Journal of Gerontology: SOCIAL SCIENCES
2007, Vol. 62B, No. 5, S349–S357

Copyright 2007 by The Gerontological Society of America

An Examination of Urban Versus Rural Mortality in
China Using Community and Individual Data
Zachary Zimmer,1 Toshiko Kaneda,2 and Laura Spess3
1

Institute of Public and International Affairs and Department of Sociology, University of Utah, Salt Lake City.
2
Population Reference Bureau, Washington, DC.
3
Population Council, New York, New York.
Objectives. Urban/rural residence is a critical health determinant and one researchers have historically found to
distinguish health experiences. In this study, we investigated variations in older adult mortality across urban and rural
areas of China and assessed mechanisms driving an urban advantage through a series of socioeconomic and health service
covariates measured at individual and community levels.
Methods. We employed 15 years of mortality data from the China Health and Nutrition Survey. We calculated average
annual age-speciﬁc death rates and used combinations of covariates to examine Cox proportional hazards models. We
employed the 2000 Chinese Census and the 2002 Demographic Yearbook descriptively to assess reliability and provide an
alternative source for mortality variation.
Results. Hazard ratios and standardized death rates showed rural mortality to be about 30% higher than urban
mortality. Cadre status, amenities within the community, and average wage within the community are important
determinants of mortality, and adjusting for these covariates reduced the urban advantage.
Discussion. There is great differentiation in economic and social life between urban and rural China, and this appears
to be negatively inﬂuencing survival chances of older adults in rural areas. The policy implications are fairly clear:
Investment in rural China is needed to reduce health inequalities.

P

LACE of residence has, for centuries, been implicated as
a health determinant. In the developed world, differences
in urban and rural health go back to medieval times when urban
residence was harmful owing to communicable diseases more
easily transferred across populations living in cramped and
unclean surroundings (Kearns, 1988; Landers, 1987; National
Research Council, 2003; Woods, 2003). Although more recent
trends suggest less robust differentials, several studies have
noted that an urban mortality advantage exists in the United
States (Clifford & Brannon, 1985; Hayward, Pienta, &
McLaughlin, 1997; House et al., 2000; Kitagawa & Hauser,
1973; Smith, Anderson, Bradham, & Longino, 1995). Yet very
little research has examined urban/rural mortality trends in
developing countries (Eastwood & Lipton, 2000; National
Research Council, 2003). What research does exist suggests
that public health factors, such as access to services and safe
water, make living in a city in developing countries
advantageous (National Research Council, 2003). Those living
in urban areas are also thought to earn higher incomes and have
higher education, factors considered to be robust predictors of
health (Antonovsky, 1967; Mackenbach et al., 1997; Marmot,
Shipley, & Rose, 1984; Preston & Taubman, 1994). As a result,
life expectancy is higher in urban areas in developing countries
(Andrews, 2001; Kinsella, 2001; Langmore, 2001; National
Research Council, 2003). Yet evidence also shows higher risks
for lifestyle-related diseases, such as diabetes and cardiovascular disease, beginning to arise in some urban areas in the
developing world, and substantial intra-urban variations in
health (Bradley, Stephens, Harpham, & Cairncross, 1992;
Harpham & Tanner, 1995; Stephens, 1996; Zhai & McGarvey,

1992). Therefore, although the little research that exists does
point to an urban advantage, the robustness of the association,
whether it holds across the entire developing world, why it
exists, and whether it will continue to hold into the future are all
issues requiring further attention.
Studies of urban/rural health variations can be hampered by
difﬁculties in deﬁning urban and rural areas, which often encroach on one another. But in China, urban and rural distinctions are quite sharp due to legal designations implemented
after the establishment of the People’s Republic of China in
1949, most notably, the household registration system (Kirkby,
1985). Discrete economic, population, social, and health policies treated sectors differently, and advantages, including
medical coverage and economic investment, accrued to urban
areas and residents. If anything, the distinction has been widening, in part a byproduct of China’s rapid economic development and subsequent differential investment (England, 2005).
Urban China is experiencing a well-publicized boom while
development in rural areas proceeds at a slower pace. The gap
in health services has also widened. China’s economic reform
has ushered in a decline in cooperative medicine and an
increase in privatized fee-for-service, which has changed the
way health care is provided in rural areas and has resulted in
expensive service and decreasing access (Beach, 2001; Chen,
Hu, & Lin 1993; Meng, Liu, & Shi, 2000; Shi, 1996). In the
meantime, most urban residents maintain subsidized care
through insurance programs ﬁnanced from the national budget,
have access to qualiﬁed medical personnel, and can draw on an
array of resources. There are indeed few places in the world
where urban and rural differences in social and economic life

