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Trends i n Life E xpe cta ncy and Life Disparity in the Older Population in
China by Regions, 1981–2020
Yuteng Yan
School of Population and Health, Renmin University of China, Beijing, China
Corresponding author:
Yuteng Yan, School of Population and Health, Renmin University of China, No. 59
Zhongguancun Street, Haidian District, Beijing 100872, China.
Email: yanyuteng@ruc.edu.cn
ORCID iD: Yuteng Yan https://orcid.org/0000-0001-5739-7174
Abstract
This paper examines the trends in life expectancy and life disparity among the older population in China
and its sub-national regions based on China National Population Census data from 1981 to 2020, using
the life disparity indicator developed by Zhang and Vaupel. It is shown that, first, the life expectancy of
the older population in both China and its sub-national regions has experienced a significant upward
trend; however, the growth is uneven across regions. Second, life disparities due to deaths among the
elderly in China and its sub-national regions followed a downward trend, but life disparities due to
deaths among the elderly as a ratio of total life disparities increased, indicating an increasing importance
of deaths among the elderly in life disparities amidst overall mortality improvements. Third, multiple
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linear regression models indicate that variations in life expectancy and life disparity among the older
population across regions in China may stem from uneven development in the health transition process
and resulting disparities. The eastern region of China has experienced a faster health transition
compared to the western region, as evidenced by the greater impact of investments in medical facilities
and economic development levels on increasing old-age life expectancy and reducing old-age life
disparities in the eastern region. This paper reveals that the life expectancy and life disparity of China’s
older population has improved considerably over the past four decades, and its relatively lagging
improvement compared to that of developed countries over the same period may be related to the
relatively lagging health transition in the western region of the country.
Keywords: Old-age mortality, life expectancy, life disparity, health transitions, regional disparities
Introduction
As the most populous country globally, China is grappling with a swiftly expanding older population,
posing continual challenges to the sustainability of population health not only within China and East
Asia but also globally. By 2035, as per the United Nations Population Division’s medium scenario in
Wo rl d Po p ul at i on P ro sp ec ts 2 02 2, China’s population aged 60 years and older is projected to constitute
30.31% of the total population, in contrast to the global proportion of 18.07% (United Nations, 2022).
Therefore, understanding the well-being of China’s sizable older population is a crucial issue warranting
ongoing academic scrutiny.
Presently, numerous scholars have engaged in noteworthy discussions regarding the mortality
levels of and changing trends among China’s older population. These analyses commonly concur that
the life expectancy of China’s older population demonstrates a consistent upward trajectory (Huang and
Sha, 2023; Qiao, 2023; Wang and Mi, 2013; Zhang and Fu, 2022). Nevertheless, its growth rate has not
exhibited a definitive trend of catching up with that of developed countries, as typically anticipated
based on life expectancy at birth. Instead, it has shown a widening trend compared to developed
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countries, characterized by a delayed pace of growth (Guo et al., 2020). Additionally, some researchers
argue that, when considering equivalent life expectancy levels, the life expectancy of China’s female
older population historically displays a more rapid growth rate compared to that of developed countries
(Zhang and Sun, 2023).
As research has progressed, the discussion of life expectancy for the older population has gradually
extended in recent years from a single discussion of the average and the degree of dispersion to a
discussion of the disparities in deaths among the older population. In short, mortality disparity or life
disparity can be reflected by the degree of variation in age-specific mortality rates within a population,
which can be used to examine the degree of inequality in the likelihood of survival of the members of
a society. In this regard, the studies that have been conducted on life disparities in China show that
China has reduced life disparities to very low levels at the global level over the same level of income,
but this reduction is mainly due to the contribution of the rapid decline in mortality for the youngest
age groups (Zhang, 2016). In contrast to the dramatic reduction of infant mortality, China’s old-age
mortality remained concentrated in early old age until 2015, suggesting the potential for high life
disparities in China’s elderly population (Guo et al., 2020).
In comparison to the in-depth studies of mortality among the older population at the national level
in China, the level of mortality and its disparities among the older population in China’s sub-national
regions are inadequately discussed at present. Earlier research has indicated a declining trend in old-
age mortality across China's sub-national regions, with patterns of variation observed between the
eastern, central, and western regions, and has identified possible factors affecting the differences
between regions, encompassing family structure, demographic composition, economic development,
healthcare, and environmental pollution, among others. (Li and Yan , 2023; Wu and Qiao, 2023).
Furthermore, mortality in China’s rural older population has been found to be notably concentrated in
relatively early old age, highlighting substantial urban–rural discrepancies (Peng et al., 2021).
Nevertheless, two issues persist within the current regional studies of old-age mortality in the Chinese
mainland.
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First, most have focused only on the increase in life expectancy of the older population across
regions or on differences between regions, and few scholars have analyzed whether life disparities in
old age have risen or fallen within regions. Indeed, similar to the great variation in life expectancy and
life disparities that exist at the state level in the United States (Brown et al., 2023). China is likely to
have large cross-regional differences in mortality levels, and in particular large life disparities at older
ages, which have not yet been carefully discussed by scholars at the regional level in China.
It has been revealed that increases in life expectancy are strongly associated with compression of
the age-at-death distribution. Several studies have argued that increases in life expectancy parallel
increases in life disparities, that is, the distribution of deaths tends to expand with increasing life
expectancy (Smits and Monden, 2009). However, follow-up studies have pointed out that, after 1960,
as life expectancy increases beyond a certain point, life disparities do not also increase, and that, in
general, the lower the level of life disparities, the higher the life expectancy, implying that growth and
equity are compatible with each other (Vaupel et al., 2011). Consequently, life expectancy and life
disparity in the older population cannot be viewed separately and in isolation from each other, but rather
need to be examined simultaneously. For instance, when comparing two societies with the same life
expectancy, the one with lower life disparity will have a higher potential for sustainable growth. By
observing both the life expectancy and life disparity of the older population, it is possible to gain a
deeper understanding of the changes in old-age mortality in China overall, as well as in its sub-national
regions.
Second, the present debates on the utility of different influencing factors on old-age mortality in
different regions of the Chinese mainland are lacking a temporal transition perspective, and thus fail to
establish a unified explanatory framework on why a certain factor has a stronger effect in a certain
region and a weaker effect in another, and remaining at the descriptive level of the discussion. This
hinders our understanding of the deeper reasons for the variations in life expectancy and life disparities
among older populations across regions, namely, uneven development in terms of the health transition.
Within the theoretical framework of the health transition, the process has been segmented into
three stages: the “vanquishing of infectious diseases”, the “cardiovascular revolution”, and the “fight
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against aging”. Of these, the “vanquishing of infectious diseases” stage is further delineated into a three-
stage epidemiologic transition, namely, the “age of pestilence and famine”, the “age of receding
pandemics”, and the “age of degenerative and man-made diseases” (Omran, 1971). Compared to
Omran’s initial framework, a new framework, however, was developed in response to the renewed
widening of the gap in life expectancy between developing and developed countries in the 1990s due
to the collapse of the Soviet Union, the AIDS pandemic in Africa, and so on. It introduces the sub-stages
of dispersion and convergence, arguing that within each major stage, there exists a sub-stage of
dispersion followed by convergence. Health disparities across populations arise from differences in the
degree of diffusion between the accessibility of medical technology and the prevalence of healthy
lifestyles among individuals (Vallin and Meslé, 2004).
Follow-up studies further revealed that the health transition, especially in late-developing countries,
is also highly time-compressed, meaning that multiple stages of transition may take place in the same
place and at the same time and that different dynamics may be at work at the same time, which further
extends the explanatory force of the health transition theory (Soares, 2007). In other words, changes in
life expectancy and life disparities across regions are closely linked to the stage of the health transition
that each region is in. Similar increases in life expectancy may stem from declines in mortality at
different ages, while increases or decreases in intra-regional differences in life expectancy and life
disparities may stem from whether the population is in a sub-stage of expansion or convergence. The
framework thus provides this paper with a well-explained path for the horizontal differences and vertical
variations in the levels of life expectancy and life disparities across regions.
Accordingly, to respond to the above two questions, this study quantifies the life expectancy and
life disparity among China's older population by gender, province, and region spanning from 1981 to
2020, and discusses the corresponding mechanisms of the changes in the life expectancy and life
disparity of China's older population by region within the framework of the health transition through
the healthcare and economic data at the provincial level in China.
