Did the rural health insurance introduction reduce urban-rural
disparities in Health Care Utilization in China?
ERASMUS UNIVERSITY ROTTERDAM
Erasmus School of Economics
Department of Economics
Supervisors: Eddy Van Doorslaer
Name: Yimeng Wang
Exam Number : 365525
E-mail address: meng420315@gmail.com
Abstract
This thesis will analyze urban-rural disparities in health care utilization in China and further
try to find out whether NCMS can partially reduce this kind of disparity. Our finding is that
there is no significant difference in inpatient utilization. While we find that the excess urban
use of outpatient and preventative service do exist and insurance especially NCMS can
partially explain the reducing urban-rural disparities in outpatient use. And the other
insurance (exclude NCMS) can reduce the urban-rural disparity in preventative utilization.
1
Contents
1. Introduction ................................................................................................................ 1
1.1 Background ...................................................................................................... 1
1.2 Motivation ........................................................................................................ 2
1.3 Research Questions .......................................................................................... 2
1.4 Research Design............................................................................................... 3
2. Literature review ........................................................................................................ 4
2.1 Urban-rural Disparity of Utilization in China .................................................. 4
2.2 Effects of NCMS on Health Care Utilization .................................................. 5
2.3 Summary .......................................................................................................... 6
3. Methodology and Data ............................................................................................... 6
3.1 Empirical Model .............................................................................................. 6
3.2 Data and Variables ........................................................................................... 7
3.3 Descriptive Statistics ........................................................................................ 9
4. Results ...................................................................................................................... 10
4.1 Urban-rural Disparity of Utilization .............................................................. 11
4.2 Urban-rural Disparity of Utilization over Time ............................................. 11
4.3 Effects of NCMS on utilization and urban-rural disparity............................. 12
5. Conclusions .............................................................................................................. 19
5.1 Main Conclusions .......................................................................................... 19
5.2 Limitations ..................................................................................................... 20
References .................................................................................................................... 21
Appendix ...................................................................................................................... 22
I
List of Tables
Table 1 Variables and their definitions ....................................................................... 8
Table 2 Summary statistics of independent variables ................................................. 9
Table 3 Means of health care utilization: by Area .................................................... 11
Table 4 Means of health care utilization: by Year .................................................... 12
Table 5 Marginal effects estimated for inpatient care utilization ............................. 13
Table 6 Marginal effects estimated for outpatient care utilization ........................... 15
Table 7 Marginal effects estimated for preventative care utilization........................ 17
II
1. Introduction.
1.1 Background
Ever after establishing the People’s Republic of China, the Chinese government has
always been focusing on promoting the economic development for transforming its social
structure. China has experienced a period of rapid economic growth which derives from
introducing the market economy into China and opening up China to the outer world (Li,
2014).
However, China’s health care system and health insurance system didn’t benefit a lot
from this economic prosperity. Instead, studies show that because of the very imbalanced
development of factors related to health resources across different areas in China, inequity
of health care utilization has begun to emerge in this country (Shenglan Tang 2008). One
of the most noticeable inequality problems is the utilization disparities between rural and
urban areas (M. Liu, Zhang, Lu, Kwon, & Quan, 2007).
If nothing would be done to reduce the disparity between rural and urban areas in
China, it would then result in the disparity of health accordingly, thereby violating the
principle of social justice. Therefore, the central government has taken measures to reduce
the rural-urban gap of health care utilization, such as income redistribution planning and
nine-year compulsory education (Xie'e, 2009). In addition to these indirectly health-related
policies, the most direct national intervention is the rollout of what is known as the New
Cooperative Medical Scheme (NCMS), which has become one of the most important social
health insurance system in China since 2003 (Lei & Lin, 2009). By the end of 2011, the
coverage of NCMS in rural citizens had reached 97.5% (Hou, Van de Poel, Van Doorslaer,
Yu, & Meng, 2013). People can participate in the NCMS voluntarily and share the cost of
health service with governments. In 2011, the reimbursement level of inpatient and
outpatient reached 75% and 50%, respectively in some provinces(Barber & Yao)
1
NCMS was designed to cover rural residents and collect funds from individual
commitments and governments to protect them from financial risk when they need health
care, which helps to ensure the insured can use health care when they really need it.
Otherwise, they might be less likely to use health care even when they became seriously ill
because of limited ability to pay (Hou et al., 2013).From 2003, governments select at least
two or three cities (counties) as trial points in each province, municipality and autonomous
city. By the end of 2010, coverage of NCMS in rural citizens reached more than 80% in the
whole country.
1.2 Motivation
After decades of health care reform, China is on its way towards universal health
coverage. Unfortunately, the urban-rural gap in health care utilization still exists and is
gradually becoming a social relevant problem. Some studies like Xie’e (2009) explains that
different income levels lead to urban-rural health use gap. Hence, it is urgent to find out
potential reasons for such urban-rural disparity, theoretically and empirically.
Several studies have already examined the effect of NCMS on health care utilization.