S349

S350

ZIMMER ET AL.

are so severe, a reality that has prompted the Chinese government to announce a new focus on strategies aimed at reducing
gaps in economic and social development (Kahn, 2004).
Growing social and economic disparities in China across
urban and rural areas can intensify health inequalities already
thought to exist. But studies that have attempted to quantify
these inequalities are modest in number. Although researchers
have reported an urban advantage, they have less frequently
studied the mechanisms accounting for this advantage.
However, investigators suspect, usually anecdotally, that these
mechanisms include the higher socioeconomic status and
consequent superior access to health service for people living
in urban areas, and the fact that urban neighborhoods are
wealthier and consequently offer superior availability of health
services (Fang, 1993; S. Li & Sun, 2003; Lili, 1993; Zeng,
1990). This implies that mechanisms operate on two levels—
individual and community. The current study examined urban/
rural variation in mortality among those aged 50 and older in
China. We asked the following research question: How great is
the urban advantage, and to what extent is it accounted for by
a series of individual- and community-level characteristics?

METHODS

Data
Data were mainly from the China Health and Nutrition
Survey (CHNS), a multiwave longitudinal survey. The CHNS
currently consists of six waves (1989, 1991, 1993, 1997, 2000,
and 2004). It employs a multistage random cluster sampling
process to draw households from urban and rural areas in nine
provinces (Guangxi, Guizhou, Heilongjiang, Henan, Hubei,
Hunan, Jiangsu, Liaoning, and Shandong). Urban and rural
deﬁnitions for the CHNS and consequently for the current study
are based on those used for administrative purposes by the
central government of China. The CHNS classiﬁes cities and
suburban areas, deﬁned as metropolitan places with populations
of at least 100,000, as urban locales and classiﬁes other areas,
including towns and farming areas, as rural locales. The current
study was limited to household members aged 50 and older,
and sample sizes ranged from about 2,700 to 3,800 across
waves. Most households were followed up across all waves, but
there were also deletions and additions based on community
participation. Beginning in 1993, the CHNS added households
in the sample areas that had been formed by individuals
included in the previous waves. From 1997 onward, the survey
added new households and communities to replace those no
longer participating.
A community questionnaire, completed for each primary
sampling unit by knowledgeable respondents, collected information on infrastructure, services, population, and wages. A
household survey interviewed one household member but
collected information about everyone in the household. The
current analysis employed both demographic and roster
information collected from each household member as well
as information collected in the community module.
We accessed data, supporting documentation, and details on
sampling through the Carolina Population Center Web site
(http://www.cpc.unc.edu/projects/china). At the time of writing,
the Web site listed more than 100 publications, few of which

focused on older adults and none of which examined mortality.
Thus, this is an underused resource for questions related to the
health of older adults. Good examples of publications that
provide additional information on the study include Bell, Ge,
and Popkin (2001), Beydoun and Popkin (2005), Entwisle and
Chen (2002), and Wang, Bentley, Zhai, and Popkin (2002).

Measuring and Analyzing Mortality
Although the CHNS was not designed to study mortality,
such information is available from the household register
completed at each follow-up. Individuals were listed as alive
and present, moved, or died. If there was a move or a death, the
month was recorded. Missing cases resulted from households
not being contacted, which was mostly a function of the entire
community no longer participating. Community dropouts were
fairly evenly distributed across urban and rural settings (about
95% of communities covered were followed up over time in
both urban and rural areas). For the current study, we organized
information about household members into ﬁve episodes based
on survey interval (1989–1991, 1991–1993, 1993–1997, 1997–
2000, and 2000–2004) and aggregated the episodic data.
We determined exact age by date of birth and date of
interview. An individual aged 48 at the time of interview in
1989 could, depending on his or her exact date of birth and
interview, have entered the sample population for the second
interval. In this way, individuals were aged in.
We examined urban/rural variation in mortality in two ways.
We determined age-speciﬁc death rates, calculated as the average yearly rate within age category, for urban and rural areas.
We compared death rates calculated from the CHNS to those
published from the 2000 Chinese Census (CC) and the 2002
Demographic Yearbook (DY). Then we ﬁtted Cox proportional
hazards models to predict differential survival times across
urban and rural areas. We ﬁtted these models with the assumption that correlation between survival times within communities
could be accounted for by an additional random frailty effect
(Box-Steffensmeier & Jones, 2004; Goldstein, 2003; Hougaard,
1995; Pickles & Crouchley, 1995). This random effect is a
variable, with a mean of 1 and variance of h estimated by the
data, representing the shared frailty common to individuals
within communities. Using the standard error of h, we determined whether the shared frailty was signiﬁcant and therefore
should not be ignored. The model was denoted as follows:
hij ðtÞ ¼ h0 ðtÞai eðxijbÞ ;