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Data and methods
Data
To achieve t he o bjectives o f thi s paper, ai med a t delinea ting alterat ions in l ife e xpectancy a nd l ife
disparity among the older population in China overall and in its sub-national regions, data sourced from
the 1981, 1990, 2000, 2010, and 2020 National Population Censuses of China have been employed.
These censuses offer requisite mortality rate data categorized by province or municipality, sex, and age
for the purpose of this research. With the exception of Tibet, Chongqing, and Hainan in 1982, and
Chongqing in 1990, this paper obtained a total of 312 original life tables for the nation of China,
encompassing 31 provinces and municipalities (Hong Kong, Macao, and Taiwan were not included),
categorized by sex. It is important to note that due to substantially higher life expectancies attributed to
distinct socio-economic differences in Hong Kong, Macao, and Taiwan compared to Chinese mainland,
the analysis in this paper excludes these special economic zones and will not revisit them in subsequent
sections.
Meanwhile, to understand the different mechanisms of health effects in different regions, based on
the assumptions of the health transition theory, this paper introduces additional provincial-level data,
mainly covering the level of investment in medical facilities and the level of economic development.
The assessment of medical facilities encompasses the number of beds and physicians per 1000 people
while evaluating economic development involves the GDP per capita adjusted by the 1981 GDP index
and the disposable income per capita adjusted by the 1981 consumer price index (CPI_ for both urban
and rural areas. These data were sourced from the online query system available on the National Bureau
of Statistics of China (NBSC) website and the China Statistical Yearbooks. In this paper, the factors are
extracted as principal components based on the aforementioned two dimensions for convenient
interpretation. Furthermore, to account for geographic variations among provinces, the paper
incorporates the annual average temperature. This data is derived from the daily observations of the
National Climatic Data Center’s (NCDC) ISD-Lite basic meteorological elements at stations across
China and calculated as the provincial and annual averages. Finally, this paper also introduces the 2020
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regional data on causes of death released by the Chinese Center for Disease Control and Prevention
(China CDC) to gain a deeper understanding of the health transition stage of each region.
Methods
In terms of methods, this paper uses model life tables to adjust the mortality rate and then calculates the
life expectancy of the older population as a measure of the level of mortality in the older population. At
the same time, this paper uses the “life lost due to death” indicator developed by Zhang and Vaupel
(2009) (hereafter “life loss”) as an indicator of life disparity in the older population, which is used in
light of two main considerations. First, the loss of life has a clear demographic meaning, that is, the loss
of life expectancy due to death in a population, and the smaller the value, the more concentrated the
distribution of age at death in the population and the less disparity, and vice versa, the more disparity;
meanwhile, the indicator has a strong correlation with the various measures of disparity that have been
developed, with good mathematical properties. Second, the indicator has been widely used by the
international community, such as the United States, India, Iran, Japan, and other countries, and could
be incorporated into a broader international comparative analysis (Bayati and Kiadaliri, 2023; Brown
et al., 2023; Pal et al., 2022; Zheng et al., 2021).
Initially, considering the substantial prevalence of death underreporting in census data across
various countries, revising the raw mortality data becomes essential prior to commencing the analysis.
Numerous studies on death underreporting in China have indicated that conventional assessment
methods like Brass-Logit and the United Nations model life table lack adequacy in rectifying death
underreporting among the Chinese population, primarily due to the rapid health transition occurring
over the past four decades. In contrast, models like the life table developed by Wilmoth and other
scholars, notably the flexible two-dimensional mortality model, have exhibited superior adaptability (Li
et al., 2022). Hence, this paper employs the flexible two-dimensional mortality model to adjust the
provincial mortality data extracted from censuses, utilizing 991 life tables sourced from the September
2023 update of the Human Mortality Database (HMD). This model functions by estimating empirical
values derived from accessible historical data, thereafter fitting the age-specific mortality rates using
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two parameters (Wilmoth et al., 2012). Owing to space limitations, specific model details are not
elaborated upon in this paper. Concerning particular parameter configurations, this study utilizes the
life expectancy by province for all available years outlined in the 2021 China Population and
Employment Statistics Yearbook (National Bureau of Statistics of China, 2021). Additionally, it
incorporates the probability of death between the ages of 15 and 49, deemed relatively accurate within
the raw life tables, as the input parameters for the model life tables. Utilizing these inputs, this study
constructs 312 life tables for China, encompassing both national and sub-provincial levels, covering the
period from 1981 to 2020, and subsequently calculates the respective life expectancy figures.
Subsequently, based on the revised life table data, this paper was then able to measure the indicator
of life loss by period and province, denoted as 𝑒!(𝑡), which is calculated using the following formula:
𝑒!(𝑡) = ∫
"
#𝑑(𝑎, 𝑡)𝑒(𝑎, 𝑡)𝑑𝑎.
Here, 𝑒(𝑎, 𝑡)=∫
"
$𝑙(𝑥, 𝑡)𝑑𝑥/𝑙(𝑎, 𝑡) is the remaining life expectancy at age a in period t, 𝑙(𝑎, 𝑡) =
𝑒%&'
!
"()*+,-.* is the number of survivors at age a in period t, 𝜇(𝑎, 𝑡) is the likelihood of death at age a
in period t, the product of the above two, 𝑑(𝑎, 𝑡)= 𝜇(𝑎, 𝑡)𝑙(𝑎, 𝑡), is the number of deaths at age a in
period t. Note that ∫
"
#𝑑(𝑎, 𝑡)𝑑𝑎 = 1, 𝜔 is the maximum age of survival. For 𝑒!(𝑡), this can be
interpreted as the ability to live 𝑒(𝑎, 𝑡) years into the future for those who have already lived to age a
in period t, but some of these people who live to age a die at age a, and these people then lose 𝑒(𝑎, 𝑡)
years of their lives, by multiplying the number of people who die at age a, 𝑑(𝑎, 𝑡), with the life loss of
an individual, 𝑒(𝑎, 𝑡), this gives the total life loss of these people, summing the life loss at all ages from
0 to 𝜔 yields the total population's life loss. If 𝑒!(𝑡)=10, it means that the loss of life expectancy due
to death for this population at moment t is 10 years.
Also, for any modern population, there exists an old-age threshold age, denoted as 𝑎!. The life
loss of a population can be decomposed into two parts before and after 𝑎! as in the following equation:
𝑒!(𝑡) = ∫
$#
#𝑑(𝑎, 𝑡)𝑒(𝑎, 𝑡)𝑑𝑎 +∫
"
$#𝑑(𝑎, 𝑡)𝑒(𝑎, 𝑡)𝑑𝑎 = 𝑒/
!(𝑡) + 𝑒0
!(𝑡).3
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Death before 𝑎! decreases life disparity and death after 𝑎! increases life disparity; the former implies
a compression of the distribution of deaths known as early disparity, denoted 𝑒/
!, and the latter implies
an expansion of the distribution of deaths known as late disparity, denoted 𝑒0
!. The extent of the life
disparity of a population is determined based on the sum of the early disparity and the late disparity,
which move in opposite directions in the process of mortality decline. In the case of a decline of overall
life disparity for the population, there are two possible scenarios. First, the old age mortality rate
remains relatively stable while the young age mortality rate rapidly declines. This causes an upward
shift of the threshold age, constriction of the older population's age range, and diminishes early disparity,
consequently leading to an overall disparity reduction. Second, the young age mortality rate remains
stable while the old age mortality rate increases, resulting in a forward shift of the threshold age. This
results in a decrease in disparity due to late deaths, thereby driving the decline in overall disparity.
Relatively speaking, the former scenario is deemed healthier and better reflects reality. In Chinese
society, the mortality rate among the general population is rapidly decreasing, with a particularly swift
decline among young people. Consequently, the threshold age is continually pushed upward, with the
reduction in early disparity primarily dictating the overall decline in disparity over recent decades
(Zhang, 2016).
Nonetheless, with the continuous increase in life expectancy, the significance of late disparity
becomes more pronounced. This is notably the case in Japan, where late disparity prevailed from 1990
to 1995, leading to an upsurge in the overall disparity (Zhang and Li, 2020). Hence, to more accurately
depict the comparative significance between early disparity and late disparity, this paper calculates the
ratio of late disparity (the expansion component) to total disparity, denoted as RET = 𝑒0
!(𝑡)/𝑒!(𝑡).
