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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 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.

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Tables ...... v Figures ...... ix Abstract ...... xi Acknowledgements ...... xiii Chapter 1 Introduction ...... 1 Chapter 2 Background ...... 3 2.1 Health insurance reform in China ...... 3 2.1.1 Collapse of health insurance schemes in the 1970s and 1980s ...... 4 2.1.2 Early efforts in the 1980s and early 1990s ...... 5 2.1.3 Health insurance reform since the late 1990s ...... 6 2.1.4 Healthcare reform after 2009 ...... 9 2.2 Three Major Health Insurance Schemes ...... 10 2.2.1 The Basic Medical Insurance for Urban Employees ...... 10 2.2.2 The Basic Medical Insurance for Urban Residents...... 11 2.2.3 The New Rural Cooperative Medical Insurance ...... 13 2.3 Trends and Current Status of Healthcare Disparities ...... 13 Chapter 3 Literature Review and Study Objectives ...... 19 3.1 Existing Research ...... 19 3.1.1 Literature on Rural–Urban Disparities in Healthcare Utilization ...... 19 3.1.2 Literature on Disparities in Out‐of‐Pocket Expenditure and Healthcare Costs ...... 21 3.1.3 Literature on Disparities in Health Insurance Coverage ...... 22 3.1.4 Methodological Issues ...... 22 3.2 Gap in the Existing Literature ...... 26 3.3 Objectives and Research Questions ...... 27 Chapter 4 Study Design ...... 28 4.1 Data ...... 28 4.2 Study Periods ...... 30 4.3 Conceptual Model and Variable Selection ...... 30 4.3.1 Dependent Variables ...... 31 4.3.2 Independent Variables ...... 33 4.4 Analytic Approach ...... 38 4.4.1 Difference‐in‐Differences Analysis with Multiple Groups and Multiple Time Periods ...... 38 4.4.2 Multivariate Regression for the Variables that do not meet the Assumption of Parallel Trends ...... 44 4.5 Sensitivity analysis ...... 46 4.5.1 Controlling for Insurance Status ...... 46 4.5.2 Dropping the Richest Province or the Poorest Province ...... 4 6

iii 4.5.3 Including Interaction Terms with Household Income ...... 4 7 4.5.4 DID Analysis Results for Variables in Which Parallel Trends did not Hold ...... 47 Chapter 5 Results: Disparities in Healthcare Utilization ...... 48 5.1 Descriptive Analysis ...... 48 5.2 DID Analysis for Formal Care Utilization and Outpatient Utilization ...... 51 5.3 Multivariate Analysis Controlling for Existing Trends for Inpatient Utilization ...... 57 5.4 Sensitivity Analysis ...... 64 5.4.1 Controlling for Insurance Status ...... 64 5.4.2 Dropping the Richest Province or the Poorest Province ...... 7 1 5.4.3 Including Interaction Terms with Household Income ...... 8 0 5.4.4 DID Analysis for Inpatient Care ...... 84 5.5 Summary of Findings ...... 85 Chapter 6 Results: Disparities in healthcare costs ...... 88 6.1 Descriptive Analysis ...... 88 6.2 Multivariate Analysis Controlling for Existing Trends ...... 91 6.3 Sensitivity Analysis ...... 103 6.3.1 controlling for health insurance status ...... 103 6.3.2 dropping the richest province or the poorest province...... 107 6.3.3. Including interaction terms with household income ...... 116 6.3.4 DID analysis results for cost variables ...... 131 6.4 Summary of Findings ...... 133 Chapter 7 Conclusion, Discussion, and Policy Implications ...... 135 7.1 Conclusion ...... 135 7.2 Discussion ...... 137 7.2.1 Comparing With the Published Research ...... 137 7.2.2 Strengths ...... 138 7.2.3 Limitations ...... 139 7.2.4 Future Directions ...... 140 7.3 Policy Implications ...... 140 Appendix ...... 143 Reference ...... 145

iv Tables

Table 4.1 Sample Size by Rural and Urban Residences and Registrations ...... 29

Table 4.2 Descriptive Statistics of Independent Variables by Rural and Urban Residences and Registrations ...... 37

Table 4.3 Results of DID Analysis Using 1993 and 1997 Waves for Healthcare Utilization ...... 42

Table 4.4 Results of DID Analysis Using 1993 and 1997 Waves for Healthcare Costs ...... 44

Table 5.1 DID Analysis Results for Formal Care Utilization and Outpatient Utilization ...... 54

Table 5.2 Test Results for DID Analysis of Formal Care Utilization and Outpatient Utilization ...... 55

Table 5.3 Multivariate Analysis Results for Inpatient Care Utilization ...... 59

Table 5.4 Test Results of Disparities for Inpatient Care Utilization ...... 60

Table 5.5 Test Results of Change in Disparities for Inpatient Care Utilization ...... 62

Table 5.6 DID Analysis Results of Formal Care and Outpatient Utilization (Controlling for Insurance Status) ...... 65

Table 5.7 Test Results for DID Analysis of Healthcare Utilization (Controlling for Insurance Status) ...... 66

Table 5.8 Multivariate Analysis Results for Inpatient Care Utilization (Controlling for Insurance Status) ...... 67

Table 5.9 Test Results of Disparities for Inpatient Care Utilization (Controlling for Insurance Status) ...... 69

Table 5.10 Test Results of Change in Disparities for Inpatient Care Utilization (Controlling for Insurance Status) ...... 70

Table 5.11 DID Analysis Results for Formal Care and Outpatient Utilization (Dropping the Richest Province) ...... 73

Table 5.12 Test Results for Formal Care and Outpatient Utilization (Dropping the Richest Province) ...... 74

Table 5.13 DID Analysis Results for Formal Care and Outpatient Utilization (Dropping the Poorest Province) ...... 75

Table 5.14 Test Results for Formal Care and Outpatient Utilization (Dropping the Poorest Province) ...... 76

v Table 5.15 Multivariate Analysis Results for Inpatient Utilization (Dropping the Richest/Poorest Province) ...... 77

Table 5.16 Test Results of Disparities in Inpatient Utilization (Dropping the Richest/poorest Province) ...... 78

Table 5.17 Test Results of Change in Disparities for Inpatient Care Utilization (Dropping the Richest/poorest Province) ...... 79

Table 5.18 DID Analysis Results for Formal Care and Outpatient Utilizations (Including Interaction Term with Household Income) ...... 82

Table 5.19 Test Results for Formal Care and Outpatient Utilizations (Including Interaction Term with Household Income) ...... 83

Table 5.20 DID Analysis Results for Inpatient Care Utilization ...... 84

Table 5.21 Test Results for Inpatient Care Utilization (DID Analysis) ...... 85

Table 6.1 Multivariate Analysis Results for OOP Exceeding Certain Percentage of Household Income ...... 93

Table 6.2 Multivariate Analysis Results for Total Healthcare Costs ...... 95

Table 6.3 Test Results of Disparities for OOP Exceeding Certain Percentage of Household Income ...... 100

Table 6.4 Test Results of Changes in Disparities for OOP Exceeding Certain Percentage of Household Income ...... 101

Table 6.5 Bootstrap Results for Disparities in Total Health Costs ...... 103

Table 6.6 Multi‐variate Analysis Results for OOP Exceeding Certain Percentage of Household Income (Controlling for Insurance) ...... 104

Table 6.7 Test Results of Disparities for OOP Exceeding Certain Percentage of Household Income (Controlling for Insurance) ...... 105

Table 6.8 Test Results of Changes in Disparities for OOP Exceeding Certain Percentage of Household Income (Controlling for Insurance) ...... 106

Table 6.9 Bootstrap Results for Disparities in Total Health Cost (Controlling for Insurance) ...... 107

Table 6.10 Multi‐variate Analysis Results for OOP Exceeding Certain Percentage of Household Income (Dropping the Richest Province) ...... 109

Table 6.11 Test Results of Disparities for OOP Exceeding Certain Percentage of Household Income (Dropping the Richest Province) ...... 110

vi Table 6.12 Test Results of Changes in Disparities for OOP Exceeding Certain Percentage of Household Income (Dropping the Richest Province) ...... 111

Table 6.13 Multi‐variate Analysis Results for OOP Exceeding Certain Percentage of Household Income (Dropping the Poorest Province) ...... 112

Table 6.14 Test Results of Disparities for OOP Exceeding Certain Percentage of Household Income (Dropping the Poorest Province) ...... 113

Table 6.15 Test Results of Changes in Disparities for OOP Exceeding Certain Percentage of Household Income (Dropping the Poorest Province) ...... 114

Table 6.16 Bootstrap Results for Disparities in Total Health Costs (Dropping the Richest Province) ...... 115

Table 6.17 Bootstrap Results for Disparities in Total Health Cost (Dropping the Poorest Province) ...... 116

Table 6.18 Multi‐variate Analysis Results for OOP Exceeding Certain Percentage of Household Income (Low‐income Families) ...... 118

Table 6.19 Test Results of Disparities for OOP Exceeding Certain Percentage of Household Income (Low‐income Families) ...... 119

Table 6.20 Test Results of Changes in Disparities for OOP Exceeding Certain Percentage of Household Income (Low‐income Families) ...... 120

Table 6.21 Multi‐variate Analysis Results for OOP Exceeding Certain Percentage of Household Income (Medium‐income Families) ...... 122

Table 6.22 Test Results of Disparities for OOP Exceeding Certain Percentage of Household Income (Medium‐income Families) ...... 123

Table 6.23 Test Results of Changes in Disparities for OOP Exceeding Certain Percentage of Household Income (Medium‐income Families) ...... 124

Table 6.24 Multi‐variate Analysis Results for OOP Exceeding Certain Percentage of Household Income (High‐income Families) ...... 126

Table 6.25 Test Results of Disparities for OOP Exceeding Certain Percentage of Household Income (High‐income Families) ...... 127

Table 6.26 Test Results of Changes in Disparities for OOP Exceeding Certain Percentage of Household Income (High‐income Families) ...... 128

Table 6.27 Bootstrap Results for Disparities in Total Health Costs (Low‐income Families) ...... 129

Table 6.28 Bootstrap Results for Disparities in Total Health Costs (Medium‐income Families) ...... 130

vii Table 6.29 Bootstrap Results for Disparities in Total Health Costs (High‐income Families) ...... 130

Table 6.30 DID Analysis Results for OOP Exceeding Certain Percentage of Household Income ...... 132

Table 6.31 Test Results for OOP Exceeding Certain Percentage of Household Income (DID Analysis) ...... 132

Table 6.32 Bootstrap Results for Disparities in Total Health Costs (DID Analysis) ...... 133

viii Figures Figure 2.1 Health Insurance Coverage in Urban and Rural Areas in China, Selected Years 1993‐2008 ...... 15

Figure 2.2 Health Service Utilization in Urban and Rural Areas in China (2003) ...... 16

Figure 2.3 Healthcare Spending in China, by Source and Year ...... 17

Figure 2.4 Per Capita Out‐of‐Pocket Health Expenses as a Percentage of Income ...... 18

Figure 4.1 Updated Structure of Anderson Model ...... 31

Figure 5.1 Probability of Formal Care Utilization in 4 Weeks by Rural and Urban Residences and Registrations ...... 48

Figure 5.2 Probability of Outpatient Care Utilization in 4 Weeks by Rural and Urban Residences and Registrations ...... 49

Figure 5.3 Probability of Inpatient Care Utilization in 4 Weeks by Rural and Urban Residences and Registrations ...... 50

Figure 5.4 Predicted Probability of Formal Care Utilization in 4 Weeks by Rural and Urban Residences and Registrations ...... 56

Figure 5.5 Predicted Probability of Outpatient Care Utilization in 4 Weeks by Rural and Urban Residences and Registrations ...... 57

Figure 5.6 Predicted Probability of Inpatient Care Utilization in 4 Weeks by Rural and Urban Residences and Registrations ...... 63

Figure 6.1 Probability of Having Out‐of‐pocket Medical Expense Exceeding 20% of Household Income by Rural and Urban Residences and Registrations ...... 8 9

Figure 6.2 Probability of Having Out‐of‐pocket Medical Expense Exceeding 40% Household Income by Rural or Urban Residences and Registrations ...... 90

Figure 6.3 Total Healthcare Costs by Rural and Urban Residences and Registrations ...... 91

Figure 6.4 Predicted Probability of Having OOP Exceeding 20% of Household Income by Rural and Urban Residences and Registrations ...... 97

Figure 6.5 Predicted Probability of Having OOP Exceeding 40% of Household Income by Rural and Urban Residences and Registrations ...... 98

Figure 6.6 Predicted Total Healthcare Costs by Rural and Urban Residences and Registrations ...... 98

ix

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 (1993‐2011). 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 2003. 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 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 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

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 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 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 universal coverage to all residents, and to target on disadvantage population to improve the healthcare service for them and reduce disparities.

2.1.1 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 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.

2.1.2 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 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.

2.1.3 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

2007. In rural areas, the New Rural Cooperative Medical Insurance (NRCM) was

established in 2003. 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 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 2003. 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 program was launched in 2005. 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 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 2009. 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 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

2009.

2.2 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.

2.2.1 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 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 2003. 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.

2.2.2 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 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 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 2007. 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 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 2003.

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 urban‐ rural 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 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 1993-2008

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

2003. On one hand, the urban‐rural disparity on healthcare utilization decreased from

1993 to 2003. 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 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 2003. 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.

Health Service Utilization in Urban and Rural Areas in China, by Income (2003) 100% 90% 98% 80% 70% 85% 81% 60% pregnancy 50% health care 40% 30% 45% give birth in 20% hospital 10% 0% lowest highest lowest highest percentile percentile percentile percentile Urban Rural Source: China Ministry of Health, The Third National Health Services Survey Report (in Chinese), 2004, http://www.moh.gov.cn/publicfiles///business/cmsresources/mohwsbwstjxxzx/cmsrsdocument/doc9908.pdf (accessed Aug. 28, 2012)

Figure 2.2 Health Service Utilization in Urban and Rural Areas in China (2003)

16

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 2001. 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.

Healthcare Spending in China, by Source and Year 70

60 Individual Patient, 50 38.2

40 Social Programs, 34.6 30

Percentage Government, 27.2 20

10

0 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008

Source: China Ministry of Health, China Health Statistics Yearbook(in Chinese), 2010, http://www.moh.gov.cn/publicfiles/business/htmlfiles/zwgkzt/ptjnj/year2010/index2010.html

Figure 2.3 Healthcare Spending in China, by Source and Year

17 Per Capita Out‐of‐pocket Health Expenses as a

30.0% Percentage of Income

25.0%

20.0%

15.0%

10.0% 1993 5.0% 1998 2003 0.0% lowest middle highest lowest middle highest percentile percentile percentile percentile Urban Rural Source: China Ministry of Health, The Third National Health Services Survey Report (in Chinese), 2004, http://www.moh.gov.cn/publicfiles///business/cmsresources/mohwsbwstjxxzx/cmsrsdocument/doc9908.pdf (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, out‐ of‐pocket payments for healthcare services constituted 26.7% of their total income in 2003, a large increase from the percentage ten years earlier.