ð1Þ

where i indicates 1 through n communities and j indicates 1
through n observations within communities. Therefore, h
indicates the hazard of an individual within a speciﬁc community dying at any instant, a represents the shared community
effect, and xijb represents the effects (b) of independent
covariates (x) for individuals within communities on survival
time. We measured survival time as the number of months of
observation from the start of an episode until death or censoring, with those surviving considered censored at time of
follow-up and those moving censored at time of move (movers
were very evenly distributed across urban and rural areas). We
used the Breslow method for ties. We initially tested the
suitability of this Cox model by graphing Kaplan–Meier and
Nelson–Aalen estimators and interacting variables with time.

RURAL VERSUS URBAN MORTALITY IN CHINA

We did not ﬁnd that variables violated the proportionality
assumption. Because standard errors of the hazard ratios were
conditioned on h, signiﬁcance was robust to within-community
correlation. We conducted the Cox hazards models and all other
procedures using STATA 9.2 software.

Covariates
Individual characteristics came from the household survey and
varied across individuals. Community characteristics came from
the community survey and were constant across individuals
within communities. In choosing covariates, we ﬁrst considered
speculation from previous literature that socioeconomic status
and health service characteristics could explain an urban
advantage. We operationalized this at an individual level as
indicators of socioeconomic status and related access to health
service. On a community level, we operationalized this as
community economic status and related availability of health
services. Because we used episodic information, we treated as
time varying covariates that might have carried a different value
for the same person or community over time.
As for individual level measures, we coded education as
no formal, some primary, or completed primary and above. We
coded occupation as white collar and skilled, unskilled or
agricultural, and other. The ‘‘other’’ category included student,
homemaker, no occupation, or other occupation unclassiﬁed or
difﬁcult to classify, but the category mostly consisted of an
unstated occupation or never worked. An individual who was not
currently working recorded their primary occupation if they had
ever worked; therefore, no occupation only included those who
had never worked. A cadre is a public ofﬁcial holding a responsible or managerial position in the party and government
and therefore may have access to some privileges. The CHNS
asked whether each household member was a cadre, and we
coded this dichotomously. Following Filmer and Pritchett (2001)
and Rutstein and Johnson (2004), we created an index for
household wealth using a principal component technique and a
series of survey questions about whether the following products
were contained in the household: bicycle, motorbike, car, television, camera, fan, and radio. We subsequently divided ranked
scores between 0 and 100 into quintiles when included in
multivariate equations. We dichotomously coded having health
insurance. In addition, in order to determine whether urban/rural
differences in mortality were a function of health differences
across place of residence, we also considered a measure of ill
health, which came from a single item asking whether the older
adult was currently in ill health or had been sick or injured within
the 4 weeks preceding the interview. This was dichotomously
measured.
There was a degree of interplay between the various
individual-level measures. Having insurance is clearly a direct
measure of health care access. Cadre status is directly linked to
access through speciﬁc health care privileges granted to cadres,
whereas the other measures are proximate. For instance, those
with higher education likely have higher a greater understanding of the health care system. Household wealth relates to
income that is necessary to purchase health care.
We considered four community characteristics. Number of
amenities summed the following infrastructure items existing in
the community: telegraph, telephone, post ofﬁce, newspaper,
movie theater, paved roads, and 24-hour electricity. Average

S351

Table 1. China Health and Nutrition Survey Survival
Information for the Sample Aged 50 and Older
Survey
Interval
1989–1991
1991–1993
1993–1997
1997–2000
2000–2004
Total

Survived, Lived in Survived, Moved
Starting Same Residence
Before End
Status
N
at End of Interval
of Interval
Died Unknown
2,799
2,742
2,708
3,190
3,828

2,435
2,351
1,826
2,464
2,994

105
52
104
129
84

109
131
185
153
200

150
208
593
444
550

15,267

12,070

474

778

1,945

wage for an ordinary male worker was reported in Yuan and
logged. We imputed about 12% of cases with missing responses using related community variables. The other two
measures were related to health services. We categorized a
question for distance from the center of the community to the
nearest health care facility (less than ½ km, ½ to 2 km, more
than 2 km), and we included a separate code for a moderate
number of nonresponses (about 15% of cases). We summed
number of health care facilities in the community, eliminating
any that were used solely for reproductive health care.