Since the threshold age 𝑎! is dynamic and early disparity and late disparity are in a reciprocal
relationship, the indicator compares the reduction in old-age mortality with the reduction in youth
mortality, highlighting their relative changes. Also, it reflects the relative efficacy of deaths within the
older population amidst an ongoing decrease in mortality rates across all age groups and a continual
upward shift in the threshold age. Additionally, the ratio of expansion to compression (REC) serves to
compare the relative effects of two age groups, denoted as REC = 𝑒0
!(𝑡)/𝑒/
!(𝑡). RET and REC are
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essentially equivalent, although there are slight differences in their explanations, with
RET=REC/(1+REC). Considering that the threshold age tends to be 3 to 6 years away from the life
expectancy of the 0-year-old population, this paper refers to the threshold age (𝑎!) as the old-age
threshold age, early disparity (𝑒/
!) is referred to as young-adulthood disparity, and late disparity (𝑒0
!) is
referred to as old-age disparity.
Analytical strategy
Building on the preceding work, this paper conducts its analysis through two primary approaches. First,
it describes and analyzes the changes in life expectancy in China in general and in its sub-national
regions, especially in the eastern, central, western, and northeastern regions. The changes are described
at the regional level rather than in provinces and cities, mainly because of the large number of provinces
in China, the strong homogeneity within regions, and weak homogeneity between regions, thereby
discussing the aggregation at the regional scale. For detailed information on changes in provinces and
municipalities, please refer to Appendix Tables A1 and A2. Then, the description of the changes in each
region can be further divided into two parts, beginning with a description of the changes in the average
life expectancy of the older population in each region, followed by a description of the changes in old-
age disparity and the ratio of old-age disparity to total disparity in each region. At the same time, this
study employs corresponding demographic decomposition methods as necessary supplements to further
understand the convergence phenomenon among various indicators. The above work provides a
comprehensive overview for understanding the changes in life expectancy and life disparity among the
older population in China and its regions over the past four decades.
Second, multiple linear regressions are used to analyze the mechanisms affecting life expectancy
and life disparity among the older population in China's sub-national regions, and for the same reason,
provinces and municipalities are grouped into four regions for ease of discussion. Under the framework
of health transition theory, the differences in mechanisms in turn reflect the different stages of the health
transition of the older population in different regions of China. Moreover, the discussion of the cause-
of-death data further confirms this judgment, providing a theoretical discussion for understanding the
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relative lag of improvement in the older population's survival status within the context of the significant
increase in life expectancy in China.
Results
Descriptive statistics
For a clearer depiction of changes in life expectancy within the older age group, this paper selects the
population aged 60–64 as representative of early old age and the population aged 80–84 as
representative of late old age. Subsequently, it illustrates the changes in average life expectancy for
these two age groups across genders and regions.
In terms of the average life expectancy, as shown in Figures 1 and 2, overall, during the period
from 1981 to 2020, the life expectancy of the older population in China has shown a significant upward
trend, regardless of whether it is for males or females, and whether it is in the early-old-age group or
the late-old-age group. In 1981 and 2020, respectively, life expectancy for the early-old-age group was
15.99 years and 19.29 years for males, 17.85 years and 23.07 years for females, and in the late-old-age
group, life expectancy was 5.35 years and 6.51 years for males, and 5.91 years and 7.92 years for
females. For the early-old-age group, the average annual increases were 0.53% and 0.75% for males
and females respectively, and for the late-old-age group, the average annual increases were 0.56% and
0.87% for males and females respectively.
Between 1981 and 2020, except in the northeastern and western regions, which experienced a
slight decline between 1981 and 1990, life expectancy in all regions of China increased significantly.
The rate of the increase in life expectancy declines in the order of the eastern, northeastern, central and
western regions, excepting that the rate in the central region is slightly higher than the northeastern
region in the early-old-age group of males. In terms of the average annual increase, the western region
has the lowest growth rate for both males and females and both the early- and late-old-age groups.
Considering the higher level of initial mortality in the western region, the gap between the average life
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expectancy of the eastern region and the western region has widened over the past four decades. In the
early-old-age group, the gap between the eastern and western regions for men and women widened
from 0.57 and 1.76 years in 1981 to 1.95 and 2.28 years in 2020, and in the late-old-age group, the gap
between east and west for men and women widened from 0.31 and 0.63 years in 1981 to 0.63 and 1.06
years in 2020.
Average LE for the male older population
Average LE for the female older population
Figure 1. Trends in average life expectancy (LE) for both genders aged 60–64 in China and its four
sub-national regions over the period 1981–2020.
Average LE for the male older population
Average LE for the female older population
Figure 2. Trends in average life expectancy (LE) for both genders aged 80–84 in China and its four
sub-national regions over the period 1981–2020.
12
14
16
18
20
22
24
26
1981 1990 2000 2010 2020
life expectancy (Years)
Yea r
CN East Central West Northeast
12
14
16
18
20
22
24
26
1981 1990 2000 2010 2020
life expectancy (Years)
Year
CN East Central West Northeast
4
6
8
10
1981 1990 2000 2010 2020
life expectancy (Years)
Year
CN East Central West Northeast
4
6
8
10
1981 1990 2000 2010 2020
life expectancy (Years)
Year
CN East Central West Northeast
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As mentioned earlier, however, the significant rise in the average life expectancy across regions
does not determine whether life disparities across regions are growing or declining, or whether this
increase is equitable and the growth in life expectancy in old age has reached the majority of the
population. To respond to this question, this paper first plots the trend in the old-age threshold by gender
and by region on average, as shown in Figure 3.
Average 𝑎!, males
Average 𝑎!, females
Figure 3. Trends in average old-age threshold age (𝑎!) in China and its four sub-national regions over
the period 1981–2020.
Figure 3 shows that between 1981 and 2020, China's overall old-age threshold has continued to
shift upward, increasing from 65.42 and 68.68 years in 1981 to 72.51 and 77.98 years in 2020 for males
and females, respectively. This is mainly due to the rapid decline in China's infant and adolescent
mortality rates, which has shifted the threshold age upward (Wang et al., 2016); in other words, as
overall mortality has declined, the threshold age at which one is considered old has risen.
Regional observations reveal that, except in the western and northeastern regions, where there was
a slight decline between 1981 and 1990, the old-age threshold increased significantly in all regions of
China, and regardless of gender the threshold age was the highest in the eastern region and the lowest
in the western region, which is highly consistent with the trends established in previous discussion on
life expectancy. However, unlike the previous observations, in terms of average annual increases, the
western region has the highest average annual increase for females between 1981 and 2020, at 0.39%,
60
65
70
75
80
85
1981 1990 2000 2010 2020
a+ (Years)
Yea r
CN East Central West Northeast
60
65
70
75
80
85
1981 1990 2000 2010 2020
a+ (Years)
Year
CN East Central West Northeast
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which is slightly higher than the eastern region’s 0.31%. In conjunction with previous studies, this may
be related to the faster decline in the western region in the context of higher initial infant mortality
levels (Li and Yan, 2023), whereby the more rapidly declining infant and adolescent mortality rates
have led to a relatively more rapid increase in threshold age in the western region.
Next, in the context of declining mortality across the general population, Figures 4 and 5 indicate
trends in old-age disparity in China generally, as well as across regions, and variations in the relative
contribution of old-age disparity.
Concerning variations in old-age disparity for males, between 1981 and 2020, China’s overall old-
age disparity declined from 4.49 to 3.95 years, with an average annual rate of decline of 0.31%. At the
same time, the ratio of old-age disparity to total disparity increased from 30.78% to 37.05%, with an
average annual increase of 0.52%. These two opposite trends show that old-age disparity in China has
been relatively improved, but the impact of old-age disparity has risen comparatively.
Observing the old-age disparity among males by region, it can be seen that if old-age disparities
are ranked in descending order by region, between 1981 and 2020, the western region (4.45 years in
1981) moved from being only higher than the eastern region to being the highest of all the regions (4.08
years in 2020), except the eastern region, which consistently has the lowest disparities (4.38 years in
1981 and 3.77 years in 2020). Similarly, if the ratio of old-age disparity to total disparity in each region
is ranked in descending order, between 1981 and 2020, except for the northeastern region, which was
higher in 1981, the eastern region is consistently the highest (33.09% in 1981 and 38.04% in 2020), and
the western region is consistently the lowest (27.27% in 1981 and 35.59% in 2020). The above data
show persistent and stable regional differences in old-age disparity in the context of continued
improvement in overall old-age disparity in China, and reflect, in particular, a relative disadvantage in
the western region and a relative advantage in the eastern region. More specifically, the western region,
with its high total life disparity, also has the highest loss of life due to death in old age. However, the
relative contribution of old-age disparity to total disparity in the western region is, instead, the weakest
among the regions, and the western region also had the lowest average annual decline in old-age
disparity (0.21% in the west and 0.36% in the east). But as the health transition deepens in China, as
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well as in each region, the relative contribution of old-age disparity is increasing, as reflected in the
data that the western region has the highest average annual increase in the ratio of old-age disparity to
total disparity among the regions between 1981 and 2020 (0.78%). Thus, the western region is rapidly
approaching the stage of the health transition that is dominated by improvements in mortality levels
among the elderly, but remains different from the eastern, northeastern, and central regions because it
is experiencing the slowest decline in absolute old-age disparity.