For example, Baorong Yu et al., (2010) have found that NCMS does not significantly
increase outpatient service utilization in two province-Shandong and Ningxia after
controlling income level and location. Regarding NCMS has become the most significant
health insurance scheme in rural China, the question naturally arises whether it has had an
impact on reducing the urban-rural disparity of utilization and to what extent NCMS can
decrease the inequality of health care utilization.
1.3 Research Questions
Studies only focus on rural residents and select several rural areas to assess the health
utilization effect of NCMS. Far fewer studies combine the urban-rural gap and NCMS
effect. Therefore, in terms of the background and motivation discussed above, the research
2
question of this thesis is summarized as two parts:
One is that whether there is excess health care use in urban regions than rural areas.
The hypothesis is:
𝐻1 : The urban health utilization is equal to the rural health utilization
Such comparisons would be done using either samples from 2000 or samples from
2011. The former samples can provide information about disparity before the establishment
of NCMS in 2003. The latter samples are used to describe disparity after NCMS has been
implemented. This then leads to the second hypothesis:
𝐻2 : Urban-rural disparity of health care utilization is increasing over time.
There is some evidence suggesting that there is a widening gap in health utilization,
but the empirical analysis focus on the period of Chinese transition from command
economy to marketed economy (Y. Liu, Hsiao, & Eggleston, 1999). Some researchers claim
that the health service utilization declined in both urban and rural areas with the mortality
rates decreasing, but more rapidly in urban areas (Gao, Qian, Tang, Eriksson, & Blas, 2002).
While there is no clear trend for urban and rural gap in health utilization, this paper aimed
to analyze whether the disparity has changed from 2000 to 2011.
After testing these two hypothesis, this thesis wants to address another question: if
urban-rural disparity of health care utilization does exist in reality, whether and to what
extent has NCMS contributed to a reduction of such disparity. So the third hypothesis is:
𝐻3 : The development of rural health insurance, especially NCMS, can partially
explain the urban-rural gap and has an impact on the trend of such gap.
This hypothesis is used to test what factors can explain reduce the gap.
1.4 Research Design
The thesis is organized as follows.
In Chapter 2, this thesis will review relevant literatre to this thesis.
Chapter 3 will first introduce a methodology for analyzing the urban-rural disparity,
change of disparity from 2000 to 2011 and the effects of NCMS on health care use. Then
3
it will introduce data sources for empirical analysis in this thesis.
Chapter 4 reports and discusses empirical results. First, we test whether there is an
urban-rural gap in utilization health with the t-test. If there is a gap, we will compare such
gap between 2000 and 2011, trying to find out patterns of time trends of such gap. Then,
the logit estimation results will be discussed to see whether health insurance especially
NCMS can be attributed to explain those findings related to the urban-rural disparity of
utilization.
2. Literature review
2.1 Urban-rural Disparity of Utilization in China
Some researches describe the current situation of urban-rural health utilization in
China. For instance, Liu and Zhang (2007) use data from the Third National Health Service
Survey in 2003 to examine whether there is a gap between rural and urban in terms of health
care use. They conclude that there is a significant disparity in urban-rural health service
utilization. More specifically, rural residents visited physicians more frequently than urban
residents. While rural minority residents visited physicians less than people in the urban
area. Most of the fixed variables such as income and education also will be used in this
paper, while this paper not only analyze the urban-rural gap in health service use but also
test the change of the urban-rural disparity.
In view of reasons of disparity urban-rural gap, Xie’e (2009) does research about this
topic. He selects data from China Health Nutrition Survey (CHNS), which covers urban
and rural areas in nine provinces. The author chooses utilization of health service as the
dependent variable and average household income as the individual income as the
independent variable then including other explanatory variables such as health condition,
gender, location, jobs, education and so on. This paper exploits that there are differences
between rural and urban areas in China, but the author did not consider the health service
4
disparity between urban and rural, they only separately describe the urban-rural difference
in income, gender, age and so on. This paper shows that income is associated with health
service use. Inequitable income makes inpatient utilization unequal. People who are in
high-level of income will use more health service than that of people who have lower
income. When it comes to the influence of urban-rural gap to health care utilization, the
paper attributes it to economic imbalanced development. Even though this paper also takes
insurance into account, it only considers about compulsory insurance scheme and believes
that the coverage of voluntary insurance is relatively low before 2004. That is one possible
reason that the influence of insurance is not significant to health care use.
2.2 Effects of NCMS on Health Care Utilization
In the last decade, many studies have tested the effect of this kind of insurance policy
on health care utilization. For example, Xiaoyan Lei and Wangchuan Lin (2009) investigate
that whether rapid increase health insurance (NCMS) coverage improve individuals’ health
and utilization of medical service. The results show that insured people are healthier than
the uninsured group, if the paper regarded feeling sick or injured in four weeks as the health
measurement. Besides that, they distinguish physical examinations and other preventative
care service and find many people participate NCMS in order to get free annual general
physical examinations. It indicates that people who are insured are inclined to use more
preventative care service than the uninsured group. However, on top of medical care
utilization, the probability of insured group is not significantly larger than that of the
uninsured group.