18 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.

3.1.1 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 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 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

2008. 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 self‐

discharge 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 high‐

income groups, while low‐income group enjoyed fewer benefits.

3.1.2 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 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.

3.1.3 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 1997. 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 2006. 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.

3.1.4 Methodological Issues

3.1.4.1 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 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.

3.1.4.2 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 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 2003. 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 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‐of‐ pocket expenses. However, unlike Lei & Li (2009), they found that NRCM did significantly increase healthcare utilization.

25 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 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 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 1993. 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 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 Urban Residents Residents

Rural Urban Rural Urban Wave Registration Registration Registration Registration Total

1993 7,663 2,253 1,433 2,470 13,819

1997 7,255 2,492 1,661 2,801 14,209

2000 7,956 2,601 1,563 3,015 15,135

2004 6,016 2,081 1,188 2,858 12,143

2006 5,774 2,059 1,228 2,679 11,740

2009 5,931 2,064 1,241 2,688 11,924

2011 6,489 2,874 1,420 4,717 15,500

Total 47,084 16,424 9,734 21,228 94,470

29 4.2 Study Periods For this analysis, I classified the study period of 1993–2011 into four periods:

1. 1993–1997, a period before the major health insurance expansion in China

2. 2000, a period after the initiation of UEBMI in 1998

3. 2004–2006, a period after the initiation of NRCM in 2003

4. 2009–2011, a period after the initiation of URBMI in 2007

4.3 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 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.

4.3.1 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 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 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 out‐ of‐pocket costs.

4.3.2 Independent Variables

4.3.2.1 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 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 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.

4.3.2.2 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.

4.3.2.3 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 4.3.2.4 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 Table 4.2 Descriptive Statistics of Independent Variables by Rural and Urban Residences and Registrations Group RR Group RU Group UR Group UU N=47,084 N=16,424 N=9,734 N=21,228 gender male 0.496 0.514 0.482 0.485 female 0.504 0.486 0.518 0.515 Ethnicity Han 0.843 0.873 0.826 0.945 Minority 0.156 0.121 0.173 0.049 unreported 0.001 0.006 0.002 0.006 age age equal or below 5 0.063 0.044 0.057 0.035 age between 6 and 17 0.179 0.141 0.177 0.114 age between 18 and 60 0.621 0.632 0.642 0.625 age equal or above 61 0.136 0.183 0.123 0.225 unreported 0.001 0.001 0.002 0.001 marital status married 0.602 0.640 0.602 0.665 never married 0.332 0.281 0.328 0.251 other(divorced, widowed, or separated) 0.059 0.071 0.063 0.076 unreported 0.007 0.008 0.007 0.008 education level primary school 0.623 0.381 0.560 0.329 middle school 0.295 0.293 0.307 0.251 high school 0.074 0.256 0.116 0.287 college and above 0.003 0.063 0.011 0.126 unreported education status 0.005 0.006 0.006 0.007 whether still in school whether still in school 0.141 0.136 0.157 0.118 not in school 0.839 0.847 0.831 0.873 unreported whether in school 0.020 0.017 0.012 0.009 income groups low income group 0.384 0.261 0.314 0.173 medium income group 0.345 0.351 0.325 0.305 high income group 0.270 0.387 0.359 0.517 unreported 0.001 0.001 0.002 0.006 Note: 1. Income was adjusted for inflation to 2011 value 2. Adjusted per‐capita household income was used

37 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.

4.4.1 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 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.

4.4.1.1 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, , ,,

wherepˆ 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 residence/registration,  r is the effect of each different time period, xrt is the interaction

term of residence/registration and time periods, zirt 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   rtrtirt   zx    irtrtrtirt ,

1, 2, 3, 4, , , ,

40 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.

4.4.1.2 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 1993–1997 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 0.560 to 0.789, indicating significant disparities in the year 1993. The DID estimator shows change in disparity in 1997. I observed no significant results in the change of disparity for formal care utilization, indicating similar disparities from 1993 to 1997. 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 1993. 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 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

Robust Robust Robust Odds Ratio Std. Err. Odds Ratio Std. Err. Odds Ratio Std. Err.

Disparity with Group UU in 1993

Group RR 0.674*** 0.080 0.695** 0.095 0.885 0.244

Group RU 0.789 0.113 0.666* 0.117 1.526 0.453

Group UR 0.560*** 0.100 0.591* 0.120 0.525 0.252

Change in disparity in 1997

Group RR 0.866 0.130 0.915 0.155 0.307** 0.116

Group RU 0.740 0.144 0.874 0.199 0.396* 0.165

Group UR 1.376 0.303 1.440 0.353 0.834 0.506 Note: 1. Significance level: *** 0.001, ** 0.01, * 0.05. 2. 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 1997. For the second part, there was significant decrease in disparities for all three groups. The results indicate that the parallel

42 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 1998. 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 Table 4.4 Results of DID Analysis Using 1993 and 1997 Waves for Healthcare Costs OOP>20% OOP>40% Having any Household Income Household Income Healthcare Cost Total Healthcare Cost

Odds Robust Odds Robust Odds Robust Robust Ratio Std. Err. Ratio Std. Err. Ratio Std. Err. Coef. Std. Err.

Disparity with Group UU in 1993

Group RR 1.096 0.217 1.261 0.318 0.880 0.107 95.617 266.246

Group RU 1.110 0.260 1.448 0.410 0.752 0.117 511.350 334.908

Group UR 1.283 0.339 1.370 0.452 0.846 0.144 344.013 371.668

Change in disparity in 1997

Group RR 0.499** 0.120 0.457** 0.135 0.627** 0.094 ‐662.942* 302.294

Group RU 0.505* 0.151 0.411* 0.146 0.753 0.148 ‐1212.681*** 375.975

Group UR 0.589 0.192 0.517 0.209 0.977 0.201 ‐856.702* 424.727 Note: 1. Significance level: *** 0.001, ** 0.01, * 0.05. 2. Results for other independent variables are omitted.

4.4.2 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, , , , wherepˆ 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 interaction between groups and year dummy variables, is the interaction term of

residence/registration and time periods, zirt is the individual specific covariate,  rt is the

unobserved time/group effect, and irt is the individual specific error. Thus,  was the policy effect that I planned to estimate.

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 4.4.1.1. 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 4.5 Sensitivity analysis I performed several sensitivity analyses in addition to the baseline results, which I discuss in the following section.

4.5.1 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‐of‐

pocket 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.

4.5.2 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 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.

4.5.4 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 Chapter 5 Results: Disparities in Healthcare Utilization

5.1 Descriptive Analysis 0.20 1.40

0.18 care 1.20 Group RR UU 0.16 Group RU

medical 1.00 Group 0.14

weeks to

4 Group UR 0.12 using

0.80 past

0.10 groups Group UU

the 0.60 0.08 redients study

Ratio: Group

of RR/Group UU 0.06 0.40 during other

Ratio: Group of

0.04 Ru/Group UU 0.20 0.02 Ratio: Group Ratio Proportion UR/Group UU 0.00 0.00 1234 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 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.

0.20 1.40

care Group RR

0.18 UU 1.20 0.16 Group RU 0.14 1.00 group to

outpatient

weeks

Group UR

4 0.12 0.80 using

groups

past 0.10 Group UU 0.60 the

0.08 study

residents Ratio: Group 0.06 of 0.40 RR/Group UU other during of 0.04 Ratio: Group 0.20 Ru/Group UU

0.02 Ratio Ratio: Group Proportion 0.00 0.00 UR/Group UU 1234 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 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.

0.2 1.4

0.18 UEBMI, URBMI, duing

Group RR

2007 1.2 UU 1998 0.16 NRCM, care 2003 Group RU 0.14 1 group to

inpatient 0.12 0.8 Group UR weeks

groups

4 0.1 using

0.6 Group UU past

study

0.08 the

residents 0.06 Ratio: Group 0.4 other of

of RR/Group UU 0.04 0.2 Ratio: Group 0.02 Ratio RU/Group UU

Proportion 0 0 Ratio: Group 1993 1997 2000 2004 2006 2009 2011 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 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 0.586 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 1998. 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 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 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 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*** 0.046 0.628*** 0.055 Group RU 0.652*** 0.064 0.609*** 0.068 Group UR 0.688*** 0.077 0.783* 0.094 periods period 1 1 n/a 1 n/a period 2 1.368*** 0.126 1.495*** 0.151 period 3 2.359*** 0.175 2.561*** 0.210 period 4 2.055*** 0.153 2.159*** 0.178 change in disparities Group RR in period 2 0.807 0.095 0.801 0.103 Group RR in period 3 1.145 0.106 1.143 0.115 Group RR in period 4 1.248* 0.115 1.234* 0.124 Group RU in period 2 0.872 0.133 0.985 0.167 Group RU in period 3 1.383** 0.160 1.513*** 0.196 Group RU in period 4 1.263* 0.143 1.386** 0.176 Group UR in period 2 0.740 0.138 0.659* 0.132 Group UR in period 3 1.093 0.151 0.974 0.142 Group UR in period 4 1.219 0.166 1.108 0.161 gender male 0.886*** 0.025 0.862*** 0.025 female 1 n/a 1 n/a ethnicity minority 0.788*** 0.044 0.752*** 0.044 Han 1 n/a 1 n/a age age equal or below 5 2.458*** 0.203 2.548*** 0.226 age between 6 and 17 1.036 0.108 1.087 0.123 age between 18 and 60 1 n/a 1.000 n/a age equal or above 61 2.282*** 0.080 2.150*** 0.081 marital status married 1 n/a 1 n/a never married 0.558*** 0.037 0.557*** 0.039 other (divorced, widowed or separated) 0.997 0.047 0.997 0.050 education level primary school 1 n/a 1 n/a middile school 0.730*** 0.027 0.739*** 0.029 high school 0.712*** 0.033 0.726*** 0.035 college or higher 0.727*** 0.050 0.715*** 0.052 whether still in school in school 0.948 0.093 0.983 0.105 not in school 1 n/a 1 n/a adjusted per capita household income low household income 1.039 0.034 1.057 0.037 medium household income 1 n/a 1 n/a high household income 1.026 0.033 1.039 0.035 province Jiangsu 1 n/a 1 n/a Liaoning 0.682*** 0.046 0.671*** 0.047 Heilongjiang 0.444*** 0.033 0.454*** 0.036 Shandong 0.641*** 0.041 0.610*** 0.042 Henan 1.233*** 0.069 1.214*** 0.072 Hubei 0.851** 0.052 0.814** 0.053 Hunan 0.856** 0.050 0.831** 0.052 Guangxi 1.300*** 0.072 1.349*** 0.079 Guizhou 0.759*** 0.051 0.774*** 0.055 Beijing 2.583*** 0.213 2.914*** 0.248 Shanghai 2.749*** 0.212 3.188*** 0.253 Chongqing 1.216* 0.111 1.324** 0.125 Note: 1. Significance level: *** 0.001, ** 0.01, * 0.05.

54 Table 5.2 Test Results for DID Analysis of Formal Care Utilization and Outpatient Utilization Formal Care Outpatient

chi2 Prob>chi chi2 Prob>chi

Group RR

Change in disparity in period 2 = Change in disparity in period 3 8.19** 0.0042 7.85** 0.0051

Change in disparity in period 3 = Change in disparity in period 4 0.07 0.7977 0.09 0.7636

Group RU

Change in disparity in period 2 = Change in disparity in period 3 10.83*** 0.0010 8.25** 0.0041

Change in disparity in period 3 = Change in disparity in period 4 2.09 0.1478 1.56 0.2114

Group UR

Change in disparity in period 2 = Change in disparity in period 3 4.87* 0.0274 4.25* 0.0393

Change in disparity in period 3 = Change in disparity in period 4 0.12 0.7331 0.36 0.5460 Note: 1. Significance level: *** 0.001, ** 0.01, * 0.05.

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 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.

0.20 1.40 0.18

UU Group RR 1.20 medical 0.16

group Group RU using 0.14 1.00 weeks to

4 0.12 Group UR 0.80 past redients

groups 0.10 of

the

Group UU 0.60

0.08 study

during Ratio: Group 0.06 0.40 other RR/Group UU probability

of care 0.04 Ratio: Group 0.20 RU/Group UU

0.02 Ratio Ratio: Group Predicted 0.00 0.00 UR/Group UU 1234 Period

Figure 5.4 Predicted Probability of Formal Care Utilization in 4 Weeks by Rural and Urban Residences and Registrations

56 0.20 1.40

UU

0.18 1.20 Group RR

using 0.16

weeks

group 4 1.00 Group RU 0.14 to

past 0.12 Group UR

residents 0.80 the groups

of 0.10 Group UU 0.60

0.08 study during

Ratio: Group 0.06 0.40 care

RR/Group UU other probability Ratio: Group 0.04 of 0.20 RU/Group UU 0.02 Ratio: Group Ratio Predicted outpatient 0.00 0.00 UR/Group UU 1234 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 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 0.012. 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 2009. 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 Table 5.3 Multivariate Analysis Results for Inpatient Care Utilization Independent Variables Coef. Robust Std. Err. disparity with Group UU in 1993 Group RR ‐0.235 0.261 Group RU 0.319 0.291 Group UR ‐0.688 0.465 trend in 1990s and change in later waves Group RR trend in 1990s ‐0.204** 0.066 deviation from trend in 2000 0.566 0.464 deviation from trend in 2004 1.812** 0.687 deviation from trend in 2006 2.370** 0.809 deviation from trend in 2009 3.328*** 0.996 deviation from trend in 2011 4.159*** 1.121 Group RU trend in 1990s ‐0.131 0.077 deviation from trend in 2000 0.171 0.524 deviation from trend in 2004 0.882 0.774 deviation from trend in 2006 1.554 0.916 deviation from trend in 2009 2.106 1.129 deviation from trend in 2011 2.390 1.284 Group UR trend in 1990s 0.047 0.135 deviation from trend in 2000 ‐1.018 0.905 deviation from trend in 2004 0.143 1.253 deviation from trend in 2006 0.063 1.509 deviation from trend in 2009 0.229 1.895 deviation from trend in 2011 0.180 2.160 Group UU trend in 1990s 0.105 0.066 deviation from trend in 2000 ‐0.756* 0.380 deviation from trend in 2004 ‐0.820 0.599 deviation from trend in 2006 ‐1.125 0.728 deviation from trend in 2009 ‐1.481 0.918 deviation from trend in 2011 ‐1.163 1.041 constant ‐4.586*** 0.274 Note: 1. Significance level: *** 0.001, ** 0.01, * 0.05.