Average 𝑒"
!, males
Average RET, males
Figure 4. Trends in average male old-age disparity (𝑒0
!) and its ratio to total disparity (RET) in China
and its four sub-national regions over the period 1981–2020.
Average 𝑒"
!, females
Average RET, females
Figure 5. Trends in average female old-age disparity (𝑒0
!) and its ratio to total disparity (RET) in
China and its four sub-national regions over the period 1981–2020.
3.0
3.5
4.0
4.5
5.0
1981 1990 2000 2010 2020
LD (Years)
Year
CN East Central West Northeast
25
30
35
40
45
1981 1990 2000 2010 2020
Ratio (%)
Yea r
CN East Central West Northeast
3.0
3.5
4.0
4.5
5.0
1981 1990 2000 2010 2020
LD (Years)
Year
CN East Central West Northeast
25
30
35
40
45
1981 1990 2000 2010 2020
Ratio (%)
Year
CN East Central West Northeast
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Similarly, concerning variations in old-age disparity among women, between 1981 and 2020, the
overall old-age disparity declined from 4.24 years to 3.58 years, with an average annual rate of decline
of 0.40%. Meanwhile, the ratio of old-age disparity to total disparity rose from 30.14% to 39.17%, with
an average annual increase of 0.77%. In line with the previous observations on male old-age disparities,
inter-regional observations reveal that the relative disadvantage of the western region and the relative
advantage of the eastern region still exist and that there are no significant gender differences in terms
of the ratio of old-age disparity to total disparity.
The above data show that China and its subregions exhibit significant differences in various
indicators. However, the regions also demonstrate an essentially consistent pattern characterized by a
highly synchronized trend: an increase in life expectancy in old age, a decrease in old-age disparity, and
an increase in the ratio of old-age disparity to total disparity.
How can we interpret this phenomenon? First, increases in life expectancy are universally
associated with declines in life disparity. This paper plots the correlation between life expectancy and
the total, early, and late disparity in China's provinces and municipalities from 1981 to 2020, with no
distinction between genders, as shown in Figure 6. It can be seen that life expectancy shows a significant
negative correlation pattern with the total disparity, young-adulthood disparity, and old-age disparity in
China, which is very consistent with the negative correlation patterns of countries around the world
(Vaupel et al., 2011). Meanwhile, while young-adulthood disparity has remained dominant, it is clear
that as life expectancy increases, total disparity and young-adulthood disparity are declining rapidly,
and the contribution of old-age disparity is becoming more and more important.
Second, in Chinese society, the decline in mortality among the early-old-age population is
gradually becoming the main force behind the increase in life expectancy and the improvement in life
disparity for the overall population. However, the improvement in mortality levels among the late-old-
age population is serving as an increasing impediment to improvements in total life disparity, thus
undermining the decline in old-age disparity. This phenomenon explains the simultaneous growth of
old-age life expectancy and the ratio of old-age disparity to total disparity. To elucidate this, this paper
decomposes the contribution of age-specific mortality rates to the growth of life expectancy and the
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contribution of age-specific mortality rates to life disparity in China between 1981 and 2020. This is
done based on the Arriaga (1984) method and the Shkolnikov and Andreev (2010) method, respectively,
as depicted in Figures 7 and 8.
Figure 6. The relationship between life expectancy and life disparity by province and municipality in
China between 1981 and 2020.
Males
Females
Figure 7. Contribution of age-specific mortality rates to the increase in life expectancy in China
between 1981 and 2020.
Figure 7, based on China's census data, indicates that the contribution of infant and child mortality
to the increase in life expectancy is rapidly declining. Concurrently, the contribution of the early-old-
age population to the increase in life expectancy is rapidly increasing, which accounts for the large
0
5
10
15
20
25
55 60 65 70 75 80 85 90
Life Disparity (Years)
Life Expectancy (Years)
Total Life Disparity Early-Life Disparity Late-Life Disparity
Expon. (Total Life Disparity) Expon. (Early-Life Disparity) Expon. (Late-Life Disparity)
0
5
10
15
20
25
30
35
40
0~1
1~4
5~9
10~14
15~19
20~24
25~29
30~34
35~39
40~44
45~49
50~54
55~59
60~64
65~69
70~74
75~79
80~84
85~89
90~94
95~99
100~104
105~109
110+
Precentage (%)
Age Groups
1981~1990 1990~2000 2000~2010 2010~2020
0
5
10
15
20
25
30
35
40
0~1
1~4
5~9
10~14
15~19
20~24
25~29
30~34
35~39
40~44
45~49
50~54
55~59
60~64
65~69
70~74
75~79
80~84
85~89
90~94
95~99
100~104
105~109
110+
Precentage (%)
Age Groups
1981~1990 1990~2000 2000~2010 2010~2020
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18
increase in life expectancy for the population as a whole and the elderly. For instance, in the case of
males, the contribution of the decline in infant mortality to the increase in life expectancy falls from
38.86% between 1990 and 2000 to 24.98% between 2010 and 2020. In contrast, the contribution of the
decline in mortality among the early-old-age population, aged 60 to 79, to the increase in life expectancy
rose from 15.05% between 1990 and 2000 to 31.96% between 2010 and 2020. Similarly, the
contribution from mortality improvement among the late-old-age population, aged 80 to 99, rose from
2.31% to 6.36 % over the same period. Therefore, while the mortality improvement of China's early-
old-age older population has been relatively slow, as mentioned earlier, its impact on the increase in life
expectancy for the whole population has significantly exceeded that of the infant group, and life
expectancy in old age has improved significantly, owing to the larger decline in absolute numbers.
Males
Females
Figure 8. Contribution of age-specific mortality rates to the decline in life disparity in China
between 1981 and 2020.
Figure 8 shows that the contribution of declining infant mortality to the improvement of total life
disparity is diminishing, whereas the contribution of the early-old-age population is increasing. For
instance, in the case of males, the contribution of infant mortality to the improvement of life disparity
declines from 55.10% between 1990 and 2000 to 49.48% between 2010 and 2020. Concurrently, the
contribution of the early-old-age population increases from -3.99% to 7.74% over the same period,
shifting from a negative to a positive contribution. In contrast, over the same period, the contribution
-20
-10
0
10
20
30
40
50
60
0~1
1~4
5~9
10~14
15~19
20~24
25~29
30~34
35~39
40~44
45~49
50~54
55~59
60~64
65~69
70~74
75~79
80~84
85~89
90~94
95~99
100~104
105~109
110+
Precentage (%)
Age Groups
1981~1990 1990~2000 2000~2010 2010~2020
-20
-10
0
10
20
30
40
50
60
0~1
1~4
5~9
10~14
15~19
20~24
25~29
30~34
35~39
40~44
45~49
50~54
55~59
60~64
65~69
70~74
75~79
80~84
85~89
90~94
95~99
100~104
105~109
110+
Precentage (%)
Age Groups
1981~1990 1990~2000 2000~2010 2010~2020
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19
of the late-old-age population to the overall reduction in life disparity declines further from -9.02% to
-24.13%.
Thus, the data indicate that the old-age threshold has consistently risen in China and its subregions,
enabling a growing proportion of the elderly to survive to advanced ages. This trend expands the range
of age groups considered in the calculation of young-adulthood disparity. Additionally, the increased
mortality rate among the early-old-age population has contributed significantly to further reducing the
young-adulthood disparity. On the other hand, while the threshold age has continued to move up, the
loss of life expectancy due to deaths in the late-old-age population has acted as an increasing barrier to
the decline in life disparity and may have thereby weakened the decline in life disparity produced by
the improved mortality level late-old-age population. As a result, the relatively slower decline in old-
age life disparity at a time of rapid decline in total life disparity and young-adulthood disparity has led
to an increase in its ratio to total life disparity instead.