Besides this paper, some studies focus on the effect of NCMS on specific provinces
such as Shandong and Ningxia. In the paper of Hou et al., (2013), they observe people who
insured NCMS and identify the causal effect of this insurance coverage on access to care
and health expenditure. They find the increased coverage of NCMS lead to raising the
probability of using inpatient and outpatient care. Furthermore, NCMS incentives enrollees
to use more outpatient service, compared to the other health insurance. NCMS also reduce
5
the share of out-of-pocket spending on inpatient and outpatient service.
2.3 Summary
In the review of literature, it is found that there are mixed evidences about the effects
of NCMS on the utilization in terms of inpatient care, outpatient care or even preventative
service. Besides that, fewer studies analyze that whether NCMS can explain the urban-rural
gap of health care utilization.
Therefore, in my thesis, I focus on the utilization gap between rural and urban, among
which disparity of inpatient care, outpatient care and preventative care will all be analyzed.
I will also study the effect of NCMS on different types of health care utilization and discuss
whether such effect would help to improve equality of use between rural and urban areas
in China.
3. Methodology and Data
This chapter will first introduce the empirical model used to estimate the effects of
NCMS on health care utilization. And then, I will explain the data used for empirical
analysis. Variables used in this thesis will finally be introduced and described statistically.
3.1 Empirical Model
Health care utilization is often measured with a dummy, namely whether one uses
health care or not. Parallel with most of previous related studies, I use such dummy to
measure health care utilization too1. Since the dependent variables are 0-1 variable, logit
model would be adopted, which is widely applied in health economics field. Hou et-al
(2009) use a conditional logit model to estimate health care use and the choice of medical
1
For example, CHNS reports that if the outcome of inpatient (outpatient or preventative service) is one (zero), it means
that the interviwee does (not) utilize health care service during the last four weeks.
6
facilities. The model is written as:
exp⁡(𝛽 +𝛽2 𝑥𝑖 )
𝑝𝑖 =Pr[𝑦𝑖 = 1|𝑥𝑖 ] = 1+exp⁡(1𝛽
1 +𝛽2 𝑥𝑖 )
Which clearly ensures that⁡0 < 𝑝𝑖 < 1. 𝑥𝑖 represents all covariates that can be used to
explain health care utilization. To be specific, it includes demographic conditions (e.g. age
and gender), socioeconomic conditions (e.g. log income and education) and health status
(e.g. number of chronic conditions and stroke) as well as health insurance variables (e.g.
NCMS). Fixed provincial effects are controlled.
The implied marginal effect for logit model is:
̂1 + 𝛽
̂2 𝑥⁡
𝛿𝑝𝑖
𝑒𝑥𝑝(𝛽
̅)
̂
⁡=
2 𝛽2
𝛿𝑥̅
̂+𝛽
̂𝑥⁡
(1 + 𝑒𝑥𝑝(𝛽
̅ ))
1
2
The interpretation of the marginal effect is that the independent variable changes one
point, how many percentage points change in health care use (A. COLIN CAMERON,
2005).
3.2 Data and Variables
3.2.1 Data
Data for this thesis are collected from China Health and Nutrition Survey (CHNS)1, a
very famous survey data which sampled from urban and rural areas in 12 provinces in
China. These provinces covered various geographic and demographic characteristics. Year
2000 and 2011 are chosen in order to test the change of the urban-rural utilization gap,
which yielding 17018 observations in two years together
In 2000, the number of rural residents and urban residents is 4542 and 2944,
respectively. In 2011, the number of people from urban has been increased to nearly two
times as compared to the last 11 years. Meanwhile, the number of people from rural areas
nearly keeps stable (4542 in 2000, 4725 in 2011). Additionally, the sample excludes
1
CHNS: http://www.cpc.unc.edu/projects/china.
7
children who are below 18 years old.
3.2.2 Variables
The key dependent variables in the thesis are health utilization at the individual level.
Inpatient, outpatient and preventative service will all be analyzed as a measure of health
care utilization because they are frequently discussed in assessment of NCMS.
The independent variables are categorized into several types:
1. Demographic variables (e.g. age and gender). Because different ages have different
health conditions, which will influence the utilization of health care, this thesis separates
age into three categories (age below 45 years old; age between 45 and 65 years old and age
above 65 years old).
2. Socioeconomic variables (e.g. income, education, hukou and locations). Household
income level is used as the indicator of ability to pay because individual income and
household income are highly related to each other and I cannot use both in the same
regression. Compared to individual-level income, people often share expenditure of health
utilization in the same household. Furthermore, this thesis transforms household income to
logarithm household income, which allows recovering the income elasticity of utilization
of health care service. Note that urban and rural people are identified based on their hukous
that are recorded in the system of a household registration(unknown, 2015).
3. Health status (e.g. the number of chronic conditions and stroke or not).
4. Health insurance status (e.g. NCMS or not).
All variables used in this thesis is summarized in Table 1.
Table 1 Variables and their definitions
Variables
Inpatient care
Outpatient Care
Preventive Care
Definition
1=Use inpatient care during the last 4 weeks;
0=
1=Use outpatient care during the last 4 weeks;
0=
1=Use preventive care during the last 4 weeks;
8
0=
Gender
1=female, 0=male
Age
0=No being educated; 1=Primary school; 2=Middle School; 3=High School;
Education
4=College and above
Province
1=a specific province; 0=not the specific province
Income
log income household
Stroke
1=stroke; 0=Not having stroke
Diseases
Number of chronic diseases
NCMS
1=Insured by NCMS; 0=Not insured by NCMS.