RESULTS
Table 1 describes the episodic data. We based the analysis on
an aggregated data set involving 15,267 persons aged 50 years
or older at time of the household interview that began an
episode. There were a total of 778 deaths. Table 2 provides
descriptive information about the covariates by urban/rural
residence. All except gender differed signiﬁcantly across area.
Whereas most socioeconomic measures displayed a consistent
and signiﬁcant urban advantage, persons in urban areas were
more likely to have been reported sick or injured currently or in
the 4 weeks preceding the interview.
Table 3 compares age-speciﬁc death rates for total, urban,
and rural populations across three sources. We present the table
ﬁrst to establish reliability of the CHNS for measuring mortality
and second, to provide an initial descriptive comparison of
urban/rural mortality. Deaths in the DY, based on vital registration records, were not provided by urban/rural residence, so
we present only the total for this source.
Death rates from the CHNS generally corresponded to those
from the CC and DY. The bottom row of the table shows death
rates for the population aged 50 and older standardized using
a common age and gender distribution (from the CC). The
standardized death rate from the CHNS, which was 22.8, was
quite similar to that from the CC (24.4) and the DY (22.4). But
we should note that additional analyses revealed that death rates
from CHNS varied across intervals, from a standardized rate of
about 19 to 29. Signiﬁcant variation over intervals could be
expected given the relatively small sample sizes upon which the
death rate calculation in any interval was based. So, although
individual intervals were not necessarily reliable, the aggregate
of the intervals seemed to be. Age-speciﬁc death rates from the
CHNS were also quite similar to those from the CC and DY.
Both the CHNS and the CC conﬁrmed a substantially higher
mortality rate in rural areas. We calculated standardized rates
for urban areas to be 19.5 and 20.3 from the CHNS and CC,

ZIMMER ET AL.

S352

Table 2. Descriptive Information for Covariates by
Rural/Urban Residence

Table 3. Age-Speciﬁc Death Rates in China per 1,000 Population
From Three Sources by Urban/Rural Residence

Covariate
Demographics
Mean age
Gender (% male)

Age
61.8
48.5

62.5***
47.1

47.5
37.9
14.7

38.3***
34.5***
27.1***

2.4

7.6***

% White collar/skilled
% Unskilled/agricultural
% Never worked/other/missing

9.2
65.3
25.5

22.4***
38.6***
39.0***

Wealth (mean wealth score)a
Insurance (% who have insurance)

43.7
17.1

60.8***
50.5***

15.5
4.0
2.1

17.8***
5.2***
3.0***

40.7
28.3
22.7
8.4

35.3***
27.4
21.3*
15.9***

10.3

13.3***

Individual
Education
% No education
% Primary
% More than primary
Cadre status (% who is a cadre)
Occupation

2000 CCa

CHNS

Rural
Urban
(N ¼ 8,834) (N ¼ 4,491)

50–54
55–59
60–64
65–69
70–74
75–79
80 þ
Standardized
death ratec

DYb

Total
Total
Total
Population Urban Rural Population Urban Rural Population
6.7
9.0
12.6
24.6
37.9
58.2
121.7
22.8

5.2
7.3
8.1
9.6
8.5 14.9
21.0 26.6
29.0 43.1
50.6 63.7
120.9 124.9
19.5

24.8

5.5
8.7
14.8
24.4
42.3
66.5
134.5
24.4

4.3
6.1
6.9
9.6
11.7 16.5
20.1 26.6
35.2 45.8
56.2 71.2
117.0 142.8
20.3

26.4

4.6
8.6
15.3
24.3
38.4
57.6
117.7
22.4

Notes: CHNS ¼ China Health and Nutrition Survey; CC ¼ 2000 Chinese
Census; DY ¼ 2002 Demographic Yearbook.
a
Source: China Data Center (2006), Tables 6a, 6b, 6c.
b
Source: United Nations (2002a), Tables 7 and 19.
c
Standardized death rate using the age composition of the total sample in the
2000 Census.