The descriptive discussion above has sketched out the increasing trend in life expectancy, the
decreasing trend in life disparity for the older population, and the increasing trend in the ratio of old-
age disparity to total disparity in China overall and across its regions from 1981 to 2020. Although there
are relatively large differences in mortality levels across China's regions, all of China's regions have
generally kept in line with the overall pattern of change, with the eastern region experiencing faster
changes than the western region. However, the descriptive analysis has not yet addressed the reasons
for regional variations in these indicators among the older population, and in particular, why there are
large differences in old-age mortality between the eastern and western regions. To answer this question,
it is necessary to introduce the discussion in the subsequent section, on uneven development in the
health transition across regions. In particular, the improvement in the mortality levels of the older
population has been asynchronous across regions, which is reflected in the fact that the same health
improvement mechanisms will show differences in their effects in different regions. Furthermore, this
judgment is further supported by data on causes of death at the regional level.
Regression results
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20
Initially, this section examines health mechanisms across diverse regions to observe the sequential
stages of the health transition, offering vital insights into regional disparities in old-age health levels.
Aligned with the descriptive analysis section, this segment focuses on life expectancies of individuals
aged 60–64 and 80–84, alongside old-age disparity indicators. Additionally, to underscore the relative
significance of old-age disparity within the total disparity framework, this study includes young-
adulthood disparity for comparative purposes. These four indicators serve as dependent variables, while
the associated influence mechanisms function as independent variables to construct a multivariate linear
model. The outcomes of this model are depicted in Table 1.
First, life expectancy, as shown in Models 1 to 4 in Table 1. In general, medical facility investments
have a significant effect on increasing the life expectancy of the older population aged 60–64 and 80–
84, while the effect of the level of economic development shows some fluctuations due to the interaction
term. At the regional level, life expectancy in old age is consistently higher in the eastern region than
in the western region, and observing the regional interaction terms, it was found that for life expectancy
at 60–64 and 80–84, both medical facility investments and the level of economic development are
significantly more effective in increasing life expectancy in the eastern region compared to the western
region.
Differences in the effects of the above impact mechanisms can be well understood in the context
of the health transition framework. In general, both medical facility investments and the level of
economic development significantly contribute to reducing mortality. However, it is important to note
that variations exist in the targeted age groups for these mechanisms. Specifically, in the stage of the
vanquishing of infectious diseases, the decline in infant and child mortality, which was the main force
behind the increase in life expectancy of the population as a whole, was very much dependent on
investments in basic health facilities. But after the stage of the cardiovascular revolution, when the main
force of mortality reduction transitions from infancy and adulthood to old age, the role of medical
facility investments gradually weakens, as opposed to the level of economic development, which
becomes the more important influencing force. This is mainly because basic medical facilities have
limited effectiveness in addressing deaths from cardiovascular and cerebrovascular diseases, and
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21
whether advanced medical technology can be spread among the older population depends on local
economic development and the average education level.
Table 1 Models of the impact mechanisms of life expectancy (𝑒!""and
"
𝑒#"), old-age disparity
(𝑒$
%), and young-adulthood disparity (𝑒&
%).
𝒆𝟔𝟎
𝒆𝟖𝟎
𝒆𝒄
%
𝒆𝒆
%
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Factors:
Med (Medical Factor)
0.101*
0.189***
0.091+
0.181***
–0.113*
–0.212***
–0.365***
–0.175***
(0.047)
(0.031)
(0.055)
(0.036)
(0.055)
(0.036)
(0.072)
(0.046)
Eco (Economic Factor)
0.223***
0.054
0.276***
0.117
–0.168**
0.015
0.036
–0.344**
(0.047)
(0.070)
(0.055)
(0.082)
(0.055)
(0.082)
(0.072)
(0.105)
Region: (Western)
Eastern
0.221***
0.229***
0.220***
0.227***
–0.244***
–0.253***
–0.431***
–0.412***
(0.024)
(0.024)
(0.028)
(0.028)
(0.028)
(0.028)
(0.037)
(0.036)
Central
0.089***
0.094***
0.087***
0.090***
–0.101***
–0.107***
–0.191***
–0.183***
(0.020)
(0.020)
(0.023)
(0.024)
(0.024)
(0.024)
(0.031)
(0.030)
Northeastern
0.112***
0.118***
0.157***
0.168***
–0.065*
–0.070**
–0.307***
–0.264***
(0.023)
(0.019)
(0.026)
(0.022)
(0.027)
(0.022)
(0.034)
(0.028)
Interaction
Eastern * Med
0.071*
0.068+
–0.082*
0.144**
(0.034)
(0.039)
(0.039)
(0.051)
Central * Med
0.028
0.041
–0.026
0.072*
(0.022)
(0.025)
(0.025)
(0.033)
Northeastern * Med
0.022
0.032
–0.024
0.099**
(0.023)
(0.026)
(0.026)
(0.034)
Eastern * Eco
0.132***
0.120**
–0.146***
0.284***
(0.036)
(0.042)
(0.042)
(0.053)
Central * Eco
0.046*
0.045+
–0.053*
0.102**
(0.021)
(0.025)
(0.025)
(0.031)
Northeastern * Eco
0.049*
0.054*
–0.052*
0.115***
(0.019)
(0.022)
(0.022)
(0.028)
Controls
✓
✓
✓
✓
✓
✓
✓
✓
N
302
302
302
302
302
302
302
302
R-squared
0.913
0.916
0.883
0.886
0.882
0.885
0.801
0.814
Note: *** p < 0.001, ** p < 0.01, * p < 0.05, + p < 0.1; robust standard errors in parentheses; controls include
years and temperature factor.
In terms of the results of this study, on the one hand, this is reflected in the standardized coefficients,
where the level of economic development generally has a stronger influence on life expectancy in the
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22
elderly population compared to the basic medical facility investments. On the other hand, at the regional
level, considering that life expectancy in old age is higher in the eastern region, the mechanisms of the
medical facility investments and the level of economic development have a stronger effect on the
increase of life expectancy of the older population in the eastern region, which is also a reflection of a
faster health transition in the eastern region than in the western region (Li and Yan , 2023).
Second, regarding life disparity, as indicated in Models 5 and 6 presented in Table 1, broadly
speaking, investments in medical facilities exhibit a significant impact on old-age life disparity, whereas
the effect of economic development levels fluctuates, influenced by the interaction term. At the regional
scale, old-age life disparity consistently exhibits lower levels in the eastern region compared to the
western region. Further observation of the regional interaction terms reveals that both investments in
medical facilities and the level of economic development demonstrate a significantly greater influence
on reducing old-age life disparity in the eastern region than in the western region.
Meanwhile, in Models 7 and 8, the life disparity in young adulthood is lower in the eastern region,
while the efficacy of the two mechanisms—medical facility investments and economic development
level—exhibits a weaker impact in the eastern region compared to the western region. In other words,
these mechanisms do not demonstrate a proportionally stronger impact in improving conditions in the
eastern region, as observed in the context of old-age life expectancy and old-age life disparity.
The underlying reason for this disparity in mechanism effects requires further exploration within
the health transition framework. It has been noted that a sub-stage of initial divergence followed by
eventual convergence prevails at all stages of the health transition. Moreover, the decline in life disparity,
as an indicator of the concentration of the age distribution of deaths in a population, suggests a
compression or convergence of the distribution of deaths within the framework of the health transition.
Although life disparity has continued to decline in China and its subregions, with all regions showing
a convergence trend, each region is at a different stage of the health transition, and the improvements
in their mortality levels have focused on different age groups. While the eastern region has transitioned
to a stage of health transition characterized by declining deaths among the older population, the western
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23
region, with persistently high mortality levels among infants and young children as well as adults, is
still lagging behind the country as a whole.
Therefore, as outlined in the descriptive analysis, regarding the life disparity in young adulthood,
the rate of decline in the western region is significantly higher than that in the eastern region, indicating
a catching-up trend between the western and eastern regions. Conversely, regarding the life disparity in
old age, the rate of decline in the western region is significantly lower than that in the eastern region,
indicating a diverging trend between the western and eastern regions. The mechanism effect also reflects
this difference, considering that infant and child deaths are mainly attributed to infectious diseases, that
universal access to basic medical facilities is effective in ameliorating inequality across regions, and
that inexpensive and easily accessible basic medical facilities are effective in reducing higher levels of
life disparity among young adults in the western region. Meanwhile, deaths among middle-aged and
older people are mainly attributed to degenerative diseases, and their access to medical technology is
heavily reliant on local economic factors. China's uneven economic distribution, in turn, further
exacerbates the differences in old-age life disparity between the east and west. Nevertheless, since
health transitions in late-developing countries are often multi-stage and overlapping, investment in
medical facilities and the level of economic development are not mutually exclusive for the eastern and
western regions but rather work in tandem.