Other insurance
Not insured
1=Insured by other insurance except NCMS;
0=Not insured by other insurance.
1= insured by any type of health insurance;
0= Insured by any type of health insurance.
3.3 Descriptive Statistics
Table 2 illustrates summary statistics of all dependent variables.
Table 2 Summary statistics of
Urban
Rural
independent variables
N
Mean
N
Mean
age
8040
49.00
9148
46.00
age below 45(1/0)
8040
39%
9148
46%
age1 below 65(1/0)
8040
42%
9148
44%
age above 65(1/0)
8040
18%
9148
9.7%
gender(female=1, male=0)
8040
49%
9148
51%
Education
8040
2.57
9148
1.33
hhinc(RMB)
8040
52238.43
9148
26044.04
Stroke(1/0)
8040
1.60%
9148
0.47%
Num of chron
8040
0.2349
9148
0.094
NCMS (1/0)
8040
6.80%
9148
50%
Other health insurance (1/0)
8040
73.89%
9148
4.41%
Demographic variables:
Socioeconomic variables:
Health status:
Health insurance status:
9
Not insured(1/0)
8040
20.95%
9148
46.10%
80% urban citizens insured while only half of the rural citizens have insurance. Among
the different kinds of insurances, this thesis compares NCMS and non-NCMS insurance
effect and shows that half of rural citizens participate in the NCMS insurance while about
74 percent of urban people have non-NCMS insurance. It means most rural people would
like to participate NCMS and urban people are willing to choose the other insurances.
In addition to health insurance status, the other individual characteristics are
summarized as follows. First, the mean age of urban citizens (49) is slightly higher than
rural people’s age (46). More specifically, this thesis divides age variable into three age
groups: one group is below 45; one is between 45 and 65 years old; the last is above 65
years old. From the mean of these three age groups, it illustrates that more than 80%
observations are below 65 years old. From the whole sample, we can see that the share of
urban older people who are above 65 years old is more than that of rural older people. Next,
the table shows that male-female ratio is relative the same between urban and rural (50-50).
Third, while data of education and household income variables between urban and rural
have different distributions. The average education level of urban citizens is between
middle and high school, which is higher than that of rural citizens (between primary and
middle school). Fourth, the average urban household income level is nearly two times as
much as the average rural one. Finally, as for health conditions, the stroke data illustrates
that there is 1.6 percent probability to get stroke among urban citizens, which is higher than
that of rural citizens. The average of the number of chronic conditions for urban or rural
citizens is less than one.
4. Results
This chapter shows the results from table 3 to table 7 so that to enable one to decide
whether or not to reject the hypotheses stated previously.
10
4.1 Urban-rural Disparity of Utilization
Table 3 illustrates health care utilization and the differences in the means of health
care utilization between rural and urban areas.
Table 3 Means of health care utilization: by Area
Urban
Rural
Mean of urban-rural
obs
mean
obs
mean
inpatient visit last 4 weeks (1/0)
8142
1.4%
9267
0.84%
0.57%
outpatient visit last 4 weeks(1/0)
8142
9267
8.71%
3.45%
preventative service(1/0)
8029
9150
2.8%
4%
12.16%
6.8%
From Table 3, 1.4% people from urban areas use inpatient service in the last four
weeks, which is higher than rural citizens (0.84%). Furthermore, 12.16% urban residents
occupied outpatient service care in the last four weeks, while only 8.7% rural people paid
a visit to the outpatient care in the same period. Moreover, more people generally choose
to use outpatient or preventative service care rather than inpatient care service.
In order to test the first hypothesis, one has to make the comparison between the
respective mean of the inpatient, outpatient and preventative utilizations in both urban and
rural areas. After making such comparison, it can be seen that more urban citizens tend to
utilize such health care service than rural citizens do. The last column (urban-rural)
demonstrates that compared to rural populations, more urban citizens pay a visit to the
health care service, no matter in 2000 or 2011.Furthermore, t-test also proves that the there
is a significant excess urban use in health care utilization. Therefore, we can reject the first
hypothesis.
4.2 Urban-rural Disparity of Utilization over Time
As for the second hypothesis, after analyzing the gap between the health care
utilization in urban areas and the health care utilization in rural areas for 2000 and 2011
respectively, we can compare the difference between the utilization of health care service
11
in urban and rural areas in different years and check if such difference varies with time.
The results are shown in Table 4.