Community
Average wage (in Yuan)
Number of amenities in community
Mean number medical facilities in community
Distance to nearest facility
%
%
%
%

0–.50 km
.51–2.00 km
2.01þ km
Missing information

Health (% in ill health)

Notes: aWealth score divided into quintiles for multivariate analysis.
*p , .10; ***p , .01, comparing urban and rural, two-tailed test.

respectively, compared to 24.8 and 26.4 for rural areas. Thus,
given similar age and gender distributions, we would expect
about 30% more deaths among rural elders. Both the CHNS
and CC data showed urban residents to be advantaged regardless of age group.
Table 4 presents hazard ratios from Cox proportional hazards
models using shared frailty to account for a latent withincommunity random effect. The table provides the results of
a number of models. Column 1 shows a base model that
adjusted for age and gender. We tested several parameterizations for age and found the best to be a categorization into
5-year intervals. Column 2 presents the results of 10 models
(i–x), each of which includes base plus one additional covariate. Column 3 simultaneously adjusts for individual- and
community-level covariates. Column 4 adds a variable for ill
health in order to determine whether the effect of urban/rural
residence works indirectly through health. We further examined
models that included various combinations of covariates, adjusted for age and gender, and monitored their impact on the
urban/rural coefﬁcient and the degree to which they inﬂuenced
the À2 log likelihood, which we used to approximate the
explanatory power of covariates. Column 5 shows a parsimonious model that included covariates that, when added, reduced
the urban/rural coefﬁcient and substantially increased the À2
log likelihood. Because there are clear expectations regarding
associations (e.g., urban morality lower than rural, higher
education associated with lower mortality), one-tailed tests of

signiﬁcance might be suitable. We report more the conventional
two-tailed results but note signiﬁcance up to p , .10.
The base model showed a hazard ratio for rural of 1.32,
meaning that after controlling for age and gender, mortality was
32% higher in rural areas. The results also showed a rapid
increase in the hazard with increasing age, with men having
a greater risk than women.
Column 2 shows partial results of ten models. Although not
shown, effects for age and gender remained signiﬁcant and
consistent throughout. The urban/rural effect never declined to
a point of insigniﬁcance across these models, and its hazard
ratio varied between about 1.3 and 1.2 depending upon the
speciﬁc covariate being included. Most individual-level variables were signiﬁcant. Those with no education had a signiﬁcantly higher hazard of mortality than those with more than
primary education. Cadres had a signiﬁcantly lower risk of
mortality. Those with unskilled or agricultural occupations and
those categorized as having an ‘‘other’’ occupation were at
higher risk for mortality than skilled and white collar workers,
and those with insurance were at lower risk. Those with lower
than the highest wealth quintile were at higher risk for
mortality, therefore the variable was in the expected direction,
but the effect did not reach a level of statistical signiﬁcance. For
community-level variables, higher average wage within a community and a greater number of amenities signiﬁcantly decreased mortality. Distance to nearest facility and number of
facilities played no role. Those reporting ill health were
substantially more likely to die than were others.
Column 3 shows effects when we considered individual and
community characteristics simultaneously. The urban advantage was reduced, with the rural hazard ratio falling to 1.18. Of
the individual- and community-level covariates, two of each
remained statistically signiﬁcant. Speciﬁcally, being a cadre
reduced the hazard of dying, as did having a skilled or white
collar occupation versus being in the never worked/other/
missing occupation category. At the community level, number
of amenities and the average wage in the community remained
statistically signiﬁcant when other things were adjusted.
Column 4 adds the health variable to determine whether the

RURAL VERSUS URBAN MORTALITY IN CHINA

S353

Table 4. Cox Models Showing Hazard Ratios for Dying (N ¼ 13,325)a
Base

Rural residence

Individual þ Community
Characteristics

Adding
Ill Health

Parsimonious
Modelc

1

Variable

(i–x), Single
Covariatesb
2

3

4

5

1.32**

1.18

1.19*

1.22**

Age (ref ¼ 50–54)
55–59
60–64
65–69
70–74
75–79
80 þ

1.43*
2.01***
4.03***
6.37***
9.93***
22.20***

1.37*
1.86***
3.61***
5.55***
8.31***
18.79***

1.40**
1.95***
3.82***
6.01***
9.17***
21.42***

1.49***

1.65***

1.66***

1.53***

1.27*
1.11

1.10
0.99

1.12
1.01

0.42***

0.49**

0.49**

1.38**
1.42**

1.16
1.34*

1.11
1.28

1.11
1.09
1.18
1.11

0.95
1.02
1.08
1.05

0.88
0.98
1.03
1.01

0.83*
0.89**
0.94***
1.01

0.92
0.89**
0.95**
1.01

0.88
0.86***
0.94***
1.01

0.99
1.10
0.78*

Gender (1 ¼ male)

