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1 CHILDREN AND FAMILIES EDUCATION AND THE ARTS ENERGY AND ENVIRONMENT HEALTH AND HEALTH CARE INFRASTRUCTURE AND TRANSPORTATION INTERNATIONAL AFFAIRS LAW AND BUSINESS NATIONAL SECURITY POPULATION AND AGING PUBLIC SAFETY SCIENCE AND TECHNOLOGY TERRORISM AND HOMELAND SECURITY The RAND Corporation is a nonprofit institution that helps improve policy and decisionmaking through research and analysis. This electronic document was made available from as a public service of the RAND Corporation. Skip all front matter: Jump to Page 16 Support RAND Browse Reports & Bookstore Make a charitable contribution For More Information Visit RAND at Explore the Pardee RAND Graduate School View document details Limited Electronic Distribution Rights This document and trademark(s) contained herein are protected by law as indicated in a notice appearing later in this work. This electronic representation of RAND intellectual property is provided for noncommercial use only. Unauthorized posting of RAND electronic documents to a non-rand website is prohibited. RAND electronic documents are protected under copyright law. Permission is required from RAND to reproduce, or reuse in another form, any of our research documents for commercial use. For information on reprint and linking permissions, please see RAND Permissions.

2 This product is part of the Pardee RAND Graduate School (PRGS) dissertation series. PRGS dissertations are produced by graduate fellows of the Pardee RAND Graduate School, the world s leading producer of Ph.D. s in policy analysis. The dissertation has been supervised, reviewed, and approved by the graduate fellow s faculty committee.

3 Dissertation China s Health Insurance Reform and Disparities in Healthcare Utilization and Costs A Longitudinal Analysis Henu Zhao C O R P O R A T I O N

4 Dissertation China s Health Insurance Reform and Disparities in Healthcare Utilization and Costs A Longitudinal Analysis Henu Zhao This document was submitted as a dissertation in October 2014 in partial fulfillment of the requirements of the doctoral degree in public policy analysis at the Pardee RAND Graduate School. The faculty committee that supervised and approved the dissertation consisted of Hao Yu (Chair), Emmett Keeler, and Gema Zamarro. PARDEE RAND GRADUATE SCHOOL

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6 Table of Contents Tables... v Figures... ix Abstract... xi Acknowledgements... xiii Chapter 1 Introduction... 1 Chapter 2 Background Health insurance reform in China Collapse of health insurance schemes in the 1970s and 1980s Early efforts in the 1980s and early 1990s Health insurance reform since the late 1990s Healthcare reform after Three Major Health Insurance Schemes The Basic Medical Insurance for Urban Employees The Basic Medical Insurance for Urban Residents The New Rural Cooperative Medical Insurance Trends and Current Status of Healthcare Disparities Chapter 3 Literature Review and Study Objectives Existing Research Literature on Rural Urban Disparities in Healthcare Utilization Literature on Disparities in Out of Pocket Expenditure and Healthcare Costs Literature on Disparities in Health Insurance Coverage Methodological Issues Gap in the Existing Literature Objectives and Research Questions Chapter 4 Study Design Data Study Periods Conceptual Model and Variable Selection Dependent Variables Independent Variables Analytic Approach Difference in Differences Analysis with Multiple Groups and Multiple Time Periods Multivariate Regression for the Variables that do not meet the Assumption of Parallel Trends Sensitivity analysis Controlling for Insurance Status Dropping the Richest Province or the Poorest Province iii

7 4.5.3 Including Interaction Terms with Household Income DID Analysis Results for Variables in Which Parallel Trends did not Hold Chapter 5 Results: Disparities in Healthcare Utilization Descriptive Analysis DID Analysis for Formal Care Utilization and Outpatient Utilization Multivariate Analysis Controlling for Existing Trends for Inpatient Utilization Sensitivity Analysis Controlling for Insurance Status Dropping the Richest Province or the Poorest Province Including Interaction Terms with Household Income DID Analysis for Inpatient Care Summary of Findings Chapter 6 Results: Disparities in healthcare costs Descriptive Analysis Multivariate Analysis Controlling for Existing Trends Sensitivity Analysis controlling for health insurance status dropping the richest province or the poorest province Including interaction terms with household income DID analysis results for cost variables Summary of Findings Chapter 7 Conclusion, Discussion, and Policy Implications Conclusion Discussion Comparing With the Published Research Strengths Limitations Future Directions Policy Implications Appendix Reference iv

8 Tables Table 4.1 Sample Size by Rural and Urban Residences and Registrations Table 4.2 Descriptive Statistics of Independent Variables by Rural and Urban Residences and Registrations Table 4.3 Results of DID Analysis Using 1993 and 1997 Waves for Healthcare Utilization Table 4.4 Results of DID Analysis Using 1993 and 1997 Waves for Healthcare Costs Table 5.1 DID Analysis Results for Formal Care Utilization and Outpatient Utilization Table 5.2 Test Results for DID Analysis of Formal Care Utilization and Outpatient Utilization Table 5.3 Multivariate Analysis Results for Inpatient Care Utilization Table 5.4 Test Results of Disparities for Inpatient Care Utilization Table 5.5 Test Results of Change in Disparities for Inpatient Care Utilization Table 5.6 DID Analysis Results of Formal Care and Outpatient Utilization (Controlling for Insurance Status) Table 5.7 Test Results for DID Analysis of Healthcare Utilization (Controlling for Insurance Status) Table 5.8 Multivariate Analysis Results for Inpatient Care Utilization (Controlling for Insurance Status) Table 5.9 Test Results of Disparities for Inpatient Care Utilization (Controlling for Insurance Status) Table 5.10 Test Results of Change in Disparities for Inpatient Care Utilization (Controlling for Insurance Status) Table 5.11 DID Analysis Results for Formal Care and Outpatient Utilization (Dropping the Richest Province) Table 5.12 Test Results for Formal Care and Outpatient Utilization (Dropping the Richest Province) Table 5.13 DID Analysis Results for Formal Care and Outpatient Utilization (Dropping the Poorest Province) Table 5.14 Test Results for Formal Care and Outpatient Utilization (Dropping the Poorest Province) v

9 Table 5.15 Multivariate Analysis Results for Inpatient Utilization (Dropping the Richest/Poorest Province) Table 5.16 Test Results of Disparities in Inpatient Utilization (Dropping the Richest/poorest Province) Table 5.17 Test Results of Change in Disparities for Inpatient Care Utilization (Dropping the Richest/poorest Province) Table 5.18 DID Analysis Results for Formal Care and Outpatient Utilizations (Including Interaction Term with Household Income) Table 5.19 Test Results for Formal Care and Outpatient Utilizations (Including Interaction Term with Household Income) Table 5.20 DID Analysis Results for Inpatient Care Utilization Table 5.21 Test Results for Inpatient Care Utilization (DID Analysis) Table 6.1 Multivariate Analysis Results for OOP Exceeding Certain Percentage of Household Income Table 6.2 Multivariate Analysis Results for Total Healthcare Costs Table 6.3 Test Results of Disparities for OOP Exceeding Certain Percentage of Household Income Table 6.4 Test Results of Changes in Disparities for OOP Exceeding Certain Percentage of Household Income Table 6.5 Bootstrap Results for Disparities in Total Health Costs Table 6.6 Multi variate Analysis Results for OOP Exceeding Certain Percentage of Household Income (Controlling for Insurance) Table 6.7 Test Results of Disparities for OOP Exceeding Certain Percentage of Household Income (Controlling for Insurance) Table 6.8 Test Results of Changes in Disparities for OOP Exceeding Certain Percentage of Household Income (Controlling for Insurance) Table 6.9 Bootstrap Results for Disparities in Total Health Cost (Controlling for Insurance) Table 6.10 Multi variate Analysis Results for OOP Exceeding Certain Percentage of Household Income (Dropping the Richest Province) Table 6.11 Test Results of Disparities for OOP Exceeding Certain Percentage of Household Income (Dropping the Richest Province) vi

10 Table 6.12 Test Results of Changes in Disparities for OOP Exceeding Certain Percentage of Household Income (Dropping the Richest Province) Table 6.13 Multi variate Analysis Results for OOP Exceeding Certain Percentage of Household Income (Dropping the Poorest Province) Table 6.14 Test Results of Disparities for OOP Exceeding Certain Percentage of Household Income (Dropping the Poorest Province) Table 6.15 Test Results of Changes in Disparities for OOP Exceeding Certain Percentage of Household Income (Dropping the Poorest Province) Table 6.16 Bootstrap Results for Disparities in Total Health Costs (Dropping the Richest Province) Table 6.17 Bootstrap Results for Disparities in Total Health Cost (Dropping the Poorest Province) Table 6.18 Multi variate Analysis Results for OOP Exceeding Certain Percentage of Household Income (Low income Families) Table 6.19 Test Results of Disparities for OOP Exceeding Certain Percentage of Household Income (Low income Families) Table 6.20 Test Results of Changes in Disparities for OOP Exceeding Certain Percentage of Household Income (Low income Families) Table 6.21 Multi variate Analysis Results for OOP Exceeding Certain Percentage of Household Income (Medium income Families) Table 6.22 Test Results of Disparities for OOP Exceeding Certain Percentage of Household Income (Medium income Families) Table 6.23 Test Results of Changes in Disparities for OOP Exceeding Certain Percentage of Household Income (Medium income Families) Table 6.24 Multi variate Analysis Results for OOP Exceeding Certain Percentage of Household Income (High income Families) Table 6.25 Test Results of Disparities for OOP Exceeding Certain Percentage of Household Income (High income Families) Table 6.26 Test Results of Changes in Disparities for OOP Exceeding Certain Percentage of Household Income (High income Families) Table 6.27 Bootstrap Results for Disparities in Total Health Costs (Low income Families) Table 6.28 Bootstrap Results for Disparities in Total Health Costs (Medium income Families) vii

11 Table 6.29 Bootstrap Results for Disparities in Total Health Costs (High income Families) Table 6.30 DID Analysis Results for OOP Exceeding Certain Percentage of Household Income Table 6.31 Test Results for OOP Exceeding Certain Percentage of Household Income (DID Analysis) Table 6.32 Bootstrap Results for Disparities in Total Health Costs (DID Analysis) viii

12 Figures Figure 2.1 Health Insurance Coverage in Urban and Rural Areas in China, Selected Years Figure 2.2 Health Service Utilization in Urban and Rural Areas in China (2003) Figure 2.3 Healthcare Spending in China, by Source and Year Figure 2.4 Per Capita Out of Pocket Health Expenses as a Percentage of Income Figure 4.1 Updated Structure of Anderson Model Figure 5.1 Probability of Formal Care Utilization in 4 Weeks by Rural and Urban Residences and Registrations Figure 5.2 Probability of Outpatient Care Utilization in 4 Weeks by Rural and Urban Residences and Registrations Figure 5.3 Probability of Inpatient Care Utilization in 4 Weeks by Rural and Urban Residences and Registrations Figure 5.4 Predicted Probability of Formal Care Utilization in 4 Weeks by Rural and Urban Residences and Registrations Figure 5.5 Predicted Probability of Outpatient Care Utilization in 4 Weeks by Rural and Urban Residences and Registrations Figure 5.6 Predicted Probability of Inpatient Care Utilization in 4 Weeks by Rural and Urban Residences and Registrations Figure 6.1 Probability of Having Out of pocket Medical Expense Exceeding 20% of Household Income by Rural and Urban Residences and Registrations Figure 6.2 Probability of Having Out of pocket Medical Expense Exceeding 40% Household Income by Rural or Urban Residences and Registrations Figure 6.3 Total Healthcare Costs by Rural and Urban Residences and Registrations Figure 6.4 Predicted Probability of Having OOP Exceeding 20% of Household Income by Rural and Urban Residences and Registrations Figure 6.5 Predicted Probability of Having OOP Exceeding 40% of Household Income by Rural and Urban Residences and Registrations Figure 6.6 Predicted Total Healthcare Costs by Rural and Urban Residences and Registrations ix

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14 Abstract China s economic success during the past 30 years was not mirrored in its health care system. As a result, the rural urban disparities in health insurance coverage and the related health care areas became prominent. Since the late 1990s, China has been expanding insurance coverage, in order to provide accessible and affordable health care to all residents. My study analyzes whether the insurance expansion reduces rural urban disparities in terms of health care utilization and financial protection. To my knowledge, this is the first study to address the disparity issue by examining China s health care reform policies over an extended 18 year period ( ). It is also the first study to address the dynamic phenomenon of rural urban migration during the study period by separating the study group into 4 subgroups in terms of respondents in residential areas versus household registration type. Drawing on seven waves of data from the China Health and Nutrition Survey and applying multivariate analysis techniques, such as difference in difference analysis and generalized linear model, I find that rural urban disparities in formal care and outpatient utilization were significantly reduced by the expanded health insurance coverage in rural area in The rural urban disparity in total health costs is also significantly reduced. However, no evidence shows that the policy changes in health insurance coverage had impact on disparities in inpatient utilization or having high out of pocket payments. By conducting several sets of sensitivity analyses, my study also finds that the expanded health insurance coverage impacted richer province more than poorer provinces, and impact high income families more than medium and low income families. xi

15 The study findings have important policy implications for China s ongoing health care reform. First, China s policy makers should provide better health care coverage and more health care resources to rural areas to further reduce the rural urban disparity. Second, since prior policy changes affected rich province more than poor province, new policy should target specifically poor provinces. Third, given the finding that the positive impact on health care utilization of policy change in 2003 happening mainly in high income groups, new policy change should focus more on medium and low income group. xii

16 Acknowledgements I am grateful for the support provided by my wonderful dissertation committee: Dr. Hao Yu, Dr. Gema Zamarro, and Dr. Emmett Keeler. The successful completion of this dissertation was a consequence of their excellent guidance. I am especially thankful for mentorship of my Committee Chair, Hao. His timely feedbacks on our weekly meetings were crucial to keep me on the right track. I would also like to thank Gema and Emmett for their insightful and constructive advices on the policy context and methodological issues. I also want to thank my outside reader Teh wei Hu, Professor Emeritus of Health Economics, University of California, Berkeley, for his helpful and responsive comments on my dissertation. I would also like to thank my research mentor Nelson Lim. He taught me how to do research and how to write, and provided me with advices and encouragement during my dissertation work. I would also like to thank the PRGS faculty, staff and students for their help during my dissertation writing. The dissertation would not have been possible without the generous financial support provided by the Rosenfeld Dissertation Award. Lastly, I would like to extend special thanks to my parents for their trust and encouragement, and to my husband, Yong Fu, for his love and support. xiii

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18 Chapter 1 Introduction China experienced rapid economic growth in the past two decades, benefiting many sectors of the economy. However, the economic success was not mirrored in the healthcare system. Instead, the transition from a centrally planned economy to a market oriented economy has caused problems in the public health arena. For example, after the economic reforms started in 1978, the existing health insurance providers faced increased operational challenges, and as a result, many residents lacked any form of health insurance. The condition was especially troublesome in rural areas, revealing sharp rural urban disparities in health insurance coverage and related healthcare services and costs. Since the late 1990s, there have been attempts to expand public health insurance coverage to both rural and urban residents in order to provide accessible and affordable healthcare to all residents. Another goal of the healthcare reforms was to provide healthcare to the poor and disadvantaged populations. As of the end of 2011, three health insurance programs, called schemes, were established, covering most of the rural and urban residents with some form of health insurance. However, the performance of the current health insurance schemes has not been well examined. Mixed findings have been presented regarding this issue. My dissertation focuses on the role of health insurance in reducing the rural urban disparities in terms of healthcare utilization and financial protection, in the context of the current health insurance schemes. The dissertation is organized as follows: Chapter 2 provides the background of the policy change. The chapter briefly reviews the history of the Chinese health insurance system reform, including the collapse and re establishment of the systems. I also provide 1

19 statistics of the trends and current status of rural urban healthcare disparities. Chapter 3 reviews existing literature on the topic of rural urban healthcare disparities and summarizes the research questions. Chapter 4 presents the study design, including data used, conceptual framework, and analytical approach. Chapters 5 and 6 present the results of the study. In Chapter 7, I conclude the study and present policy implications. 2

20 Chapter 2 Background The great economic reform in China brought changes to all areas of the economy, including the healthcare system. Unfortunately, as a result, many residents lost health insurance coverage. The existing health insurance schemes experienced difficulties in providing sufficient healthcare to insured residents. The cooperative medical scheme (CMS) providing rural health insurance experienced the greatest damage. In response to the emerging problems in its healthcare system, China has made numerous attempts to rebuild universal coverage system since the late 1990s. Through decades of effort, the Chinese government has developed three systems, in both urban and rural areas, which provide coverage for more than 90% of the population. During the launch of each new health insurance scheme, the government also proposed other measures to provide more healthcare resources to the targeted population. These measures work together with the health insurance systems to provide sufficient and affordable healthcare to all residents. Although there has been great progress, the health insurance system is far from perfect. The health insurance reform is still underway, and the effect of the expanded insurance coverage in China is still under debate. 2.1 Health insurance reform in China In this section, I review the history of health insurance reform in China. The health insurance system collapsed in the late 1970s, and a great number of residents left uninsured. Starting from the late 1990s, the government established three new health insurance systems in both rural and urban areas. In 2009, the government started a new round of healthcare reform. In the new round of reform, the major goal was to provide 3

21 universal coverage to all residents, and to target on disadvantage population to improve the healthcare service for them and reduce disparities Collapse of health insurance schemes in the 1970s and 1980s Since the late 1970s, the Chinese economic reforms have led to a period of prosperity. However, the economic success was not mirrored in the healthcare system. Instead, the economic transition caused problems in the public health arena. Prior to the economic reforms, there were three basic forms of insurance, which covered almost all Chinese citizens. The Government Insurance Scheme (GIS) covered government employees. The Labor Insurance Scheme (LIS) covered state owned enterprise (SOE) workers. Finally, the cooperative medical scheme (CMS) covered the rural agricultural workers. The economic reforms brought changes to the healthcare sector, weakening all three forms of insurance to some extent. First, the government run hospitals under the GIS experienced financial difficulties and thus were hard pressed to provide sufficient healthcare service to those insured under GIS. One reason for the financial crisis was that the economic reforms led to relaxation of price controls, and as a result, the costs incurred by the government run hospitals increased. Another reason is that the government contributed less to public hospitals: Government contributions shrank from 50% in the 1980s to less than 10% in 2000 (Wang 2004). Second, during the reform, financial autonomy was granted to the SOEs. As a result, a large number of SOEs closed, and many employees lost their jobs. Thus, the number of those insured by the LIS was reduced. Even those who kept their jobs found that their SOE employers faced difficulties in financing health insurance for workers (Li 2008). Finally, in the rural areas, the basic production unit 4