Table 4 Means of health care utilization: by Year
Year
Urban
Rural
urban-rural
inpatient visit last 4
2000
2944
0.75%
4542
0.39%
weeks (1/0)
2011
5198
1.79%
4725
1.27%
outpatient visit last
2000
2944
6.42%
4542
5.15%
4 weeks(1/0)
2011
5198
15.4%
4725
12.1%
3.28%
2000
2840
1.23%
4425
0.41%
0.82%
2011
5198
9.87%
4725
5.04%
preventative
service(1/0)
0.35%
0.52%
1.27%
2011-2000
0.17%
2.01%
4.01%
4.83%
In this section, we still use comparison-means method to test utilization gap. From
Table 4, it can be seen that more people (urban and rural) use health care service in 2011,
compared with that of 2000. However, within the same year, there is still a difference
between people from urban and rural in the health utilization, which the urban-rural gap
experienced an increase in 2011, compared to 2000. In the eighth column (gap urban-rural)
in table 4, the excess urban health care use in 2011 tends to be larger than that in 2000,
because the difference in the gap between urban and rural areas (gap in 2011-gap in 2000)
is positive. Hence, I cannot reject the second hypothesis.
4.3 Effects of controlling for health insurance coverage on
utilization and urban-rural disparity
In this section, we use a three-step approach to test the urban-rural disparity and
further to find out whether insurance especially NCMS insurance has an effect on the health
utilization. Furthermore, after adding dummy other insurance (not included NCMS) in
model (2) and adding dummy NCMS and other insurance variables in model（3）, we can
find the changes of the urban-rural gap in health utilization
12
4.3.1 Effects of controlling for health insurance coverage on
inpatient care utilization
At first, we use logit regression to test urban-rural gap in health care use, given that
having controlled for a series of social conditions and health status. Table 5 shows that
marginal effect on the probability of hospitalization in the last four weeks, with a positive
and insignificant effect for both measures on urban-rural gap and change of the gap. It
means that there is no significant difference between urban and rural in the utilization of
inpatient care, after controlling for other determinants of use. Moreover, the urban-rural
gap in the probability of hospitalization has not changed significantly over time.
Table 5 Marginal effects estimated for hospitalization
（1）
（2）
（3）
Urban×2011
-0.000237
-0.00242
-0.00205
Urban
0.00403
0.000655
0.000496
Year (1=2011, 0=2000)
0.0110***
0.0111***
0.0106***
Insured and not NCMS
0.00751***
NCMS
0.000543
Other health insurance
0.00780*
age 45-65
0.00654**
0.00629**
0.00627**
age above 65
0.0118***
0.0111***
0.0111***
Female
-0.00126
-0.00123
-0.00124
ln (household income)
-0.000735
-0.00102
-0.00102
number of chronic diseases
0.00564***
0.00553***
0.00553***
stroke
0.0127***
0.0128***
0.0128***
no education
0.000603
0.00179
0.00180
primary school
0.00332
0.00427
0.00426
middle school
0.000896
0.00163
0.00164
high school
-0.00132
-0.000941
-0.000932
Beijing
-0.00707***
-0.00744***
-0.00743***
Liaoning
-0.00310***
-0.00322***
-0.00320***
Heilongjiang
-0.00605***
-0.00609***
-0.00609***
Shanghai
-0.00596***
-0.00645***
-0.00643***
Jiangsu
-0.000334
-0.000879
-0.000890
13
Shandong
-0.000115
-0.000441
-0.000444
Henan
0.000401
0.000221
0.000237
Hubei
0.00173***
0.00176***
0.00178***
Hunan
0.00441***
0.00429***
0.00430***
Guangxi
0.000965
0.000973
0.000983
Guizhou
0.00338***
0.00327***
0.00327***
N
17018
17018
17018
Notes: ***, ** , * indicates significance of the marginal effects estimated at 1%, 5% and 10%. Standard
errors adjusted for clustering at province level.
Regarding NCMS-related variables, column 2 illustrates that if a person is insured by
health insurance other than NCMS, this had a positive and significant effect on his
probability of hospitalization. From column 3, it is observed that when one is insured with
other health insurance except NCMS, his propensity to use inpatient care will be 0.78
percentage points higher than that of a person who is not insured by any health insurance.
Nonetheless, NCMS does not have a significant influence on the probability of using
inpatient utilization.
Furthermore, Table 5 shows that education level, income and gender do not have a
significant effect on the probability of hospitalization. While variables such as year, age
and health status have effects on the likelihood of using inpatient care. Specifically, the
probability of hospitalization significantly increases around 1.1 percentage points in 2011
than in 2000. And the probability of being hospitalized for the elder people (above 45 years
old) is significantly higher than people who are younger than 45 years old. Namely, the
older people are, the higher probability of inpatient utilization is. Regarding the health
status, stroke and the number of chronic conditions both have positive and significant
effects on the probability of inpatient utilization. In other words, if a person gets a stroke
or additional chronic conditions, his probability of using inpatient care will increase by
1.27 percentage points and 0.56 percentage points respectively.
An interesting finding is that provincial dummies have different effects on inpatient
care utilization. Some provinces such as Hebei and Hunan have significantly positive effect
on the probability of hospitalization compared to Chongqing, while some provinces such
14
as Beijing and Liaoning have the lower probability of using inpatient service.
4.3.2 Effects of controlling for health insurance coverage on
outpatient care utilization
Table 6 shows the estimated marginal effect for the probability of outpatient care use.
Table 6 illustrates that the urban-rural gap in the probability of utilization of outpatient care
significantly decreases over the time. It means ceteris paribus, the urban-rural gap of the
probability of using outpatient in 2011 has decreased 3.15 percentage points compared to
the urban-rural gap of probability of outpatient visits in 2000. But the urban-rural gap itself
does not have a significant difference in the probability of outpatient care.