1.40**
1.91***
3.77***
5.82***
8.81***
19.05***

0.97
1.06
0.80

1.00
1.07
0.83

À6808.0
32.7***
0.093***

2.15***
À6775.6
94.4***
0.103***

Education (ref ¼ more than primary)
No education
Primary education
Is a cadre

0.44***

Occupation (ref ¼ skilled/white collar)
Unskilled/agricultural
Never worked/other/missing
Wealth quintile (ref ¼ highest)
Lowest quintile
Second quintile
Third quintile
Fourth quintile
Has insurance
Log average wage
Number of amenities
Number medical facilities

0.89**
0.94***

Distance to nearest facility (ref ¼ 0–.50 km)
.51–2.00 km
2.01þ km
Missing distance information
Ill health
LL
Á –2LL from base
h

2.06***
À6824.3
0.111***

2.15***
À6781.7
84.2***
0.112***

Notes: i–x ¼ 10 models (i–x), each of which includes base plus one additional covariate; LL ¼ log likelihood.
a
Results of a shared frailty hazard model with standard errors of hazard ratios conditional on h. N based on number of measured episodes.
b
Results represent a series of models that all adjusted for residence, age, and gender. Results for residence, age, gender, LL, and Á –2LL not shown.
c
Addition of other covariates did not substantially change the urban coefﬁcient or the Á –2LL from base.
*p , .10; **p , .05; ***p , .01, two-tailed.

impact of urban/rural residence on mortality works primarily
through health. We did not ﬁnd evidence for this. Although
being in ill health greatly increased the hazard of dying, the
hazard ratio for the rural area was 1.19 and statistically
signiﬁcant. Therefore, even accounting for whether an individual was in poor or good health, we found mortality to be
higher in rural areas of China. In fact, Table 2 shows that those
in rural areas were less, not more, likely to report ill health. The
indirect inﬂuence of rural residence on the hazard of dying
through ill health was therefore negative.
Column 5 includes the combination of covariates that most
efﬁciently accounted for the urban advantage. Cadre status,
average wage within a community, and number of amenities
within a community best represented the impact of individualand community-level factors on mortality. The descriptive
results shown above suggested that people in urban areas were
more likely to be cadres, and urban communities were more

likely to have higher wages and a greater number of amenities
than rural communities. In turn, cadres and those living in
communities with higher average wages and more amenities
were less likely to die than others, a result that accounted for
some of the urban advantage. These three covariates alone
reduced the hazard of rural residence from 1.32 in the base
model to 1.22. We also included the health covariate in the ﬁnal
model, and those in ill health had a much higher hazard of
dying than others.
Figure 1 shows visually the net impact of urban versus rural
residence on mortality based on this ﬁnal parsimonious model.
The ﬁgure represents underlying survival curves for a 65- to 69year-old woman. We chose this age and gender for heuristic
purposes. A person of different age and gender would have
a different but proportional survival curve. Although the risk of
dying remained fairly similar for urban residents in the base and
parsimonious models, the risk of dying for rural residents

S354

ZIMMER ET AL.

Figure 1. Implied survival curves for a 65–69 year old woman by urban and rural residence assuming base and parsimonious models.

neared urban residents when considering the parsimonious
model. For instance, from the base model we determined that
after 12 months an urban woman aged 65 to 69 had a .9803
probability of survival, compared with .9740 for her rural counterpart, an overall difference of .0063. When we held cadre status,
average wage, number of amenities, and ill health constant, the
probabilities converged to .9810 for the urban woman and .9769
for the rural, decreasing the difference to .0041. So, the base
advantage was reduced by about one third. The residual two
thirds of the urban advantage remained unexplained.
Given the strong impact of cadre status, and previous literature showing substantial mortality differences by gender and
level of education, we conducted further analyses examining
interactions between these covariates and urban/rural residence.
We found nothing that was statistically signiﬁcant. Therefore,
living in an urban area had a similar advantage for women as
for men and for those who were better educated as for those
who were less educated. We did ﬁnd that being a cadre was
more advantageous in urban areas, but the small number of
rural cadres in the current sample (only about 2% of rural
residents were cadres) resulted in unstable standard errors and
a nonsigniﬁcant interaction effect. It is therefore difﬁcult to
come to a deﬁnitive conclusion on this issue.
We explored cadre status further with the notion that resources accrued and services available to a cadre may extend to
household members themselves not classiﬁed as cadres. As
such, an older person may beneﬁt from the cadre membership
of a family member. In further analyses we tested variables
measuring household cadre status. For instance, we tested a
three-category variable coded as 1 ¼ respondent is a cadre, 2 ¼
respondent is not a cadre but someone else in household is, and
3 ¼ no one in household is a cadre. These analyses indicated

that the important determinant of mortality was cadre status of
the older person, whereas cadre status of other household
members, though having a small negative impact, was less
important and statistically nonsigniﬁcant.