Subsequently, for further understanding of the differences in mechanism effects across regions,
this paper introduces data on causes of death for individuals aged 65 years and over in each region, as
shown in Figure 9. These data strongly suggest that behind the differences in mechanism effects is an
asynchronous decline in the level of deaths among the older population in each region, and that the
older population in each region is at different stages of the health transition.
On the one hand, in terms of the mortality level, the probability of death between the ages of 60
and 79 is much lower in the eastern region than in the western region, decreasing from 63.38% and
66.65% in 1981 to 45.32% and 54.28% in 2020 for males, respectively. Regarding the mortality level
of the early-old-age population, Gansu in the western region lags behind Shanghai in the eastern region
by about 20 years in absolute value, and the gap continues to widen due to the difference in the rate of
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24
decline (Lu et al., 2019). On the other hand, when it comes to causes of death, the available data on the
top four causes of death for those aged 65 years and older show that the western region has a
significantly higher proportion of respiratory diseases in the distribution of causes of death. Given that
respiratory diseases, as published by the China CDC based on the ICD-10 classification, include
infectious diseases J00–J22, this data suggests that the older population in the western region still faces
significant infectious diseases. In contrast, the eastern region exhibits a significantly higher composition
of deaths due to malignant tumors and a relatively lower composition of deaths due to cardiovascular
diseases at a lower mortality level. The regional cause-of-death data suggest that the older population
in the eastern region has progressively transitioned to a more advanced stage of the health transition.
This is very much in line with recent research indicating that, with technological advances, malignant
tumors have replaced cardiovascular disease as the leading cause of death among middle-aged and older
people in middle- and high-income regions, unlike low-income regions that are still plagued by
cardiovascular disease (Caselli, 2015; Dagenais et al., 2020). Interestingly, the distribution of causes of
death in the central region remains high for cardiovascular and cerebrovascular diseases, but the share
of respiratory diseases has decreased significantly, which indicates that the central region is in a stage
of the health transition that is between those of the eastern and western regions.
27.22
24.95
20.57
10.69
26.95
23.72 23.11
8.75
30.07
26.42
19.90
8.89
23.84 24.97
17.41 16.20
0
5
10
15
20
25
30
35
Cardiovascular diseases Cerebrovascular diseases Malignant tumors Respiratory diseases
Distribution of causes of death (%)
The top 4 causes of death
CN East Central Wes t
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25
Figure 9. Composition of the top four causes of death of the older population aged 65 and
over by region in China, 2020 (%).
Notes: For available cause-of-death data, the eastern region includes the north-eastern region.
Thus, in combination with the data already discussed in the previous descriptive section, that the
western region has the lowest life expectancy, the highest old-age disparity, and the lowest old-age
disparity as a ratio to total life disparity among the regions shows that it is significantly lagging behind
the eastern region in the health transition. The discussion in the section on mechanisms of health effects
and the components of causes of death reaffirms this assertion and is consistent with existing research
indicating that the western region is in a stage of the health transition characterized by a significant
improvement in mortality at older ages, yet there are still significant gaps between it and the eastern
region (Chen et al., 2018). In this sense, the lagging health improvement of China's older population is
strongly related to the uneven development of the health transition in different regions, and the relatively
lagging health transition in the western region may partly explain the relatively slow improvement in
China's overall old-age mortality.
Conclusion and discussion
Building on previous research, this paper derives two primary conclusions. First, through an
examination of life expectancy and life disparity among the older population, this paper notes a
consistent upward trend in life expectancy across China and its various regions. Throughout the period
spanning 1981 to 2020, despite earlier studies suggesting a delayed growth in life expectancy among
China's older population, this paper acknowledges significant efforts by the Chinese government to
enhance old-age health, evident in the life expectancy data presented herein. Within the 60–64 age
group, life expectancy for males and females has risen from 15.99 to 19.29 years and from 17.85 to
23.07 years, respectively, between 1981 and 2020; within the 80–84 age group, life expectancy for
males and females increased from 5.35 to 6.51 years and from 5.91 to 7.92 years, respectively, during
the same period. Nevertheless, the increase in life expectancy has been uneven across regions. Among
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26
the older population aged 60–64 years, the gap between the eastern and western regions for males and
females has widened from 0.57 years and 1.76 years in 1981 to 1.95 years and 2.28 years in 2020,
respectively; among the older population aged 80–84 years, the gap between the eastern and western
regions for males and females has widened from 0.31 years and 0.63 years in 1981 to 0.63 years and
1.06 years in 2020, respectively.
At the same time, the examination of old-age disparity reveals that old-age disparity within China
and its regions has been declining year by year and that the ratio of old-age disparity to total disparity
has been rising year by year. For China as a whole, old-age disparity for males and females has declined
from 4.49 and 4.24 years in 1981 to 3.95 and 3.58 years in 2020, respectively, and the ratio of old-age
disparity to total disparity ratios has risen from 30.78% and 30.14% in 1981 to 37.05% and 39.17% in
2020, respectively. Across all years, excluding 1981, the eastern region consistently exhibits the lowest
absolute level of old-age disparity across genders. Moreover, the eastern region consistently maintains
the highest ratio of old-age disparity to total disparity among all regions, while the western region
consistently records the lowest figures among all regions. The descriptive analyses presented above
show the persistent inter-regional differences in the levels of old-age mortality and old-age life disparity
in China.
Second, the discussion of the effects of medical facility investments and the level of economic
development in different regions under the health transition framework finds that the same mechanisms
have different intensities of effects in different regions: relative to the western region, medical facility
investments and the level of economic development both improve the life expectancy of the elderly and
old-age disparity to a greater extent in the eastern region, whereas they improve infant, child, and
young-adult disparities to a lesser extent. The differences in the intensity of the effects of the same
mechanism in different regions are largely the result of the stage-specific characteristics of China's
overall health transition, and the regional data on causes of death reaffirm this judgment, indicating that
while the eastern region has already advanced to the stage of improving health in old age, the western
region may not yet formally have advanced to this stage, or the central and western regions are still in
the transitional stage, which may have delayed the overall improvement in the level of mortality of the
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27
older population in China. This paper reveals that although all regions of China have experienced high
rates of life expectancy growth among the elderly, this growth has not been balanced or synchronized
at the same rate across regions, and due to the vastness of the territory of China, its health transition is
a multi-stage process taking place in parallel.
Undoubtedly, certain limitations persist within this paper, particularly evident being that the
discussion of the health transition solely relies on mortality and socio-economic data as a peripheral
approach to understanding health transition stages and investment in fundamental medical facilities, as
represented by the number of beds, may have underestimated the role of private medical care in the
health of the older population. Moving forward, a more comprehensive understanding can be achieved
by integrating data on more detailed cause-of-death data, bio-health data, and relevant indicators that
reflect the specificities of the older population. Recent research on global health disparities reveals that
as global life expectancy increases, the root causes of health disparities are shifting from death-related
disparities to disparities centered on illness and disability (Permanyer et al., 2023). With the substantial
growth of China's aging population, ensuring a long and quality life for the elderly will emerge as a
fresh challenge in maintaining health equity within Chinese society. Consequently, academics should
persist in monitoring the emerging health trends among the older population, while the government
ought to prioritize accounting for regional disparities to tailor and implement more targeted, region-
specific health policies.
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13.
Accepted Manuscript
30
Appendix
Table A1. Indicator estimates of the male older population in China by province and municipality.
1981
1990
2000
2010
2020
Province
𝑒!"
𝑒#"
𝑎!
𝑒$
%
𝑅𝐸𝑇
𝑒!"
𝑒#"
𝑎!
𝑒$
%
𝑅𝐸𝑇
𝑒!"
𝑒#"
𝑎!
𝑒$
%
𝑅𝐸𝑇
𝑒!"
𝑒#"
𝑎!
𝑒$
%
𝑅𝐸𝑇
𝑒!"
𝑒#"
𝑎!