Table 6 Marginal effects estimated for outpatient care utilization
（1）
（2）
（3）
Urban×2011
-0.0315**
-0.0362***
-0.00582
Urban
0.0109
0.00507
-0.00535
Year (1=2011, 0=2000)
0.0452***
0.0453***
0.00491
Insured and not NCMS
0.0148
NCMS
0.0484***
Other health insurance
0.0400***
age 45-65
0.0480***
0.0475***
0.0464***
age above 65
0.0712***
0.0699***
0.0695***
Female
0.0264***
0.0264***
0.0266***
ln (household income)
-0.00140
-0.00191
-0.00226
number of chronic diseases
0.0684***
0.0682***
0.0686***
stroke
-0.00697
-0.00705
-0.00638
no education
0.0256**
0.0283***
0.0277**
primary school
0.0103
0.0124
0.0122
middle school
-0.00138
0.000149
0.000379
high school
-0.0115*
-0.0107*
-0.00985
Beijing
0.0358***
0.0351***
0.0366***
Liaoning
-0.116***
-0.116***
-0.114***
Heilongjiang
-0.138***
-0.138***
-0.137***
Shanghai
0.0411***
0.0396***
0.0412***
15
Jiangsu
-0.0638***
-0.0650***
-0.0683***
Shandong
-0.0839***
-0.0841***
-0.0858***
Henan
-0.0387***
-0.0389***
-0.0366***
Hubei
-0.0967***
-0.0967***
-0.0947***
Hunan
-0.0732***
-0.0733***
-0.0718***
Guangxi
-0.0400***
-0.0398***
-0.0380***
Guizhou
-0.0952***
-0.0953***
-0.0943***
N
17018
17018
17018
Notes: ***, ** , * indicates significance of the marginal effects estimated at 1%, 5% and 10%. Standard
errors adjusted for clustering at province level.
It is found in Table 6 that insured by health insurance other than NCMS does not have
significant influence on outpatient visits. But, after controlling for the variable ,which
describes whether the NCMS health insurance or other insurance is used the coefficient of
the change of urban-rural gap in outpatient visits becomes significantly less than that in
column 1. It means the urban-rural gap in probability of using outpatient in 2011 decreases
3.6 percentage points compared the urban-rural gap in probability for an outpatient visit in
2000 if people’s health insurance status are held to be the same, which is higher than that
shown in column 1. Column 3 introduces NCMS as an additional variable to explain the
effect of health insurance on probability of outpatient utilization. The change of the urbanrural gap in the probability of outpatient utilization with time in column 3 does not have a
significant difference. It means after controlling NCMS, the difference in the urban-rural
gap of outpatient utilization between 2000 and 2011 is not significant. Moreover, the year
effect is not significant to the probability of outpatient use either. Besides, both NCMS and
other health insurance have significant positive influence on outpatient utilization. The
probability of outpatient utilization for a person who has NCMS insurance will be 4.8
percentage points higher than a person without any insurance. Column 1, 2 and 3 show that
with the intervention of NCMS, the change of urban-rural gap over time becomes
insignificant.
Furthermore, the probability of outpatient use rises with time and aging. Additionally,
generally speaking, females increase 2.6 percentage points in the probability of outpatient
16
visits than that of males. In terms of health status, having stroke does not significantly
influence the probability of outpatient care use. However, an increase in the number of
chronic conditions by one unit will induce an increase in the probability of outpatient visits
by 6.8 percentage points. Furthermore, only people with no education have a significant
positive effect on outpatient visits, compared to college education level. The other
educational levels do not have a significant difference with a college education in the
utilization of outpatient.
Again, we find that provincial dummies have significant effects on the probability of
outpatient use. To name a few, people residing in Beijing and Shanghai are more likely to
use outpatient care while people living in Jiangsu, Shandong and Henan are less likely to
use outpatient care.
4.3.3 Effects of NCMS on preventative care utilization
Table 7 shows the results of the urban-rural gap and the effect of NCMS on
preventative care utilization. The probability of preventative visits for an urban citizen is
about 3.12 percentage points higher than that of a rural citizen. Furthermore, the change of
the urban-rural gap over time has a significantly negative influence on the preventative
utilization. It means ceteris paribus, the disparity of using preventative care between urban
and rural areas in 2011 decreases by 3 percentage points compared to 2000.