59 Table 5.4 Test Results of Disparities for Inpatient Care Utilization Disparity Chi2 Group RR disparity (Group UU probability‐Group RR probability) 1997 0.0117 0.13 2000 0.0065 13.24*** 2004 0.0088 15.96*** 2006 0.0067 9.50** 2009 0.0037 2.71 2011 0.0076 7.96** Group RU disparity (Group UU probability‐Group RU probability) 1997 0.0070 0.44 2000 0.0033 1.48 2004 0.0061 3.71 2006 0.0008 0.07 2009 ‐0.0017 0.28 2011 0.0063 3.37 Group UR disparity (Group UU probability‐Group UR probability) 1997 0.0091 2.39 2000 0.0073 4.89* 2004 0.0042 0.99 2006 0.0028 0.50 2009 0.0094 0.10 2011 0.0169 2.05 Note: 1. Significance level: *** 0.001, ** 0.01, * 0.05.

Table 5.5 shows test results for the change in disparities. The major health insurance policy changes occurred in 1998, 2003, and 2007. 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 1997. 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 1997. The changes were significant for 2009 and 2011. The disparity was reduced by 0.8% in 2009 and by 0.4% in 2011. Compared to the disparity with Group UU in 2000, the disparity was larger in 2004 and 2011 and smaller in 2009. However, none of the changes was significant. Compared with disparity in 2006, the disparity was smaller in 2009 and larger in 2011. 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 2006.

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 2011. However, no evidence shows that it was due to the policy change in 2000.

61 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 1997 2000 ‐0.0052 0.96 2004 ‐0.0029 1.58 2006 ‐0.0050 3.75 2009 ‐0.0080 9.61** 2011 ‐0.0041 9.64** compare with disparities with Group UU in 2000 2004 0.0023 0.04 2006 0.0002 0.71 2009 ‐0.0027 3.74 2011 0.0012 3.24 compare with disparities with Group UU in 2006 2009 ‐0.0030 1.45 2011 0.0009 1.01 Group RU compare with disparities with Group UU in 1997 2000 ‐0.0038 0.24 2004 ‐0.0010 0.01 2006 ‐0.0062 2.02 2009 ‐0.0087 3.92 2011 ‐0.0007 0.49 compare with disparities with Group UU in 2000 2004 0.0028 0.14 2006 ‐0.0024 0.64 2009 ‐0.0050 1.65 2011 0.0031 0.01 compare with disparities with Group UU in 2006 2009 ‐0.0025 0.30 2011 0.0055 0.86 Group UR compare with disparities with Group UU in 1997 2000 ‐0.0018 0.36 2004 ‐0.0049 1.10 2006 ‐0.0063 1.60 2009 0.0003 4.14* 2011 0.0077 1.26 compare with disparities with Group UU in 2000 2004 ‐0.0031 1.97 2006 ‐0.0045 2.46 2009 0.0021 4.51* 2011 0.0095 2.12 compare with disparities with Group UU in 2006 2009 0.0066 0.59 2011 0.0140 0.10 Note: 1. Significance level: *** 0.001, ** 0.01, * 0.05.

62 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 2011.

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.

0.025 1.4

UEBMI URBMI Launch, 1998

Launch, 2007 UU 1.2 Group RR

using NRCM 0.02 weeks

Launch, 4

2003 1 group Group RU to

past

residents 0.015 of the 0.8

Group UR groups

Group UU during 0.6

study

0.01 probability care

Ratio: Group

0.4 other

of RR/Group UU 0.005 0.2 Ratio: Group inpatient Ratio

Prediceted RU/Group UU

0 0 Ratio: Group 1993 1997 2000 2004 2006 2009 2011 UR/Group UU Wave

Figure 5.6 Predicted Probability of Inpatient Care Utilization in 4 Weeks by Rural and Urban Residences and Registrations

63 5.4 Sensitivity Analysis

5.4.1 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 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 Robust Robust Independent Variable Odds Ratio Std. Err. Odds Ratio Std. Err. disparities with Group UU in period 1 Group RR 0.645*** 0.051 0.680*** 0.060 Group RU 0.688*** 0.068 0.635*** 0.071 Group UR 0.741** 0.083 0.829 0.100 periods period 1 1 n/a 1 n/a period 2 1.401*** 0.129 1.524*** 0.154 period 3 2.411*** 0.179 2.605*** 0.213 period 4 1.940*** 0.146 2.059*** 0.171 change in disparities Group RR in period 2 0.792* 0.093 0.789 0.101 Group RR in period 3 1.071 0.100 1.083 0.110 Group RR in period 4 1.091 0.102 1.108 0.113 Group RU in period 2 0.864 0.132 0.978 0.165 Group RU in period 3 1.342* 0.155 1.476** 0.191 Group RU in period 4 1.185 0.135 1.317* 0.167 Group UR in period 2 0.730 0.136 0.652* 0.130 Group UR in period 3 1.063 0.147 0.953 0.139 Group UR in period 4 1.103 0.150 1.023 0.149 whether having insurance insurance 1.264*** 0.043 1.208*** 0.044 not having insurance 1 n/a 1 n/a Note: 1. Significance level: *** 0.001, ** 0.01, * 0.05. 2. Results for other independent variables are omitted.

65 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 8.19** 0.0042 7.85** 0.0051 disparity in period 3 Change in disparity in period 3 = Change in 0.07 0.7977 0.09 0.7636 disparity in period 4

Group RU

Change in disparity in period 2 = Change in 10.83*** 0.0010 8.25** 0.0041 disparity in period 3 Change in disparity in period 3 = Change in 2.09 0.1478 1.56 0.2114 disparity in period 4

Group UR

Change in disparity in period 2 = Change in 4.87* 0.0274 4.25* 0.0393 disparity in period 3 Change in disparity in period 3 = Change in 0.12 0.7331 0.36 0.5460 disparity in period 4 Note: 1. Significance level: *** 0.001, ** 0.01, * 0.05.

66 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 0.138 0.272 Group RU 0.472 0.292 Group UR ‐0.418 0.468 trend in 1990s and change in later waves Group RR trend in 1990s ‐0.219*** 0.066 deviation from trend in 2000 0.657 0.464 deviation from trend in 2004 1.921** 0.687 deviation from trend in 2006 2.269** 0.808 deviation from trend in 2009 3.000** 0.994 deviation from trend in 2011 3.850*** 1.119 Group RU trend in 1990s ‐0.112 0.077 deviation from trend in 2000 0.116 0.524 deviation from trend in 2004 0.713 0.774 deviation from trend in 2006 1.281 0.918 deviation from trend in 2009 1.539 1.133 deviation from trend in 2011 1.750 1.289 Group UR trend in 1990s 0.054 0.135 deviation from trend in 2000 ‐1.014 0.905 deviation from trend in 2004 0.106 1.254 deviation from trend in 2006 ‐0.130 1.510 deviation from trend in 2009 ‐0.307 1.898 deviation from trend in 2011 ‐0.381 2.161 Group UU trend in 1990s 0.125 0.066 deviation from trend in 2000 ‐0.792* 0.381 deviation from trend in 2004 ‐0.922 0.600 deviation from trend in 2006 ‐1.316 0.730 deviation from trend in 2009 ‐1.879* 0.920 deviation from trend in 2011 ‐1.634 1.044 whether having insurance insurance 0.655*** 0.107 not having insurance 0 n/a constant ‐5.121*** 0.288 Note: 1. Significance level: *** 0.001, ** 0.01, * 0.05. 2. 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 5.10. 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 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 2011. For Group RU and UR, the disparities were also positive, but only the disparity between Group RU and UU in 2011 was significant.

68 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) 1997 0.0069 0.26 2000 0.0240 7.37** 2004 0.0064 9.99** 2006 0.0081 9.16** 2009 0.0075 4.57* 2011 0.0023 11.29** Group RU disparity (Group UU probability‐Group RU probability) 1997 0.0037 0.91 2000 0.0220 0.67 2004 0.0049 2.76 2006 0.0039 0.00 2009 0.0043 0.17 2011 0.0012 3.93* Group UR disparity (Group UU probability‐Group UR probability) 1997 0.0049 1.46 2000 0.0248 3.44 2004 0.0027 0.23 2006 0.0050 0.20 2009 0.0049 0.00 2011 0.0016 2.77 Note: 1. Significance level: *** 0.001, ** 0.01, * 0.05.

69 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 1997 2000 0.0171 1.07 2004 ‐0.0005 1.37 2006 0.0012 1.75 2009 0.0006 4.36* 2011 ‐0.0046 4.25* compare with disparities with Group UU in 2000 2004 ‐0.0176 0 2006 ‐0.0159 0.04 2009 ‐0.0165 0.81 2011 ‐0.0217 0.55 compare with disparities with Group UU in 2006 2009 ‐0.0005 0.67 2011 ‐0.0057 0.41 Group RU compare with disparities with Group UU in 1997 2000 0.0183 0.20 2004 0.0012 0.00 2006 0.0003 1.42 2009 0.0006 2.26 2011 ‐0.0024 0.04 compare with disparities with Group UU in 2000 2004 ‐0.0171 0.24 2006 ‐0.0181 0.40 2009 ‐0.0177 0.81 2011 ‐0.0207 0.11 compare with disparities with Group UU in 2006 2009 0.0004 0.09 2011 ‐0.0027 1.45 Group UR compare with disparities with Group UU in 1997 2000 0.0199 0.36 2004 ‐0.0021 0.98 2006 0.0001 1.05 2009 0.0000 2.00 2011 ‐0.0033 0.28 compare with disparities with Group UU in 2000 2004 ‐0.0220 1.86 2006 ‐0.0198 1.93 2009 ‐0.0199 2.80 2011 ‐0.0232 1.06 compare with disparities with Group UU in 2006 2009 ‐0.0001 0.14 2011 ‐0.0035 0.47 Note: 1. Significance level: *** 0.001, ** 0.01, * 0.05.

70 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 2011. However, the direction of

change was different in 2009. 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.

5.4.2 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 5.13. 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

5.14.

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 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 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*** 0.053 0.614*** 0.057

Group RU 0.655*** 0.071 0.556*** 0.068

Group UR 0.706** 0.086 0.764* 0.097

periods

period1 1 n/a 1 n/a

period2 1.481*** 0.150 1.510*** 0.166

period3 2.533*** 0.206 2.491*** 0.220

period4 2.186*** 0.179 2.078*** 0.184

changes in disparities

Group RR in period 2 0.735* 0.094 0.778 0.107

Group RR in period 3 1.060 0.106 1.166 0.126

Group RR in period 4 1.136 0.113 1.242* 0.133

Group RU in period 2 0.777 0.132 0.934 0.175

Group RU in period 3 1.329* 0.169 1.606*** 0.227

Group RU in period 4 1.191 0.147 1.467** 0.201

Group UR in period 2 0.737 0.145 0.676 0.141

Group UR in period 3 1.017 0.150 0.963 0.149

Group UR in period 4 1.160 0.169 1.130 0.173 Note: 1. Significance level: *** 0.001, ** 0.01, * 0.05. 2. Results for other independent variables are omitted.

73 Table 5.12 Test Results for Formal Care and Outpatient Utilization (Dropping the Richest Province) Formal care Outpatient

chi2 Prob>chi chi2 Prob>chi

Group RR

Change in disparity in period 2 = Change in disparity in period 3 10.35** 0.0013 10.97*** 0.0009

Change in disparity in period 3 = Change in disparity in period 4 0.81 0.3676 0.60 0.4392

Group RU

Change in disparity in period 2 = Change in disparity in period 3 12.97*** 0.0003 11.39*** 0.0007

Change in disparity in period 3 = Change in disparity in period 4 1.40 0.2363 0.83 0.3614

Group UR

Change in disparity in period 2 = Change in disparity in period 3 3.31 0.0690 3.39 0.0657

Change in disparity in period 3 = Change in disparity in period 4 1.34 0.2475 1.69 0.1938 Note: 1. Significance level: *** 0.001, ** 0.01, * 0.05.

74 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*** 0.054 0.734*** 0.069

Group RU 0.727** 0.076 0.718** 0.085

Group UR 0.633*** 0.081 0.723* 0.100

periods

period1 1 n/a 1 n/a

period2 1.417*** 0.137 1.584*** 0.171

period3 2.586*** 0.202 2.908*** 0.255

period4 2.273*** 0.179 2.486*** 0.219

changes in disparities

Group RR in period 2 0.796 0.098 0.770 0.104

Group RR in period 3 1.043 0.102 0.991 0.106

Group RR in period 4 1.112 0.108 1.050 0.112

Group RU in period 2 0.810 0.130 0.900 0.160

Group RU in period 3 1.273* 0.155 1.334* 0.181

Group RU in period 4 1.090 0.130 1.143 0.153

Group UR in period 2 0.708 0.150 0.596* 0.139

Group UR in period 3 1.130 0.174 0.987 0.162

Group UR in period 4 1.316 0.200 1.191 0.195 Note: 1. Significance level: *** 0.001, ** 0.01, * 0.05. 2. Results for other independent variables are omitted.

75 Table 5.14 Test Results for Formal Care and Outpatient Utilization (Dropping the Poorest Province) Formal care Outpatient

chi2 Prob>chi chi2 Prob>chi

Group RR

Change in disparity in period 2 = Change in disparity in period 3 6.04* 0.0140 4.55* 0.0330

Change in disparity in period 3 = Change in disparity in period 4 0.76 0.3830 0.54 0.4616

Group RU

Change in disparity in period 2 = Change in disparity in period 3 10.32** 0.0013 6.85** 0.0088

Change in disparity in period 3 = Change in disparity in period 4 3.05 0.0809 2.67 0.1021

Group UR

Change in disparity in period 2 = Change in disparity in period 3 5.91* 0.0151 5.57* 0.0183

Change in disparity in period 3 = Change in disparity in period 4 1.67 0.1959 2.19 0.1387 Note: 1. Significance level: *** 0.001, ** 0.01, * 0.05.

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 5.17.