22 became households as the collective farms were dismantled. The CMS also collapsed with this change. In the 1990s, the vast majority of the rural population lacked any form of health insurance coverage (Hsiao 1984; Liu 2004). As mentioned, all three major health insurance systems experienced damages as a result of the changes brought by the economic reforms, and among them, the rural health insurance scheme CMS faced the biggest challenge. By 1998, the percentage of rural residents with any form of health insurance coverage had dropped to 13%, compared to 56% for residents covered in urban areas (China Ministry of Health, 2004). As the urban rural gap widened, the urban rural disparity in health insurance started to draw more attention Early efforts in the 1980s and early 1990s Before the major health reforms began in the late 1990s, there had been attempts to improve the existing health insurance systems. Even since the 1980s, actions had been taken in urban areas to relieve the financial burden on the health insurance systems. By introducing demand and supply side cost sharing, the attempts in the 1980s focused on reducing costs. These actions curbed the rapid healthcare cost growth, but they were not able to solve the fundamental financial problems (Liu 2002). Beginning in the early 1990s, the government introduced more actions to increase the level of risk pooling. In 1995, the government introduced a new model combining individual responsibility and social protection with city wide risk pooling. However, pilot programs of the new system were launched in only two cities and were not spread nationwide until the late 1990s. In rural areas, debate and research has focused on how to maintain the collapsing corporative insurance scheme from the 1980s and 1990s. The central government s effort 5

23 mainly focused on urban area; the local governments were advised to develop and complete the current CMS systems based on local economic conditions. However, the local actions only slightly increased the health insurance coverage in rural areas. Most of the coverage concentrated only on developed provinces and cities, such as Shanghai, Jiangsu, Guangdong, and Shandong. By the end of 1990s, most of the rural residents were left uninsured Health insurance reform since the late 1990s In response to the emerging problems in its healthcare system, China has made numerous attempts to rebuild universal coverage since the late 1990s. The goal of universal coverage is to provide safe, effective, convenient, and affordable basic medical services to all urban and rural residents (State Council, 2009). One of the most important components of universal coverage is health insurance. Before this goal of universal coverage was officially introduced in 2009 with the Chinese government s announcement of the blueprint for health system reform, health insurance reforms in both urban and rural areas had resulted in greater health insurance coverage. Three major health insurance schemes were established. The Urban Employees Basic Medical Insurance was launched in urban areas in 1998, and the Urban Residents Basic Medical Insurance was launched in In rural areas, the New Rural Cooperative Medical Insurance (NRCM) was established in In 2008, the two urban health insurance schemes covered about 65% of urban residents, and the rural scheme covered about 90% of rural residents (National Health Services Survey, 2008). The three major health insurance schemes are discussed in detail in the next section. 6

24 The expanded health insurance coverage provided residents with more financial protection and encouraged residents to use healthcare when needed. However, the utilization of healthcare was also subjected to medical resources available. Instead of only providing health insurance coverage to residents, the healthcare reform was a comprehensive system with other measures and actions. These measures and actions worked together with health insurance expansion, providing residents with more healthcare resources and granting them adequate healthcare access. First, the medical service system with basic facilities was constructed in rural areas. In 2003, together with the launch of NRCM, the State Council announced other measures designed to rebuild the rural medical system (State Council, 2002). One of the measures was to construct the medical service system with basic facilities. In order to achieve this goal, central and local governments increased their financial support to the medical system each year. From 2003 to 2010, the increased funding was partially used on the construction of the medical system. Local governments at the county level were responsible for the operational cost of the local medical facilities. The central government and local governments at the province level provided undeveloped areas with subsidies for infrastructure construction. Second, a medical assistance program was established in both rural and urban areas. In rural areas, the program was launched in The program was to provide financial assistance to low income households. The assistance could either be used to treat catastrophic disease or be used as premiums to join the local NRCM. Government subsidies for the program have been increasing since the program was launched. In urban areas, the 7

25 program was launched in The targeted populations were (a) urban residents living below the poverty line who did not join the Urban Residents Basic Medical Insurance; and (b) urban residents who joined the URBMI but were still carrying heavy financial burdens. The program was designed and funded by local governments. The central government also provided assistance through government transfers. Third, training of medical professionals was enhanced in rural areas. In its 2002 document No. 13, the State Council announced measures to improve the quality of medical professionals in rural areas. In post secondary medical schools, the Council introduced a 5 year program after middle school and a 3 year program after high school, in an effort to produce more medical professionals, especially for rural areas. Medical graduates and retired medical professionals from urban areas were encouraged to go back to work in rural areas (State Council, 2002). As a reflection of ongoing progress, measures to improve education and training of medical professional were introduced again in a new round of health reform (State Council, 2009). Healthcare workers were encouraged to attend formal education programs and obtain official licenses. The training of general practitioners for rural areas was included in the Ministry of Education 2010 work plan. The government provided the training costs (Meng and Tang 2010). Finally, the government undertook other actions to refine the whole medical system, such as regulation of drug policy, allocation of medical funding, and strengthening of administration and supervision system. All the measures worked as a whole to improve the medical service for both rural and urban areas. 8

26 2.1.4 Healthcare reform after 2009 As mentioned in the previous section, the goal of universal coverage was brought up by the State Council in The goal was published in the Opinions on Deepening the Reform of the Healthcare System (State Council, 2009), which marked a new era of health care reform in China. In this round of healthcare reform, the State Council set up the goal of the universal coverage for the first time. It was also the first time for the Chinese governments to break the urban rural dichotomy and to provide equivalent public healthcare service to both urban and rural residents. In order to achieve the goal of universal coverage, all three existing health insurance programs were to be improved. In addition to extending insurance coverage to the uninsured population, the benefit coverage of the insured was to be increased and expanded to cover catastrophic illnesses and outpatient visits. Another goal of the new round of health insurance reform was to provide better healthcare coverage to vulnerable population, such as rural residents, low income families, unemployed former SOE employees, senior population, the retired, the disabled and children. The rural urban gap of benefit coverage was expected to be closed, and the medical assistant programs were going to be strengthened. In addition to improving the health insurance system, the State Council also launched other initiatives to change the health care system (State Council, 2009). The first was to provide equivalent public healthcare service to both rural and urban residents. The public healthcare service included preventative care, healthcare education, as well as health service for women and children. The second was to establish basic drug supply system. In order to ensure the supply of affordable basic drugs, the central government 9

27 established a list of essential drugs, and guaranteed the supply of the listed drugs to all levels of medical facilities. Moreover, the health insurance programs provided more coverage for these basic drugs. The third was to strengthen the grass root level medical service system. In rural areas, a comprehensive medical system, including medical facilities in county, town and village levels, was to be established, in order to provide medical service at each local level. In urban areas, community medical facilities were to be strengthened. Training for medical professionals were also improved at local levels. Finally, pilot programs for public hospital reform were started by the central government after Three Major Health Insurance Schemes As discussed in the last section, China is now implementing ambitious reforms of the health insurance system, and three types of health insurance schemes have been launched. These three schemes were launched in different years targeting different population groups. Two insurance schemes cover the urban residents, and the third one covers the rural residents The Basic Medical Insurance for Urban Employees In 1998, the Chinese State Council issued the Decision of the State Council on Establishing the Urban Employees Basic Medical Insurance System. This was the first step in re establishing the health insurance system in urban areas. The Urban Employees Basic Medical Insurance (UEBMI) is compulsory based on employment. It provides basic medical insurance coverage for urban employees in both the public and private sectors (State Council, 1998). Local governments, mainly at the municipal level, set the level of deductibles, copayments, and reimbursement caps according to local economic levels. 10

28 The policy was launched in early 1999, and in late 1999, it was expanded nationwide. By the end of 2002, about 94 million people participated in the UEBMI. In order to further expand the coverage, the Ministry of Human Resources and Social Security issued Notification of Further Expanding the Coverage of the Urban Employees Basic Insurance Coverage in By the end of 2008, the number of insured totaled 200 million. The UEBMI is financed by premiums from both employers and employees. In their decision, the State Council suggested that the employers contribution be 6% of the employee s salary and the employees percentage be 2%. The revenue collected from premiums is distributed evenly into two independent accounts: the Medical Savings Account (MSA) and the Social Pooling Account (SPA). All employees contributions and about 30% of employers contributions go into the MSA, and the remainder of the employers contributions goes to SPA. The two accounts are managed separately and pay for different services: the MSA covers outpatient and emergency services and drug expenses, and the SPA covers inpatient services The Basic Medical Insurance for Urban Residents In 2007, the State Council issued guidelines to launch the Urban Residents Basic Medical Insurance (URBMI). According to the guidelines, the URBMI covers primary and secondary school students who are not covered by the UEBMI (including students in professional senior high schools, vocational middle schools, and technical schools), young children, and other unemployed urban residents on a voluntary basis (State Council, 2007). The main purpose of the guidelines is to provide coverage for urban residents without 11

29 formal employment with the intention of eliminating impoverishment resulting from chronic or fatal diseases, which can lead to catastrophic medical expenditures. The URBMI was piloted in 79 cities, including two to three cities in each of the provinces that were able to participate, and expanded to more cities in 2008 and 2009, with the objective of covering 80% of all cities in the participating provinces. In 2010, this insurance scheme was expanded nationwide and gradually extended to all unemployed urban residents. The number of insured was about 43 million by the end of 2007 and increased to 118 million by late 2008 (China Ministry of Health, 2010). The financing of this insurance program mainly comes from participants premiums. The government also provides a smaller amount of subsidies, compared to the premium contributions. The premium of the policy is determined by the local government, according to the local economic level, the medical care expense level, and the participants household income level. When the policy was launched, the government contribution was at least 40 Yuan per participant. From this amount, the central government transfers 20 Yuan to central and western areas residents. There are extra government subsidies for low income families, disabled students, and young children (State Council, 2007). The URMBI mainly targets people with chronic and fatal diseases; therefore, it covers more expenses for inpatient services. In 2008, the URMBI covered 45% of expenses from inpatient service related to chronic and fatal diseases, which equaled 1436 Yuan per inpatient stay (State Council Evaluation Group for the URBMI Pilot Program, 2008). 12

30 2.2.3 The New Rural Cooperative Medical Insurance In 2003, the State Council issued the Decision to Further Enhance the Rural Health Care System, aimed at re establishing the Rural Cooperative Medical Insurance (NRCM). The NRCM scheme covered the rural residents on a voluntary basis in order to avoid impoverishment caused by catastrophic expenses from infectious and endemic diseases. The NRCM was piloted in 2003 in selected counties. In 2006, coverage increased to 40% of all counties, and about 60% in In 2010, the NRCM covered more than 90% of all rural residents. The NRCM was funded by premiums from both the insured and by subsidies from the local and central governments. In 2003, the central government provided a subsidy of 10 Yuan for each insured resident. The Council s 2003 decision also required local governments to provide no less than 10 Yuan. In 2011, the subsidized amount was raised to a total of 200 Yuan. The NRCM provides partial coverage for all kinds of medical expenses, excluding some outpatient expenses and drug expenses. The reimbursement caps vary by local economic development levels. 2.3 Trends and Current Status of Healthcare Disparities China is a vast country with uneven economic development. Rural and urban residents are categorized separately according to the household registration system. The government financing systems for rural and urban sectors are also separate. Most of the government revenue comes from the urban economy, and most is spent on urban economy as well. This is especially true in public service areas, resulting in the urban rural disparity. 13

31 As mentioned before, by 1998, the urban rural disparity in health insurance coverage had become prominent. The coverage gap persisted in subsequent years. For example, in 2003, the urban health insurance coverage rate was still more than 50%, while only about 20% of the rural residents were covered by some form of health insurance coverage, and about half of the 20% was covered by pure commercial health insurance. This is shown in Figure 2.1, which presents the percentage of residents covered by health insurance in both urban and rural areas over time. During the selected period, public health insurance coverage was reduced year by year in both rural and urban areas until However, the percentage of coverage had always been much lower in rural areas than in urban areas. Then, in 2008, there was a large increase in insurance coverage, especially for rural areas. Coverage increased to more than 90%, and a larger portion of rural residents was covered by health insurance at this time, compared to the portion of urban residents. We can also observe the shift in the urban rural ratio (the green line). Before 2003, the urbanrural ratio of health insurance coverage was extremely high; however, in 2008, the ratio decreased to less than 1, indicating more coverage in rural areas. Between the two time points, there were several policy changes that affected health insurance coverage. In the urban areas, the basic medical insurance for urban employees was launched in 1998, and in 2007, the basic medical insurance for urban residents was established. In the rural areas, in 2003, the government started to rebuild the cooperative health insurance system (NRCM), which influenced a very large population. Most of the rural coverage in 2008 was from NRCM. Therefore, I believe the initiation and expansion of the NRCM diminished the disparities in health insurance coverage; however, it is still unknown whether the 14

32 expansion helped reduce disparities in other healthcare areas, such as healthcare utilization and cost. Figure 2.1 Health Insurance Coverage in Urban and Rural Areas in China, Selected Years Disparity was also observed in other healthcare issues related to health insurance coverage, such as in healthcare utilization and out of pocket cost, especially before the year On one hand, the urban rural disparity on healthcare utilization decreased from 1993 to For example, in 1993, the percentages of hospital outpatient service use in the two weeks prior to the survey for urban and rural residents were 19.9% and 16.0%, respectively; in 2003, the percentages became 11.8% and 13.9%, respectively (China Ministry of Health, 2004). On the other hand, in 2003, about half of the residents in rural areas who sought outpatient services went to informal healthcare institutions instead of to formal hospitals, while the percentage in urban areas was only about 25%. The shrinkage 15

33 of the urban rural gap of healthcare utilization was due to the reduction in informal healthcare institutions in urban areas (China Ministry of Health, 2004). Moreover, the percentage of unmet needs was highest among the low income population in rural areas (China Ministry of Health, 2004). The healthcare utilization disparity was most prominent in the health service area. Figure 2.2 shows the percentage of pregnancy healthcare utilization and the percentage of women who gave birth in hospital in We can see that rural women used less of these services, especially low income women. By 2008, the disparity in health service utilization had been relieved but still existed. The percentage of pregnancy healthcare utilization had risen to 93.7% for rural women. Compared to the 97.6% ratio for urban women, the rate of healthcare utilization was still lower but the gap between urban and rural had become narrower. 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Health Service Utilization in Urban and Rural Areas in China, by Income (2003) 85% lowest percentile Urban 98% highest percentile 45% lowest percentile Rural 81% highest percentile pregnancy health care give birth in hospital Source: China Ministry of Health, The Third National Health Services Survey Report (in Chinese), 2004, (accessed Aug. 28, 2012) Figure 2.2 Health Service Utilization in Urban and Rural Areas in China (2003) 16

34 Driven by limited health insurance coverage and rapidly growing healthcare costs, high out of pocket expenses comprised a major challenge for those seeking healthcare. China became one of the Asian countries with the highest ratio of out of pocket cost to total healthcare costs in 2002 (Yip and Hsiao 2008). At that time, the out of pocket ratio was 60% (Hu, Tang et al. 2008), and rural residents bore an even higher ratio. The trend of health spending is shown in Figure 2.3. The percentage of out of pocket payments by individual patient rose steadily from 1980 to This trend indicates that the financial burden of healthcare shifted more and more to the individual patients during that period. However, after 2001, the government and social programs started to take on more of the cost, and this resulted in a downward influence on individual out of pocket payments. Percentage Healthcare Spending in China, by Source and Year Individual Patient, 38.2 Social Programs, 34.6 Government, Source: China Ministry of Health, China Health Statistics Yearbook(in Chinese), 2010, Figure 2.3 Healthcare Spending in China, by Source and Year 17

35 30.0% Per Capita Out of pocket Health Expenses as a Percentage of Income 25.0% 20.0% 15.0% 10.0% 5.0% 0.0% lowest percentile middle Urban highest percentile lowest percentile middle Rural highest percentile Source: China Ministry of Health, The Third National Health Services Survey Report (in Chinese), 2004, (accessed Aug. 28, 2012) Figure 2.4 Per Capita Out-of-Pocket Health Expenses as a Percentage of Income Figure 2.4 shows the per capita out of pocket health expenditure as a percentage of income by urban and rural areas. Rural residents paid for medical service with a larger portion of their incomes than did urban residents. Among the poorer rural residents, outof pocket payments for healthcare services constituted 26.7% of their total income in 2003, a large increase from the percentage ten years earlier. 18

36 Chapter 3 Literature Review and Study Objectives 3.1 Existing Research Two research areas inform my study. The first area comprises research on healthcare disparities. As discussed, urban rural disparities in health and healthcare have drawn attention in China in recent years. Many studies have provided empirical evidence on the conditions, trends, and associated factors of such disparities in health status, healthcare utilization, healthcare costs, and related issues such as health insurance coverage. Other research in this area has focused on examining the determinants of the disparities. The second area of research includes assessments of the insurance schemes in China in terms of impact on healthcare utilization, out of pocket cost, and health outcomes. Although these studies are usually not focused on healthcare disparities, I viewed them as a good foundation for my research. I also found these studies helpful in terms of data and methodology. In the next section, I review some of the key research Literature on Rural Urban Disparities in Healthcare Utilization Recent studies have provided empirical evidence on the conditions and trends of rural urban healthcare disparities (Liu, Hsiao et al. 1999; Zhao 2006; Tang, Meng et al. 2008; Meng, Zhang et al. 2012). Liu, Hsiao, and Eggleston (1999) examined the changes in disparity in health status and healthcare utilization in China from 1985 to 1993 and found that the gap in health status and healthcare utilization between urban and rural residents widened during the transitional period when the Chinese economy was shifting from a command economy to a market economy. The authors concluded that the trends were correlated with the reduction of rural health insurance coverage. Zhao (2006) provided evidence for later years, showing that the rural urban disparities in morbidity and 19

37 mortality levels were associated with disparities in healthcare access. Meng, Zhang et al. (2012) provided similar evidence on disparities in maternal and under five mortality rates. Tang, Meng et al. (2008) pointed out that there were rural urban disparities in a set of child health indicators, including infant mortality rate, level of malnutrition, child stunting, and underweight status. However, the researchers believed that China has the ability to carry out the necessary reforms to improve health equity. Several researchers specifically examined disparities in healthcare access and utilization to identify the determinants of healthcare utilization. (Gao, Tang et al. 2001; Wang, Yip et al. 2005; Gao, Raven et al. 2007; Liu, Zhang et al. 2007; Fang, Chen et al. 2009; Jian, Chan et al. 2010; Long, Zhang et al. 2010; Feng, Guo et al. 2011; Xu and Short 2011; Liu, Tang et al. 2012; Meng, Zhang et al. 2012). Among these studies, researchers presented mixed findings. Generally, the authors agreed that most healthcare resources were being allocated to urban areas and that urban residents use more formal healthcare than do rural residents. However, Fang, Chen et al. (2009) examined the evolution of rural urban disparities in healthcare utilization from 1997 to 2006 and concluded that rural residents actually visit physicians more often than do urban residents when they are ill. Some of the researchers pointed out that better insurance coverage was associated with increased healthcare utilization. Liu, Zhang et al. (2007) noted that hospital utilization was lower among the uninsured. Some of the studies focused on certain subpopulations and reached similar conclusions. Gao, Raven et al. (2007) examined the trend of inpatient utilization among the elderly in urban China, and they found that within this subpopulation, the insured were 20