Table 7 Marginal effects estimated for preventative care utilization
（1）
（2）
（3）
Urban×2011
-0.0302*
-0.0335*
-0.0224
Urban
0.0312**
0.0258*
0.0200
Year (1=2011, 0=2000)
0.0856***
0.0854***
0.0705***
Insured and not NCMS
0.0121
NCMS
0.0187*
Other health insurance
0.0243***
age 45-65
0.0102**
0.00995**
0.00961**
age above 65
0.0195***
0.0187***
0.0187***
Female
0.00985***
0.00976***
0.00978***
17
ln (household income)
0.0111***
0.0108***
0.0108***
number of chronic diseases
0.0280***
0.0278***
0.0278***
stroke
-0.00216
-0.00214
-0.00192
no education
-0.0162***
-0.0143***
-0.0146***
primary school
-0.0172**
-0.0158**
-0.0160**
middle school
-0.0103**
-0.00931*
-0.00932*
high school
-0.0134***
-0.0130***
-0.0127***
Beijing
0.00933**
0.00879**
0.00891**
Liaoning
-0.0349***
-0.0350***
-0.0347***
Heilongjiang
-0.0757***
-0.0757***
-0.0757***
Shanghai
-0.000324
-0.00140
-0.00127
Jiangsu
-0.0109***
-0.0117***
-0.0120***
Shandong
-0.00852***
-0.00893***
-0.00911***
Henan
-0.0354***
-0.0355***
-0.0353***
Hubei
-0.00422**
-0.00402**
-0.00393**
Hunan
-0.0273***
-0.0276***
-0.0275***
Guangxi
-0.0252***
-0.0247***
-0.0244***
Guizhou
-0.0400***
-0.0399***
-0.0400***
N
16818
16818
16818
Notes: ***, ** , * indicates significance of the marginal effects estimated at 1%, 5% and 10%. Standard
errors adjusted for clustering at province level.
Column 2 controls for the variables that describe the number of individuals who are
insured by the health insurance other than NCMS, but it does not have significantly positive
effect on the probability of preventative visits. However, in column 3, NCMS shows
significantly positive effect on the probability of preventative utilization. People who have
insured by NCMS will be 1.87 percentage points higher to use the preventive care than
those without any health insurance. Furthermore, other health insurance has more effect on
the probability of preventative visits than that of NCMS. Given the same baseline (a person
without any health insurance), the probability of utilizing preventative care for a person
who has other health insurances will increase by 2.43 percentage points. After controlling
for NCMS and other health insurances, the positive urban-rural gap and negative change
of the gap over time on preventative utilization becomes insignificant.
18
Besides age and number of chronic conditions, variables such as household income
and educational level have a significantly positive and negative effect on the probability of
using preventative service. A 1-point increase in household income raises the probability
of using the preventative service by 1.11 percentage points. Higher educational level will
lead to higher probability of preventative visits. For example, the probability of
preventative utilization for a person with high school educational background decreases 1.3
percentage points compared to the probability of a person with college educational
background.
Similar to the results of the provincial dummies found in Table 5 and Table 6, the
province variables such as Beijing and Liaoning still have various effects on the probability
of preventative utilization.
5. Conclusions
This last chapter will conclude all the main results found in Chapter 4. And we will
state the limitations of my thesis finally.
5.1 Main Conclusions
From the early 1990s, health care use is imbalanced between urban and rural citizens
in China(Y. Liu et al., 1999). Most researchers focus on analyzing reasons for the urbanrural gap but not taking into account of the effect of NCMS. This thesis merges these two
together to study the effect of NCMS on the urban-rural disparity of health care utilization
using CHNS from 2000 to 2011.
From the results of the first hypothesis, this thesis finds that there is a significantly
excess use of urban in health utilization. According to the results of the second hypothesis,
the trend of the excess use of urban will become larger. It means the urban-rural gap in
health utilization in 2011 becomes larger than that of 2000.
They are important results but primary because they are based on the mean difference
19
comparison and t-test, there are some other influential variables which can have impacts
on health care utilization. Therefore, the thesis uses logit regressions to test whether there
is no significant difference between people from urban and rural areas on the probability
of hospitalization and outpatient utilization. But as for the preventative service care, the
urban-rural disparity does exist, namely urban residents’ utilization exceeds that of those
rural residents. In addition, this thesis also finds that the change of urban-rural gap over
time in inpatient utilization is not significant. But as for the outpatient and preventative
utilization, the urban-rural gap in health utilization becomes smaller over time. It means
the urban excess use of outpatient care and preventative care is reducing as time goes by.
Notice that the effect of excessive use of urban and the change of the urban-rural gap of
outpatient and preventative service over time become insignificant after controlling for
NCMS, which means that NCMS can partially decrease the excess urban outpatient or
preventative service use.
On top of the effect of NCMS, this thesis demonstrates that NCMS do not have the
effect on the probability of inpatient utilization. However, NCMS has significant positive
influence on outpatient and preventative service use. Although NCMS has a bigger positive
effect on the outpatient utilization than that of other health insurance, it turns out that in the
other way around when it comes to preventative care utilization.
To sum up, the urban-rural disparity of health care utilization does exist in terms of
outpatient care use and preventive care use. But, as the most important health coverage in
rural areas in China, NCMS is significantly contributed to closing the urban-rural gap in
outpatient care use. And also the urban-rural gap in preventative service utilization is
reducing after controlling for other health insurance.
5.2 Limitations
In drawing conclusions, there are two limitations in this thesis.
Firstly, this thesis does not take consider the quality of the health care use into account.
For example, this thesis does not include the quality of hospitals or the number of doctors
20
and nurses which are associated with health utilization into independent variables.