76 Table 5.15 Multivariate Analysis Results for Inpatient Utilization (Dropping the Richest/Poorest Province) Dropping the Richest Province Dropping the Poorest Province Robust Robust Independent Variables Coef. Std. Err. Coef. Std. Err. disparity with Group UU in 1993 Group RR 0.452 0.339 ‐0.384 0.276 Group RU 0.998** 0.364 0.288 0.303 Group UR ‐0.634 0.654 ‐0.711 0.503 trend in 1990s and change in later waves Group RR trend in 1990s ‐0.230*** 0.070 ‐0.168* 0.071 deviation from trend in 2000 0.736 0.496 0.397 0.492 deviation from trend in 2004 2.016** 0.739 1.615* 0.727 deviation from trend in 2006 2.643** 0.869 1.979* 0.860 deviation from trend in 2009 3.673*** 1.070 2.833** 1.060 deviation from trend in 2011 4.528*** 1.204 3.566** 1.197 Group RU trend in 1990s ‐0.131 0.079 ‐0.136 0.082 deviation from trend in 2000 0.185 0.542 ‐0.037 0.575 deviation from trend in 2004 0.757 0.801 0.899 0.823 deviation from trend in 2006 1.503 0.957 1.634 0.974 deviation from trend in 2009 1.974 1.165 2.129 1.201 deviation from trend in 2011 2.261 1.326 2.374 1.367 Group UR trend in 1990s 0.206 0.170 0.064 0.147 deviation from trend in 2000 ‐1.500 0.957 ‐1.325 1.030 deviation from trend in 2004 ‐1.120 1.449 ‐0.027 1.356 deviation from trend in 2006 ‐1.475 1.773 ‐0.137 1.632 deviation from trend in 2009 ‐1.740 2.261 ‐0.129 2.052 deviation from trend in 2011 ‐2.146 2.596 ‐0.079 2.336 Group UU trend in 1990s 0.224* 0.088 0.111 0.069 deviation from trend in 2000 ‐1.055* 0.461 ‐0.830* 0.394 deviation from trend in 2004 ‐1.506* 0.758 ‐0.852 0.621 deviation from trend in 2006 ‐1.994* 0.928 ‐1.258 0.757 deviation from trend in 2009 ‐2.608* 1.183 ‐1.606 0.954 deviation from trend in 2011 ‐2.654* 1.350 ‐1.292 1.083 constant ‐5.360*** 0.333 ‐4.552*** 0.281 Note: 1. Significance level: *** 0.001, ** 0.01, * 0.05. 2. 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 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) 1997 0.0085 0.82 0.0125 0.38 2000 0.0047 8.62** 0.0066 12.41*** 2004 0.0077 14.34*** 0.0093 14.97*** 2006 0.0065 10.02** 0.0066 8.86** 2009 0.0051 5.23* 0.0040 2.99 2011 0.0076 9.20** 0.0086 9.54** Group RU disparity (Group UU probability‐Group RU probability) 1997 0.0039 2.61 0.0081 0.04 2000 0.0017 0.49 0.0047 2.92 2004 0.0057 3.86* 0.0073 4.66* 2006 0.0014 0.19 0.0004 0.01 2009 0.0012 0.15 ‐0.0009 0.07 2011 0.0069 4.80* 0.0080 5.07* Group UR disparity (Group UU probability‐Group UR probability) 1997 0.0057 1.83 0.0095 2.45 2000 0.0054 3.72 0.0077 4.30* 2004 0.0043 1.32 0.0049 1.02 2006 0.0034 0.85 0.0022 0.26 2009 0.0006 0.02 ‐0.0002 0.00 2011 0.0065 2.51 0.0059 1.42 Note: 1. Significance level: *** 0.001, ** 0.01, * 0.05.

78 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 1997 2000 ‐0.0037 0.93 ‐0.0059 0.85 2004 ‐0.0007 0.70 ‐0.0032 1.81 2006 ‐0.0020 1.86 ‐0.0058 3.52 2009 ‐0.0034 4.61* ‐0.0084 8.71** 2011 ‐0.0009 5.40* ‐0.0039 8.03** compare with disparities with Group UU in 2000 2004 0.0030 0.04 0.0027 0.10 2006 0.0017 0.09 0.0001 0.70 2009 0.0003 1.07 ‐0.0025 3.33 2011 0.0029 1.21 0.0020 2.53 compare with disparities with Group UU in 2006 2009 ‐0.0014 0.69 ‐0.0026 1.18 2011 0.0011 0.85 0.0019 0.59 Group RU compare with disparities with Group UU in 1997 2000 ‐0.0022 0.13 ‐0.0034 0.01 2004 0.0018 0.25 ‐0.0008 0.00 2006 ‐0.0025 0.49 ‐0.0077 2.67 2009 ‐0.0027 0.60 ‐0.0090 3.69 2011 0.0030 0.02 ‐0.0001 0.30 compare with disparities with Group UU in 2000 2004 0.0040 0.68 0.0026 0.00 2006 ‐0.0004 0.08 ‐0.0043 1.72 2009 ‐0.0005 0.10 ‐0.0056 2.41 2011 0.0052 0.31 0.0033 0.13 compare with disparities with Group UU in 2006 2009 ‐0.0001 0.00 ‐0.0013 0.07 2011 0.0056 1.03 0.0076 1.77 Group UR compare with disparities with Group UU in 1997 2000 ‐0.0003 0.45 ‐0.0018 0.56 2004 ‐0.0014 0.22 ‐0.0046 0.75 2006 ‐0.0024 0.43 ‐0.0073 1.53 2009 ‐0.0052 1.65 ‐0.0097 2.87 2011 0.0008 0.28 ‐0.0036 1.24 compare with disparities with Group UU in 2000 2004 ‐0.0011 1.10 ‐0.0029 1.84 2006 ‐0.0021 1.42 ‐0.0055 2.59 2009 ‐0.0049 2.76 ‐0.0080 3.65 2011 0.0010 1.25 ‐0.0019 2.30 compare with disparities with Group UU in 2006 2009 ‐0.0028 0.40 ‐0.0024 0.19 2011 0.0031 0.05 0.0037 0.09 Note: 1. Significance level: *** 0.001, ** 0.01, * 0.05.

79 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 5.18. 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 5.19.

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 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 low‐

income 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 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*** 0.059 0.627*** 0.073 Group RU low income 0.812 0.114 0.822 0.131 Group UR low income 0.468*** 0.100 0.578* 0.131 Group UU low income 1.102 0.078 1.168* 0.090 Group RR medium income 0.674*** 0.072 0.763* 0.089 Group RU medium income 0.695* 0.103 0.637** 0.112 Group UR medium income 1.088 0.174 1.335 0.224 Group RR high income 0.645*** 0.074 0.731* 0.092 Group RU high income 0.566*** 0.096 0.571** 0.108 Group UR high income 0.700* 0.115 0.777 0.138 Group UU high income 1.145* 0.064 1.195** 0.071 periods period1 1 n/a 1 n/a period2 1.340*** 0.123 1.476*** 0.149 period3 2.313*** 0.170 2.525*** 0.206 period4 2.004*** 0.149 2.120*** 0.174 changes in disparities Group RR low income in period 2 0.988 0.144 0.960 0.154 Group RR low income in period 3 1.224 0.142 1.243 0.158 Group RR low income in period 4 1.456*** 0.169 1.440** 0.184 Group RU low income in period 2 0.904 0.197 1.030 0.246 Group RU low income in period 3 1.527** 0.246 1.594** 0.285 Group RU low income in period 4 1.251 0.209 1.372 0.255 Group UR low income in period 2 1.203 0.374 0.891 0.310 Group UR low income in period 3 1.687* 0.417 1.475 0.382 Group UR low income in period 4 2.099** 0.501 1.886* 0.477 Group RR medium income in period 2 0.773 0.123 0.773 0.131 Group RR medium income in period 3 1.020 0.123 0.987 0.129 Group RR medium income in period 4 1.177 0.140 1.144 0.147 Group RU medium income in period 2 0.903 0.212 1.099 0.283 Group RU medium income in period 3 1.311 0.225 1.510* 0.300 Group RU medium income in period 4 1.318 0.218 1.521* 0.292 Group UR medium income in period 2 0.644 0.172 0.610 0.169 Group UR medium income in period 3 0.878 0.178 0.675 0.146 Group UR medium income in period 4 0.784 0.153 0.681 0.141 Group RR high income in period 2 0.693* 0.123 0.685* 0.130 Group RR high income in period 3 1.348* 0.177 1.318 0.186 Group RR high income in period 4 1.208 0.158 1.177 0.167 Group RU high income in period 2 0.928 0.227 0.919 0.250 Group RU high income in period 3 1.534* 0.289 1.618* 0.337 Group RU high income in period 4 1.473* 0.270 1.502* 0.306 Group UR high income in period 2 0.561 0.188 0.514 0.186 Group UR high income in period 3 1.056 0.216 1.086 0.234 Group UR high income in period 4 1.279 0.262 1.161 0.257 Note: 1. Significance level: *** 0.001, ** 0.01, * 0.05. 2. Results for other independent variables are omitted.

82 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 3 16.99*** 0.0000 14.78*** 0.0001 change in disparity in period 3 = Change in disparity in period 4 1.13 0.2876 1.07 0.3012 medium income group change in disparity in period 2 = Change in disparity in period 3 3.65 0.0560 2.57 0.1091 change in disparity in period 3 = Change in disparity in period 4 2.39 0.1224 2.30 0.1290 low income group change in disparity in period 2 = Change in disparity in period 3 2.67 0.1022 3.36 0.0668 change in disparity in period 3 = Change in disparity in period 4 3.84* 0.0500 2.42 0.1197 Group RU high income group change in disparity in period 2 = Change in disparity in period 3 5.96* 0.0146 6.24* 0.0125 change in disparity in period 3 = Change in disparity in period 4 0.12 0.7345 0.33 0.5630 medium income group change in disparity in period 2 = Change in disparity in period 3 3.16 0.0755 2.08 0.1488 change in disparity in period 3 = Change in disparity in period 4 0.00 0.9662 0.00 0.9589 low income group change in disparity in period 2 = Change in disparity in period 3 7.06* 0.0079 4.43* 0.0353 change in disparity in period 3 = Change in disparity in period 4 2.04 0.1532 1.03 0.3103 Group UR high income group change in disparity in period 2 = Change in disparity in period 3 3.91* 0.0479 4.61* 0.0317 change in disparity in period 3 = Change in disparity in period 4 1.17 0.2786 0.13 0.7235 medium income group change in disparity in period 2 = Change in disparity in period 3 1.52 0.2174 0.14 0.7036 change in disparity in period 3 = Change in disparity in period 4 0.45 0.5043 0.00 0.9645 low income group change in disparity in period 2 = Change in disparity in period 3 1.60 0.2062 2.88 0.0897 change in disparity in period 3 = Change in disparity in period 4 1.66 0.1980 1.84 0.1754 Note: 1. Significance level: *** 0.001, ** 0.01, * 0.05.

83 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

5.21.

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*** 0.082 Group RU 0.851 0.173 Group UR 0.438** 0.133 periods period 1 1 n/a period 2 0.764 0.180 period 3 1.040 0.191 period 4 1.290 0.224 change in disparities Group RR in period 2 0.757 0.257 Group RR in period 3 0.924 0.234 Group RR in period 4 1.427 0.324 Group RU in period 2 0.784 0.305 Group RU in period 3 0.869 0.242 Group RU in period 4 0.980 0.254 Group UR in period 2 0.592 0.403 Group UR in period 3 1.689 0.684 Group UR in period 4 1.883 0.683 Note: 1. Significance level: *** 0.001, ** 0.01, * 0.05. 2. 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 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 3 0.35 0.5566 Change in disparity in period 3 = Change in disparity in period 4 4.33* 0.0375 Group RU Change in disparity in period 2 = Change in disparity in period 3 0.07 0.7869 Change in disparity in period 3 = Change in disparity in period 4 0.23 0.6321 Group UR Change in disparity in period 2 = Change in disparity in period 3 2.47 0.1162 Change in disparity in period 3 = Change in disparity in period 4 0.12 0.7296 Note: 1. Significance level: *** 0.001, ** 0.01, * 0.05.

5.5 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 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 observed no significant impact. In the low‐income group, the positive impact

was observed only in rural residents with urban household registration.

87 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 1998. 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 observation was contrary to my hypothesis that more health insurance coverage reduces

financial risk.

0.20 1.40 Group RR

UU 0.18 the 1.20 Group RU OOP of

0.16 Group 1.00 to

20% 0.14 Group UR

whose

0.12 0.80

income Group UU

groups 0.10 exceeds

redients 0.08 0.60 Ratio: Group study of RR/Group UU 0.06 household 0.40 expense Ratio: Group

0.04 other RU/Group UU of 0.20 Ratio: Group

Proportion 0.02

medical UR/Group UU

0.00 0.00 Ratio 1234 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 0.20 1.40

Group RR UU

0.18 1.20

medical Group RU 0.16 Group household

OOP 1.00 0.14 to Group UR

the 0.12 of

0.80 Group UU whose

groups 0.10 40%

income 0.60 Ratio: Group 0.08 study RR/Group UU redients Ratio: Group of 0.06

exceeds 0.40

other RU/Group UU

0.04 of Ratio: Group 0.20 0.02 UR/Group UU expense Ratio Proportion 0.00 0.00 1234 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 2003. Urban residents with rural registration also began to pay more compared to

Group UU after the health insurance expansion in 2003. However, their total healthcare cost shrank compared to Group UU when health insurance covered more urban residents in 2007.

90

350.00 1.40 UU

300.00 1.20 Group RR Group 250.00 1.00 Group RU to

expense 200.00 0.80 Group UR groups

Group UU

medical 150.00 0.60

study

Ratio: Group 100.00 0.40 RR/Group UU other Average

of Ratio: Group

50.00 0.20 RU/Group UU Ratio: Group Ratio 0.00 0.00 UR/Group UU 1234 period

Figure 6.3 Total Healthcare Costs by Rural and Urban Residences and Registrations

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 1993. Group UR was more likely to have high OOP compared with Group