38 more likely to use inpatient care. Jian, Chan et al. (2010) analyzed changes in the rural urban gap for patients with chronic disease, drawing on data collected between 2003 and They concluded that the gap between urban and rural residents was narrowed in terms of hospital admission rates; however, there was no change in terms of early selfdischarge from hospital. Liu, Tang et al. (2012) analyzed the impact of health insurance on utilization of outpatient and inpatient services. They concluded that having health insurance coverage had no significant impact on outpatient service utilization; however, inpatient service utilization increased. Some of the researchers found that changes in disparities and the impacts of health insurance coverage were different among different income groups. Gao, Tang et al. (2001) concluded that from 1993 to 1998, healthcare access for low income groups shrank more than did healthcare access for high income group. Liu, Tang et al. (2012) pointed out that the effect of insurance coverage on inpatient service utilization was greatest for highincome groups, while low income group enjoyed fewer benefits Literature on Disparities in Out of Pocket Expenditure and Healthcare Costs Several studies focused on the disparities and determinants of out of pocket expenditures and healthcare cost (Pan, Dib et al. 2009; Sun, Jackson et al. 2009; Long, Zhang et al. 2010). The researchers generally agreed that rural residents tended to be at increased risk for high and catastrophic medical payments; the current insurance schemes in rural areas offer limited financial protection. Pan, Dib et al. (2009) concluded that hospitalization costs were higher among insured patients because the insured generally stayed longer in hospital than did the uninsured. Long, Zhang et al. (2010) found that 21

39 participating in the NRCM reduced out of pocket expenditures on average, but the rural poor were still faced with high payment problems. Sun, Jackson et al. (2008) pointed out that out of pocket payments remained a burden for rural residents after the initiation of NRCM Literature on Disparities in Health Insurance Coverage Research has focused on the trends of disparities in health insurance coverage (Akin, Dow et al. 2004; Xu, Wang et al. 2007; Xu and Short 2011). Akin, Dow & Lance (2004) examined changes in health insurance coverage from 1989 to 1997 and concluded that the overall coverage decreased slightly, from 26% in 1989 to 23% in They further pointed out that urban areas (cities and towns) experienced reductions in health insurance coverage, while rural area coverage increased. However, the changes were very small, and the rural urban disparity in health insurance coverage persists. Xu, Wang et al. (2007) used data from the National Health Services Surveys of 1998 and 2003 to examine the impact of the reform on population coverage, and they concluded that the overall health insurance coverage stayed almost the same among urban residents. Xu and Short (2011) examined the trends of health insurance coverage from 1997 to They pointed out a sharp increase of coverage in 2006 in rural residents, which resulted in a smaller gap in health insurance coverage between rural and urban residents Methodological Issues Definition of Rural and Urban Two definitions are used to determine rural and urban status in China. The first definition classifies residents by geographical residential areas, which are officially divided into urban and rural areas by the National Bureau of Statistics of China, according to 22

40 China s administrative divisions. The second definition is by household registration type. China classifies people as either agricultural (rural) or nonagricultural (urban). These categorization data are recorded by the household registration (Hukou, 户 口 ) system. These two definitions of rural and urban status are not entirely consistent. Different definitions of rural areas can lead to different results when studying health policy, because the definition of rural areas affects the resources to which people have access (Hart, Larson et al. 2005). However, few existing studies address the definition specifically. For most of the studies, I identified the authors definitions of rural/urban areas only by the terminology used. For example, if the authors used terms such as residents, areas, or geographic regions, I viewed these terms as being consistent with the first definition. If the authors mentioned household registration or used the term population, I viewed these terms as consistent with the second definition. In all of the cited papers, the researchers adopted the first definition except for one study assessing NRCM. Lei & Lin (2009) adopted both the first and second definitions when they evaluated NRCM. However, they restricted their sample by only including people who lived in rural areas and were with rural household registration Modelling In terms of methodology, most of the studies mentioned were descriptive, and some of the papers used cross sectional data to fit logit/probit models. The researchers emphasized the problem of urban rural disparities in healthcare in China and clarified the trends and current conditions, as well as provided direction for further study of this issue. However, no research has provided a complete picture of how the disparities in health 23

41 insurance coverage, healthcare utilization, and healthcare cost change over time. Little research has focused on the role of health insurance coverage on closing the rural urban gap in healthcare utilization and healthcare costs, while considering all major insurance changes. As discussed before, some researchers have evaluated NRCM, and this type of research provided me with methodological help. Wagstaff & Lindelow (2009) drew on multiple data sources to study the insurance and financial risk in China before They applied fixed effect models for two panel datasets and an instrumental variable (IV) technique for a cross sectional dataset, and they concluded that having health insurance in China does not always reduce financial risk. They explained this curious phenomenon by adverse selection, i.e., people with higher risk of high medical expense tend to join the insurance scheme. The advantage of this research is that it used panel data and advanced analysis techniques. However, there were still drawbacks in this study s methodology. Their longest panel had only four waves, and these waves covered a time period before the NRCM was launched. As discussed before, all health insurance systems had experienced changes to some extent at that time. It would be more comprehensive and convincing to extend the research by incorporating the most recent data. More recently, three other papers addressed the NRCM using different data and methodologies, reaching mixed conclusions (Lei and Lin 2009; Yu, Meng et al. 2010; Lu, Liu et al. 2012). In the first study, Lei & Lin (2009) concentrated on evaluating the healthcare service and health outcome after the initiation of NRCM. They used panel data from the China Health and Nutritious Survey to estimate fixed effect and IV models, and they also 24

42 applied a difference in differences estimation with propensity score matching. The researchers found no evidence that the NRCM decreased out of pocket expenditures or increased utilization of healthcare service. Therefore, they concluded that the impact of the NRCM was limited. In their study, they included only three waves of data, one before NRCM was launched and two waves after. This panel could still be expanded to include richer information. In the second study, Yu, Meng et al. (2010) used data from six counties in two provinces to conduct a cross sectional study to examine whether the launch of NRCM increased healthcare utilization. They found that NRCM did not significantly increase outpatient service utilization in rural areas, while inpatient service in general increased. Further, they pointed out the association between household income and healthcare utilization. The authors concluded that the increase happened only among the most affluent. For people with middle and lower incomes, the increase was not significant. In the third study, Lu, Liu et al. (2012) used data from the 2001 China Health Surveillance Baseline Survey to investigate whether the launch of NRCM led to an increase in healthcare utilization and a decrease in possible catastrophic medical expense for rural residents. Similar to the method used by Lei & Li (2009), Lu, Liu et al. also used propensity score matching, and applied the IV method. They found that NRCM did not decrease out ofpocket expenses. However, unlike Lei & Li (2009), they found that NRCM did significantly increase healthcare utilization. 25

43 3.2 Gap in the Existing Literature To sum up, current research provides empirical evidences on the rural urban disparities in health insurance coverage, healthcare utilization, and healthcare costs. However, current research could be improved in several ways. First, in current studies, researchers have examined rural urban disparities in different time periods, but have not provided a complete picture of the trends in rural urban disparities. Second, the determinants of rural urban disparities have not been well examined. The impact of health insurance status, which can be a very important policy intervention to reduce disparities, has not been well studied. Third, in the papers on health insurance or healthcare disparities, the authors have not drawn consistent conclusions; the studies could be improved in terms of data quality and methodology. Fourth, the papers on the impact of health insurance usually focus on certain population groups. For example, when studying the effects of NRCM, researchers usually focus only on rural residents. The first possible expansion to existing literature is to include more waves of data to show a more complete picture of the trends of change in rural urban disparity in health insurance coverage, healthcare utilization, and healthcare cost. The second possible expansion to these studies is to include more waves of data and to use advanced techniques to examine the determinants of the disparities and thus provide policy suggestions on ways to further relieve the disparities. In addition, among the factors associated with the disparities, health insurance is an important issue to study. The third area of expansion is to include urban areas as a control group when examining the impact of health insurance expansion. To address these gaps in the existing literature, I explored all possibilities in my research. 26

44 3.3 Objectives and Research Questions The objectives of my research were to examine the status and trends of rural urban disparities in healthcare utilization and costs, to analyze the role of health insurance coverage in reducing these disparities, and to provide evidence and suggestions to policy makers about how to further reduce rural urban healthcare disparities. My research questions were: 1. What do the rural urban disparities in healthcare utilization and costs look like? How do the disparities change along with major health insurance policy changes? 2. Does more health insurance coverage in rural area reduce the rural urban disparities in healthcare utilization? 3. Does more health insurance coverage in rural area reduce the disparities in high out of pocket healthcare expenditure and total healthcare costs? 4. Does the impact of health insurance on disparities differ by income group and by region? 27

45 Chapter 4 Study Design 4.1 Data For this study, I drew on the detailed individual level longitudinal data from the China Health and Nutrition Survey (CHNS), which is a collaborative project between the Carolina Population Center at the University of North Carolina at Chapel Hill and the National Institute of Nutrition and Food Safety at the Chinese Center for Disease Control and Prevention. As a panel survey, CHNS started in 1989 and has been conducted roughly every other year. I used the most recent seven waves of data (1993, 1997, 2000, 2004, 2006, 2009, and 2011) in the analysis. The 1989 and 1991 datasets were not used because these datasets did not contain health insurance information or household registration information. CHNS used a multistage, random cluster sampling approach, and was conducted in nine provinces, 1 which are mostly representative of Central and Eastern China and vary substantially in geography, economic development, public resources, and health indicators. Counties in the nine provinces were stratified into three layers by income, and a weighted sampling scheme was used to randomly select four counties in each province. Villages and townships (the CHNS definition of communities) within the counties and urban and suburban neighborhoods within the cities were then selected randomly into primary sampling units (PSUs). The same households were surveyed over time whenever possible and newly formed households were included beginning in In the sample, rural communities had populations ranging from 125 to 14,964 people, and urban communities 1 The nine provinces are Guangxi, Guizhou, Heilongjiang, Henan, Hubei, Hunan, Jiangsu, Liaoning, and Shandong. In the 2011 wave, three municipalities (Beijing, Shanghai and Chongqing) were added into the sample. 28

46 had populations ranging from 167 to 86,733 people. In this study, I included all respondents who responded to the health insurance section. This final sample included more than 90,000 respondents. The sample sizes are shown in Table 4.1. CHNS was a good data source for the research because it provided detailed information on insurance coverage, medical providers, health services use, and healthcare costs. Therefore, CHNS allowed me to look at how insurance coverage affects health service use and health financing. Questions about healthcare accessibility, time and travel costs to health facilities, and perceived quality of care were also asked. Table 4.1 Sample Size by Rural and Urban Residences and Registrations Rural Residents Urban Residents Wave Rural Registration Urban Registration Rural Registration Urban Registration Total ,663 2,253 1,433 2,470 13, ,255 2,492 1,661 2,801 14, ,956 2,601 1,563 3,015 15, ,016 2,081 1,188 2,858 12, ,774 2,059 1,228 2,679 11, ,931 2,064 1,241 2,688 11, ,489 2,874 1,420 4,717 15,500 Total 47,084 16,424 9,734 21,228 94,470 29

47 4.2 Study Periods For this analysis, I classified the study period of into four periods: , a period before the major health insurance expansion in China , a period after the initiation of UEBMI in , a period after the initiation of NRCM in , a period after the initiation of URBMI in Conceptual Model and Variable Selection The variable selection was based on the Andersen model (Andersen 1968). The model focused on the individual as the unit of analysis and, when first developed, was used to explain why people use healthcare services. After several generations, the model grew to include other endpoints of interest, such as healthcare quality and health outcomes (Andersen 1995). Figure 4.1 shows the most recent Andersen model. This figure depicts the interaction between environment, population characteristics, health behavior, and health outcomes. Specifically, the healthcare system includes policy, resources, and organizations; predisposing characteristics include demographic characteristics, health beliefs, and social structure; enabling resources includes income, health insurance, and other resources for healthcare services. All these characteristics can impact the decision to use health services and further influence healthcare outcomes. Health behavior can influence enabling resources; health outcomes can affect enabling resources and health behaviors (Andersen 1995). Therefore, by including personal demographic information, family and social structure, income, insurance status, health conditions, and policy change in the model, I was able to examine how these factors affected peoples healthcare seeking behaviors and 30

48 healthcare costs. The variables of health insurance coverage and types of coverage are viewed as enabling factors in the model. By including location information about urban versus rural areas, I also controlled the impact of the external environment. Figure 4.1 Updated Structure of Anderson Model Moreover, Andersen assigned a degree of mutability to the model components when he developed the model. According to Andersen, the most mutable population characteristic component was enabling resources, which included insurance coverage. In my analysis, status of health insurance was affected by policy changes. Therefore, when interpreting the results, I focused on the impact of health insurance coverage on healthcare utilization and costs, and the resulting policy implications Dependent Variables The analysis focused on urban rural disparities in healthcare utilization and healthcare costs. All the healthcare utilization questions in CHNS focused on a four week period right before the interview. For healthcare utilization, I constructed three variables: 31

49 formal care utilization, outpatient care utilization, and inpatient care utilization. Formal care utilization is a binary variable indicating whether the respondent sought formal medical care from a hospital or clinic in the four weeks before the interview. The formal care utilization variable was constructed from several raw variables: (a) whether the respondent was sick or injured or suffered from a chronic or acute disease, (b) whether the respondent sought care from a formal medical provider, and (c) what the respondent did when he or she was ill or injured. If the answer to the first question was yes, the respondent was asked the second and third questions. If the answer to the second question was yes or the answer to the third question was saw a doctor (clinic, hospital), I considered the respondent to have sought formal medical care in the past four weeks. There were some inconsistences in the question setting and wording across waves. In waves 1993 to 2000, CHNS only asked the second question, and repeated the question for a second facility. In the latter waves, CHNS asked both questions. 2 The outpatient and inpatient utilization were also binary variables. They were constructed from the raw variable of whether the visit was an inpatient or outpatient visit. For healthcare expenses, I constructed two types of variables. The first type of variable involved the amount of total healthcare costs. The second type contained several binary variables indicating whether the out of pocket healthcare costs were more than a certain percentage of the household income. I used two cut off points for the percentage: 20% and 40%. The amount of total healthcare costs was derived from the raw variables underlying the treatment costs. The amount of out of pocket costs was constructed from 2 There has been a jump of percentage of people who use formal medical care since the 2004 wave. However, the change is not a result from the setting of the questions. 32

50 the total treatment costs and percentage of treatment costs paid by insurance and other cost of treating the illness or injury. These variables were also constrained to the four week period before the interview. I inflated the amounts to 2011 values using the index from CHNS data. In the survey, the question about household income referred to a time period of one year. Therefore, I multiplied the out of pocket healthcare expenses by 12 to match the two time frames. The healthcare costs variables measured the costs within 4 weeks before the interview, thus the costs could be from acute illness and be overestimated when transported to costs in one year. Therefore, I did not pick a lower cut off point for high outof pocket costs Independent Variables Key Independent Variable: Dummies Indicating the Respondents Residence and Household Registration Type My key independent variable was a set of dummies indicating the respondents resident area and household registration type. There are two definitions of rural and urban in China. The first consists of geographic residential areas, which are officially divided into urban and rural areas. The National Bureau of Statistics of China officially assigns these levels. This variable was directly created from the primary sampling units of CHNS, which drew samples from cities, suburbs, towns, or villages. The first two designations cities and suburbs are considered urban areas; the latter two are classified as rural areas. The second definition of rurality is by type of household registration. China classifies people as either agricultural (rural) or nonagricultural (urban) population, recorded by the household registration (Hukou, 户 口 ) system. These two definitions are not completely consistent, for three possible reasons: (a) there are areas in China called urban rural mixed 33

51 areas ( 城 乡 结 合 部 ), but they can only be classified as either urban or rural area; (b) increasing numbers of people with rural household registration migrate to urban areas to work, but their household registrations do not change; and (c) some people with urban household registration, especially in recent years, have chosen to live in rural areas. Most of the agricultural population resides in rural areas. In my CHNS sample, 75% of people with agricultural household registration lived in rural areas, and 67% of people with nonagricultural household registration lived in urban areas. These percentages stayed relatively consistent across waves; therefore, my assumption was that the sample covered few migrating rural workers. If this were not the case, there should be greater numbers of rural workers migrating to urban areas as the economy develops and the control of residency relaxes. As discussed in the literature review, most of the studies on the disparity issue used residential area to define rurality, while most studies evaluating NRCM used the household registration system to define rurality. In my research, I sought to examine the changes in disparities, as well as to establish a link between insurance and disparity. Therefore, I used both of the two classifications to divide people into four categories: rural residents with rural registration (Group RR), rural residents with urban registration (Group RU), urban residents with rural registration (Group UR) and urban residents with urban registration (Group UU). I used Group UU as the reference group and compared the three other groups with it. By adopting the four categories, I was able to track all three health insurance policy changes that expanded health insurance coverage to people with certain household 34

52 registration types and to people living in certain areas. I was also able to examine how the disparity levels changed with the residing environment. As discussed, the policy changes also included construction of healthcare facilities, training of medical service workers, and drug policy changes. These are all applied to the residing environment and can affect the residents healthcare utilization and costs Descriptive Statistics of Independent Variables Other independent variables included basic demographic characteristics, family size and wealth, health measures, and health insurance status. Table 4.2 shows descriptive statistics of all the independent variables. In order to reflect the difference between rural and urban residents, I report the statistics separately for rural and urban residents. From the descriptive statistics, rural and urban residents were substantially different. In my sample, rural residents contained a slightly larger portion of males and minorities than urban residents. Rural residents were younger than urban residents, on average, although I observed aging trends in both groups. More urban residents were married, but rural residents usually had larger household sizes. Urban residents had higher education levels and incomes than did rural residents Equivalence Scale for Adjusting Household Income In order to provide a more accurate measure of household income, I used the equivalence scale to adjust the size of household and then computed the per capita household income using the adjusted household size. I chose to apply one of the most commonly used scales, the square root scale, which involves dividing household income by the square root of household size. This scale was adopted by some recent OECD publications on income inequality and poverty (e.g., OECD 2011). 35

53 Missing Value Imputation for Independent Variables I performed basic imputation for missing values. For marital status, I replaced the missing values with never married if the respondent was younger than 18. According to China s marriage law, the youngest age to get married is 18. For household size and household income, I imputed the missing values using other household members answers. For household registration type, if the value was missing in one wave, but the previous and post waves had the same values, I assigned this value to the missing wave. For missing values in education years, I assigned 0 to the variable if the respondent was younger than seven. If the values in the previous and post waves were equal, I assigned the same value to the missing wave. If the values in last two waves did not change, I assigned the same value to the missing wave. If the respondent was older than 30, I assigned the value from the previous wave to the missing wave. I used the value from the variable indicating years of formal education to impute the missing values in highest level of formal education, which was used in the analysis. For missing values for the variable of whether the respondent was still in school, I replaced the value with 0 if the respondent was younger than seven or older than 30. For missing values in the variable of having any medical insurance coverage, I assigned 1 to the variable if the respondent claimed to have any type of medical insurance. After the basic imputation, there were still a few missing values. The percentage of missing values was generally less than 1%. In order to better use the information in the dataset, I created additional categories in each variable indicating whether the value was missing and included the categories in my analysis. 36