DISCUSSION
Policies have treated urban and rural areas in China differently, and a byproduct of China’s rapid development is growing
differentiation between urban and rural social and economic
life. Whereas urban China witnesses expansions of infrastructure, less investment has been made in rural China and the
availability of health services may have declined owing to the
dismantling of cooperative services and the introduction of payfor-service. It is not surprising, then, that data have documented
an urban mortality advantage. Still, the numbers of studies are
modest, and little research has focused on old-age mortality or
the mechanisms driving an urban advantage. Nevertheless, the
issue is of critical importance, especially for a country undergoing population aging and a subsequent increase in the proportion at very old ages (Andrews, 2001; Kinsella, 2001;
Kinsella & Phillips, 2005; Langmore, 2001; United Nations,
2002b; Zeng & George, 2001). It is reasonable to expect that
these age structure changes will alter the provision of health
care, making understanding the determinants of health among
elders critical for the development and implementation of
policy (Mayhew, 1999). The current study examined variation
in mortality between urban and rural China among adults aged
50 and older. We found mortality to be about 30% higher in
rural areas when controlling for age and gender based on
standardized mortality rates and on results of a Cox proportional hazards model. The advantage was fairly consistent
when comparing the CHNS and the 2000 Census.

RURAL VERSUS URBAN MORTALITY IN CHINA

The analysis also examined the extent to which variables
representing socioeconomic and health service characteristics
of individuals and the communities in which they live explain
the urban advantage. Three turned out to be particularly important. On the individual level, it was cadre status. Urban residents were more than 3 times more likely to be cadres, and
being a cadre is beneﬁcial for survival. Traditionally, cadres
were elite ofﬁcials holding responsible positions within the
party, and cadre status provided them with privileges that the
average citizen may not have accrued (Lee, 1991, Y. Li, 2005;
Nee, 1996). For instance, they may have had better access to
health service, housing, or employment. Some have argued that
cadres have recently become a kind of elite civil service, but
they nonetheless are well positioned to take advantage of
China’s economic growth, being able to acquire good positions
within the government employment system. As such, cadre
status may be a proxy for a host of other socioeconomically
related factors not accounted for in the current study and for
access to health services. It might also relate to psychological
resources inﬂuencing health, such as level of stress and feelings
of control and self-worth. It is unsurprising that cadres have
lower mortality, but it is surprising that cadre status is so
important in comparison to education, occupation, and insurance. The result here supports a long-standing ﬁnding about
the connection between socioeconomic status and mortality
(Antonovsky, 1967; Mackenbach et al., 1997; Marmot et al.,
1984; Preston & Taubman, 1994), but it suggests that the
speciﬁc indicator of socioeconomic status that is critical may
be culturally dependent.
On the community level, important measures were number of
amenities within the community and the average wage for an
ordinary male worker. Urban communities have on average
a greater number of amenities and a higher wage structure. In
turn, individuals living in communities with a large number of
amenities and higher average wages have a lower risk of mortality. Average wage has a clear association with community
wealth, which may consequently result in the availability of
better and more technologically advanced resources for community members. Number of amenities, constructed by summing seven items, is also an indicator of resources available
within a community as well as remoteness and level of infrastructure development. As such, amenities may be a proxy
for a congregation of other services, including those both
wealth and health related. Some amenities, like telephone
service, facilitate communication, whereas others, like paved
roads, facilitate transportation. Both of these factors may be
important for transferring health information to older adults,
moving both formal and informal care into the community, and
allowing older adults to be in contact with family members. The
latter may have psychological beneﬁts and result in more
efﬁcient informal assistance.
Given that the amenities measure was a composite of seven
individual items, separate analyses not presented here predicted
survival given existence versus nonexistence of each amenity,
adjusting for age, gender, and residence. Telegraph service,
electricity, telephone service, and paved roads are most important, in that order. Post ofﬁce, movie theaters, and newspapers
are least important. There are several possible explanations for
this. One is that amenities that are most important facilitate
communication and transportation, which are important for the