91 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 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 ‐0.054 0.194 0.081 0.244 Group RU ‐0.016 0.234 0.228 0.282 Group UR 0.253 0.261 0.341 0.324 trend in 1990s and change in later waves Group RR trend in 1990s ‐0.001 0.032 0.013 0.038 deviation from trend in 2000 0.304 0.190 0.256 0.219 deviation from trend in 2004 0.959** 0.304 0.795* 0.356 deviation from trend in 2006 0.926* 0.369 0.614 0.429 deviation from trend in 2009 1.061* 0.460 0.747 0.540 deviation from trend in 2011 0.814 0.527 0.568 0.616 Group RU trend in 1990s 0.008 0.054 ‐0.010 0.061 deviation from trend in 2000 0.414 0.319 0.631 0.361 deviation from trend in 2004 1.107* 0.505 1.220* 0.573 deviation from trend in 2006 1.178 0.607 1.282 0.686 deviation from trend in 2009 0.997 0.769 1.185 0.870 deviation from trend in 2011 0.747 0.874 1.045 0.986 Group UR trend in 1990s 0.027 0.064 0.025 0.078 deviation from trend in 2000 ‐0.171 0.369 0.065 0.437 deviation from trend in 2004 0.805 0.580 0.875 0.708 deviation from trend in 2006 0.564 0.707 0.463 0.864 deviation from trend in 2009 0.672 0.894 0.651 1.092 deviation from trend in 2011 0.316 1.015 0.453 1.247 Group UU trend in 1990s 0.171*** 0.051 0.199** 0.064 deviation from trend in 2000 ‐0.390 0.253 ‐0.345 0.306 deviation from trend in 2004 ‐0.356 0.437 ‐0.494 0.541 deviation from trend in 2006 ‐0.945 0.538 ‐1.181 0.668 deviation from trend in 2009 ‐1.572* 0.692 ‐1.865* 0.859 deviation from trend in 2011 ‐1.907* 0.785 ‐2.185* 0.978 constant ‐3.709*** 0.184 ‐4.236*** 0.232 Note: 1. Significance level: *** 0.001, ** 0.01, * 0.05. 2. 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 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 2004. This suggests that Group RR was more likely to have had healthcare costs after the second policy change in 2003. 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 1993. 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 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 ‐0.121 0.118 0.236 0.359 Group RU ‐0.280 0.155 0.664 0.399 Group UR ‐0.037 0.167 0.305 0.449 trend in 1990s and change in later waves Group RR trend in 1990s 0.038 0.021 ‐0.051 0.057 deviation from trend in 2000 ‐0.006 0.121 0.671* 0.342 deviation from trend in 2004 0.904*** 0.192 0.744 0.525 deviation from trend in 2006 0.653** 0.232 0.787 0.635 deviation from trend in 2009 0.798** 0.292 1.020 0.797 deviation from trend in 2011 0.495 0.333 1.743 0.910 Group RU trend in 1990s 0.086* 0.038 ‐0.218** 0.077 deviation from trend in 2000 ‐0.066 0.208 1.443** 0.456 deviation from trend in 2004 0.639 0.341 2.107** 0.692 deviation from trend in 2006 0.414 0.414 2.114* 0.845 deviation from trend in 2009 0.162 0.525 3.688*** 1.067 deviation from trend in 2011 ‐0.148 0.600 4.486*** 1.213 Group UR trend in 1990s 0.147*** 0.040 ‐0.077 0.110 deviation from trend in 2000 ‐0.616** 0.222 1.012 0.655 deviation from trend in 2004 ‐0.174 0.359 1.651 1.045 deviation from trend in 2006 ‐0.543 0.437 0.844 1.226 deviation from trend in 2009 ‐0.867 0.556 1.307 1.535 deviation from trend in 2011 ‐1.344 0.631 1.810 1.761 Group UU trend in 1990s 0.166*** 0.031 0.193* 0.087 deviation from trend in 2000 ‐0.533** 0.160 0.512 0.396 deviation from trend in 2004 ‐0.121 0.270 ‐1.269 0.715 deviation from trend in 2006 ‐0.716* 0.330 ‐1.743 0.892 deviation from trend in 2009 ‐1.317** 0.420 ‐2.028 1.143 deviation from trend in 2011 ‐1.742*** 0.479 ‐2.354 1.307 constant ‐2.618*** 0.111 6.342*** 0.336 Note: 1. Significance level: *** 0.001, ** 0.01, * 0.05. 2. 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 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 2011. 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 2011. 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 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.

0.12 1.4

UU Group RR 1.2

OOP 0.1

income Group Group RU 1 to

having 0.08 Group UR of 0.8 groups

household 0.06 Group UU of 0.6 study

20%

probability Ratio: Group

0.04 0.4 RR/Group UU other Ratio: Group of 0.02 0.2 RU/Group UU Predicted exceeding Ratio: Group Ratio 0 0 UR/Group UU 1993 1997 2000 2004 2006 2009 2011 Wave

Figure 6.4 Predicted Probability of Having OOP Exceeding 20% of Household Income by Rural and Urban Residences and Registrations

97 0.12 1.4

Group RR 1.2 UU

0.1 OOP

income Group RU 1 Group

having 0.08 to

Group UR of 0.8

household 0.06 Group UU groups of

0.6 40% probability

0.04 Ratio: Group 0.4 study

of RR/Group UU

Ratio: Group 0.02 0.2 Ratio RU/Group UU Predicted exceeding Ratio: Group 0 0 UR/Group UU 1993 1997 2000 2004 2006 2009 2011 Wave

Figure 6.5 Predicted Probability of Having OOP Exceeding 40% of Household Income by Rural and Urban Residences and Registrations

300 1.6 Group RR 1.4 UU 250 1.2 Group RU cost

200 Group to 1 Group UR health

150 0.8

groups Group UU total

0.6 100

study Ratio: Group

0.4 of RR/Group UU Predicted 50 Ratio: Group

0.2 Ratio RU/Group UU 0 0 Ratio: Group 1993 1997 2000 2004 2006 2009 2011 UR/Group UU Wave Figure 6.6 Predicted Total Healthcare Costs by Rural and Urban Residences and Registrations

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 2000. In 2004, the ratio started to increase, and continued to

98 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 2011. For

Group UR, the disparities were significant in 2000, 2004, and 2011. For Group UR, the disparity was significant in 2000. For OOP exceeding 40% of household income, disparities

were also significant in years 2006 and 2011.

99 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) 1997 0.0237 0.04 0.0257 0.05 2000 0.0217 17.66*** 0.0324 16.57*** 2004 0.0445 37.25*** 0.0600 32.01*** 2006 0.0259 13.63*** 0.0445 16.22*** 2009 0.0106 2.32 0.0380 4.50* 2011 0.0241 17.85*** 0.0438 19.60*** Group RU disparity (Group UU probability‐Group RU probability) 1997 0.0220 0.24 0.0214 1.31 2000 0.0148 4.45* 0.0221 2.03 2004 0.0272 7.49** 0.0430 6.16* 2006 0.0006 0.00 0.0245 0.03 2009 0.0034 0.14 0.0237 0.17 2011 0.0175 5.94* 0.0316 4.88* Group UR disparity (Group UU probability‐Group UR probability) 1997 0.0123 0.1 0.0222 0.02 2000 0.0208 6.15* 0.0295 3.90 2004 0.0142 1.26 0.0482 1.42 2006 0.0075 0.41 0.0383 1.91 2009 ‐0.0145 1.67 0.0288 0.16 2011 0.0072 0.61 0.0357 0.92 Note: 1. Significance level: *** 0.001, ** 0.01, * 0.05.

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 2000. For

Group RU, the disparities in 2006 and 2009 were significantly smaller than the disparity in

1997. 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 2000.

100 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 1997 2000 0.88 0.3482 0.04 0.8417 2004 0.41 0.5198 0.00 0.9648 2006 3.17 0.0752 0.34 0.5598 2009 9.75** 0.0018 3.23 0.0724 2011 3.22 0.0728 0.57 0.4493 compare with disparity with Group UU in 2000 2004 0.16 0.6862 0.08 0.7739 2006 0.74 0.3890 0.17 0.6816 2009 5.35* 0.0207 3.10 0.0784 2011 0.72 0.3950 0.38 0.5391 compare with disparity with Group UU in 2006 2009 2.84 0.0921 2.27 0.1323 2011 0.00 0.9480 0.03 0.8523 Group RU compare with disparity with Group UU in 1997 2000 1.60 0.2058 1.52 0.2183 2004 2.02 0.1556 0.89 0.3462 2006 8.19** 0.0042 4.75* 0.0293 2009 6.74** 0.0094 3.82 0.0505 2011 2.78 0.0956 1.34 0.2475 compare with disparity with Group UU in 2000 2004 0.00 0.9687 0.18 0.6707 2006 2.66 0.1032 0.97 0.3248 2009 1.92 0.1660 0.64 0.4249 2011 0.09 0.7689 0.04 0.8430 compare with disparity with Group UU in 2006 2009 0.06 0.8137 0.03 0.8543 2011 2.60 0.1068 1.86 0.1725 Group UR compare with disparity with Group UU in 1997 2000 0.53 0.4659 0.11 0.7350 2004 0.39 0.5326 0.25 0.6138 2006 0.69 0.4058 0.06 0.8095 2009 4.06* 0.0440 1.90 0.1676 2011 0.73 0.3936 0.45 0.5018 compare with disparity with Group UU in 2000 2004 2.05 0.1524 0.81 0.3669 2006 2.54 0.1112 0.36 0.5496 2009 7.72** 0.0055 3.25 0.0716 2011 2.73 0.0985 1.20 0.2741 compare with disparity with Group UU in 2006 2009 2.09 0.1484 1.92 0.1662 2011 0.00 0.9797 0.24 0.6210 Note: 1. Significance level: *** 0.001, ** 0.01, * 0.05.

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 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 2006. 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 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 1993 ‐3.417 0.463 ‐4.326 ‐2.508 disparity with Group UU in 1997 76.630 0.923 74.816 78.444 disparity with Group UU in 2000 240.683 3.019 234.751 246.615 disparity with Group UU in 2004 147.003 1.904 143.262 150.744 disparity with Group UU in 2006 99.688 1.836 96.081 103.295 disparity with Group UU in 2009 115.866 2.056 111.827 119.905 disparity with Group UU in 2011 50.281 1.836 46.673 53.888 Group RU disparity with Group UU in 1993 ‐13.889 0.624 ‐15.116 ‐12.663 disparity with Group UU in 1997 80.683 0.942 78.832 82.535 disparity with Group UU in 2000 233.155 3.097 227.070 239.240 disparity with Group UU in 2004 137.568 2.005 133.629 141.506 disparity with Group UU in 2006 119.026 2.058 114.982 123.070 disparity with Group UU in 2009 52.818 2.759 47.397 58.240 disparity with Group UU in 2011 6.739 2.557 1.716 11.762 Group UR disparity with Group UU in 1993 ‐8.478 0.678 ‐9.811 ‐7.146 disparity with Group UU in 1997 59.943 1.076 57.829 62.057 disparity with Group UU in 2000 210.383 3.302 203.896 216.870 disparity with Group UU in 2004 ‐10.805 4.429 ‐19.507 ‐2.102 disparity with Group UU in 2006 95.359 2.006 91.417 99.301 disparity with Group UU in 2009 104.133 2.270 99.674 108.592 disparity with Group UU in 2011 72.882 2.046 68.862 76.902

6.3 Sensitivity Analysis

6.3.1 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 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 0.059 0.198 0.163 0.247 Group RU 0.041 0.236 0.269 0.283 Group UR 0.340 0.262 0.405 0.326 trend in 1990s and change in later waves Group RR trend in 1990s ‐0.004 0.032 0.011 0.038 deviation from trend in 2000 0.327 0.190 0.273 0.219 deviation from trend in 2004 0.984*** 0.304 0.814* 0.356 deviation from trend in 2006 0.885** 0.368 0.585 0.429 deviation from trend in 2009 0.935* 0.460 0.656 0.540 deviation from trend in 2011 0.690 0.527 0.479 0.616 Group RU trend in 1990s 0.014 0.054 ‐0.006 0.061 deviation from trend in 2000 0.404 0.319 0.624 0.361 deviation from trend in 2004 1.058* 0.504 1.184* 0.573 deviation from trend in 2006 1.095 0.606 1.221 0.686 deviation from trend in 2009 0.808 0.768 1.045 0.870 deviation from trend in 2011 0.531 0.874 0.886 0.986 Group UR trend in 1990s 0.028 0.064 0.025 0.078 deviation from trend in 2000 ‐0.162 0.369 0.074 0.437 deviation from trend in 2004 0.806 0.580 0.879 0.708 deviation from trend in 2006 0.526 0.707 0.438 0.864 deviation from trend in 2009 0.511 0.894 0.538 1.093 deviation from trend in 2011 0.153 1.016 0.338 1.248 Group UU trend in 1990s 0.179*** 0.051 0.204*** 0.064 deviation from trend in 2000 ‐0.402 0.253 ‐0.352 0.306 deviation from trend in 2004 ‐0.395 0.437 ‐0.521 0.541 deviation from trend in 2006 ‐1.014 0.539 ‐1.229 0.669 deviation from trend in 2009 ‐1.720* 0.695 ‐1.973* 0.861 deviation from trend in 2011 ‐2.090** 0.789 ‐2.317* 0.981 whether having insurance insurance 0.210*** 0.051 0.153* 0.060 not having insurance 0 n/a 0 n/a constant ‐3.879*** 0.191 ‐4.357*** 0.237 Note: 1. Significance level: *** 0.001, ** 0.01, * 0.05. 2. Results for other independent variables are omitted.

104 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) 1997 0.0194 0.06 0.0127 0.00 2000 0.0174 13.41*** 0.0154 13.81*** 2004 0.0375 30.63*** 0.0303 28.24*** 2006 0.0232 14.00*** 0.0205 16.53*** 2009 0.0111 3.72 0.0114 5.63* 2011 0.0211 20.33*** 0.0189 21.21*** Group RU disparity (Group UU probability‐Group RU probability) 1997 0.0183 0.46 0.0121 1.58 2000 0.0118 3.38 0.0072 1.56 2004 0.0230 6.44* 0.0184 5.57* 2006 ‐0.0006 0.00 0.0007 0.01 2009 0.0036 0.23 0.0032 0.24 2011 0.0150 6.40* 0.0123 5.15* Group UR disparity (Group UU probability‐Group UR probability) 1997 0.0091 0.01 0.0074 0.09 2000 0.0169 4.86* 0.0123 3.22 2004 0.0091 0.61 0.0099 0.97 2006 0.0050 0.22 0.0112 1.66 2009 ‐0.0098 1.11 ‐0.0020 0.06 2011 0.0069 0.83 0.0071 1.09 Note: 1. Significance level: *** 0.001, ** 0.01, * 0.05.

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 2007. The results indicate that the change in

105 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 1997 2000 ‐0.0020 0.88 0.0027 0.04 2004 0.0180 0.31 0.0176 0.01 2006 0.0038 1.88 0.0078 0.11 2009 ‐0.0083 6.20* ‐0.0013 1.97 2011 0.0016 1.5 0.0062 0.16 compare with disparities with Group UU in 2000 2004 0.0200 0.25 0.0149 0.12 2006 0.0058 0.18 0.0051 0.02 2009 ‐0.0064 2.65 ‐0.0040 1.75 2011 0.0036 0.06 0.0035 0.05 compare with disparities with Group UU in 2006 2009 ‐0.0122 2 ‐0.0091 1.79 2011 ‐0.0022 0.06 ‐0.0016 0.01 Group RU compare with disparities with Group UU in 1997 2000 ‐0.0065 1.55 ‐0.0049 1.48 2004 0.0047 1.72 0.0063 0.76 2006 ‐0.0189 7.42** ‐0.0114 4.36* 2009 ‐0.0146 5.43 ‐0.0089 3.19 2011 ‐0.0033 1.94 0.0003 0.97 compare with disparities with Group UU in 2000 2004 0.0112 0 0.0112 0.24 2006 ‐0.0124 2.25 ‐0.0065 0.81 2009 ‐0.0081 1.24 ‐0.0040 0.38 2011 0.0032 0 0.0052 0.14 compare with disparities with Group UU in 2006 2009 0.0043 0.17 0.0025 0.08 2011 0.0156 3.15 0.0116 2.14 Group UR compare with disparities with Group UU in 1997 2000 0.0079 0.53 0.0049 0.12 2004 0.0001 0.35 0.0025 0.23 2006 ‐0.0041 0.5 0.0038 0.03 2009 ‐0.0189 2.7 ‐0.0094 1.33 2011 ‐0.0022 0.29 ‐0.0003 0.23 compare with disparities with Group UU in 2000 2004 ‐0.0078 1.96 ‐0.0024 0.77 2006 ‐0.0120 2.18 ‐0.0011 0.27 2009 ‐0.0268 5.83* ‐0.0142 2.46 2011 ‐0.0100 1.81 ‐0.0052 0.8 compare with disparities with Group UU in 2006 2009 ‐0.0148 1.29 ‐0.0131 1.45 2011 0.0019 0.06 ‐0.0041 0.13 Note: 1. Significance level: *** 0.001, ** 0.01, * 0.05.