𝑒$
%
𝑅𝐸𝑇
China
15.99
5.35
65.42
4.49
0.31
16.13
5.39
65.86
4.44
0.31
16.82
5.64
67.73
4.31
0.33
17.89
5.98
69.91
4.21
0.36
19.29
6.51
72.51
3.95
0.37
Beijing
17.23
5.74
68.66
4.23
0.34
17.63
5.78
69.50
4.15
0.33
18.75
6.43
71.32
4.11
0.37
21.07
7.25
75.06
3.85
0.39
22.52
7.84
77.33
3.59
0.39
Tianjin
17.03
5.67
68.24
4.25
0.33
17.37
5.79
68.94
4.23
0.34
18.27
6.13
70.67
4.13
0.36
20.46
6.90
74.38
3.84
0.39
21.70
7.44
76.20
3.69
0.39
Hebei
16.65
5.61
67.34
4.34
0.33
16.51
5.52
66.96
4.35
0.32
17.08
5.80
68.22
4.32
0.35
17.94
6.10
69.83
4.26
0.37
19.20
6.46
72.39
3.95
0.37
Shanxi
16.07
5.38
65.71
4.46
0.31
16.23
5.42
66.18
4.41
0.31
16.91
5.69
67.93
4.30
0.34
18.07
6.07
70.24
4.19
0.36
19.53
6.42
73.14
3.84
0.36
Inner Mongolia
16.04
5.34
65.50
4.46
0.30
15.72
5.24
64.38
4.52
0.29
16.38
5.51
66.63
4.40
0.33
17.78
5.92
69.76
4.20
0.35
19.10
6.55
71.96
4.04
0.37
Liaoning
16.74
5.68
67.49
4.36
0.34
16.46
5.56
66.86
4.39
0.33
17.40
5.94
68.80
4.30
0.36
18.73
6.55
70.91
4.21
0.37
19.88
7.03
72.78
4.04
0.37
Jilin
16.14
5.58
65.90
4.56
0.35
15.74
5.37
64.86
4.62
0.33
17.33
6.06
68.29
4.40
0.36
18.64
6.39
71.12
4.13
0.37
19.48
6.71
72.55
4.00
0.37
Heilongjiang
16.05
5.44
65.76
4.50
0.33
15.51
5.28
64.18
4.62
0.31
16.91
5.81
67.73
4.38
0.35
18.33
6.27
70.54
4.20
0.37
19.45
6.83
72.16
4.08
0.37
Shanghai
17.35
5.74
68.92
4.20
0.33
18.08
6.02
70.35
4.15
0.36
19.77
6.70
73.27
3.91
0.38
20.93
7.06
75.09
3.81
0.39
22.35
7.45
77.45
3.48
0.39
Jiangsu
16.15
5.44
66.02
4.46
0.32
16.66
5.61
67.36
4.34
0.33
17.57
5.90
69.28
4.24
0.35
18.89
6.36
71.80
4.01
0.37
20.25
6.70
74.24
3.79
0.38
Zhejiang
16.30
5.48
66.44
4.41
0.32
16.76
5.66
67.58
4.33
0.34
17.84
6.17
69.43
4.32
0.37
19.42
6.63
72.59
3.97
0.37
20.91
6.87
75.28
3.72
0.39
Anhui
16.16
5.48
66.05
4.48
0.33
16.30
5.46
66.40
4.40
0.32
17.05
5.70
68.25
4.26
0.33
18.06
5.99
70.33
4.14
0.35
19.38
6.48
72.76
3.91
0.37
Fujian
15.55
5.37
64.36
4.67
0.33
15.88
5.35
65.20
4.54
0.32
16.95
5.75
67.96
4.33
0.34
18.23
6.16
70.50
4.17
0.37
19.53
6.59
72.92
3.92
0.37
Jiangxi
15.76
5.26
64.58
4.53
0.29
15.83
5.26
64.70
4.51
0.29
16.58
5.51
67.12
4.31
0.32
17.71
5.91
69.60
4.21
0.35
21.30
7.62
74.87
3.94
0.38
Shandong
16.43
5.56
66.75
4.41
0.33
16.42
5.55
66.75
4.40
0.33
17.46
6.03
68.78
4.33
0.36
18.61
6.37
71.05
4.14
0.37
19.96
6.87
73.36
3.94
0.38
Henan
16.14
5.50
66.01
4.50
0.33
16.16
5.49
66.07
4.48
0.33
16.87
5.64
67.87
4.28
0.33
17.66
5.90
69.50
4.22
0.35
18.88
6.40
71.71
4.04
0.37
Hubei
15.37
5.20
63.58
4.60
0.30
15.72
5.28
64.65
4.56
0.31
16.85
5.60
67.80
4.27
0.32
18.14
5.98
70.53
4.10
0.35
19.50
6.65
72.74
3.96
0.37
Hunan
15.75
5.24
64.42
4.52
0.29
15.98
5.30
65.17
4.48
0.29
16.80
5.57
67.68
4.27
0.32
17.88
5.95
69.93
4.20
0.35
19.42
6.78
72.22
4.06
0.37
Guangdong
16.31
5.57
66.44
4.46
0.34
16.69
5.70
67.33
4.39
0.34
17.14
5.81
68.36
4.30
0.35
18.59
6.26
71.24
4.07
0.37
20.08
6.66
73.96
3.81
0.38
Guangxi
16.41
5.53
66.72
4.39
0.33
16.09
5.41
65.83
4.47
0.32
16.47
5.61
66.84
4.42
0.34
17.50
6.04
68.83
4.33
0.36
19.35
6.98
71.38
4.22
0.37
Hainan
16.20
5.39
66.05
4.41
0.31
17.17
5.76
68.51
4.26
0.34
18.33
6.06
70.91
4.06
0.35
19.59
6.48
73.20
3.85
0.37
Chongqing
16.70
5.73
67.32
4.40
0.35
18.22
6.37
69.91
4.33
0.37
19.79
6.98
72.68
4.04
0.37
Sichuan
15.46
5.17
63.50
4.56
0.28
15.77
5.26
64.63
4.53
0.30
16.53
5.64
66.99
4.41
0.34
17.74
6.17
69.15
4.34
0.37
19.10
6.50
72.07
4.01
0.37
Guizhou
16.01
5.21
63.88
4.43
0.25
15.96
5.22
64.36
4.45
0.26
15.67
5.23
64.29
4.54
0.29
16.32
5.53
66.48
4.44
0.33
17.68
6.19
68.94
4.37
0.37
Accepted Manuscript
31
Yunnan
15.95
5.18
63.31
4.43
0.24
16.00
5.21
64.07
4.43
0.25
15.78
5.23
64.41
4.51
0.28
16.22
5.40
66.09
4.41
0.31
17.26
5.82
68.66
4.27
0.35
Tibet
14.99
4.99
59.79
4.78
0.24
14.86
5.09
61.89
4.74
0.29
15.87
5.34
65.14
4.55
0.31
17.95
5.73
69.92
4.07
0.30
Shaanxi
15.77
5.24
64.43
4.51
0.29
16.04
5.34
65.51
4.46
0.30
16.69
5.56
67.41
4.30
0.32
18.07
6.05
70.26
4.18
0.36
19.52
6.40
73.15
3.83
0.36
Gansu
15.79
5.28
64.74
4.54
0.30
16.04
5.35
65.54
4.47
0.30
16.43
5.40
66.50
4.32
0.29
17.49
5.73
69.20
4.16
0.32
18.56
6.11
71.39
4.00
0.35
Qinghai
15.70
5.14
62.78
4.49
0.24
15.61
5.11
62.06
4.53
0.24
15.83
5.25
64.62
4.50
0.29
16.40
5.49
66.69
4.37
0.32
18.09
5.85
70.42
4.05
0.33
Ningxia
16.36
5.33
65.83
4.34
0.27
16.73
5.41
66.84
4.23
0.27
16.98
5.55
68.00
4.20
0.30
17.36
5.87
68.83
4.27
0.35
19.05
6.39
72.15
3.97
0.37
Xinjiang
16.36
5.26
64.43
4.37
0.24
16.07
5.22
64.15
4.42
0.25
16.43
5.37
66.27
4.32
0.28
17.28
5.70
68.77
4.19
0.33
18.42
6.20
70.95
4.10
0.36
Accepted Manuscript
32
Table A2. Indicator estimates of the female older population in China by province and municipality.
1981
1990
2000
2010
2020
Province
𝑒!"
𝑒#"
𝑎!
𝑒$
%
𝑅𝐸𝑇
𝑒!"
𝑒#"
𝑎!
𝑒$
%
𝑅𝐸𝑇
𝑒!"
𝑒#"
𝑎!