54 Table 4.2 Descriptive Statistics of Independent Variables by Rural and Urban Residences and Registrations gender Group RR Group RU Group UR Group UU N=47,084 N=16,424 N=9,734 N=21,228 male female Ethnicity Han Minority unreported age age equal or below age between 6 and age between 18 and age equal or above unreported marital status married never married other(divorced, widowed, or separated) unreported education level primary school middle school high school college and above unreported education status whether still in school whether still in school not in school unreported whether in school income groups low income group medium income group high income group unreported Note: 1. Income was adjusted for inflation to 2011 value 2. Adjusted per capita household income was used 37

55 4.4 Analytic Approach Difference in differences (DID) analysis comprised my main analyzing technique. DID analysis assumes parallel trends in control and treatment groups before the policy intervention. For the variables for which the parallel trends did not hold, I performed multivariate models, controlling for existing trends. I also performed several sensitivity analyses, each of which had different focuses, as discussed in the next section Difference in Differences Analysis with Multiple Groups and Multiple Time Periods Using the longitudinal data collected in seven waves between 1993 and 2011 enabled me to take a DID approach in my empirical analysis. This approach has become increasingly popular in the empirical literature on the effects of public policy interventions. The DID estimation is based on the simple idea of comparing the difference in outcomes before and after an intervention for groups affected by it to the difference for unaffected groups. The great appeal of DID estimation comes from its simplicity as well as from its potential to mitigate biases in the comparison between the treatment and control group that could be the result of permanent differences between those groups, as well as to mitigate biases from the pre post comparison of the treatment group that could be the result of secular trends unrelated to the intervention (Card and Krueger 2000; Athey and Imbens 2002; Bertrand, Duflo et al. 2004; Abadie 2005; Conley and Taber 2005). My research focused on the change in disparities. Further, the setting of the research questions made DID the most suitable approach. The DID analysis can be expanded to include more than two time periods (Bertrand, Duflo et al. 2004; Hansen 2007). As discussed, there have been three major policy changes 38

56 in health insurance in China. I included all three major policy interventions on health insurance in my model. My main hypothesis was that the second policy change, which expanded insurance coverage in rural areas in 2003 helped reduce rural urban disparities in healthcare utilization and costs. However, it was important to take the other two policy changes in urban areas into consideration and separate the effects from different policy changes. After the DID model, I interpreted the results using the whole sample to make predictions for different residence and registration groups in each period. The results are presented in bar graphs. Using the adjusted outcome variables, I was able to observe the trends in disparities Econometric Models In this section, I elaborate on how I built econometric models to perform the analysis based on the conceptual framework. For different outcomes, I applied different techniques. Considering the dichotomous variables, such as whether a person used outpatient care, I applied logistic regression model and a general framework considered by Bertrand, Duflo et al. (2004) and Hansen (2007). Empirically:, 1,2,3,4,,,, where pˆ denotes the probability that the dependent variable equals 1, and 1 pˆ is the probability that the dependent variable equals 0, t is the effect of rural or urban 39

57 residence/registration, is the effect of each different time period, x is the interaction r rt term of residence/registration and time periods, z irt is the individual specific covariates, rt is the unobserved time/group effect, and irt is the individual specific error. Thus, was the policy effect that I planned to estimate. For the continuous variable of the amount of total healthcare expenditures, I estimated a two part model, which was developed to address two problems typical of expenditures data first, that many individuals have zero expenditure and that the distribution of nonzero expenditures is highly skewed (Duan Manning et al. 1983). The first part of the model was a logit model on the probability of having nonzero total health expenditures, and the second part focused on the amount of health expenditures conditional on nonzero health expenditures. For the second part of the model, I used a generalized linear model (GLM; Manning and Mullahy 2001). I performed Box Cox test to select the appropriate link function and a GLM family test (Park test) to select GLM family. Based on the test results, gamma family and log link were selected. Empirically: Part 1:, 1,2,3,4,,,, Part 2: y irt x z t r rt irt rt rt irt, 1, 2, 3, 4,,,, 40

58 To test the results of the two part model, I performed a bootstrap approach when producing the prediction after the model fitting. I provide the 95% confidence interval of the adjusted results Test of Trends Before the Policy Intervention The DID analyses assumed similar trends in the study outcome, such as healthcare costs, among the study populations before the expansion of health insurance coverage. To test this assumption, I examined trends in the study outcomes among the study populations by analyzing the data, which reflected the situation before the dramatic expansion of health insurance in the late 1990s. Table 4.3 shows the test results using the 1993 and 1997 data. Column 1 shows results of whether the respondent used any formal care in the previous four weeks. As seen in the results, the initial rural urban disparity estimators range from to 0.789, indicating significant disparities in the year The DID estimator shows change in disparity in I observed no significant results in the change of disparity for formal care utilization, indicating similar disparities from 1993 to These results rule out the possibility of changes in disparities before the policy interventions, suggesting that the parallel trend holds for the variable of formal care. Therefore, I concluded that standard DID analysis was suitable for formal care utilization. Similar results were observed for outpatient utilization, which are shown in column 2. Again, all groups used less formal medical care than Group UU in The changes in disparity in 1997 were not significant for any of the three groups. Therefore, DID analysis was also suitable for outpatient utilization. Results for inpatient utilization are shown in 41

59 column 3. For this variable, however, I did not observe any significant results in the initial disparity in 1993, although the changes in disparity in 1997 were significant for Group RR and Group RU. Therefore, the parallel trend assumption did not hold for inpatient care utilization, preventing me from using standard DID analysis for this variable. Table 4.3 Results of DID Analysis Using 1993 and 1997 Waves for Healthcare Utilization Formal care Outpatient Inpatient Odds Ratio Robust Std. Err. Odds Ratio Robust Std. Err. Odds Ratio Robust Std. Err. Disparity with Group UU in 1993 Group RR 0.674*** ** Group RU * Group UR 0.560*** * Change in disparity in 1997 Group RR ** Group RU * Group UR Note: 1. Significance level: *** 0.001, ** 0.01, * Results for other independent variables are omitted. Table 4.4 shows test results for healthcare costs. The results are similar to those observed for inpatient care utilization. Columns 1 and 2 show results for whether having OOP exceeding 20%/40% of household income. No significant disparities were observed in 1993, while the disparities significantly decreased in 1997 for Group RR and Group RU. Columns 3 and 4 show results for the two part model for total healthcare cost. From the first part, no significant results were observed for initial disparities in 1993, while there was a significant increase in disparity for Group RR in For the second part, there was significant decrease in disparities for all three groups. The results indicate that the parallel 42

60 trends did not hold for these variables. Therefore, DID analysis was not suitable for any of the variables. As discussed in Chapter 2, in 1990s, policy changes have been implemented in urban areas to alleviate financial problems, and these measures may have increased costs. In rural areas, however, the situation was not improved during the same period. Therefore, for some of the outcome variables, I observed significant changes in rural urban disparity during 1990s, even before the first major health insurance expansion in Assuming the trends continued in the following years, I estimated the following models, which included variables to control for the trends before 1998, and then I examined the deviation from the existing trends in each of the subsequent waves. 43

61 Table 4.4 Results of DID Analysis Using 1993 and 1997 Waves for Healthcare Costs OOP>20% Household Income OOP>40% Household Income Having any Healthcare Cost Total Healthcare Cost Odds Ratio Robust Std. Err. Odds Ratio Robust Std. Err. Odds Ratio Robust Std. Err. Coef. Robust Std. Err. Disparity with Group UU in 1993 Group RR Group RU Group UR Change in disparity in 1997 Group RR 0.499** ** ** * Group RU 0.505* * *** Group UR * Note: 1. Significance level: *** 0.001, ** 0.01, * Results for other independent variables are omitted Multivariate Regression for the Variables that do not meet the Assumption of Parallel Trends For the dependent variables in which the parallel trends did not hold, I applied another technique to account for the pre existing trends in 1990s. Considering the dichotomous variables, such as use of inpatient care, I applied logistic regression model. Empirically: 1993, 1993, 1997,, 2011,,,, where pˆ denotes the probability that the dependent variable equals 1, and 1 pˆ is the probability that the dependent variable equals 0, is the effect of rural or urban residence/registration, 1993 is the trend in 1990s for different groups, is the 44

62 interaction between groups and year dummy variables, is the interaction term of residence/registration and time periods, z is the individual specific covariate, is the unobserved time/group effect, and is the individual specific error. Thus, was the policy effect that I planned to estimate. irt irt rt After the multivariate model was completed, I carried out a Wald test to examine whether the disparities were significant in each wave and to examine whether the change in disparities between different waves was significant. For the continuous variable of the amount of total healthcare expenditures, I estimated a two part model, discussed in detail in section Empirically: Part 1: 1993, 1993, 1997,, 2011,,,, Part 2: 1993, 1993, 1997,, 2011,,,, To test the results of the two part model, I performed a bootstrap approach when producing the prediction after the model fitting. I provided the 95% confidence interval of the adjusted results. 45

63 4.5 Sensitivity analysis I performed several sensitivity analyses in addition to the baseline results, which I discuss in the following section Controlling for Insurance Status In the base case, I did not control for insurance status. Insurance coverage is one of the aspects that the Chinese healthcare reform has been designed to change. I planned to examine how insurance coverage changes the disparities. However, there were policy changes other than insurance coverage occurring in the same period. As discussed in Chapter 2, there were usually other measures implemented while China provided more health insurance coverage to residents. For example, when providing more health insurance coverage for rural residents in 2003, the government also provided funding for medical facility construction and training of medical workers. Medical assistance programs were also established in both rural and urban areas in different years. These measures could also be important in promoting healthcare utilization, as well as reducing out ofpocket costs. Therefore, I performed the DID models while controlling for insurance status as a sensitivity analysis to examine the impact of other policy changes. I then compared how much disparity changed with and without controlling for insurance Dropping the Richest Province or the Poorest Province My CHNS sample contained nine provinces and three municipalities, and these provinces varied in terms of economic development. In order to examine different effects of the policy changes in different provinces with uneven development, I performed analysis without the richest and poorest provinces (in terms of GDP in 2012, see Appendix for details) and compared the results with results from models using the whole sample. 46

64 4.5.3 Including Interaction Terms with Household Income When studying the impact of NRCM, several researchers found different effects among residents with different income levels. In order to examine whether the policy effect differed among different income groups, I included an interaction term of household income with rural/urban residences and registrations. In this analysis, I classified residents into three categories by adjusted per capita household income. The three groups are high, medium, and low income groups, representing the three different quintiles of adjusted per capita household income. By including this term, I was able to study the different policy effects among different income groups DID Analysis Results for Variables in Which Parallel Trends did not Hold As discussed previously, the parallel trends did not hold for inpatient care utilization, OOP exceeding 20%/40% of household income, and total healthcare costs. Therefore, I used a model controlling for existing trends before policy intervention as the base model for these variables. In these models, I assumed the existing trends continued in the following years. I also performed DID analysis to determine whether the results were different when not controlling for existing trends. 47

65 Chapter 5 Results: Disparities in Healthcare Utilization 5.1 Descriptive Analysis Proportion of redients using medical care during the past 4 weeks Ratio of other study groups to Group UU Group RR Group RU Group UR Group UU Ratio: Group RR/Group UU Ratio: Group Ru/Group UU Ratio: Group UR/Group UU Period Figure 5.1 Probability of Formal Care Utilization in 4 Weeks by Rural and Urban Residences and Registrations Columns in Figure 5.1 show the trends of proportion of residents seeking formal medical care by rural/urban residences and registration types. There are also lines showing the ratios, using urban residents with urban registration as the base group. Among the four groups, residents in Group UU had always been using the most formal medical care, and Group RR residents had always been using the least. Group RU and Group UR remained in the middle. However, the ratios between groups changed over time. In period 1, before the first policy change in 1998, Group RR used about 60% as much formal medical care as did Group UU. Group UR used more formal care than did Group RU. In period 2, after the policy change in 1998 and before the 2003 policy change, Group UU used a greater amount of medical care than in period 1, and utilization within Group RR and RU also increased 48

66 slightly. However, Group UR used less formal care than in period 1. As a result, all the ratios decreased in this period, and the ratio between Group UR and UU dropped the most. In period 3, after the 2003 rural policy change, utilization within all groups increased dramatically. Utilization within Group RR RU and UR increased more than Group UU utilization, resulting in higher ratios. In period 4, after the policy change in 2007, Group UU utilization increased steadily again while utilization within the other groups only increased slightly in this period. Therefore, the ratios dropped in this period Proportion of residents using outpatient care during the past 4 weeks Ratio of other study groups to group UU Group RR Group RU Group UR Group UU Ratio: Group RR/Group UU Ratio: Group Ru/Group UU Ratio: Group UR/Group UU Period Figure 5.2 Probability of Outpatient Care Utilization in 4 Weeks by Rural and Urban Residences and Registrations Similar trends were observed in outpatient care utilization. Figure 5.2 shows the trends of proportion of residents using outpatient care by rural/urban residences and registration types. Again, Group RR had always been using less outpatient services than other groups, and Group UU had been using the most. Utilization within all groups 49

67 increased along the periods, except that the utilization of Group UR decreased in period 2. The ratios decreased after the 1998 policy change, and it decreased most for Group UR. Then the ratios increased after the 2003 policy change, and finally dropped following the 2007 policy change Proportion of residents using inpatient care duing the past 4 weeks UEBMI, 1998 NRCM, 2003 URBMI, Ratio of other study groups to group UU Group RR Group RU Group UR Group UU Ratio: Group RR/Group UU Ratio: Group RU/Group UU Ratio: Group UR/Group UU Wave Figure 5.3 Probability of Inpatient Care Utilization in 4 Weeks by Rural and Urban Residences and Registrations Similar to the results found for overall formal care and outpatient utilization, Group RR almost always used less inpatient care than did all the other groups. However, inpatient care utilization shows different trends. The ratios also dropped in the 1990s and increased in the 2000s, with different slopes for different groups. Note that the amount of inpatient care was very small in the sample. Fewer than 1% of the respondents used inpatient care 50

68 within the four week period before the interview. Therefore, data that are more informative might be needed to discern the real pattern. 5.2 DID Analysis for Formal Care Utilization and Outpatient Utilization The DID analysis results for rural urban disparities in formal healthcare utilization and outpatient utilization are presented in Table 5.1. These DID models included four categories of different rural and urban settings, using Group UU as the reference group. Table 5.1 column 1 reports results for formal care. Using Group UU as the reference group, the initial rural urban disparity estimators ranged from to 0.688, indicating that there were great rural urban disparities going back to the early 1990s. Among the three groups, Group RR used the least formal care; Group UR used the most. Change in disparities can be indicated from the DID estimators. The disparities increased for all three groups in period 2 since the policy change in Subsequently, in periods 3 and 4, the disparities decreased compared with the initial period. However, most of the changes were not significant except for Group RR in period 4 and Group RU in periods 3 and 4. In order to test the change of disparities between two adjacent periods, I performed Wald tests after the DID analysis. If the test result was significant, I rejected the null hypothesis that change in period 2 equaled change in period 3. The test results are shown in Table 5.2. For formal care utilization, test results comparing the change in period 2 with change in period 3 were significant for all three groups. Therefore, I rejected the null hypothesis that the change in period 2 equaled change in period 3. These results show that Groups RR, RU, and UR all improved after the policy change in 2003, compared with their counterparts from Group UU. 51

69 I also observed significant effects in other independent variables. Male respondents used less formal medical care than did females. Minorities used less formal medical care than did Han Chinese. Children under the age of six and seniors over the age of 60 used more formal medical care than middle aged groups. People who were never married used less formal medical care than did those in the married group. People whose highest education level was lower than primary school used more medical care, but this may because the sample included children who were still in school. Finally, there were differences across different provinces. Using the province with the highest GDP level as the reference group, the other provinces generally used less formal medical care, except for Guangxi and Henan. 3 This difference may have been due to different healthcare policies in different provinces. Similar results were observed for outpatient care utilization. In the first period, Groups RR, RU, and UR used about 60% to 78% of outpatient services compared to the amount used by urban residents. In period 2, however, the disparities increased for all three groups, as determined from DID estimators smaller than 1. In period 3, the disparities shrank compared with the first two periods. Finally, in period 4, the disparities diminished, compared with period 1. However, compared with the adjacent period 3, the disparities increased slightly for Group RU. The fluctuation of disparities over the four periods indicates that rural urban disparities in outpatient care utilization increased after the policy change in 1998, diminished after the policy change in 2003, and slightly decreased after the policy change in 2007 (except for Group RU). The Wald test results for outpatient care were significant for all groups in periods 2 and 3, showing that all three 3 Jiangsu province, which had the biggest GDP value in 2012, was used as the reference group. 52

70 groups had improved outcomes after the 2003 policy change, compared with their counterparts from Group UU. The other independent variables show the same effects for outpatient care utilization as for overall formal care utilization. 53

71 Table 5.1 DID Analysis Results for Formal Care Utilization and Outpatient Utilization Formal care Outpatient Independent Variable Odds Ratio Robust Std. Err. Odds Ratio Robust Std. Err. disparities with Group UU in period 1 Group RR 0.586*** *** Group RU 0.652*** *** Group UR 0.688*** * periods period 1 1 n/a 1 n/a period *** *** period *** *** period *** *** change in disparities Group RR in period Group RR in period Group RR in period * * Group RU in period Group RU in period ** *** Group RU in period * ** Group UR in period * Group UR in period Group UR in period gender male 0.886*** *** female 1 n/a 1 n/a ethnicity minority 0.788*** *** Han 1 n/a 1 n/a age age equal or below *** *** age between 6 and age between 18 and 60 1 n/a n/a age equal or above *** *** marital status married 1 n/a 1 n/a never married 0.558*** *** other (divorced, widowed or separated) education level primary school 1 n/a 1 n/a middile school 0.730*** *** high school 0.712*** *** college or higher 0.727*** *** whether still in school in school not in school 1 n/a 1 n/a adjusted per capita household income low household income medium household income 1 n/a 1 n/a high household income province Jiangsu 1 n/a 1 n/a Liaoning 0.682*** *** Heilongjiang 0.444*** *** Shandong 0.641*** *** Henan 1.233*** *** Hubei 0.851** ** Hunan 0.856** ** Guangxi 1.300*** *** Guizhou 0.759*** *** Beijing 2.583*** *** Shanghai 2.749*** *** Chongqing 1.216* ** Note: 1. Significance level: *** 0.001, ** 0.01, *

72 Table 5.2 Test Results for DID Analysis of Formal Care Utilization and Outpatient Utilization Group RR Formal Care Outpatient chi2 Prob>chi chi2 Prob>chi Change in disparity in period 2 = Change in disparity in period ** ** Change in disparity in period 3 = Change in disparity in period Group RU Change in disparity in period 2 = Change in disparity in period *** ** Change in disparity in period 3 = Change in disparity in period Group UR Change in disparity in period 2 = Change in disparity in period * * Change in disparity in period 3 = Change in disparity in period Note: 1. Significance level: *** 0.001, ** 0.01, * Based on the results from DID analysis, I predicted the probabilities of formal care and outpatient in four weeks by rural and urban residences and registrations for four time periods. The results are shown in Figure 5.4 and Figure 5.5. Figure 5.4 shows predicted probability of formal medical care utilization in four weeks. All the ratios to Group UU had always been less than 1, but changed over time. The ratios decreased between periods 1 and 2 and increased between periods 2 and 3. Subsequently, the ratio for Group RU decreased slightly between the last two periods and increased slightly for Groups RR and UR. These trends were consistent with what I observed in descriptive figures and show that the policy changes resulted in first more, 55