106

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 1993 ‐10.911 0.479 ‐11.851 ‐9.971 disparity with Group UU in 1997 71.176 0.926 69.358 72.995 disparity with Group UU in 2000 242.659 3.167 236.437 248.882 disparity with Group UU in 2004 132.532 1.981 128.641 136.423 disparity with Group UU in 2006 99.663 1.831 96.066 103.261 disparity with Group UU in 2009 109.833 1.811 106.275 113.391 disparity with Group UU in 2011 53.515 1.591 50.390 56.640 Group RU disparity with Group UU in 1993 ‐18.136 0.640 ‐19.394 ‐16.878 disparity with Group UU in 1997 77.388 0.944 75.533 79.243 disparity with Group UU in 2000 240.529 3.255 234.134 246.924 disparity with Group UU in 2004 131.123 2.067 127.062 135.184 disparity with Group UU in 2006 117.277 2.041 113.268 121.286 disparity with Group UU in 2009 49.269 2.431 44.493 54.044 disparity with Group UU in 2011 8.129 2.212 3.783 12.476 Group UR disparity with Group UU in 1993 ‐15.588 0.758 ‐17.077 ‐14.098 disparity with Group UU in 1997 54.204 1.103 52.037 56.372 disparity with Group UU in 2000 207.627 3.540 200.672 214.582 disparity with Group UU in 2004 ‐47.990 5.096 ‐58.002 ‐37.977 disparity with Group UU in 2006 92.671 2.005 88.731 96.611 disparity with Group UU in 2009 99.117 1.985 95.216 103.018 disparity with Group UU in 2011 68.205 1.779 64.710 71.700

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.

6.3.2 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 6.10. 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 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 2011. 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 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 0.015 0.212 0.133 0.262 Group RU ‐0.030 0.261 0.184 0.310 Group UR 0.319 0.283 0.274 0.358 trend in 1990s and change in later waves Group RR trend in 1990s 0.009 0.035 0.021 0.041 deviation from trend in 2000 0.272 0.201 0.253 0.231 deviation from trend in 2004 0.896** 0.325 0.719 0.379 deviation from trend in 2006 0.818* 0.394 0.503 0.458 deviation from trend in 2009 0.927 0.493 0.639 0.577 deviation from trend in 2011 0.670 0.565 0.431 0.659 Group RU trend in 1990s 0.054 0.060 0.036 0.068 deviation from trend in 2000 0.178 0.345 0.379 0.391 deviation from trend in 2004 0.740 0.550 0.885 0.625 deviation from trend in 2006 0.738 0.664 0.900 0.752 deviation from trend in 2009 0.321 0.845 0.442 0.959 deviation from trend in 2011 0.034 0.962 0.346 1.087 Group UR trend in 1990s 0.028 0.068 0.045 0.086 deviation from trend in 2000 ‐0.070 0.385 0.126 0.462 deviation from trend in 2004 0.709 0.619 0.695 0.769 deviation from trend in 2006 0.619 0.752 0.354 0.941 deviation from trend in 2009 0.724 0.949 0.481 1.187 deviation from trend in 2011 0.325 1.079 0.205 1.358 Group UU trend in 1990s 0.155** 0.057 0.172* 0.070 deviation from trend in 2000 ‐0.191 0.288 ‐0.083 0.347 deviation from trend in 2004 ‐0.091 0.493 ‐0.159 0.605 deviation from trend in 2006 ‐0.553 0.606 ‐0.683 0.745 deviation from trend in 2009 ‐1.080 0.775 ‐1.247 0.954 deviation from trend in 2011 ‐1.475 0.879 ‐1.605 1.085 constant ‐4.313*** 0.200 ‐4.707*** 0.244 Note: 1. Significance level: *** 0.001, ** 0.01, * 0.05.

109 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) 1997 0.0104 0.18 0.0066 0.22 2000 0.0130 13.92*** 0.0118 13.20*** 2004 0.0273 28.93*** 0.0227 25.45*** 2006 0.0209 18.08*** 0.0191 20.98*** 2009 0.0130 6.84** 0.0125 8.55** 2011 0.0169 18.77*** 0.0160 20.75*** Group RU disparity (Group UU probability‐Group RU probability) 1997 0.0077 0.04 0.0053 0.08 2000 0.0085 4.09* 0.0071 2.49 2004 0.0138 5.19* 0.0114 3.25 2006 0.0002 0.13 0.0005 0.01 2009 0.0087 2.66 0.0103 3.33 2011 0.0115 7.09** 0.0105 5.17* Group UR disparity (Group UR probability‐Group 1997 0.0041 0.04 0.0036 0.00 2000 0.0112 4.08* 0.0089 0.00 2004 0.0144 2.95 0.0111 2.14 2006 0.0075 0.83 0.0115 2.65 2009 ‐0.0050 0.39 0.0011 0.03 2011 0.0068 1.21 0.0069 1.45 Note: 1. Significance level: *** 0.001, ** 0.01, * 0.05.

110

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 1997 2000 0.0026 0.02 0.0053 0.23 2004 0.0170 0.03 0.0161 0.61 2006 0.0105 0.09 0.0125 0.54 2009 0.0026 1.68 0.0060 0.10 2011 0.0066 0.30 0.0094 0.11 compare with disparities with Group UU in 2000 2004 0.0144 0.11 0.0109 0.10 2006 0.0079 0.04 0.0073 0.07 2009 0.0000 1.66 0.0007 0.85 2011 0.0040 0.22 0.0042 0.04 compare with disparities with Group UU in 2006 2009 ‐0.0079 1.69 ‐0.0066 1.82 2011 ‐0.0040 0.09 ‐0.0031 0.29 Group RU compare with disparities with Group UU in 1997 2000 0.0008 0.06 0.0018 0.03 2004 0.0062 0.25 0.0060 0.09 2006 ‐0.0075 2.39 ‐0.0048 1.50 2009 0.0010 0.56 0.0050 0.02 2011 0.0038 0.16 0.0052 0.02 compare with disparities with Group UU in 2000 2004 0.0053 0.07 0.0043 0.02 2006 ‐0.0083 1.97 ‐0.0066 1.41 2009 0.0002 0.29 0.0033 0.00 2011 0.0030 0.02 0.0034 0.00 compare with disparities with Group UU in 2006 2009 0.0085 0.96 0.0099 1.74 2011 0.0113 2.38 0.0101 2.20 Group UR compare with disparities with Group UU in 1997 2000 0.0071 0.71 0.0052 0.24 2004 0.0103 0.10 0.0075 0.01 2006 0.0034 0.01 0.0079 0.10 2009 ‐0.0090 1.07 ‐0.0025 0.38 2011 0.0027 0.01 0.0032 0.01 compare with disparities with Group UU in 2000 2004 0.0032 0.42 0.0022 0.20 2006 ‐0.0037 1.11 0.0027 0.04 2009 ‐0.0161 4.07 ‐0.0077 1.50 2011 ‐0.0044 1.18 ‐0.0020 0.49 compare with disparities with Group UU in 2006 2009 ‐0.0124 1.37 ‐0.0104 1.42 2011 ‐0.0007 0.00 ‐0.0046 0.29 Note: 1. Significance level: *** 0.001, ** 0.01, * 0.05.

111 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 ‐0.102 0.205 0.049 0.257 Group RU ‐0.032 0.248 0.250 0.296 Group UR 0.303 0.275 0.455 0.338 trend in 1990s and change in later waves Group RR trend in 1990s 0.028 0.034 0.039 0.040 deviation from trend in 2000 0.166 0.198 0.108 0.228 deviation from trend in 2004 0.727* 0.319 0.604 0.372 deviation from trend in 2006 0.630 0.385 0.341 0.449 deviation from trend in 2009 0.643 0.482 0.353 0.565 deviation from trend in 2011 0.323 0.552 0.123 0.645 Group RU trend in 1990s 0.022 0.057 ‐0.013 0.063 deviation from trend in 2000 0.256 0.334 0.564 0.379 deviation from trend in 2004 0.988 0.529 1.262* 0.604 deviation from trend in 2006 1.098 0.634 1.357 0.719 deviation from trend in 2009 0.723 0.807 1.163 0.913 deviation from trend in 2011 0.523 0.913 1.060 1.030 Group UR trend in 1990s 0.022 0.068 0.008 0.082 deviation from trend in 2000 ‐0.180 0.399 0.038 0.467 deviation from trend in 2004 0.791 0.623 0.927 0.751 deviation from trend in 2006 0.644 0.760 0.716 0.914 deviation from trend in 2009 0.753 0.960 0.822 1.156 deviation from trend in 2011 0.412 1.089 0.705 1.317 Group UU trend in 1990s 0.177*** 0.053 0.202 0.067 deviation from trend in 2000 ‐0.449 0.262 ‐0.367 0.319 deviation from trend in 2004 ‐0.386 0.454 ‐0.471 0.564 deviation from trend in 2006 ‐1.035 0.559 ‐1.223 0.698 deviation from trend in 2009 ‐1.657* 0.720 ‐1.858* 0.897 deviation from trend in 2011 ‐1.995* 0.816 ‐2.205* 1.022 constant ‐3.725*** 0.192 ‐4.251*** 0.242 Note: 1. Significance level: *** 0.001, ** 0.01, * 0.05.

112 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) 1997 0.0230 0.14 0.0138 0.21 2000 0.0203 14.56*** 0.0175 14.91*** 2004 0.0458 35.88*** 0.0353 30.27*** 2006 0.0238 10.73** 0.0208 13.20*** 2009 0.0122 2.95 0.0149 6.73** 2011 0.0268 21.22*** 0.0230 22.16*** Group RU disparity (Group UU probability‐Group RU probability) 1997 0.0235 0.03 0.0138 0.93 2000 0.0189 5.51* 0.0103 2.67 2004 0.0343 8.15** 0.0222 6.29* 2006 ‐0.0005 0.32 ‐0.0025 0.09 2009 0.0132 0.98 0.0072 0.86 2011 0.0225 6.66** 0.0154 5.66* Group UR disparity (Group UU probability‐Group UR probability) 1997 0.0123 0.02 0.0083 0.05 2000 0.0197 4.88* 0.0148 3.59 2004 0.0193 1.97 0.0171 2.12 2006 0.0026 0.04 0.0059 0.33 2009 ‐0.0171 2.03 ‐0.0020 0.04 2011 0.0056 0.33 0.0058 0.49 Note: 1. Significance level: *** 0.001, ** 0.01, * 0.05.

113 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 1997 2000 ‐0.0026 0.69 0.0037 0.00 2004 0.0228 0.13 0.0216 0.09 2006 0.0008 2.79 0.0071 0.20 2009 ‐0.0108 7.02** 0.0011 1.33 2011 0.0038 1.48 0.0092 0.05 compare with disparities with Group UU in 2000 2004 0.0254 0.35 0.0178 0.07 2006 0.0034 0.72 0.0033 0.29 2009 ‐0.0082 3.66 ‐0.0026 1.77 2011 0.0064 0.11 0.0055 0.10 compare with disparities with Group UU in 2006 2009 ‐0.0116 1.50 ‐0.0060 0.77 2011 0.0030 0.41 0.0021 0.08 Group RU compare with disparities with Group UU in 1997 2000 ‐0.0046 0.82 ‐0.0036 0.94 2004 0.0108 1.42 0.0084 0.69 2006 ‐0.0241 9.38** ‐0.0163 5.45* 2009 ‐0.0104 4.19* ‐0.0066 2.39 2011 ‐0.0010 2.10 0.0016 0.95 compare with disparities with Group UU in 2000 2004 0.0154 0.05 0.0120 0.05 2006 ‐0.0194 4.96* ‐0.0127 2.14 2009 ‐0.0057 1.41 ‐0.0030 0.40 2011 0.0036 0.22 0.0051 0.01 compare with disparities with Group UU in 2006 2009 0.0137 1.32 0.0097 0.80 2011 0.0230 4.76* 0.0179 3.39 Group UR compare with disparities with Group UU in 1997 2000 0.0074 0.40 0.0065 0.24 2004 0.0070 0.11 0.0088 0.02 2006 ‐0.0097 1.03 ‐0.0024 0.35 2009 ‐0.0294 3.95* ‐0.0103 1.18 2011 ‐0.0067 0.79 ‐0.0024 0.41 compare with disparities with Group UU in 2000 2004 ‐0.0004 1.07 0.0023 0.46 2006 ‐0.0171 2.73 ‐0.0089 1.21 2009 ‐0.0368 6.94** ‐0.0168 2.69 2011 ‐0.0142 2.47 ‐0.0089 1.49 compare with disparities with Group UU in 2006 2009 ‐0.0197 1.43 ‐0.0079 0.37 2011 0.0030 0.05 0.0000 0.00 Note: 1. Significance level: *** 0.001, ** 0.01, * 0.05.

