent variable of choosing a private facility. 29

33 Additional bias in this analysis may result from the possibility that poor people who choose a public facility may be more likely to die or be too sick to participate in the survey, causing them to be unobserved in the data. In a hypothetical example, if patients with the most severe illnesses enroll in an insurance scheme and visit public hospitals but their very illness makes it less likely that they could answer the survey form, this would bias the estimated relationship between insurance and public hospital usage. In the present case such bias is expected to be minor. Another potential bias includes the manner in which the study was conducted. Because the health bureaus of the three provinces were each asked to suggest urban and rural counties that were known to have private medical practice, the health bureaus might have chosen places that had an abnormal concentration of private health care facilities compared to the population of China which may create bias on the coefficients of the included variables if the results are to be representative of the entire country. However, any region-specificity within the results only applies to the three provinces and does not necessarily bias the results for the three provinces. Selection bias may also be a potential problem in this analysis. The survey interviewed the head of a household either male or female over 18-years-old, but that criterion is unlikely to cause a high degree of bias as it is unlikely that those under 18 years of age are responsible for health care decision-making. The exclusion criteria also included health-related reasons which may cause some bias on the coefficient of the selection of health care facilities. These exclusions included mentally or cognitively impaired; too sick or weak to answer questions; and physical impairments. 30

34 Households suffering from these serious health-related conditions may have been more likely to visit public facilities, thus causing upward bias on the coefficient estimate of the utilization of private facilities. Issues of multicollinearity may also arise within this analysis. If one of the independent variables in the model is explained as a linear function of one of the other independent variables, multicollinearity may cause biases in the analysis. The variables of income, education, and occupation may be correlated, creating difficulties for reliable statistical inference. Despite these potential problems, the dataset overall has excellent representation from three very different provinces in China. This paper, therefore, may still valuably contribute to the broader discussion of health insurance correlations with health care delivery in China. DESCRIPTIVE STATISTICS The following three tables include descriptive statistics of the variables which are included in the model used here. Table 1 gives descriptive statistics for the various binary dependent variables to be used in all models. Table 2 lists descriptive statistics for the independent variables to be used in the first model without interactions. Table 3 lists descriptive statistics for the independent variables which are interacted with the variable of having health insurance. 31