73 then less rural urban disparity in formal care utilization. As discussed previously, the change between periods 2 and 3 was significant. Among the three groups, Group RR had always been the lowest. Figure 5.5 shows the predicted probability of outpatient care utilization. A similar pattern was observed in this figure. Predicted probability of redients using medical care during the past 4 weeks Ratio of other study groups to group UU Group RR Group RU Group UR Group UU Ratio: Group RR/Group UU Ratio: Group RU/Group UU Ratio: Group UR/Group UU Period Figure 5.4 Predicted Probability of Formal Care Utilization in 4 Weeks by Rural and Urban Residences and Registrations 56

74 Predicted probability of residents using outpatient care during the past 4 weeks Ratio of other study groups to group UU Group RR Group RU Group UR Group UU Ratio: Group RR/Group UU Ratio: Group RU/Group UU Ratio: Group UR/Group UU Period Figure 5.5 Predicted Probability of Outpatient Care Utilization in 4 Weeks by Rural and Urban Residences and Registrations 5.3 Multivariate Analysis Controlling for Existing Trends for Inpatient Utilization For inpatient care, I applied multivariate regression, controlling for existing trends, and the results are shown in Table 5.3. The initial coefficients of disparities were smaller than 0 for Group RR and UR, meaning that the two groups used less inpatient care than did Group UU. Group RU used more inpatient care compared with Group UU. However, none of the disparities was significant. For Group RR, the trend in the 1990s was negative, and the result was significant. If the trend persisted, Group RR would use less and less inpatient care in the following years. However, this group experienced a positive deviation from the trend in all of the subsequent years. This deviation could because the policy change in 2003 provided more health insurance coverage for Group RR. The deviations in all years after 2004 were significant. This indicates the policy impact persisted in the subsequent years. Group RU followed the same pattern as Group RR. However, none of the results for Group 57

75 RU was significant. For Group UR, the trend was positive; deviation in 2000 was negative, and then all the deviations in the subsequent years were positive. For Group UU, the trend was positive but not significant. In all the following years, the deviation from trend was negative, and the deviation in 2000 was significant. Table 5.4 shows test results of disparities between Group UU and other groups. As discussed, the disparity is the difference between the probability of having any inpatient care visit for Group UU, compared to the other groups. Column 1 shows disparities, and the test results are in columns 2 and 3. For Group RR, disparity with Group UU in 1997 is 0.012, indicating that the probability of having inpatient care visit was greater in Group UU than in Group RR. The difference in probabilities was The test result shows that the disparity was not significant. For Group RR, disparities with Group UU were all positive, meaning that Group RR had always been using less inpatient care compared with Group UU. In 2000, 2004, 2006, and 2011, the disparities were significant. For Group RU, similarly, the disparities were all positive except for the disparity in For Group UR, disparities were all positive. However, none of the results was significant for Group RU and only significant in 2000 for Group UR. 58

76 Table 5.3 Multivariate Analysis Results for Inpatient Care Utilization Independent Variables Coef. Robust Std. Err. disparity with Group UU in 1993 Group RR Group RU Group UR trend in 1990s and change in later waves Group RR trend in 1990s 0.204** deviation from trend in deviation from trend in ** deviation from trend in ** deviation from trend in *** deviation from trend in *** Group RU trend in 1990s deviation from trend in deviation from trend in deviation from trend in deviation from trend in deviation from trend in Group UR trend in 1990s deviation from trend in deviation from trend in deviation from trend in deviation from trend in deviation from trend in Group UU trend in 1990s deviation from trend in * deviation from trend in deviation from trend in deviation from trend in deviation from trend in constant 4.586*** Note: 1. Significance level: *** 0.001, ** 0.01, *

77 Table 5.4 Test Results of Disparities for Inpatient Care Utilization Disparity Chi2 Group RR disparity (Group UU probability Group RR probability) *** *** ** ** Group RU disparity (Group UU probability Group RU probability) Group UR disparity (Group UU probability Group UR probability) * Note: 1. Significance level: *** 0.001, ** 0.01, * Table 5.5 shows test results for the change in disparities. The major health insurance policy changes occurred in 1998, 2003, and Therefore, I compared the disparities in the years before the initiation of each policy intervention (1997, 2000, and 2006) with all the waves that occurred afterward and then tested for the significance of the change in disparities. For Group RR, the disparity decreased in 2000 by 0.5%, compared with the disparity in However, the change was not significant, as shown by the test results in columns 2 and 3. In all the subsequent waves, the disparities were smaller than in 60

78 1997. The changes were significant for 2009 and The disparity was reduced by 0.8% in 2009 and by 0.4% in Compared to the disparity with Group UU in 2000, the disparity was larger in 2004 and 2011 and smaller in However, none of the changes was significant. Compared with disparity in 2006, the disparity was smaller in 2009 and larger in Again, the changes were not significant. For Group RU, the change was not significant for any of the following years compared with disparities in 1997, 2000, or For Group UR, the disparity increased in 2009 compared with 1997 and 2000, and the change was significant. In sum, there was no significant change in disparities in the years immediately after the major policy interventions. The disparity between Group RR and UU decreased from 1997 in 2009 and However, no evidence shows that it was due to the policy change in

79 Table 5.5 Test Results of Change in Disparities for Inpatient Care Utilization Change In Disparity Chi2 Group RR compare with disparities with Group UU in ** ** compare with disparities with Group UU in compare with disparities with Group UU in Group RU compare with disparities with Group UU in compare with disparities with Group UU in compare with disparities with Group UU in Group UR compare with disparities with Group UU in * compare with disparities with Group UU in * compare with disparities with Group UU in Note: 1. Significance level: *** 0.001, ** 0.01, *

80 Figure 5.6 shows the predicted probability of inpatient care utilization. For Group RR, the ratio decreased in the 1990s, increased in the 2000s, and finally decreased in For Group RU and UR, the ratio does not show any pattern. As discussed before, the variable only measured inpatient visits in a four week period, and the proportion of residents using inpatient care was very small. The data may not be sufficient to show the real pattern, and more detailed data is needed Prediceted probability of residents using inpatient care during the past 4 weeks UEBMI Launch, 1998 NRCM Launch, 2003 URBMI Launch, Ratio of other study groups to group UU Group RR Group RU Group UR Group UU Ratio: Group RR/Group UU Ratio: Group RU/Group UU Ratio: Group UR/Group UU Wave Figure 5.6 Predicted Probability of Inpatient Care Utilization in 4 Weeks by Rural and Urban Residences and Registrations 63

81 5.4 Sensitivity Analysis Controlling for Insurance Status My first sensitivity analysis involved controlling for insurance status. After the analysis, I also performed tests to examine whether there were significant changes between adjacent periods/waves. The regression results for formal care and outpatient utilization are shown in Table 5.6, and the test results are shown in Table 5.7. From these models, I observed similar effects as were observed in the base models. Column 1 shows results for formal care utilization. Having health insurance coverage had a positive effect on formal care utilization. When controlling for insurance status, there were rural urban disparities in period 1, as the odds ratio for all groups were less than 1. Group RR was still the worst performing in terms of using formal medical care. Compared with models not controlling for insurance, the odds ratios were larger. The results indicate that having insurance coverage could explain part of the disparities in formal care utilization. The magnitude of changes in disparities was smaller compared with the base models. However, the disparities in the last three waves were generally not significant from period 1. The trends of changes in disparities were similar with the base models. For Group RR and UR, the disparities increased in period 2 and decreased in periods 3 and 4. For Group RU, the disparities increased in period 2, decreased in period 3, and finally increased again in period 4. The Wald test results indicated that the changes in disparities for all groups from periods 2 to 3 were significant. This was also consistent with the base models. The odds ratio for change in disparities decreased compared with base models. After controlling for insurance status, the changes in disparities were still significant, but smaller. 64

82 The results indicate that the disparities were reduced not only because of more health insurance coverage but also because of other policy interventions. I observed the same results for outpatient care utilization. Table 5.6 DID Analysis Results of Formal Care and Outpatient Utilization (Controlling for Insurance Status) Formal Care Outpatient Independent Variable Odds Ratio Robust Std. Err. Odds Ratio Robust Std. Err. disparities with Group UU in period 1 Group RR 0.645*** *** Group RU 0.688*** *** Group UR 0.741** periods period 1 1 n/a 1 n/a period *** *** period *** *** period *** *** change in disparities Group RR in period * Group RR in period Group RR in period Group RU in period Group RU in period * ** Group RU in period * Group UR in period * Group UR in period Group UR in period whether having insurance insurance 1.264*** *** not having insurance 1 n/a 1 n/a Note: 1. Significance level: *** 0.001, ** 0.01, * Results for other independent variables are omitted. 65

83 Table 5.7 Test Results for DID Analysis of Healthcare Utilization (Controlling for Insurance Status) Formal Care Outpatient chi2 Prob>chi chi2 Prob>chi Group RR Change in disparity in period 2 = Change in disparity in period 3 Change in disparity in period 3 = Change in disparity in period ** ** Group RU Change in disparity in period 2 = Change in disparity in period 3 Change in disparity in period 3 = Change in disparity in period *** ** Group UR Change in disparity in period 2 = Change in disparity in period 3 Change in disparity in period 3 = Change in disparity in period 4 Note: 1. Significance level: *** 0.001, ** 0.01, * * *

84 Table 5.8 Multivariate Analysis Results for Inpatient Care Utilization (Controlling for Insurance Status) Independent Variables Coef. Robust Std. Err. disparity with Group UU in 1993 Group RR Group RU Group UR trend in 1990s and change in later waves Group RR trend in 1990s 0.219*** deviation from trend in deviation from trend in ** deviation from trend in ** deviation from trend in ** deviation from trend in *** Group RU trend in 1990s deviation from trend in deviation from trend in deviation from trend in deviation from trend in deviation from trend in Group UR trend in 1990s deviation from trend in deviation from trend in deviation from trend in deviation from trend in deviation from trend in Group UU trend in 1990s deviation from trend in * deviation from trend in deviation from trend in deviation from trend in * deviation from trend in whether having insurance insurance 0.655*** not having insurance 0 n/a constant 5.121*** Note: 1. Significance level: *** 0.001, ** 0.01, * Results for other independent variables are omitted. Multivariate analysis results for inpatient care utilization are shown in Table 5.8, and the corresponding test results are shown in Tables 5.9 and Having insurance coverage had a positive effect on using inpatient care. Similar to the results seen for the base model, disparities in inpatient care utilization for all other groups with Group UU in 67

85 1993 were not significant. Looking at the trends, there was a significant trend in the 1990s only for Group RR. The trend for Group RR was negative, and there was a significant deviation from the trend in later years. For other groups, similar results were observed as those observed in the base model, and the results were generally not significant. After controlling for insurance status, the magnitudes of other coefficients were generally smaller. The results indicate that the change in disparities could partly be explained by insurance status. Test results, shown in Table 5.9, were consistent with the base model. The disparities between Group UU and Group RR were positive in all years, indicating that Group RR was less likely to use inpatient care compared with Group UU. The disparities were significant from years 2000 to For Group RU and UR, the disparities were also positive, but only the disparity between Group RU and UU in 2011 was significant. 68

86 Table 5.9 Test Results of Disparities for Inpatient Care Utilization (Controlling for Insurance Status) Disparity Chi2 Group RR disparity (Group UU probability Group RR probability) ** ** ** * ** Group RU disparity (Group UU probability Group RU probability) * Group UR disparity (Group UU probability Group UR probability) Note: 1. Significance level: *** 0.001, ** 0.01, *

87 Table 5.10 Test Results of Change in Disparities for Inpatient Care Utilization (Controlling for Insurance Status) Change In Disparity Chi2 Group RR compare with disparities with Group UU in * * compare with disparities with Group UU in compare with disparities with Group UU in Group RU compare with disparities with Group UU in compare with disparities with Group UU in compare with disparities with Group UU in Group UR compare with disparities with Group UU in compare with disparities with Group UU in compare with disparities with Group UU in Note: 1. Significance level: *** 0.001, ** 0.01, *

88 Table 5.10 shows test results of change in disparities. After controlling for insurance, the change was still significant for Group RR in 2009 and However, the direction of change was different in This was also true for changes in disparities for other groups. However, the results were not significant for any of the changes in disparities for other groups. After controlling for insurance, some of the changes in disparities were not significant, as seen in the base model. This may be because the change in disparities can partly be explained by insurance status. However, the magnitude of change in disparities was very small. As discussed before, the proportion of residents using inpatient care was very small. Further data collection is needed to reveal the pattern of inpatient care utilization Dropping the Richest Province or the Poorest Province The second set of sensitivity analysis techniques involved dropping one of the provinces from the analysis to check whether the results still held. I dropped the richest province, Jiangsu, in the first set of models, and then dropped the poorest province, Guizhou, in the second set of models. The results for formal care and outpatient utilization are shown in Table 5.11 and Table After the regression, I also performed Wald tests to examine the change between two periods, and the results are shown in Tables 5.12 and As shown in Table 5.11, column 1, for formal care utilization, the results were very similar to the base model after dropping Jiangsu, the richest province. The odds ratios for all three groups were smaller than 1, indicating that there was rural urban disparity in terms of formal care utilization initially in period 1. The change in disparity in period 2 was smaller than 1, and in periods 3 and 4 were greater than 1. This indicates that the 71

89 disparities were larger in period 2 compared with period 1, and in periods 3 and 4, the disparities were smaller. The changes in disparity for Groups RR and UR kept increasing from periods 2 to 4. This trend indicates that the disparities shrank throughout the last three periods. As shown in Table 5.12, column 1, there was significant change in disparities between periods 2 and 3 for Groups RR and RU. The change was associated with the 2003 policy change in rural area. No significant change in disparity was observed between other periods. The difference with the base model was that no significant change in disparity was observed between periods 2 and 3 for Group UR. Although Group UR was also under rural household registration and provided more health insurance coverage between periods 2 and 3, no significant policy effect was observed after dropping the richest province. The observation indicates that the policy was more effective in reducing disparities in formal care utilization in rich provinces. When dropping the richest province, the effect disappeared. The reason I still observed positive effects in Groups RR and RU may come from the other measures affecting rural residents, such as the construction of basic facilities in rural areas. The same results were observed for outpatient care utilization. When dropping Guizhou, the poorest province, exactly the same results and trends were observed as in the base models. 72

90 Table 5.11 DID Analysis Results for Formal Care and Outpatient Utilization (Dropping the Richest Province) Formal care Outpatient Independent Variables Odds Ratio Robust Std. Err. Odds Ratio Robust Std. Err. disparities in period 1 Group UU 1 n/a 1 n/a Group RR 0.624*** *** Group RU 0.655*** *** Group UR 0.706** * periods period1 1 n/a 1 n/a period *** *** period *** *** period *** *** changes in disparities Group RR in period * Group RR in period Group RR in period * Group RU in period Group RU in period * *** Group RU in period ** Group UR in period Group UR in period Group UR in period Note: 1. Significance level: *** 0.001, ** 0.01, * Results for other independent variables are omitted. 73

91 Table 5.12 Test Results for Formal Care and Outpatient Utilization (Dropping the Richest Province) Group RR Formal care Outpatient chi2 Prob>chi chi2 Prob>chi Change in disparity in period 2 = Change in disparity in period ** *** Change in disparity in period 3 = Change in disparity in period Group RU Change in disparity in period 2 = Change in disparity in period *** *** Change in disparity in period 3 = Change in disparity in period Group UR Change in disparity in period 2 = Change in disparity in period Change in disparity in period 3 = Change in disparity in period Note: 1. Significance level: *** 0.001, ** 0.01, *

92 Table 5.13 DID Analysis Results for Formal Care and Outpatient Utilization (Dropping the Poorest Province) Formal care Outpatient Independent Variables Odds Ratio Robust Std. Err. Odds Ratio Robust Std. Err. disparities in period 1 Group UU 1 n/a 1 n/a Group RR 0.649*** *** Group RU 0.727** ** Group UR 0.633*** * periods period1 1 n/a 1 n/a period *** *** period *** *** period *** *** changes in disparities Group RR in period Group RR in period Group RR in period Group RU in period Group RU in period * * Group RU in period Group UR in period * Group UR in period Group UR in period Note: 1. Significance level: *** 0.001, ** 0.01, * Results for other independent variables are omitted. 75

93 Table 5.14 Test Results for Formal Care and Outpatient Utilization (Dropping the Poorest Province) Group RR Formal care Outpatient chi2 Prob>chi chi2 Prob>chi Change in disparity in period 2 = Change in disparity in period * * Change in disparity in period 3 = Change in disparity in period Group RU Change in disparity in period 2 = Change in disparity in period ** ** Change in disparity in period 3 = Change in disparity in period Group UR Change in disparity in period 2 = Change in disparity in period * * Change in disparity in period 3 = Change in disparity in period Note: 1. Significance level: *** 0.001, ** 0.01, * The analysis results for inpatient care utilization are shown in Table 5.15, and the corresponding test results are shown in Tables 5.16 and Table

94 Table 5.15 Multivariate Analysis Results for Inpatient Utilization (Dropping the Richest/Poorest Province) Dropping the Richest Province Dropping the Poorest Province Independent Variables Coef. Robust Std. Err. Coef. Robust Std. Err. disparity with Group UU in 1993 Group RR Group RU 0.998** Group UR trend in 1990s and change in later waves Group RR trend in 1990s 0.230*** * deviation from trend in deviation from trend in ** * deviation from trend in ** * deviation from trend in *** ** deviation from trend in *** ** Group RU trend in 1990s deviation from trend in deviation from trend in deviation from trend in deviation from trend in deviation from trend in Group UR trend in 1990s deviation from trend in deviation from trend in deviation from trend in deviation from trend in deviation from trend in Group UU trend in 1990s 0.224* deviation from trend in * * deviation from trend in * deviation from trend in * deviation from trend in * deviation from trend in * constant 5.360*** *** Note: 1. Significance level: *** 0.001, ** 0.01, * Results for other independent variables are omitted. After dropping the richest province, the results were similar to the base model. The only difference was that the trend in the 1990s became significant for Group UU. The trend 77

95 was positive, and the deviation from trend in later years was negative and also significant. The results indicate that in poorer provinces, efforts in 1990s affected inpatient utilization. However, the impact was not maintained in later years. After dropping the poorest province, the results were the same as those seen in the base model. As shown in Tables 5.16 and 5.17, the levels of disparities and changes in disparities were the same as those in the base model after dropping the richest/poorest provinces. Table 5.16 Test Results of Disparities in Inpatient Utilization (Dropping the Richest/poorest Province) Dropping the Richest Province Dropping the Poorest Province Disparity Chi2 Disparity Chi2 Group RR disparity (Group UU probability Group RR probability) ** *** *** *** ** ** * ** ** Group RU disparity (Group UU probability Group RU probability) * * * * Group UR disparity (Group UU probability Group UR probability) * Note: 1. Significance level: *** 0.001, ** 0.01, *