114 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 1993 ‐16.903 0.406 ‐17.700 ‐16.106 disparity with Group UU in 1997 41.546 0.708 40.156 42.936 disparity with Group UU in 2000 256.342 4.029 248.427 264.258 disparity with Group UU in 2004 89.262 2.162 85.014 93.509 disparity with Group UU in 2006 94.805 1.721 91.423 98.186 disparity with Group UU in 2009 133.149 2.207 128.813 137.486 disparity with Group UU in 2011 27.326 1.516 24.348 30.305 Group RU disparity with Group UU in 1993 ‐17.351 0.511 ‐18.354 ‐16.347 disparity with Group UU in 1997 42.071 0.734 40.629 43.513 disparity with Group UU in 2000 258.348 4.028 250.435 266.262 disparity with Group UU in 2004 103.216 2.355 98.589 107.844 disparity with Group UU in 2006 115.019 1.810 111.462 118.576 disparity with Group UU in 2009 87.561 2.670 82.316 92.806 disparity with Group UU in 2011 ‐5.687 2.058 ‐9.731 ‐1.644 Group UR disparity with Group UU in 1993 ‐25.492 0.798 ‐27.059 ‐23.925 disparity with Group UU in 1997 19.287 1.003 17.316 21.259 disparity with Group UU in 2000 212.697 4.294 204.261 221.132 disparity with Group UU in 2004 ‐51.705 5.136 ‐61.795 ‐41.614 disparity with Group UU in 2006 83.536 1.860 79.881 87.190 disparity with Group UU in 2009 120.392 2.249 115.974 124.811 disparity with Group UU in 2011 42.984 1.688 39.668 46.300

115 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 1993 ‐4.675 0.512 ‐5.681 ‐3.670 disparity with Group UU in 1997 83.959 1.049 81.898 86.019 disparity with Group UU in 2000 280.757 4.073 272.756 288.759 disparity with Group UU in 2004 148.829 2.331 144.249 153.408 disparity with Group UU in 2006 91.114 1.845 87.490 94.739 disparity with Group UU in 2009 125.656 2.192 121.349 129.963 disparity with Group UU in 2011 64.322 1.787 60.812 67.833 Group RU disparity with Group UU in 1993 ‐15.870 0.683 ‐17.212 ‐14.528 disparity with Group UU in 1997 93.248 1.027 91.230 95.265 disparity with Group UU in 2000 298.455 3.954 290.688 306.223 disparity with Group UU in 2004 151.651 2.438 146.862 156.440 disparity with Group UU in 2006 103.990 1.976 100.108 107.873 disparity with Group UU in 2009 81.023 2.561 75.992 86.055 disparity with Group UU in 2011 17.365 2.395 12.659 22.071 Group UR disparity with Group UU in 1993 ‐14.102 0.851 ‐15.774 ‐12.430 disparity with Group UU in 1997 66.376 1.365 63.694 69.058 disparity with Group UU in 2000 233.949 4.566 224.978 242.919 disparity with Group UU in 2004 ‐69.786 6.544 ‐82.643 ‐56.928 disparity with Group UU in 2006 66.581 2.177 62.304 70.857 disparity with Group UU in 2009 107.848 2.257 103.413 112.283 disparity with Group UU in 2011 69.363 2.013 65.407 73.318

6.3.3. 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 6.19

116 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 2006. However, the results were not significant except for changes in disparities between Group RU and UU after 2006.

117 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* 0.382 1.401* 0.603 Group RU low income 0.798 0.435 1.512* 0.645 Group UR low income 1.298** 0.466 1.882** 0.685 Group UU low income 0.771 0.506 1.751** 0.681 trend in 1990s and change in later waves Group RR low income trend in 1990s 0.027 0.047 0.061 0.055 deviation from trend in 2000 0.264 0.269 0.144 0.306 deviation from trend in 2004 0.906* 0.439 0.591 0.506 deviation from trend in 2006 0.765 0.533 0.311 0.611 deviation from trend in 2009 0.918 0.668 0.418 0.773 deviation from trend in 2011 0.640 0.765 0.106 0.885 Group RU low income trend in 1990s 0.114 0.077 0.116 0.086 deviation from trend in 2000 ‐0.232 0.428 ‐0.122 0.475 deviation from trend in 2004 0.269 0.692 0.247 0.774 deviation from trend in 2006 0.387 0.831 0.383 0.929 deviation from trend in 2009 ‐0.383 1.066 ‐0.526 1.201 deviation from trend in 2011 ‐0.630 1.218 ‐0.543 1.358 Group UR low income trend in 1990s ‐0.104 0.111 ‐0.133 0.138 deviation from trend in 2000 0.969 0.638 1.467 0.788 deviation from trend in 2004 2.303* 1.055 2.804* 1.320 deviation from trend in 2006 2.040 1.263 2.563 1.592 deviation from trend in 2009 2.725 1.593 3.295 1.991 deviation from trend in 2011 2.902 1.808 3.491 2.268 Group UU low income trend in 1990s 0.147 0.105 0.046 0.111 deviation from trend in 2000 ‐0.365 0.514 0.140 0.572 deviation from trend in 2004 0.281 0.880 1.103 0.962 deviation from trend in 2006 ‐0.278 1.085 0.865 1.175 deviation from trend in 2009 ‐0.553 1.404 0.887 1.510 deviation from trend in 2011 ‐0.858 1.601 0.816 1.718 Note: 1. Significance level: *** 0.001, ** 0.01, * 0.05.

118

Table 6.19 Test Results of Disparities for OOP Exceeding Certain Percentage of Household Income (Low- income Families) OOP>20% household income OOP>40% household income Disparity Chi2 Disparity Chi2 Group RR disparity (Group UU probability‐Group RR probability) 1997 0.0186 3.81 0.0087 4.53* 2000 0.0091 1.12 0.0096 4.29* 2004 0.0754 0.04 0.0618 2.23 2006 0.0482 0.27 0.0556 2.13 2009 0.0482 0.44 0.0542 2.81 2011 0.0666 0.07 0.0696 1.88 Group RU disparity (Group UU probability‐Group RU probability) 1997 0.0049 2.17 ‐0.0015 5.51* 2000 0.0034 1.48 0.0003 5.45* 2004 0.0431 0.63 0.0326 3.99* 2006 ‐0.0355 3.61 ‐0.0215 7.31** 2009 0.0379 0.68 0.0499 2.92 2011 0.0383 0.68 0.0342 4.02* Group UR disparity (Group UU probability‐Group UR probability) 1997 0.0194 7.24** 0.0153 9.51** 2000 ‐0.0059 2.14 ‐0.0099 6.56* 2004 0.0278 1.03 0.0149 4.97* 2006 0.0421 0.37 0.0422 2.77 2009 0.0258 1.05 0.0332 3.92* 2011 0.0278 1.01 0.0419 3.50 Note: 1. Significance level: *** 0.001, ** 0.01, * 0.05.

119 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 1997 2000 ‐0.0096 0.62 0.0009 0.02 2004 0.0568 0.53 0.0531 1.40 2006 0.0296 0.01 0.0469 1.42 2009 0.0295 0.01 0.0455 0.72 2011 0.0480 0.40 0.0609 2.30 compare with disparities with Group UU in 2000 2004 0.0664 2.75 0.0522 2.00 2006 0.0391 0.99 0.0460 1.93 2009 0.0391 0.64 0.0446 1.09 2011 0.0575 2.56 0.0600 3.17 compare with disparities with Group UU in 2006 2009 0.0000 0.08 ‐0.0014 0.25 2011 0.0184 0.40 0.0140 0.11 Group RU compare with disparities with Group UU in 1997 2000 ‐0.0015 0.01 0.0018 0.01 2004 0.0382 0.46 0.0341 0.80 2006 ‐0.0404 1.14 ‐0.0200 0.15 2009 0.0330 0.36 0.0514 1.83 2011 0.0334 0.43 0.0357 0.90 compare with disparities with Group UU in 2000 2004 0.0396 0.6 0.0323 0.65 2006 ‐0.0389 0.89 ‐0.0218 0.27 2009 0.0345 0.47 0.0495 1.63 2011 0.0348 0.56 0.0339 0.73 compare with disparities with Group UU in 2006 2009 0.0734 3.89* 0.0714 4.67* 2011 0.0738 4.48* 0.0557 2.99 Group UR compare with disparities with Group UU in 1997 2000 ‐0.0253 1.40 ‐0.0253 1.90 2004 0.0084 0.35 ‐0.0004 0.67 2006 0.0227 0.02 0.0268 0.02 2009 0.0064 0.35 0.0179 0.23 2011 0.0084 0.34 0.0265 0.10 compare with disparities with Group UU in 2000 2004 0.0337 0.63 0.0249 0.63 2006 0.0480 1.43 0.0521 2.19 2009 0.0317 0.60 0.0431 1.46 2011 0.0337 0.75 0.0518 2.23 compare with disparities with Group UU in 2006 2009 ‐0.0164 0.34 ‐0.0090 0.20 2011 ‐0.0143 0.32 ‐0.0003 0.05 Note: 1. Significance level: *** 0.001, ** 0.01, * 0.05.

120 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 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 0.581 0.395 1.267* 0.613 Group RU medium income 0.917* 0.431 1.795** 0.634 Group UR medium income 1.064* 0.495 1.824** 0.697 trend in 1990s and change in later waves Group RR medium income trend in 1990s ‐0.027 0.061 ‐0.025 0.071 deviation from trend in 2000 0.374 0.363 0.477 0.413 deviation from trend in 2004 1.084 0.587 1.025 0.680 deviation from trend in 2006 1.204 0.703 1.019 0.817 deviation from trend in 2009 1.452 0.883 1.205 1.022 deviation from trend in 2011 1.001 1.008 0.940 1.168 Group RU medium income trend in 1990s ‐0.144 0.100 ‐0.241* 0.121 deviation from trend in 2000 1.136 0.635 1.827* 0.786 deviation from trend in 2004 2.656** 0.986 3.536** 1.228 deviation from trend in 2006 2.621* 1.184 3.426* 1.473 deviation from trend in 2009 3.172* 1.476 4.549* 1.824 deviation from trend in 2011 2.883 1.675 4.205* 2.068 Group UR medium income trend in 1990s 0.086 0.105 0.046 0.121 deviation from trend in 2000 ‐1.335* 0.648 ‐1.208 0.778 deviation from trend in 2004 ‐0.210 0.917 ‐0.086 1.077 deviation from trend in 2006 0.041 1.117 0.052 1.318 deviation from trend in 2009 ‐0.329 1.425 ‐0.016 1.667 deviation from trend in 2011 ‐1.450 1.636 ‐0.675 1.908 Group UU medium income trend in 1990s 0.294** 0.099 0.442** 0.157 deviation from trend in 2000 ‐0.839 0.454 ‐1.001 0.626 deviation from trend in 2004 ‐1.200 0.811 ‐2.297 1.212 deviation from trend in 2006 ‐2.079* 1.009 ‐3.523* 1.525 deviation from trend in 2009 ‐3.146* 1.302 ‐4.926* 1.989 deviation from trend in 2011 ‐3.755* 1.494 ‐5.818* 2.294 Note: 1. Significance level: *** 0.001, ** 0.01, * 0.05.

122 Table 6.22 Test Results of Disparities for OOP Exceeding Certain Percentage of Household Income (Medium- income Families) OOP>20% household income OOP>40% household income Disparity Chi2 Disparity Chi2 Group RR disparity (Group UU probability‐Group RR probability) 1997 0.0220 1.49 0.0135 2.33 2000 0.0163 3.44 0.0166 4.14* 2004 0.0454 13.50*** 0.0267 6.79** 2006 0.0187 2.40 0.0287 1.30 2009 ‐0.0031 0.06 0.0036 0.13 2011 0.0210 0.06* 0.0138 3.79 Group RU disparity (Group UU probability‐Group RU probability) 1997 0.0247 4.84* 0.0181 3.96* 2000 0.0071 0.35 0.0058 0.26 2004 0.0039 0.05 ‐0.0073 0.23 2006 0.0052 0.10 0.0057 0.20 2009 ‐0.0151 0.95 ‐0.0167 1.60 2011 0.0167 2.38 0.0160 3.12 Group UR disparity (Group UU probability‐Group UR probability) 1997 ‐0.0111 5.00* ‐0.0079 0.00 2000 0.0267 3.81 0.0279 4.56* 2004 0.0185 0.74 0.0170 0.95 2006 ‐0.0421 3.40 ‐0.0126 0.52 2009 ‐0.0431 4.44* ‐0.0200 1.47 2011 0.0183 1.70 0.0062 0.27 Note: 1. Significance level: *** 0.001, ** 0.01, * 0.05.

123 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 1997 2000 ‐0.0056 0.52 0.0031 0.04 2004 0.0234 0.02 0.0132 0.01 2006 ‐0.0033 1.47 0.0152 0.70 2009 ‐0.0251 5.08* ‐0.0099 1.69 2011 ‐0.0009 0.71 0.0004 0.30 compare with disparities with Group UU in 2000 2004 0.0290 0.50 0.0101 0.01 2006 0.0024 0.23 0.0121 0.53 2009 ‐0.0195 2.52 ‐0.0130 1.54 2011 0.0047 0.00 ‐0.0028 0.16 compare with disparities with Group UU in 2006 2009 ‐0.0218 1.69 ‐0.0251 0.31 2011 0.0023 0.25 ‐0.0149 0.15 Group RU compare with disparities with Group UU in 1997 2000 ‐0.0176 1.95 ‐0.0319 1.92 2004 ‐0.0208 3.47 ‐0.0213 4.01* 2006 ‐0.0195 2.92 ‐0.0271 2.06 2009 ‐0.0398 5.81* ‐0.0398 5.54* 2011 ‐0.0080 1.39 ‐0.0326 0.72 compare with disparities with Group UU in 2000 2004 ‐0.0032 0.13 0.0106 0.49 2006 ‐0.0019 0.07 0.0048 0.00 2009 ‐0.0222 1.13 ‐0.0079 1.47 2011 0.0096 0.18 ‐0.0007 0.60 compare with disparities with Group UU in 2006 2009 ‐0.0203 0.87 ‐0.0224 1.46 2011 0.0115 0.63 0.0103 0.73 Group UR compare with disparities with Group UU in 1997 2000 0.0379 4.15* 0.0358 4.48* 2004 0.0297 1.32 0.0248 1.33 2006 ‐0.0310 0.39 ‐0.0047 0.00 2009 ‐0.0319 0.64 ‐0.0121 0.10 2011 0.0294 2.04 0.0141 0.67 compare with disparities with Group UU in 2000 2004 ‐0.0082 1.58 ‐0.0373 1.74 2006 ‐0.0688 6.73** ‐0.0065 4.74* 2009 ‐0.0698 7.54** ‐0.0327 5.99* 2011 ‐0.0085 0.96 ‐0.0245 2.54 compare with disparities with Group UU in 2006 2009 ‐0.0010 0.04 ‐0.0074 0.10 2011 0.0604 4.93* ‐0.0180 0.77 Note: 1. Significance level: *** 0.001, ** 0.01, * 0.05.

124 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 high‐ income 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 high‐ income 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

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 0.550 0.406 1.316* 0.620 Group RU high income ‐0.137 0.573 0.349 0.821 Group UR high income 0.053 0.546 0.378 0.821 Group UU high income 0.556 0.423 0.945 0.654 trend in 1990s and change in later waves Group RR high income trend in 1990s ‐0.037 0.069 ‐0.052 0.078 deviation from trend in 2000 0.317 0.414 0.227 0.483 deviation from trend in 2004 0.882 0.669 0.910 0.765 deviation from trend in 2006 0.904 0.803 0.609 0.925 deviation from trend in 2009 0.621 1.001 0.493 1.150 deviation from trend in 2011 0.937 1.137 1.054 1.293 Group RU high income trend in 1990s 0.002 0.152 0.084 0.184 deviation from trend in 2000 0.866 0.835 0.600 0.942 deviation from trend in 2004 0.553 1.410 ‐0.731 1.666 deviation from trend in 2006 0.928 1.694 ‐0.241 1.988 deviation from trend in 2009 0.890 2.142 ‐0.438 2.526 deviation from trend in 2011 0.806 2.437 ‐0.474 2.881 Group UR high income trend in 1990s 0.095 0.137 0.197 0.178 deviation from trend in 2000 ‐0.253 0.815 ‐0.459 0.959 deviation from trend in 2004 0.442 1.258 ‐0.397 1.562 deviation from trend in 2006 ‐0.337 1.525 ‐1.705 1.921 deviation from trend in 2009 ‐0.307 1.918 ‐1.973 2.433 deviation from trend in 2011 ‐0.571 2.188 ‐2.065 2.773 Group UU high income trend in 1990s 0.159* 0.071 0.229* 0.090 deviation from trend in 2000 ‐0.420 0.373 ‐0.710 0.451 deviation from trend in 2004 ‐0.798 0.632 ‐1.252 0.778 deviation from trend in 2006 ‐1.319 0.776 ‐2.104 0.959 deviation from trend in 2009 ‐2.111* 0.983 ‐3.211** 1.222 deviation from trend in 2011 ‐2.393* 1.120 ‐3.446* 1.391 Note: 1. Significance level: *** 0.001, ** 0.01, * 0.05.