Secondly, although many literatures use data from CHNS survey, the indicators of the
measurement for the health utilization (outpatient, inpatient and preventative service in the
last four weeks) do have some limitations. The recall period is so short that many observers
respond as no inpatient or outpatient service, which do not provide sufficient evidence to
make a comparison or to take a difference between urban and rural observers. This can be
improved in the future research by adding more control variables and collecting the data of
health utilization with the longer observation period, for instance one year.
References
A. COLIN CAMERON, P. K. T. (Ed.) (2005). Microeconometrics. Cambridge: Cambridge
University Press.
Barber, S. L., & Yao, L. Development and status of health insurance systems in China. The
International journal of health planning and management, 26(4), 339-356.
Gao, J., Qian, J., Tang, S., Eriksson, B., & Blas, E. (2002). Health equity in transition from
planned to market economy in China. Health policy and Planning, 17(suppl 1), 2029.
Hou, Z., Van de Poel, E., Van Doorslaer, E., Yu, B., & Meng, Q. (2013). Effects of NCMS
on access to care and financial protection in China. Health economics, 23(8), 917934.
Lei, X., & Lin, W. (2009). The new cooperative medical scheme in rural China: Does more
coverage mean more service and better health? Health economics, 18(2), S25.
Li, E. X. (2014). How China is Reoranizing for the Future. Party of the Century.
Liu, M., Zhang, Q., Lu, M., Kwon, C.-S., & Quan, H. (2007). Rural and urban disparity in
health services utilization in China. Medical care, 45(8), 767-774.
Liu, Y., Hsiao, W. C., & Eggleston, K. (1999). Equity in health and health care: the Chinese
experience. Social science & medicine, 49(10), 1349-1356.
Shenglan Tang , Q. M., Lincoln Chen, Henk Bekedam,Tim Evans,Margaret Whitehead
(2008). Tackling the challenges to health equity in China. The Lancet, 372, 14951501.
unknown. (2015). Hukou system. free encyclopedia.
Xie'e (2009). study about the relation between inequity health utilization and inbalanced
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21
Appendix
This thesis focuses on these variables in different years separately. (2000 and 2011)
table 1 shows that average urban household income level in 2000 is around 20000 RMB,
which is much more than rural income level (11895.4 RMB). The other variables such as
gender, education and health status are not changed a lot in 2000 compared to data in table.
While for the insurance status, in 2000 nearly half of urban citizens participated insurance
and less than 10% of rural citizens have insurance. For NCMS insurance, fewer people
want to participate this kind of insurance in 2000. This phenomenon is reasonable because
fewer counties are tested of NCMS in 2000 and spread to the whole country from 2003 .
For the other types of insurances, most insured people are from urban and only 2% rural
citizens have insurance.
Table 1 Summary statistic of independent variables in 2000
Urban
Rural
Obs
Mean
Obs
Mean
age
2941.0000
46.0000
4539.0000
42.7300
age below 45(1/0)
2944.0000
0.5075
4542.0000
0.5550
age1 below 65(1/0)
2944.0000
0.3526
4542.0000
0.3786
age above 65(1/0)
2944.0000
0.1399
4542.0000
0.0650
gender(female=1, male=0)
2944.0000
0.4700
4542.0000
0.5140
edu
2885.0000
2.3244
4487.0000
1.2590
hhinc(RMB)
2944.0000
20468.5500
4542.0000
11895.4000
stroke(1/0)
2847.0000
0.0100
4424.0000
0.0020
numchron
2925.0000
0.1390
4500.0000
0.0530
full insurance(1/0)
2894.0000
0.5150
4458.0000
0.0888
NCMS insurance(1/0)
2944.0000
0.0391
4542.0000
0.0670
other insurance(1/0)
2944.0000
0.4640
4542.0000
0.0200
non-insurance(1/0)
2944.0000
0.4969
4542.0000
0.9120
social conditions:
health status:
insurance:
22
Compared data in 2000, the ratio of elder people showed in table 2 from urban or rural
(above 65 years old) increased in 2011. Furthermore, average household income no matter
urban or rural families increased to three times as compared with last 11 years, while there
is still a big difference between people from urban and rural in household income level.
For the insurance part, more than 90% citizens no matter urban or rural already have
insurance. Among them, rural citizens chose NCMS insurance and urban people would like
to participate other types of insurance.
Table 2 Summary statistic of independent variables in 2011
Urban
Rural
Obs
Mean
Obs
Mean
age
5198
51.0000
4725
49.2584
age below 45(1/0)
5198
0.3367
4725
0.3706
age1 below 65(1/0)
5198
0.4677
4725
0.5000
age above 65(1/0)
5198
0.1957
4725
0.1287
gender(female=1, male=0)
5198
0.4900
4725
0.5162
edu
5198
2.7000
4720
1.3877
hhinc(RMB)
5198
70231.9800
4725
39644.7000
stroke(1/0)
5198
0.0189
4724
0.0072
numchron
5198
0.2888
4724
0.1336
full insurance(1/0)
5198
0.9590
4725
0.9757
NCMS insurance(1/0)
5198
0.0587
4725
0.9069
other insurance(1/0)
5198
0.8946
4725
0.0669
non-insurance(1/0)
5198
0.0467
4725
0.0262
social conditions:
health status:
insurance:
23