96 Table 5.17 Test Results of Change in Disparities for Inpatient Care Utilization (Dropping the Richest/poorest Province) Dropping the Richest Province Dropping the Poorest Province Change in disparity Chi2 Change in disparity Chi2 Group RR compare with disparities with Group UU in * ** * ** compare with disparities with Group UU in compare with disparities with Group UU in Group RU compare with disparities with Group UU in compare with disparities with Group UU in compare with disparities with Group UU in Group UR compare with disparities with Group UU in compare with disparities with Group UU in compare with disparities with Group UU in Note: 1. Significance level: *** 0.001, ** 0.01, *

97 5.4.3 Including Interaction Terms with Household Income The third set of sensitivity analysis involved including an interaction term with household income to examine different effects within different income groups. The three different income categories were based on the adjusted household per capita income. The results for formal care and outpatient utilization are shown in Table After the regression, I also performed a set of Wald tests to check the changes in disparities in adjacent periods, and the results for formal care and outpatient utilization are shown in Table As shown in the first column of Table 5.18, all groups experienced disparities compared with Group UU in the first period, except that the disparity was reversed for medium income in Group UR. The reversed disparity was not significant. The changes in disparities generally followed the same trends as in the base models, although there were several exceptions. Disparities increased for all groups in period 2, except for low income families in Group UR. In period 3, the disparities dropped for all groups. In the fourth period, some of the groups experienced an increase in disparities, and some experienced a decrease, but the disparities in this period were smaller compared with period 1 for all groups. From the test results, I observed significant changes from periods 2 to 3 only within the high income families in Groups RR and UR. For Group RU, the changes were significant for the high and low income families. For outpatient care utilization, I observed similar results as for formal care utilization. For inpatient care, similar trends as those seen in the base models were observed, but none of the test results was significant. 80

98 In sum, by including interaction term with household income, I found significant evidence to support the conclusion that the rural urban disparity shrank after the 2003 policy change. However, this reduction in disparity only benefited high income families in terms of formal care utilization and outpatient care utilization. Only in Group RU did lowincome families also receive the benefit. This sensitivity analysis was not conducted for inpatient care because there was only small number of residents using inpatient care during a four week time period, and there were not sufficient observations in each subgroup. 81

99 Table 5.18 DID Analysis Results for Formal Care and Outpatient Utilizations (Including Interaction Term with Household Income) Formal care Outpatient Robust Robust Independent Variables Odds Ratio Std. Err. Odds Ratio Std. Err. disparities in period 1 Group UU medium income 1 n/a 1 n/a Group RR low income 0.569*** *** Group RU low income Group UR low income 0.468*** * Group UU low income * Group RR medium income 0.674*** * Group RU medium income 0.695* ** Group UR medium income Group RR high income 0.645*** * Group RU high income 0.566*** ** Group UR high income 0.700* Group UU high income 1.145* ** periods period1 1 n/a 1 n/a period *** *** period *** *** period *** *** changes in disparities Group RR low income in period Group RR low income in period Group RR low income in period *** ** Group RU low income in period Group RU low income in period ** ** Group RU low income in period Group UR low income in period Group UR low income in period * Group UR low income in period ** * Group RR medium income in period Group RR medium income in period Group RR medium income in period Group RU medium income in period Group RU medium income in period * Group RU medium income in period * Group UR medium income in period Group UR medium income in period Group UR medium income in period Group RR high income in period * * Group RR high income in period * Group RR high income in period Group RU high income in period Group RU high income in period * * Group RU high income in period * * Group UR high income in period Group UR high income in period Group UR high income in period Note: 1. Significance level: *** 0.001, ** 0.01, * Results for other independent variables are omitted. 82

100 Table 5.19 Test Results for Formal Care and Outpatient Utilizations (Including Interaction Term with Household Income) Formal care Outpatient chi2 Prob>chi chi2 Prob>chi Group RR high income group change in disparity in period 2 = Change in disparity in period *** *** change in disparity in period 3 = Change in disparity in period medium income group change in disparity in period 2 = Change in disparity in period change in disparity in period 3 = Change in disparity in period low income group change in disparity in period 2 = Change in disparity in period change in disparity in period 3 = Change in disparity in period * Group RU high income group change in disparity in period 2 = Change in disparity in period * * change in disparity in period 3 = Change in disparity in period medium income group change in disparity in period 2 = Change in disparity in period change in disparity in period 3 = Change in disparity in period low income group change in disparity in period 2 = Change in disparity in period * * change in disparity in period 3 = Change in disparity in period Group UR high income group change in disparity in period 2 = Change in disparity in period * * change in disparity in period 3 = Change in disparity in period medium income group change in disparity in period 2 = Change in disparity in period change in disparity in period 3 = Change in disparity in period low income group change in disparity in period 2 = Change in disparity in period change in disparity in period 3 = Change in disparity in period Note: 1. Significance level: *** 0.001, ** 0.01, *

101 5.4.4 DID Analysis for Inpatient Care The last set of sensitivity analysis involved DID analysis for inpatient care utilization. The results are shown in Table 5.20, and the corresponding test results are shown in Table As shown in Table 5.20, there were disparities for Groups RR, RU, and UR with Group UU. For Groups RR and UR, the disparities were significant, and both of the two groups only used less than half of inpatient care compared with the usage of Group UU in period 1. The disparity did not change significantly in any of the following periods for any of the groups. Table 5.20 DID Analysis Results for Inpatient Care Utilization Independent Variable Odds Ratio Robust Std. Err. disparities in period 1 Group UU 1 n/a Group RR 0.452*** Group RU Group UR 0.438** periods period 1 1 n/a period period period change in disparities Group RR in period Group RR in period Group RR in period Group RU in period Group RU in period Group RU in period Group UR in period Group UR in period Group UR in period Note: 1. Significance level: *** 0.001, ** 0.01, * Results for other independent variables are omitted. From Table 5.21, the change in disparity in period 4 was significantly different from the change in disparity in period 3. This indicates that the disparity was reduced between 84

102 periods 3 and 4 for Group RR. However, the disparity was not significantly different from the original disparity in period 1. There is no evidence to show that more health insurance coverage reduced disparity in inpatient care utilization. Table 5.21 Test Results for Inpatient Care Utilization (DID Analysis) chi2 Prob>chi Group RR Change in disparity in period 2 = Change in disparity in period Change in disparity in period 3 = Change in disparity in period * Group RU Change in disparity in period 2 = Change in disparity in period Change in disparity in period 3 = Change in disparity in period Group UR Change in disparity in period 2 = Change in disparity in period Change in disparity in period 3 = Change in disparity in period Note: 1. Significance level: *** 0.001, ** 0.01, * Summary of Findings 1. Rural urban disparity in formal care utilization and outpatient visit was associated with policy change in health insurance coverage, as well as other related measures. When the government provided more health insurance coverage for residents with rural registration, the disparities in formal care and outpatient utilization decreased for Groups UR and RR. 2. Only for Group RR, the negative trend of using inpatient care was alleviated during later years. However, no evidence shows that disparity in inpatient care utilization was also correlated to health insurance coverage. 3. The 2003 policy change in rural areas among residents with rural household registration reduced rural urban disparities. By providing more health insurance coverage to residents with rural household registration, the policy change reduced the disparity between Groups RR and UR, motivating residents with rural household registration to use more formal healthcare and outpatient 85

103 visits, compared to Group UU. Through other measures enabling resources in rural areas, the policy change also reduced disparities between Groups RU and Group UU. Although Group RU had urban household registration, these members resided in rural areas and benefited from the improved environment. 4. The 2003 policy change in rural areas not only reduced the disparity from the level of the 1990s, but also from the original level. This change happened for rural residents with either rural or urban household registration. 5. After controlling for insurance status, the positive effects could still be observed in all groups. This indicates that the positive effects came not only from more health insurance coverage but also from other related measures. Compared with the base model, the change in disparities shrank most for rural residents with rural household registration. This indicates that the rural residents with rural household registration benefited most from the expanded health insurance coverage. 6. The 2003 policy change affected both poor provinces and rich provinces. However, the expanded health insurance coverage was more effective in richer provinces in reducing disparities in healthcare utilization. The policy effect on poorer provinces was associated more closely with the other measures on changing the environment in rural areas, such as construction of basic medical facilities. 7. The positive impact on formal care and outpatient utilization of policy change in 2003 occurred mainly in high income families. In the medium income group, I 86

104 observed no significant impact. In the low income group, the positive impact was observed only in rural residents with urban household registration. 87

105 Chapter 6 Results: Disparities in healthcare costs 6.1 Descriptive Analysis Figure 6.1 shows the trends of proportion of respondents whose out of pocket (OOP) healthcare cost was more than 20% of the household gross income by rural and urban residences and registrations. From the figure, it can be seen that the percentage of OOP exceeding 20% household income had always been below 5%. Both of the two groups of rural residents had always been less likely to have OOP exceeding 20% of household income compared with Group UU. It seems that rural residents experienced less financial risk than their urban counterparts. However, given the fact that rural residents used less formal care, the low possibility of having high OOP may be due to a lack of formal care or foregone care. Initially, the ratio between Group UU and all other groups was less than 1, indicating that a lower proportion of the three groups had OOP exceeding 20% of household income, compared with Group UU. The ratio for Group RR dropped slightly in period 2, when more health insurance coverage was provided to urban workers in In periods 3 and 4, the ratio increased, and finally grew to more than 1. The ratio for Group RU stayed nearly consistent in period 2, and then increased in period 3. In this period, health insurance did not change for either Group RU or UU. However, more healthcare resources were allocated to rural areas. In period 4, the ratio dropped slightly. In this period, more health insurance and healthcare resources were allocated to urban residents. For Group UR, the ratio dropped in period 2, when more health insurance coverage was provided to Group UU. Subsequently, the ratio increased in periods 3 and 4, when more health insurance or more healthcare resources were allocated to rural areas. The 88

106 observation was contrary to my hypothesis that more health insurance coverage reduces financial risk. Proportion of redients whose OOP medical expense exceeds 20% of the household income Ratio of other study groups to Group UU Group RR Group RU Group UR Group UU Ratio: Group RR/Group UU Ratio: Group RU/Group UU Ratio: Group UR/Group UU Period Figure 6.1 Probability of Having Out-of-pocket Medical Expense Exceeding 20% of Household Income by Rural and Urban Residences and Registrations Similar results can be observed in Figure 6.2, which shows the trends of the proportion of respondents whose out of pocket healthcare cost was more than 40% of the household gross income by rural and urban residents. Again, the two groups of rural residents had always had a lower possibility of having very high OOP (more than 40% of household income) until the last period. The trends of ratio change are consistent with the results shown in Figure 6.1. Again, this result was contrary to my hypothesis that more health insurance coverage reduces financial risk. 89

107 Proportion of redients whose OOP medical expense exceeds 40% of the household income Ratio of other study groups to Group UU Group RR Group RU Group UR Group UU Ratio: Group RR/Group UU Ratio: Group RU/Group UU Ratio: Group UR/Group UU Period Figure 6.2 Probability of Having Out-of-pocket Medical Expense Exceeding 40% Household Income by Rural or Urban Residences and Registrations Figure 6.3 shows the trends of average healthcare cost. All three groups had always spent less on healthcare than Group UU. For Groups RR and RU, the ratio to Group UU decreased in period 2, and then increased in periods 3 and 4. For group UR, the ratio to Group UU decreased in period 2, increased in period 3, and then decreased again in period 4. This indicates that rural residents started to have more medical expenses after the rural policy change in Urban residents with rural registration also began to pay more compared to Group UU after the health insurance expansion in However, their total healthcare cost shrank compared to Group UU when health insurance covered more urban residents in

108 Average medical expense period Figure 6.3 Total Healthcare Costs by Rural and Urban Residences and Registrations Ratio of other study groups to Group UU Group RR Group RU Group UR Group UU Ratio: Group RR/Group UU Ratio: Group RU/Group UU Ratio: Group UR/Group UU 6.2 Multivariate Analysis Controlling for Existing Trends The results of an analysis assuming persistent trends for rural urban disparities in healthcare costs are presented in Tables 6.1 and 6.2. In these models, I used Group UU as the reference group, calculating the initial disparities between Group UU and other groups. The model also controls for the pre existing trends in the 1990s, and analyzes changes in the years after. Using the model, I was able to calculate the odds ratios of trends and actual values in each period for each group. After producing the results, I performed Wald tests to determine if the disparities and changes in disparities were significant. Table 6.1 shows results for the multivariate analysis for OOP exceeding 20%/40% of household income. Column 1 shows results for the indicator of OOP exceeding 20% household income. It seems that the disparities are reversed, since all the rural residents (Groups RR and RU) were less likely to have high OOP exceeding 20% of their household income initially in Group UR was more likely to have high OOP compared with Group 91

109 UU. However, the results were not significant for any of the groups. Group RR showed negative trends in the 1990s; thus, people in this group should be having a decreasing chance of having high OOP if the trend persists. In contrast, trends for the other three groups were positive, meaning an increasing likelihood of having high OOP if the trend persisted. Again, the trend in the 1990s was not significant for Groups RR, RU, and UR. The trend was significant for Group UU. In Groups RR and RU, I observed significant positive deviation from the trends in year 2004, which was right after the NRCM was initiated. The positive deviation continued to be significant for Group RR in the following years. In Group UU, I observed a negative deviation from the trend in 2009, which was right after the initiation of URBMI. Column 2 in Table 6.3 shows results for the indicator of OOP exceeding 40% household income. Similar results are shown in Column 1. All three groups were more likely to have OOP exceeding 40% of their household income compared with Group UU. The disparities were not significant for any of the groups. I observed significant positive trends in the 1990s within Group UU, and trends for other groups were not significant. Significant positive deviations were observed for Groups RR and RU in 2004, reflecting the initiation of NRCM the year before. A significant negative trend was observed in 2009 and 2011 within Group UU, occurring immediately after URBMI was initiated and continuing in the later wave. 92

110 Table 6.1 Multivariate Analysis Results for OOP Exceeding Certain Percentage of Household Income OOP>20% Household Income OOP>40% Household Income Robust Robust Coef. Std. Err. Coef. Std. Err. Disparity with Group UU in 1993 Group RR Group RU Group UR trend in 1990s and change in later waves Group RR trend in 1990s deviation from trend in deviation from trend in ** * deviation from trend in * deviation from trend in * deviation from trend in Group RU trend in 1990s deviation from trend in deviation from trend in * * deviation from trend in deviation from trend in deviation from trend in Group UR trend in 1990s deviation from trend in deviation from trend in deviation from trend in deviation from trend in deviation from trend in Group UU trend in 1990s 0.171*** ** deviation from trend in deviation from trend in deviation from trend in deviation from trend in * * deviation from trend in * * constant 3.709*** *** Note: 1. Significance level: *** 0.001, ** 0.01, * Results for other independent variables are omitted. Table 6.2 shows results for total healthcare costs. Column 1 shows results from the first part examining whether the respondent had any healthcare cost, and Column 2 shows results from the GLM model examining the total healthcare cost for users. From the results, in 1993, it can be seen that all three groups were less likely to have had any healthcare costs, compared to Group UU. This was consistent with what I found in Chapter 5: the three 93

111 groups use less medical care than Group UU. However, the results were not significant. In later waves, the trend for Group RR was positive, and the deviation from trend was still positive starting from This suggests that Group RR was more likely to have had healthcare costs after the second policy change in Trend and deviations for Group RU follow the same pattern, but the deviations from trend were not significant. For Group UR, the trend was positive, and deviations from trend were negative. However, only the deviation in 2000 was significant. Group UU followed the same pattern as Group UR, but the deviations were significant for this group. Looking at the total healthcare cost, users in Groups RR, RU, and UR paid more healthcare cost than users in Group UU in Groups RR, RU, and UR followed negative trends in the 1990s, and the deviations from trend in later years were positive. For Group RU, the deviations were all significant. Group UU had a positive trend, and the deviations were negative but not significant. For Group RR, I observed a significantly increased probability of having healthcare cost immediate after the 2003 policy change, and the effect continued in the following waves. 94

112 Table 6.2 Multivariate Analysis Results for Total Healthcare Costs Having Any Healthcare Cost Total Healthcare Cost Coef. Robust Std. Coef. Robust Std. Disparity with Group UU in 1993 Group RR Group RU Group UR trend in 1990s and change in later waves Group RR trend in 1990s deviation from trend in * deviation from trend in *** deviation from trend in ** deviation from trend in ** deviation from trend in Group RU trend in 1990s 0.086* ** deviation from trend in ** deviation from trend in ** deviation from trend in * deviation from trend in *** deviation from trend in *** Group UR trend in 1990s 0.147*** deviation from trend in ** deviation from trend in deviation from trend in deviation from trend in deviation from trend in Group UU trend in 1990s 0.166*** * deviation from trend in ** deviation from trend in deviation from trend in * deviation from trend in ** deviation from trend in *** constant 2.618*** *** Note: 1. Significance level: *** 0.001, ** 0.01, * Results for other independent variables are omitted. Using the coefficients from the models, I was able to calculate the predicted probability of OOP exceeding 20%/40% for each group in each year. I then used the 95

113 differences in the probabilities between Group UU and other groups as the measure of disparity. The predicted probabilities are shown in Figures 6.4 to 6.6. I also include ratios between other groups with Group UU to show the trend of disparities. Figure 6.4 shows the predicted probabilities of OOP exceeding 20% of household income. Again, I observed the reversed disparity. Group UU was almost always more likely to have a high chance of OOP exceeding 20% of household income, except for waves 1993 and 2009, while Group RR always enjoyed the lowest chance of having high OOP. For Group RR, in the 1990s, the ratio with Group UU decreased. In 2000, after the government provided more health insurance coverage to urban workers, the ratio for Group RR started to increase. In 2004, after the initiation of NRCM, the ratio decreased again. Then in 2006, the ratio once again increased. After the government offered more health insurance coverage for urban residents, the ratio finally decreased in From the trend, it seems that, compared with Group UU, Group RR benefited when health insurance coverage expanded for people with rural registration, but was harmed when more health insurance coverage was provided for urban residents. However, the ratio for Groups RR and RU followed similar trends, although these two groups did not have the same type of household registration. For Group UR, the ratio decreased in the 1990s, started to increase in 2004, and decreased in This indicates that providing more health insurance coverage did not always reduce financial risk, since the ratio increased in 2004 after the initiation of NRCM. For all three groups, the ratio was almost always higher than in 1997, suggesting increased disparities in later years. Instead of gaining financial protection, the three groups were losing the initial advantage. 96

114 Figure 6.5 shows the predicted probabilities of having OOP exceeding 40% of household income. Again, Group UU almost always showed a higher possibility of having extremely high OOP exceeding 40% of their household income, and Group RR always enjoyed the lowest possibility. The trends of ratio change were similar to what I observed in the previous variable, but the slopes were flatter Predicted probability of having OOP exceeding 20% of household income Ratio of other study groups to Group UU Group RR Group RU Group UR Group UU Ratio: Group RR/Group UU Ratio: Group RU/Group UU Ratio: Group UR/Group UU Wave Figure 6.4 Predicted Probability of Having OOP Exceeding 20% of Household Income by Rural and Urban Residences and Registrations 97