126 Table 6.25 Test Results of Disparities for OOP Exceeding Certain Percentage of Household Income (High- income Families) OOP>20% household income OOP>40% household income Disparity Chi2 Disparity Chi2 Group RR disparity (Group UU probability‐Group RR probability) 1997 0.0242 0.22 0.0172 2.55 2000 0.0045 0.03 ‐0.0005 0.16 2004 ‐0.0052 0.02 ‐0.0080 0.31 2006 0.0006 0.21 0.0021 0.28 2009 ‐0.0014 0.08 ‐0.0032 0.52 2011 ‐0.0031 0.54 ‐0.0051 1.18 Group RU disparity (Group UU probability‐Group RU probability) 1997 0.0328 0.62 0.0226 1.50 2000 0.0009 0.01 ‐0.0076 0.77 2004 0.0115 0.95 0.0100 0.81 2006 0.0057 0.02 0.0029 0.19 2009 ‐0.0070 0.45 ‐0.0076 1.14 2011 0.0002 0.22 ‐0.0041 0.99 Group UR disparity (Group UU probability‐Group UR probability) 1997 0.0236 0.00 0.0163 0.28 2000 0.0079 0.14 ‐0.0026 0.24 2004 ‐0.0294 1.23 ‐0.0244 1.37 2006 0.0014 0.14 0.0024 0.21 2009 ‐0.0248 2.39 ‐0.0138 1.97 2011 ‐0.0166 1.91 ‐0.0161 2.50 Note: 1. Significance level: *** 0.001, ** 0.01, * 0.05.

127 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 1997 2000 ‐0.0197 1.04 ‐0.0176 0.05 2004 ‐0.0294 0.03 ‐0.0252 0.24 2006 ‐0.0237 0.02 ‐0.0151 0.18 2009 ‐0.0257 0.21 ‐0.0204 0.55 2011 ‐0.0274 0.08 ‐0.0223 2.00 compare with disparities with Group UU in 2000 2004 ‐0.0097 0.26 ‐0.0075 0.07 2006 ‐0.0039 0.91 0.0025 0.05 2009 ‐0.0060 0.47 ‐0.0028 0.31 2011 ‐0.0076 2.00 ‐0.0046 1.66 compare with disparities with Group UU in 2006 2009 ‐0.0020 0.06 ‐0.0053 0.12 2011 ‐0.0037 0.18 ‐0.0072 1.03 Group RU compare with disparities with Group UU in 1997 2000 ‐0.0319 2.34 ‐0.0301 2.18 2004 ‐0.0213 0.20 ‐0.0126 0.52 2006 ‐0.0271 2.47 ‐0.0196 0.82 2009 ‐0.0398 4.32* ‐0.0301 2.78 2011 ‐0.0326 3.86* ‐0.0267 2.73 compare with disparities with Group UU in 2000 2004 0.0106 1.74 0.0176 5.71* 2006 0.0048 0.00 0.0105 0.42 2009 ‐0.0079 0.53 0.0000 0.10 2011 ‐0.0007 0.25 0.0035 0.02 compare with disparities with Group UU in 2006 2009 ‐0.0127 0.50 ‐0.0105 0.85 2011 ‐0.0055 0.20 ‐0.0070 0.69 Group UR compare with disparities with Group UU in 1997 2000 ‐0.0157 0.00 ‐0.0188 0.05 2004 ‐0.0530 2.43 ‐0.0406 1.13 2006 ‐0.0222 0.60 ‐0.0139 0.03 2009 ‐0.0484 4.07* ‐0.0301 1.79 2011 ‐0.0402 3.38 ‐0.0324 2.53 compare with disparities with Group UU in 2000 2004 ‐0.0373 2.25 ‐0.0218 0.60 2006 ‐0.0065 0.61 0.0050 0.00 2009 ‐0.0327 3.81 ‐0.0113 1.13 2011 ‐0.0245 3.16 ‐0.0136 1.65 compare with disparities with Group UU in 2006 2009 ‐0.0262 2.04 ‐0.0162 1.63 2011 ‐0.0180 1.37 ‐0.0185 2.07 Note: 1. Significance level: *** 0.001, ** 0.01, * 0.05.

128 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

2011. 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 1993 ‐14.94171 0.5385447 ‐16.00619 ‐13.87724 disparity with Group UU in 1997 56.83302 3.057011 50.7906 62.87543 disparity with Group UU in 2000 177.9387 12.62493 152.9845 202.8928 disparity with Group UU in 2004 134.318 7.863278 118.7757 149.8604 disparity with Group UU in 2006 167.8766 10.2749 147.5675 188.1857 disparity with Group UU in 2009 155.0509 9.737621 135.8038 174.298 disparity with Group UU in 2011 ‐8.919616 7.159147 ‐23.07021 5.230974 Group RU disparity with Group UU in 1993 ‐51.11267 2.525351 ‐56.10422 ‐46.12112 disparity with Group UU in 1997 59.15513 3.02742 53.17121 65.13905 disparity with Group UU in 2000 212.4249 12.75617 187.2114 237.6384 disparity with Group UU in 2004 79.0237 8.962753 61.30815 96.73926 disparity with Group UU in 2006 141.3675 10.31639 120.9764 161.7587 disparity with Group UU in 2009 7.849936 15.6367 ‐23.05717 38.75704 disparity with Group UU in 2011 9.953452 6.343274 ‐2.584507 22.49141 Group UR disparity with Group UU in 1993 ‐14.98737 0.7037759 ‐16.37844 ‐13.59631 disparity with Group UU in 1997 35.24916 4.008824 27.32542 43.1729 disparity with Group UU in 2000 149.1536 12.82396 123.806 174.5011 disparity with Group UU in 2004 ‐128.8861 19.50158 ‐167.4325 ‐90.3398 disparity with Group UU in 2006 164.5178 10.36703 144.0266 185.009 disparity with Group UU in 2009 157.6196 9.344997 139.1485 176.0907 disparity with Group UU in 2011 24.96881 7.429766 10.28331 39.6543

129 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 1993 ‐29.1525 1.301184 ‐31.72439 ‐26.58061 disparity with Group UU in 1997 63.15983 3.104018 57.02451 69.29516 disparity with Group UU in 2000 268.4552 13.2618 242.2423 294.6681 disparity with Group UU in 2004 105.42 5.152178 95.23633 115.6037 disparity with Group UU in 2006 15.80023 6.096454 3.750134 27.85033 disparity with Group UU in 2009 69.28482 8.371618 52.73769 85.83195 disparity with Group UU in 2011 79.61505 5.349504 69.04135 90.18874 Group RU disparity with Group UU in 1993 ‐38.86723 1.308961 ‐41.45449 ‐36.27997 disparity with Group UU in 1997 71.6066 3.059992 65.5583 77.6549 disparity with Group UU in 2000 218.0608 14.02427 190.3408 245.7808 disparity with Group UU in 2004 48.78306 7.31754 34.31939 63.24672 disparity with Group UU in 2006 69.7419 6.951842 56.00106 83.48273 disparity with Group UU in 2009 44.86233 8.349629 28.35866 61.366 disparity with Group UU in 2011 ‐190.2435 14.18549 ‐218.2822 ‐162.2048 Group UR disparity with Group UU in 1993 ‐51.46965 2.644083 ‐56.69588 ‐46.24342 disparity with Group UU in 1997 48.45871 3.596754 41.34946 55.56796 disparity with Group UU in 2000 286.6617 13.02051 260.9256 312.3977 disparity with Group UU in 2004 106.3634 6.631208 93.25627 119.4704 disparity with Group UU in 2006 ‐15.27882 7.554651 ‐30.21115 ‐0.3464823 disparity with Group UU in 2009 33.43513 9.072895 15.50187 51.36839 disparity with Group UU in 2011 93.39053 6.207767 81.12042 105.6607

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 1993 6.369809 1.974789 2.46649 10.27313 disparity with Group UU in 1997 93.67514 2.733506 88.27216 99.07812 disparity with Group UU in 2000 236.2302 8.237553 219.9481 252.5123 disparity with Group UU in 2004 123.896 6.393577 111.2586 136.5334 disparity with Group UU in 2006 103.3722 4.66994 94.14169 112.6027 disparity with Group UU in 2009 127.0916 5.445521 116.3281 137.8551 disparity with Group UU in 2011 44.33455 5.518837 33.42615 55.24294 Group RU disparity with Group UU in 1993 29.87013 1.589092 26.72917 33.01109 disparity with Group UU in 1997 94.11589 2.687684 88.80348 99.4283 disparity with Group UU in 2000 223.0315 8.303527 206.619 239.4441 disparity with Group UU in 2004 207.175 5.230706 196.8362 217.5139 disparity with Group UU in 2006 136.4541 4.550388 127.4599 145.4483 disparity with Group UU in 2009 70.0443 6.180751 57.82758 82.26102 disparity with Group UU in 2011 97.0841 4.45719 88.27413 105.8941 Group UR disparity with Group UU in 1993 11.8881 2.298468 7.345005 16.43119 disparity with Group UU in 1997 83.18063 3.040064 77.17171 89.18954 disparity with Group UU in 2000 140.3317 9.95022 120.6644 159.9991 disparity with Group UU in 2004 ‐34.97923 14.60023 ‐63.83769 ‐6.120781 disparity with Group UU in 2006 108.8807 5.119126 98.7624 118.9991 disparity with Group UU in 2009 116.541 5.593961 105.4841 127.5979 disparity with Group UU in 2011 62.56974 6.686264 49.35383 75.78564

130 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 6.30.

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 6.31. 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 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*** 0.072 0.666** 0.093 Group RU 0.651** 0.097 0.737 0.129 Group UR 0.890 0.145 0.889 0.175 periods period 1 1 n/a 1 n/a period 2 1.464** 0.202 1.712*** 0.274 period 3 2.680*** 0.300 2.865*** 0.378 period 4 2.147*** 0.241 2.373*** 0.316 change in disparities Group RR in period 2 0.923 0.157 0.807 0.158 Group RR in period 3 0.954 0.131 0.812 0.131 Group RR in period 4 1.182 0.161 0.995 0.161 Group RU in period 2 1.079 0.235 1.045 0.256 Group RU in period 3 1.274 0.221 1.105 0.224 Group RU in period 4 1.263 0.220 1.101 0.225 Group UR in period 2 0.656 0.174 0.701 0.210 Group UR in period 3 0.971 0.190 0.881 0.210 Group UR in period 4 1.145 0.221 1.053 0.244 Note: 1. Significance level: *** 0.001, ** 0.01, * 0.05.

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 3 0.05 0.8286 0.00 0.9751 change in disparity in period 3 = Change in disparity in period 4 4.51* 0.0337 2.98 0.0844 Group RU change in disparity in period 2 = Change in disparity in period 3 0.79 0.3753 0.07 0.7893 change in disparity in period 3 = Change in disparity in period 4 0.00 0.9452 0.00 0.9824 Group UR change in disparity in period 2 = Change in disparity in period 3 2.64 0.1043 0.68 0.4099 change in disparity in period 3 = Change in disparity in period 4 1.24 0.2649 1.01 0.3157 Note: 1. Significance level: *** 0.001, ** 0.01, * 0.05.

Estimated disparities in total health costs are shown in table 6.32. 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

Table 6.32 Bootstrap Results for Disparities in Total Health Costs (DID Analysis) disparity Mean Std. Err. [95% Conf. Interval] Group RR period 1 37.136 0.485 36.183 38.089 Group RR period 2 242.434 3.155 236.236 248.632 Group RR period 3 120.964 1.393 118.226 123.701 Group RR period 4 68.133 1.385 65.413 70.853 Group RU period 1 34.784 0.550 33.703 35.865 Group RU period 2 233.906 3.186 227.646 240.165 Group RU period 3 129.381 1.479 126.476 132.287 Group RU period 4 22.255 1.753 18.811 25.699 Group UR period 1 27.080 0.600 25.902 28.259 Group UR period 2 211.009 3.431 204.267 217.751 Group UR period 3 42.139 2.338 37.545 46.733 Group UR period 4 75.695 1.442 72.861 78.529

6.4 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 1997.

4. There is no evidence showing that more health insurance coverage had an immediate impact on high level of OOP.

133 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 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 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 1997. 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 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

7.2.1 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 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.

7.2.2 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 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.

7.2.3 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 2008.

139 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.

7.2.4 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 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 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 Appendix

Table A1 Major health insurance schemes

Urban Employee Basic Urban Resident Basic New Rural Cooperative Medical Insurance Medical Insurance Medical Insurance

Launching Time 1998 2007 2003

Urban Resident who are Insured Population Urban Employee Rural Resident not covered by UEBMI

Risk Pools County level City level City level

Employer and Government and insured Government and Premium Paid By Employee individual insured individual

Employer pays 6% of At least 300 CNY, in which At least 300 CNY, in Annual Premium Level employee's wage, government pays 240 CHY/ which government pays (2012) employee pays 2% of insured 240 CHY/ insured the wage

6 times of local average 6 times of local per capita 8 times of local per Reimbursement Cap (2012) salary (at least 60000 income (at least 60000 capita income (at least CNY) CNY) 60000 CNY)

Covered Services

Inpatient Services Covered Covered Covered

Outpatient Services for Catastrophic Illnesses Covered Covered Covered

Limited and vary by Limited and vary by General outpatient services Covered location location

Number of Insured at 2010 237 195 836 Year‐end (Million)

143 Table A2 GDP in 2012 of the sampled provinces

GDP in 2012 Province (Unit: billion Chinese Yuan)

Jiangsu 5405.8

Shandong 5001.3

Henan 3000.0

Liaoning 2480.1

Hubei 2225.0

Hunan 2215.4

Shanghai 2010.1

Beijing 1780.1

Heilongjiang 1369.2

Guangxi 1303.1

Chongqing 1145.9

Guizhou 680.2

144 Reference

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146 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.

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