𝑒$
%
𝑅𝐸𝑇
𝑒!"
𝑒#"
𝑎!
𝑒$
%
𝑅𝐸𝑇
𝑒!"
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𝑎!
𝑒$
%
𝑅𝐸𝑇
China
17.85
5.91
68.68
4.24
0.30
18.26
6.01
69.65
4.22
0.31
19.28
6.33
71.96
4.00
0.33
21.05
6.99
74.92
3.89
0.37
23.07
7.92
77.98
3.58
0.39
Beijing
19.26
6.44
71.71
4.05
0.34
19.85
6.62
72.89
3.95
0.35
21.37
7.21
75.35
3.85
0.38
23.98
8.36
79.13
3.54
0.41
25.81
9.33
81.52
3.34
0.41
Tianjin
18.68
6.23
70.47
4.18
0.33
19.34
6.45
71.91
4.03
0.34
20.67
6.87
74.33
3.89
0.36
22.81
7.75
77.65
3.59
0.39
24.83
8.76
80.20
3.50
0.42
Hebei
18.68
6.23
70.48
4.17
0.33
18.89
6.26
71.01
4.11
0.33
19.72
6.53
72.74
3.95
0.34
21.08
7.05
74.93
3.89
0.38
22.84
7.75
77.69
3.59
0.39
Shanxi
17.78
5.90
68.50
4.25
0.30
18.34
6.07
69.77
4.23
0.32
19.35
6.37
72.07
3.99
0.33
21.00
6.98
74.83
3.89
0.37
22.84
7.66
77.73
3.57
0.39
Inner Mongolia
17.43
5.81
67.56
4.30
0.29
17.44
5.74
67.59
4.26
0.28
18.81
6.14
70.96
4.08
0.31
21.04
6.94
74.94
3.88
0.37
22.79
7.76
77.60
3.60
0.39
Liaoning
18.67
6.30
70.21
4.23
0.33
18.66
6.20
70.47
4.17
0.33
20.05
6.67
73.30
3.93
0.35
21.85
7.43
76.12
3.76
0.38
23.60
8.30
78.57
3.57
0.39
Jilin
17.96
6.16
68.23
4.33
0.32
17.81
5.96
68.44
4.27
0.31
19.91
6.71
72.86
3.97
0.35
21.61
7.30
75.74
3.80
0.38
23.44
8.14
78.43
3.56
0.39
Heilongjiang
17.77
6.03
68.07
4.31
0.31
17.58
5.89
67.88
4.30
0.30
19.73
6.60
72.62
3.98
0.34
21.83
7.45
76.06
3.77
0.38
23.51
8.25
78.46
3.57
0.39
Shanghai
20.00
6.68
73.15
3.94
0.35
20.85
7.02
74.51
3.90
0.37
22.60
7.81
77.19
3.65
0.38
24.15
8.45
79.33
3.54
0.41
25.96
9.33
81.75
3.30
0.42
Jiangsu
18.65
6.17
70.52
4.16
0.32
19.29
6.41
71.85
4.03
0.34
20.46
6.83
73.94
3.91
0.36
21.81
7.32
76.16
3.74
0.38
23.70
8.16
78.81
3.54
0.40
Zhejiang
18.59
6.14
70.39
4.17
0.32
19.55
6.53
72.30
4.00
0.34
20.97
7.12
74.60
3.91
0.37
22.65
7.75
77.36
3.63
0.39
24.21
8.34
79.47
3.51
0.41
Anhui
18.34
6.12
69.70
4.24
0.32
18.55
6.10
70.32
4.17
0.32
19.39
6.36
72.18
3.98
0.33
21.28
7.11
75.28
3.85
0.38
22.97
7.88
77.82
3.59
0.39
Fujian
18.27
6.15
69.38
4.26
0.32
18.40
6.06
69.96
4.21
0.32
19.94
6.61
73.11
3.94
0.35
21.72
7.29
76.00
3.76
0.38
23.54
8.18
78.56
3.56
0.40
Jiangxi
17.55
5.77
67.90
4.25
0.28
17.74
5.79
68.29
4.21
0.27
18.57
5.98
70.13
4.15
0.28
20.91
6.91
74.74
3.88
0.37
22.84
7.77
77.68
3.59
0.39
Shandong
18.57
6.19
70.21
4.21
0.33
18.92
6.30
71.03
4.11
0.33
20.48
6.90
73.87
3.92
0.36
21.97
7.49
76.29
3.74
0.38
23.99
8.48
79.08
3.55
0.40
Henan
18.53
6.19
70.10
4.22
0.33
18.88
6.28
70.95
4.12
0.33
19.32
6.34
72.02
3.99
0.33
21.14
7.09
74.99
3.90
0.38
23.07
7.96
77.92
3.59
0.39
Hubei
17.27
5.76
67.07
4.33
0.29
17.82
5.90
68.61
4.24
0.30
19.14
6.30
71.63
4.03
0.33
21.06
6.97
74.95
3.88
0.37
22.86
7.86
77.66
3.60
0.39
Hunan
17.28
5.72
67.14
4.31
0.28
17.78
5.85
68.54
4.23
0.29
18.99
6.22
71.36
4.05
0.32
21.09
7.06
74.92
3.90
0.38
23.09
8.08
77.84
3.62
0.39
Guangdong
19.43
6.57
71.85
4.05
0.34
20.09
6.80
73.16
3.96
0.35
20.32
6.76
73.75
3.91
0.36
22.14
7.52
76.61
3.70
0.38
23.98
8.33
79.15
3.53
0.41
Guangxi
18.69
6.26
70.40
4.20
0.33
18.36
6.00
69.89
4.20
0.31
19.46
6.38
72.30
3.97
0.33
21.99
7.54
76.27
3.75
0.38
24.07
8.68
79.04
3.57
0.39
Hainan
19.25
6.32
71.89
4.00
0.33
20.04
6.62
73.34
3.91
0.35
22.52
7.64
77.22
3.63
0.39
24.39
8.46
79.67
3.52
0.42
Chongqing
19.40
6.51
71.95
4.03
0.34
21.75
7.46
75.83
3.80
0.38
23.73
8.43
78.68
3.58
0.39
Sichuan
16.95
5.62
66.02
4.39
0.27
17.56
5.78
67.94
4.25
0.28
19.19
6.42
71.54
4.07
0.34
21.18
7.22
74.90
3.91
0.38
23.13
7.99
78.01
3.58
0.39
Guizhou
16.71
5.52
64.54
4.46
0.24
17.35
5.68
67.07
4.27
0.26
17.89
5.81
68.60
4.19
0.27
19.63
6.42
72.64
3.94
0.33
21.78
7.43
75.95
3.78
0.38
Yunnan
16.73
5.52
64.59
4.46
0.24
17.32
5.66
66.83
4.28
0.25
17.74
5.77
68.18
4.20
0.27
19.14
6.21
71.67
4.00
0.31
21.19
6.99
75.19
3.85
0.37
Accepted Manuscript
33
Tibet
16.23
5.43
63.18
4.54
0.25
16.83
5.70
65.49
4.50
0.29
18.02
6.00
69.04
4.24
0.31
19.93
6.51
73.19
3.90
0.34
Shaanxi
17.05
5.66
66.39
4.36
0.28
17.78
5.86
68.54
4.23
0.29
18.66
6.09
70.62
4.12
0.31
20.72
6.89
74.41
3.89
0.37
22.70
7.59
77.56
3.58
0.39
Gansu
17.17
5.71
66.82
4.34
0.28
17.74
5.82
68.42
4.22
0.29
18.11
5.87
69.14
4.18
0.28
19.76
6.41
72.90
3.91
0.33
21.40
7.03
75.58
3.78
0.37
Qinghai
16.51
5.48
64.14
4.49
0.25
16.61
5.50
64.27
4.47
0.24
17.80
5.80
68.46
4.20
0.28
18.77
6.19
70.83
4.12
0.32
20.90
6.76
74.77
3.85
0.35
Ningxia
17.45
5.72
67.56
4.25
0.27
17.95
5.84
68.82
4.19
0.28
19.02
6.16
71.40
4.02
0.31
20.26
6.68
73.72
3.90
0.35
21.66
7.15
75.98
3.74
0.37
Xinjiang
16.75
5.53
64.85
4.46
0.25
16.82
5.55
65.14
4.45
0.25
18.10
5.90
69.27
4.19
0.29
19.85
6.57
72.97
3.94
0.34
21.31
7.11
75.34
3.84
0.38