115 Predicted probability of having OOP exceeding 40% of household income Ratio of study groups to Group UU Group RR Group RU Group UR Group UU Ratio: Group RR/Group UU Ratio: Group RU/Group UU Ratio: Group UR/Group UU Wave Figure 6.5 Predicted Probability of Having OOP Exceeding 40% of Household Income by Rural and Urban Residences and Registrations Predicted total health cost Wave Figure 6.6 Predicted Total Healthcare Costs by Rural and Urban Residences and Registrations Ratio of study groups to Group UU Group RR Group RU Group UR Group UU Ratio: Group RR/Group UU Ratio: Group RU/Group UU Ratio: Group UR/Group UU Figure 6.6 shows predicted total healthcare costs. Similar to the previous two variables, Group UU almost always had higher healthcare cost. The ratio of Group RR to Group UU decreased until In 2004, the ratio started to increase, and continued to 98

116 increase in the years after. The ratios for the other two groups followed similar trends. Although the slopes differed, I observed a clear increase for all groups in the 2004 wave, after the 2003 policy change in rural areas. The 2003 policy change in rural areas seemed to increase total healthcare cost for all affected groups. I then used the difference between probabilities for Group UU and other groups as an estimate for disparity. After the disparities were calculated, I performed a Wald test to determine whether the disparities were significant. The results for OOP exceeding 20%/40% of household income are shown in Table 6.3. Disparities in both of the two outcomes were greater than 0, indicating that respondents in Group UU were more likely to have OOP exceeding certain percentage of household income. The disparity was reversed in this case. Disparities between Groups RR and UU were significant in 2000, 2004, 2006, and For Group UR, the disparities were significant in 2000, 2004, and For Group UR, the disparity was significant in For OOP exceeding 40% of household income, disparities were also significant in years 2006 and

117 Table 6.3 Test Results of Disparities for OOP Exceeding Certain Percentage of Household Income OOP>20% Household Income OOP>40% Household Income Disparity Chi2 Disparity Chi2 Group RR disparity (Group UU probability Group RR probability) *** *** *** *** *** *** * *** *** Group RU disparity (Group UU probability Group RU probability) * ** * * * Group UR disparity (Group UU probability Group UR probability) * Note: 1. Significance level: *** 0.001, ** 0.01, * I also performed Wald tests to examine whether the changes in disparities were significant, and the results for OOP exceeding 20%/40% of household income are shown in Table 6.4. Years 1997, 2000, and 2006 were the waves before each policy intervention. Therefore, I compared disparities in these three years with disparities in the years after. Column 1 shows test results for OOP exceeding 20% household income. For Group RR and UR, the disparity in 2009 was significantly smaller than disparities in 1997 and For Group RU, the disparities in 2006 and 2009 were significantly smaller than the disparity in Column 2 shows test results for OOP exceeding 40% household income. The disparity was significantly different only between year 1997 and 2006 for Group RU. For Groups RR and RU, disparities in 2011 were significantly reduced from disparities in

118 From the results, no immediate reduction of disparities was observed after each policy intervention, although the disparities were finally reduced in later years. Table 6.4 Test Results of Changes in Disparities for OOP Exceeding Certain Percentage of Household Income OOP>20% Household Income OOP>40% Household Income Group RR compare with disparity with Group UU in ** compare with disparity with Group UU in * compare with disparity with Group UU in Group RU compare with disparity with Group UU in ** * ** compare with disparity with Group UU in compare with disparity with Group UU in Group UR compare with disparity with Group UU in * compare with disparity with Group UU in ** compare with disparity with Group UU in Note: 1. Significance level: *** 0.001, ** 0.01, * Table 6.5 shows estimate of disparities in total health care cost. The results are based on 500 iterations of bootstrap. For Group RR, the confidence intervals for the four 101

119 periods were not overlapped. I can conclude that the changes in disparities between adjacent periods are significant. Similar results were observed for Group RU and UR. Looking at the trends of disparities, for Group RR, the disparities increased during the 1990s, and started to decrease between 2000 and 2004, which was after the policy change in rural areas. The results indicate that the rural related groups paid more total health care costs compared with urban counterparts after the policy change. Between 2006 and 2009, which was after the policy change in urban areas, the disparity between Group RR and Group UU increased, indicating that Group RR experienced more health care costs compared with Group UU. The result is consistent with Chapter 5, where I found that the rural groups use more formal care and outpatient service after the policy change. The increased visit then led to increased total healthcare costs. For Group RR, the disparity in total costs increased when more health insurance coverage was provided to people with rural household registration, and decreased when more health insurance coverage was provided to urban residents. Group UR, which had the same household registration type with Group RR, bore the same trend as Group RR. For Group RU, the trend was also similar, except that the disparity continued to decrease after This group had urban household registration, thus no significant change in disparity in total costs with Group UU after urban groups receive more health insurance coverage. 102

120 Table 6.5 Bootstrap Results for Disparities in Total Health Costs Variable Mean Std. Err. [95% Conf. Interval] Group RR disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in Group RU disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in Group UR disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in Sensitivity Analysis controlling for health insurance status The first set of sensitivity analysis is control for health insurance status. From the results, having insurance has positive effect on having OOP exceeding 20%/40% of household income. For OOP exceeding 20% household income, after controlling for insurance status, the disparity between Group RR and Group UU became positive, indicating that Group RR was more likely to have OOP exceeding 20% of household income. The same happened for Group RU. This indicates that insurance status can explain some of 103

121 the disparities. However, the results were still not significant. The trends and changes in later waves followed the same pattern as the base models. Table 6.6 Multi-variate Analysis Results for OOP Exceeding Certain Percentage of Household Income (Controlling for Insurance) OOP>20% household income OOP>40% household income independent variables Coef. Robust Std. Err. Coef. Robust Std. Err. disparity with Group UU in 1993 Group RR Group RU Group UR trend in 1990s and change in later waves Group RR trend in 1990s deviation from trend in deviation from trend in *** * deviation from trend in ** deviation from trend in * deviation from trend in Group RU trend in 1990s deviation from trend in deviation from trend in * * deviation from trend in deviation from trend in deviation from trend in Group UR trend in 1990s deviation from trend in deviation from trend in deviation from trend in deviation from trend in deviation from trend in Group UU trend in 1990s 0.179*** *** deviation from trend in deviation from trend in deviation from trend in deviation from trend in * * deviation from trend in ** * whether having insurance insurance 0.210*** * not having insurance 0 n/a 0 n/a constant 3.879*** *** Note: 1. Significance level: *** 0.001, ** 0.01, * Results for other independent variables are omitted. 104

122 Table 6.7 Test Results of Disparities for OOP Exceeding Certain Percentage of Household Income (Controlling for Insurance) OOP>20% household income OOP>40% household income Disparity Chi2 Disparity Chi2 Group RR disparity (Group UU probability Group RR probability) *** *** *** *** *** *** * *** *** Group RU disparity (Group UU probability Group RU probability) * * * * Group UR disparity (Group UU probability Group UR probability) * Note: 1. Significance level: *** 0.001, ** 0.01, * Table 6.7 shows predicted disparities and test results for the disparities. The results were very similar to base models. The difference was that the magnitudes of disparities were generally smaller after controlling for insurance. The results suggest that having insurance can explain part of the disparities. However, significant disparities were still observed, which indicates that insurance was not the source for disparities. Table 6.8 shows the results for changes in disparities and the test results. From the results, the magnitudes of changes in disparities were generally smaller than the base model. Some of the changes were not significant any more, such as disparity for Group RU in 2009 compared with disparities in The results indicate that the change in 105

123 disparities can be partly explained by insurance coverage. In some waves, more insurance coverage is crucial for changing the disparities in OOP. Table 6.8 Test Results of Changes in Disparities for OOP Exceeding Certain Percentage of Household Income (Controlling for Insurance) OOP>20% household income OOP>40% household income change in disparity Chi2 change in disparity Chi2 Group RR compare with disparities with Group UU in * compare with disparities with Group UU in compare with disparities with Group UU in Group RU compare with disparities with Group UU in ** * compare with disparities with Group UU in compare with disparities with Group UU in Group UR compare with disparities with Group UU in compare with disparities with Group UU in * compare with disparities with Group UU in Note: 1. Significance level: *** 0.001, ** 0.01, *

124 Table 6.9 Bootstrap Results for Disparities in Total Health Cost (Controlling for Insurance) Variable Mean Std. Err. [95% Conf. Interval] Group RR disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in Group RU disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in Group UR disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in Table 6.9 shows estimate of disparities in total health care cost controlling for insurance coverage. The results are based on 500 iterations of bootstrap. Consistent with base model, the confidence intervals were not overlapped between any of the two adjacent periods, so I can conclude that the changes in disparities between adjacent periods were significant. The trend of changes in disparities is also similar to base model dropping the richest province or the poorest province The second set of sensitivity analysis is dropping the richest province or the poorest province. The results after dropping the richest province are shown in Table Column 1 shows results for OOP exceeding 20% of household income. After dropping the richest province Jiangsu, the results were similar as base model. The difference was that some of 107

125 the deviation from trends was not significant anymore compared with base models, such as deviation for Group RU in 2004, and deviation for Group UU in 2009 and In the OOP exceeding 40% of household income, the difference was more prominent. None of the deviations was significant after dropping the richest province. The results suggest that the deviations from existing trends were more significant in rich provinces. Table 6.11 shows predicted disparities and test results. The magnitude of disparities was generally smaller than in base model, but the disparities were still significant as observed in the base model. The results indicate that the disparities were more significant within rich provinces. Table 6.12 shows results for the changes in disparities. After dropping the richest province, none of the changes in disparities was significant anymore. The results indicate that the changes in disparities are also happened mainly in richer province. Table 6.13 to 6.15 show results after dropping the poorest province. I observe that the results were very similar to base models. Dropping the poorest province did not have significant impact on either the magnitude or significance of results. 108

126 Table 6.10 Multi-variate Analysis Results for OOP Exceeding Certain Percentage of Household Income (Dropping the Richest Province) OOP>20% household income OOP>40% household income independent variables Coef. Robust Std. Err. Coef. Robust Std. Err. disparity with Group UU in 1993 Group RR Group RU Group UR trend in 1990s and change in later waves Group RR trend in 1990s deviation from trend in deviation from trend in ** deviation from trend in * deviation from trend in deviation from trend in Group RU trend in 1990s deviation from trend in deviation from trend in deviation from trend in deviation from trend in deviation from trend in Group UR trend in 1990s deviation from trend in deviation from trend in deviation from trend in deviation from trend in deviation from trend in Group UU trend in 1990s 0.155** * deviation from trend in deviation from trend in deviation from trend in deviation from trend in deviation from trend in constant 4.313*** *** Note: 1. Significance level: *** 0.001, ** 0.01, *

127 Table 6.11 Test Results of Disparities for OOP Exceeding Certain Percentage of Household Income (Dropping the Richest Province) OOP>20% household income OOP>40% household income Disparity Chi2 Disparity Chi2 Group RR disparity (Group UU probability Group RR probability) *** *** *** *** *** *** ** ** *** *** Group RU disparity (Group UU probability Group RU probability) * * ** * Group UR disparity (Group UR probability Group * Note: 1. Significance level: *** 0.001, ** 0.01, *

128 Table 6.12 Test Results of Changes in Disparities for OOP Exceeding Certain Percentage of Household Income (Dropping the Richest Province) OOP>20% household income OOP>40% household income change in disparity Chi2 change in disparity Chi2 Group RR compare with disparities with Group UU in compare with disparities with Group UU in compare with disparities with Group UU in Group RU compare with disparities with Group UU in compare with disparities with Group UU in compare with disparities with Group UU in Group UR compare with disparities with Group UU in compare with disparities with Group UU in compare with disparities with Group UU in Note: 1. Significance level: *** 0.001, ** 0.01, *

129 Table 6.13 Multi-variate Analysis Results for OOP Exceeding Certain Percentage of Household Income (Dropping the Poorest Province) OOP>20% household income OOP>40% household income independent variables Coef. Robust Std. Err. Coef. Robust Std. Err. disparity with Group UU in 1993 Group RR Group RU Group UR trend in 1990s and change in later waves Group RR trend in 1990s deviation from trend in deviation from trend in * deviation from trend in deviation from trend in deviation from trend in Group RU trend in 1990s deviation from trend in deviation from trend in * deviation from trend in deviation from trend in deviation from trend in Group UR trend in 1990s deviation from trend in deviation from trend in deviation from trend in deviation from trend in deviation from trend in Group UU trend in 1990s 0.177*** deviation from trend in deviation from trend in deviation from trend in deviation from trend in * * deviation from trend in * * constant 3.725*** *** Note: 1. Significance level: *** 0.001, ** 0.01, *

130 Table 6.14 Test Results of Disparities for OOP Exceeding Certain Percentage of Household Income (Dropping the Poorest Province) OOP>20% household income OOP>40% household income Disparity Chi2 Disparity Chi2 Group RR disparity (Group UU probability Group RR probability) *** *** *** *** ** *** ** *** *** Group RU disparity (Group UU probability Group RU probability) * ** * ** * Group UR disparity (Group UU probability Group UR probability) * Note: 1. Significance level: *** 0.001, ** 0.01, *

131 Table 6.15 Test Results of Changes in Disparities for OOP Exceeding Certain Percentage of Household Income (Dropping the Poorest Province) OOP>20% household income OOP>40% household income change in disparity Chi2 change in disparity Chi2 Group RR compare with disparities with Group UU in ** compare with disparities with Group UU in compare with disparities with Group UU in Group RU compare with disparities with Group UU in ** * * compare with disparities with Group UU in * compare with disparities with Group UU in * Group UR compare with disparities with Group UU in * compare with disparities with Group UU in ** compare with disparities with Group UU in Note: 1. Significance level: *** 0.001, ** 0.01, *

132 Table 6.16 shows the estimated disparities in total healthcare costs after dropping the richest province. The estimation is based on 500 iterations of bootstrapping. Under this scenario, the 95% confidence intervals were not overlapped as the base model. This indicates that the changes in disparities between adjacent periods were significant, and this was consistent with the base model. Table 6.17 shows bootstrap estimated disparities in total healthcare costs after dropping the richest province. The trend of changes in disparities was consistent with base model. However, the magnitude of disparities was larger than the base model in general. This indicates that the disparities in total health costs were more prominent in rich provinces. Table 6.16 Bootstrap Results for Disparities in Total Health Costs (Dropping the Richest Province) Variable Mean Std. Err. [95% Conf. Interval] Group RR disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in Group RU disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in Group UR disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in

133 Table 6.17 Bootstrap Results for Disparities in Total Health Cost (Dropping the Poorest Province) Variable Mean Std. Err. [95% Conf. Interval] Group RR disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in Group RU disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in Group UR disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in Including interaction terms with household income The next set of sensitivity analysis is to take household income level into consideration. Table 6.18 to 6.26 show results for multi variate analysis for OOP exceeding certain percentage of household income. The model was estimated by a single regression including interaction term between four groups and household income groups and presented separately for low, medium and high income families. Medium income families in Group UU were used as reference group in the analysis. Table 6.18 shows regression results for multi variate analysis for low income families. Presented in Table 6.18 Column 1, in 1993, the low income families within Group RR, RU and UR all had greater probability to have OOP exceeding 20% of household income than their counterparts in Group UU. This was different from the base model. Table

134 shows the estimated disparities and the results from Wald test indicating statistical significance of the disparities. Generally, the disparities were not significant anymore when using 20% as the cut off point, indicating that rural low income families did not have significantly lower possibility to have high OOP costs than urban low income families. Table 6.20 shows results for change in disparities and test results. Different from the base model, the disparities generally increased in later years compared with disparities in 1997, 2000 and However, the results were not significant except for changes in disparities between Group RU and UU after

135 Table 6.18 Multi-variate Analysis Results for OOP Exceeding Certain Percentage of Household Income (Low-income Families) OOP>20% household income OOP>40% household income independent variables Coef. Robust Std. Err. Coef. Robust Std. Err. disparity with Group UU medium income in 1993 Group RR low income 0.798* * Group RU low income * Group UR low income 1.298** ** Group UU low income ** trend in 1990s and change in later waves Group RR low income trend in 1990s deviation from trend in deviation from trend in * deviation from trend in deviation from trend in deviation from trend in Group RU low income trend in 1990s deviation from trend in deviation from trend in deviation from trend in deviation from trend in deviation from trend in Group UR low income trend in 1990s deviation from trend in deviation from trend in * * deviation from trend in deviation from trend in deviation from trend in Group UU low income trend in 1990s deviation from trend in deviation from trend in deviation from trend in deviation from trend in deviation from trend in Note: 1. Significance level: *** 0.001, ** 0.01, *

136 Table 6.19 Test Results of Disparities for OOP Exceeding Certain Percentage of Household Income (Lowincome Families) OOP>20% household income OOP>40% household income Disparity Chi2 Disparity Chi2 Group RR disparity (Group UU probability Group RR probability) * * Group RU disparity (Group UU probability Group RU probability) * * * ** * Group UR disparity (Group UU probability Group UR probability) ** ** * * * Note: 1. Significance level: *** 0.001, ** 0.01, *

137 Table 6.20 Test Results of Changes in Disparities for OOP Exceeding Certain Percentage of Household Income (Low-income Families) OOP>20% household income OOP>40% household income change in disparity Chi2 change in disparity Chi2 Group RR compare with disparities with Group UU in compare with disparities with Group UU in compare with disparities with Group UU in Group RU compare with disparities with Group UU in compare with disparities with Group UU in compare with disparities with Group UU in * * * Group UR compare with disparities with Group UU in compare with disparities with Group UU in compare with disparities with Group UU in Note: 1. Significance level: *** 0.001, ** 0.01, *

138 Table 6.21 shows multi variate regression results for medium income families. Similar to low income families, in 1993, medium income families from Group RR, RU and UR were more likely to have high OOP than medium income families from Group UU when using 20% cut off point. This was different from the base model. Table 6.22 shows the estimated disparities for medium income families. Similar to low income families, most of the disparities were not significant, which was different from the base model. In some of the years, medium income families in Group RR, UR and RU had greater probability than medium income families in Group UU to have high OOP exceeding 20%/40% of their household income. For example, in 1997, 2006 and 2009, Group UR had higher probability to have OOP exceeding 20% of household income than Group UU. Table 6.23 shows the estimated changes in disparities. The direction of change was very similar to the base model. 121

139 Table 6.21 Multi-variate Analysis Results for OOP Exceeding Certain Percentage of Household Income (Medium-income Families) OOP>20% household income OOP>40% household income independent variables Coef. Robust Std. Err. Coef. Robust Std. Err. disparity with Group UU medium income in 1993 Group RR medium income * Group RU medium income 0.917* ** Group UR medium income 1.064* ** trend in 1990s and change in later waves Group RR medium income trend in 1990s deviation from trend in deviation from trend in deviation from trend in deviation from trend in deviation from trend in Group RU medium income trend in 1990s * deviation from trend in * deviation from trend in ** ** deviation from trend in * * deviation from trend in * * deviation from trend in * Group UR medium income trend in 1990s deviation from trend in * deviation from trend in deviation from trend in deviation from trend in deviation from trend in Group UU medium income trend in 1990s 0.294** ** deviation from trend in deviation from trend in deviation from trend in * * deviation from trend in * * deviation from trend in * * Note: 1. Significance level: *** 0.001, ** 0.01, *