S355

provision and efﬁcient transfer of formal and informal care. A
second is that amenities that are most important are more basic
and distinguish the poorest and most remote communities. A
movie theater or newspapers may arrive in a community only
after more basic infrastructure such as telephone service and
paved roads, and the earlier types of development are more
crucial for health. This suggests a threshold above which communities become more equal. Still, the summed score created
from the seven items proves to be the best indicator.
It is surprising that number of medical facilities had virtually
no inﬂuence. Unlike a measure better related to actual community wealth, number of facilities may not translate well into
quality of service or capacity of care. Small facilities may be
poorly maintained and equipped and may be staffed with less
qualiﬁed personnel. At the same time, fewer larger facilities
may provide better medical treatment, be technologically more
advanced, and be able to handle a larger collection of health
problems. Thus, the positive inﬂuence of a greater number of
facilities might be negated by the superior capability of larger
facilities. Questions regarding the impact of number of doctors,
nurses, and specialists; quality of available care; number of
available beds; quality of facilities; and other similar variables
for which we did not have appropriate measures remain.
We should note several issues regarding covariates. First, due
to inclusion criteria, like consistent measurement over time, the
study employed a limited number of covariates. Still, even with
a small number of covariates we were able to account for about
at least one third of the urban advantage. Second, there were
some fairly high correlations among covariates, such as between having more than a primary education and being a cadre
(r ¼ .30), having a skilled occupation and being a cadre (r ¼
.36), and wealth index score and having insurance (r ¼ .30).
This ‘‘shared variance’’ might be one factor accounting for
rather weak signiﬁcance among some covariates in a full model.
Third, there were some curious results. Having an ‘‘other’’
occupation in comparison to a skilled or white collar occupation
functioned to increase risk of mortality. It is difﬁcult to interpret
this result because ‘‘other’’ comprised mostly those who had
never worked or those not stating an occupation. Also, although
a health insurance inﬂuence came through when other individual level covariates were not controlled, its insigniﬁcance
in a full model circumvented common sense.
Also, we are cautious about the accuracy of mortality
reporting in China. Prior research has suggested that census and
vital registration data in China underestimate number of deaths
(Banister & Hill, 2004; Yang et al., 2005). A footnote in the
DY declared that death rates were inﬂated to calculate life
expectancies because it was assumed that there was underreporting in vital registration. Still, because death rates in the
CHNS mirror, to a degree, those in the CC and DY, we can
speculate that whatever inaccuracies exist in the CHNS parallel
those in other sources. Moreover, underreporting is thought
to be greater in rural areas. If this is true, the results shown
here represent an underestimation of the urban advantage in
mortality.
In sum, the policy implications of our ﬁndings appear to be
clear: Investment in rural infrastructure, especially in electricity,
telephone and telegraph services, and paved roads, appears
to have a beneﬁcial role in reducing health inequalities, and
this may be particularly so for the poorest and most remote

S356

ZIMMER ET AL.

communities. Investing in rural economy in general to improve
wage structure also appears to help reduce urban/rural inequalities in health. Although we were not able to study in detail how
cadre status exactly beneﬁts the health of older adults, we speculate it to be at least partly through increased health care access.
Thus, our results highlight the signiﬁcance of improving
individual-level access to various resources, thereby reducing
inequality in access. Future research should address the mechanisms through which being a cadre is associated with health
and mortality among older adults in China. Future studies
should also examine the impact of additional community factors on urban/rural differences in health, including more
detailed information about the availability of health care services, such as quality of service and capacity of care. It is also
essential to monitor changes in levels of urban/rural inequality
in health as China undergoes further rapid socioeconomic
development.
ACKNOWLEDGMENTS
The preparation of this article was supported in part by Grant R03
AG025729 from the National Institutes of Health (National Institute on
Aging). We thank three anonymous reviewers for their helpful suggestions
on an earlier version of the article. Lead author Zachary Zimmer conceived
the study; organized and managed the study team; and led all aspects of the
preparation of the article, including planning and implementing statistical
methods, data analysis and modeling, interpreting results, writing the initial
version of the article, and making revisions based on reviewer and editor
comments. Second author Toshiko Kaneda was involved in all aspects of
the study including conception, data analysis, writing, and editing. Third
author Laura Spess took primary responsibility for management of data and
methods, including data manipulation, use of statistical software, and
planning and implementation of statistical models.
CORRESPONDENCE
Address correspondence to Zachary Zimmer, Institute of Public and
International Affaris, University of Utah, 260 S. Central Campus Dr., Room
214, Salt Lake City, UT 84112. E-mail: zachary.zimmer@ipia.utah.edu
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Received October 12, 2006
Accepted May 17, 2007
Decision Editor: Kenneth F. Ferraro, PhD