140 Table 6.22 Test Results of Disparities for OOP Exceeding Certain Percentage of Household Income (Mediumincome Families) OOP>20% household income OOP>40% household income Disparity Chi2 Disparity Chi2 Group RR disparity (Group UU probability Group RR probability) * *** ** * Group RU disparity (Group UU probability Group RU probability) * * Group UR disparity (Group UU probability Group UR probability) * * * Note: 1. Significance level: *** 0.001, ** 0.01, *

141 Table 6.23 Test Results of Changes in Disparities for OOP Exceeding Certain Percentage of Household Income (Medium-income Families) OOP>20% household income OOP>40% household income change in disparity Chi2 change in disparity Chi2 Group RR compare with disparities with Group UU in * compare with disparities with Group UU in compare with disparities with Group UU in Group RU compare with disparities with Group UU in * * * compare with disparities with Group UU in compare with disparities with Group UU in Group UR compare with disparities with Group UU in * * compare with disparities with Group UU in ** * ** * compare with disparities with Group UU in * Note: 1. Significance level: *** 0.001, ** 0.01, *

142 Table 6.24 shows results for high income families. Table 6.24 shows regression results for multi variate analysis for high income families. The results were similar to the base model. Table 6.25 shows the estimated disparities and the results from Wald test indicating statistical significance of the disparities. Generally, the disparities were not significant anymore when using 20% as the cut off point, indicating that rural high income families did not have significantly lower possibility to have high OOP costs than urban highincome families. Table 6.26 shows results for change in disparities and test results. The trend of changes in disparities was consistent with base model. In sum, in the sensitivity analysis including interaction terms between four groups and income groups, the disparities in high OOP were not significant in low and highincome families. I also find that the changes in disparities were in different direction in low income families, although the changes in disparities were not significant. 125

143 Table 6.24 Multi-variate Analysis Results for OOP Exceeding Certain Percentage of Household Income (High-income Families) OOP>20% household income OOP>40% household income independent variables Coef. Robust Std. Err. Coef. Robust Std. Err. disparity with Group UU medium income in 1993 Group RR high income * Group RU high income Group UR high income Group UU high income trend in 1990s and change in later waves Group RR high income trend in 1990s deviation from trend in deviation from trend in deviation from trend in deviation from trend in deviation from trend in Group RU high income trend in 1990s deviation from trend in deviation from trend in deviation from trend in deviation from trend in deviation from trend in Group UR high income trend in 1990s deviation from trend in deviation from trend in deviation from trend in deviation from trend in deviation from trend in Group UU high income trend in 1990s 0.159* * deviation from trend in deviation from trend in deviation from trend in deviation from trend in * ** deviation from trend in * * Note: 1. Significance level: *** 0.001, ** 0.01, *

144 Table 6.25 Test Results of Disparities for OOP Exceeding Certain Percentage of Household Income (Highincome Families) OOP>20% household income OOP>40% household income Disparity Chi2 Disparity Chi2 Group RR disparity (Group UU probability Group RR probability) Group RU disparity (Group UU probability Group RU probability) Group UR disparity (Group UU probability Group UR probability) Note: 1. Significance level: *** 0.001, ** 0.01, *

145 Table 6.26 Test Results of Changes in Disparities for OOP Exceeding Certain Percentage of Household Income (High-income Families) OOP>20% household income OOP>40% household income change in disparity Chi2 change in disparity Chi2 Group RR compare with disparities with Group UU in compare with disparities with Group UU in compare with disparities with Group UU in Group RU compare with disparities with Group UU in * * compare with disparities with Group UU in * compare with disparities with Group UU in Group UR compare with disparities with Group UU in * compare with disparities with Group UU in compare with disparities with Group UU in Note: 1. Significance level: *** 0.001, ** 0.01, *

146 Table 6.27 to 6.29 show estimated disparities in total health costs for different income groups. The estimate is based on one single two part model, and presented separately for different income groups. Table 6.29 shows results for low income families. The magnitude of disparities was generally smaller within low income families. Most of the disparities were still significant, except for Group RR in 2011 and Group RU in 2009 and Different from the base model, in these later years, the disparities in low income families in Group RR and Group RU were not significant anymore. The trend of changes in disparities in total health costs was the same as the base model. Table 6.28 and 6.29 show bootstrap results for medium and high income families. The results were consistent with the base models. Table 6.27 Bootstrap Results for Disparities in Total Health Costs (Low-income Families) Variable Mean Std. Err. [95% Conf. Interval] Group RR disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in Group RU disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in Group UR disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in

147 Table 6.28 Bootstrap Results for Disparities in Total Health Costs (Medium-income Families) Variable Mean Std. Err. [95% Conf. Interval] Group RR disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in Group RU disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in Group UR disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in Table 6.29 Bootstrap Results for Disparities in Total Health Costs (High-income Families) Variable Mean Std. Err. [95% Conf. Interval] Group RR disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in Group RU disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in Group UR disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in disparity with Group UU in

148 6.3.4 DID analysis results for cost variables The last set of sensitivity analysis is DID analysis for the cost related variables. For OOP exceeding 20%/40% of household income, the results are shown in Table Column 1 shows results for OOP exceeding 20% of household income. The disparities in period 1 were smaller than 1, indicating that all three groups were less likely to have high OOP compared with Group UU. However, the changes in disparities were not significant in the following periods. This was consistent with what I found from the multi variate model. The same pattern was observed for OOP exceeding 40% of household income. The test results for disparity changes between adjacent periods are shown in Table The only significant result was between periods 3 and 4 for Group RR. From Table 6.31, the disparity reduced between periods 3 and 4 for Group RR. The disparity was reversed, so the reduction in disparity means that Group RR was more and more likely to high OOP compared with Group UU. This was consistent with what I found from the base model. The other changes in disparities between adjacent periods were not significant. 131

149 Table 6.30 DID Analysis Results for OOP Exceeding Certain Percentage of Household Income OOP>20% household income OOP>40% household income independent variable Odds Ratio Robust Std. Err. Odds Ratio Robust Std. Err. disparities in period 1 Group UU 1 n/a 1 n/a Group RR 0.616*** ** Group RU 0.651** Group UR periods period 1 1 n/a 1 n/a period ** *** period *** *** period *** *** change in disparities Group RR in period Group RR in period Group RR in period Group RU in period Group RU in period Group RU in period Group UR in period Group UR in period Group UR in period Note: 1. Significance level: *** 0.001, ** 0.01, * Table 6.31 Test Results for OOP Exceeding Certain Percentage of Household Income (DID Analysis) OOP>20% household income OOP>40% household income chi2 Prob>chi chi2 Prob>chi Group RR change in disparity in period 2 = Change in disparity in period change in disparity in period 3 = Change in disparity in period * Group RU change in disparity in period 2 = Change in disparity in period change in disparity in period 3 = Change in disparity in period Group UR change in disparity in period 2 = Change in disparity in period change in disparity in period 3 = Change in disparity in period Note: 1. Significance level: *** 0.001, ** 0.01, * Estimated disparities in total health costs are shown in table The estimates are from the DID analysis and based on 500 iterations of bootstrap. The results showed similar trend of change in disparities as the two part model. The changes in disparities between two adjacent periods were also significant. 132

150 Table 6.32 Bootstrap Results for Disparities in Total Health Costs (DID Analysis) disparity Mean Std. Err. [95% Conf. Interval] Group RR period Group RR period Group RR period Group RR period Group RU period Group RU period Group RU period Group RU period Group UR period Group UR period Group UR period Group UR period Summary of Findings 1. The disparity in having high OOP exceeding 20%/40% of household income was reversed. Rural residents and people with rural registrations were all less likely to have high OOP exceeding a certain percentage of their household income compared with Group UU. The same was true with total healthcare costs. Rural residents experienced lower healthcare costs than did urban residents. 2. Disparities in high OOP cost with Group UU were more significant in Group RR than the other two groups. 3. The disparities in high OOP were significantly reduced in 2009 compared with disparities in There is no evidence showing that more health insurance coverage had an immediate impact on high level of OOP. 133

151 5. Disparities in total health costs were associated with insurance coverage. Providing more health insurance would increase the chance of having any health cost, as well as the average amount of total health costs. 6. Having health insurance coverage could partly explain the disparities and changes in disparities. Providing more insurance coverage actually made people worse off in terms of being more likely to have high OOP expenditures. 7. The disparities and changes in disparities were more significant in rich provinces than in poor provinces. 8. The disparities in high OOP were not significant in low and high income families. The changes in disparities were in different direction in low income families, although the changes in disparities were not significant. In terms of total health costs, the magnitude of disparities was generally smaller within low income families. In later years, the disparities in low income families in Group RR and Group RU were not significant. 134

152 Chapter 7 Conclusion, Discussion, and Policy Implications 7.1 Conclusion Using DID and multivariate analysis and drawing on seven waves of longitudinal data from CHNS, I was able to illustrate the trends of rural urban disparities in healthcare utilization and cost, in conjunction with the major health insurance policy changes. I was also able to examine whether the government s health insurance policy changes affected changes in disparities. From my results, it seems clear that there have always been rural urban disparities in formal care utilization and outpatient visits. Urban residents used formal care and outpatient visits more than did rural residents. Results from DID analysis indicate that the rural urban disparities in formal care utilization and outpatient visit were significantly affected by the policy changes in health insurance coverage. When the government provided more health insurance coverage for residents with rural registration, the disparities in formal care and outpatient utilization decreased for Groups UR and RR. Only for Group RR, the negative trend of using inpatient care was alleviated during later years. However, there was no evidence showing that disparity in inpatient care utilization was also correlated with health insurance coverage. The 2003 policy change in rural areas among residents with rural household registration reduced rural urban disparities. Providing more health insurance coverage to residents with rural household registration reduced the disparity between Groups RR and UR, allowing residents with rural household registration to use more formal healthcare and outpatient visits compared with Group UU. The reform also reduced disparities between Groups RU and Group UU, suggesting that people in Group RU who had urban household 135

153 registration but resided in rural areas, benefited from the improved healthcare environment. The 2003 policy change in rural areas brought the disparity down to the original level in 1990s. This change occurred for both Group RR and UR. After controlling for insurance status, the positive effects could still be observed in the two groups. This finding indicates that the positive effects not only came from more health insurance coverage but also from other related measures that improved the healthcare environment. Compared with the base model, the change in disparities was the largest for Group RR. This indicates that the Group RR benefited most from the expanded health insurance coverage. The policy change in 2003 affected both poor and rich provinces. However, the expanded health insurance coverage was more effective in richer provinces. The policy effect on poorer province was associated more closely with other measures aimed at changing the environment in rural areas, such as construction of basic medical facilities. The positive impact on formal care and outpatient utilization of the 2003 policy change occurred mainly among high income families. In the medium income group, there was no significant impact. In the low income group, the positive impact was observed only in Group UR. The disparity in financial risk was reversed. In 2009, the disparities in high OOP were significantly reduced from the level in However, there was no evidence showing that the 2003 policy change in rural areas affected rural urban disparities in financial risk. The rural urban disparity in total healthcare costs was also reduced. When the government provided more health insurance coverage in urban area, the rural urban 136

154 disparity in healthcare costs increased, and vice versa. This was consistent with the finding for healthcare utilization. More health insurance coverage in rural areas led to a smaller rural urban disparity in healthcare utilization. In order to test the sensitivity of results, I also performed sensitivity analysis by dropping the richest and the poorest provinces from the sample. For both high OOP and total health costs, sensitivity analysis showed that the disparities and changes in disparities were more significant in the rich provinces. I further examined the different impacts for different income groups. The disparities in high OOP were not significant for the low and high income families. In terms of total health costs, the magnitude of the disparities was generally smaller within the low income families. In later years, the disparities in low income families between Group RR and UU or between Group RU and UU were no longer significant. This indicates that the disparities in total health costs finally diminished in low income families. Low income families in Groups RR and RU had similar levels of total health costs to the costs of low income families in Group UU. 7.2 Discussion Comparing With the Published Research My findings agree with previous researchers who claimed there are rural urban disparities in healthcare utilization. My research further shows that the disparities were the most significant within rural residents with rural registration, and the disparity was alleviated after a set of health policy changes. Regarding healthcare costs, my research conclusions agree with those of Wagstaff & Lindelow (2009), who claimed that providing 137

155 more health insurance coverage does not necessarily mean more financial protection. Instead, although not statistically significant, I found the disparity in high OOP was reversed. Rural residents were less likely to have high OOP compared with urban counterparts. Wagstaff & Lindelow (2009) explained this case by noting the balance between better health and higher costs. This could represent a possible explanation of the Chinese case. The insured tend to use more formal healthcare, and their total health costs are also high. However, the benefit coverage from NRCM is limited for outpatient visits, and the reimbursement cap is relatively low. Therefore, the benefit coverage may be enough to encourage the insured to use more formal care but not sufficient to provide enough financial protection. This explanation is also supported by the findings from the analyses for healthcare utilization and total healthcare costs Strengths 1. My research used a new classification of rural and urban. By classifying the respondents into four categories, I was able to obtain a more accurate estimate of the effect from insurance coverage expansion, as well as to examine the impact of the residing environment. 2. My research provided a holistic picture of trends of rural urban disparities in health insurance coverage, healthcare utilization, and healthcare cost in China over 20 years of the rapid reform era, which encompassed three major health insurance policy changes. 3. In my research, I examined the correlation between expansion of insurance coverage and healthcare utilization and healthcare cost, contributing new knowledge to a topic not well studied. 138

156 4. My DID model included three major policy changes in China, providing more thorough evidence on the impact of policy change in health insurance coverage on rural urban disparities in China. 5. I explored the policy effects in different subgroups of the population, providing new evidence to answer the research questions and enabling policy makers to examine policy effects at a deeper and more detailed level Limitations Five limitations should be mentioned. First, there might be an underestimation of the policy effect, since the definition of rural/urban residents and the definition of rural/urban household registration were not consistent. Some of the urban residents held rural household registration, and the same was true for rural residents. Therefore, no matter the definition used, I was not able to provide a precise estimate of the policy effect on rural urban disparities. Second, the three major policy changes focused on public health insurance coverage, and involved providing more coverage to certain groups of people each time. However, during the same time periods, there were other policy changes, which also affected rural and urban residents differently, such as construction of health facilities, training of health workers, and changes in drug policy. Due to the methodology, I could not separate the effect of policy expansion of health insurance coverage. Third, my study did not distinguish the effects between the 2007 insurance expansion for urban residents and the 2009 national health care reform due to a lack of data in

157 Fourth, I did not use a nationally representative sample. Fifth, inpatient care utilization constituted a very low percentage in my sample; thus, I was not able to fully examine the change of disparity in inpatient care utilization. Finally, I studied only healthcare utilization and costs; other related areas such as health outcome and mortality were outside the scope of this project Future Directions Future research should involve the following: 1. Examine the effect of different policy changes other than insurance using more detailed data. 2. Future studies need to differentiate the effects of the 2007 insurance expansion and the 2009 national health care reform. 3. Use a nationally representative sample to estimate the average policy effect in China. 4. Conduct more research on disparities in inpatient care utilization. 5. Study disparities in other healthcare related areas, such as health status and mortality. 7.3 Policy Implications Three important policy implications can be drawn from the results of this study. First, more health insurance and better benefit coverage is needed. As I found from the analysis, the policy changes that provided increased health insurance coverage to rural groups reduced rural urban disparities in healthcare utilization and total healthcare costs. However, current policy has not been able to reduce the rural urban disparity in 140

158 healthcare to the original 1980s level. Disparities still exist in the studied areas. Therefore, policy makers should provide more healthcare coverage and healthcare resources to rural areas to further reduce the disparity. I also found that rural groups were initially less likely to have high OOP, compared to the urban groups. Rural groups also had lower total health costs than did urban groups. When the government provided more health insurance to rural groups, the disparities decreased in high OOP as well as in total healthcare costs. Insurance failed to provide financial protection in this case. This result may indicate that the benefit coverage was not sufficient. Therefore, better benefit coverage should be provided to rural groups. Second, in order to reduce rural urban disparities, policy makers should also consider policy directions other than offering increased health insurance coverage, such as construction of healthcare facilities, health education, and so on. In my analysis, I found that the environment was also important because the environment determined the resources a person received. The policy actions changed the environment and provided more healthcare resources to rural residents. These actions are important policy alternatives in reducing rural urban disparities. Third, disadvantaged groups should receive more attention. In terms of healthcare utilization as well as in total health costs, current policy affects rich provinces more than it affects poor provinces. Therefore, new policy could provide more benefit coverage to rural residents in poor provinces. The positive impact on healthcare utilization of the 2003 policy change occurred mainly in high income and medium income groups. Therefore, new policy changes should focus more on low income groups in rural area. In terms of financial 141

159 protection, high income groups also benefited more than did low income groups. When designing new health insurance policy, policy makers should provide different benefit coverage to different income groups, and low income groups should receive more coverage. As discussed in Chapter 2, the new round of healthcare reform is intended to provide universal coverage to all residents; the focus of the new reform is the disadvantaged population. These actions are all consistent with my research findings. 142

160 Appendix Table A1 Major health insurance schemes Urban Employee Basic Medical Insurance Urban Resident Basic Medical Insurance New Rural Cooperative Medical Insurance Launching Time Insured Population Urban Employee Urban Resident who are not covered by UEBMI Rural Resident Risk Pools County level City level City level Premium Paid By Employer and Employee Government and insured individual Government and insured individual Annual Premium Level (2012) Employer pays 6% of employee's wage, employee pays 2% of the wage At least 300 CNY, in which government pays 240 CHY/ insured At least 300 CNY, in which government pays 240 CHY/ insured Reimbursement Cap (2012) 6 times of local average salary (at least CNY) 6 times of local per capita income (at least CNY) 8 times of local per capita income (at least CNY) Covered Services Inpatient Services Covered Covered Covered Outpatient Services for Catastrophic Illnesses Covered Covered Covered General outpatient services Covered Limited and vary by location Limited and vary by location Number of Insured at 2010 Year end (Million)

161 Table A2 GDP in 2012 of the sampled provinces Province GDP in 2012 (Unit: billion Chinese Yuan) Jiangsu Shandong Henan Liaoning Hubei Hunan Shanghai Beijing Heilongjiang Guangxi Chongqing Guizhou

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164 This product is part of the Pardee RAND Graduate School (PRGS) dissertation series. PRGS dissertations are produced by graduate fellows of the Pardee RAND Graduate School, the world s leading producer of Ph.D.s in policy analysis. The dissertation has been supervised; reviewed; and approved by the faculty committee composed of Hao Yu (Chair), Emmett Keeler, and Gema Zamarro. PARDEE RAND GRADUATE SCHOOL RGSD-345