ArticlePDF Available

Determinants of regional fertility in China during the first years of reaching below-replacement fertility

Authors:

Abstract and Figures

China reached a stable below-replacement fertility in the middle of 1990s. The turn of this century saw the population development gap in various regions across China expanding, the total fertility rate (TFR) shrinking and remaining at a relatively lower level with the passage of time. Based on China’s official statistics, the authors analyzed the characteristics of the total fertility rate at each stage of the population policy adjustment (1970s, 1982-2013, after 2013), in particular, in the regional aspect. The sub-stage of 1995-2010 – the first years of below-replacement fertility – were considered closely in sense of determinants of regional gaps in fertility. With the help of quantitative analysis, it can be proved that regional per capita GDP (wealth level) has significant links with fertility rate. The higher the per capita GDP, the lower the fertility rate. The authors concluded that the regional total fertility rates and per capita GDP were inversely related, and per capita GDP was the factor that had the greatest correlation with the regional total fertility rate. To increase the regional fertility rate, it is necessary not only to relax the family planning policy, but also to implement incentive policies related to human fertility and to strengthen social, economic, demographic, and cultural constructions.
Content may be subject to copyright.
Determinants of regional fertility
in China during the first years of reaching
below-replacement fertility
Fang Lieming,1 Ekaterina Shatalova,2 Irina Kalabikhina3
1 School of Economics and Management, Shandong Youth University of Political Science (China) 2 Data
and Analytics Department, LLC Lamoda (Moscow, Russia) 3 Faculty of Economics, Department of
Population, LMSU (Russia)
Corresponding author: Fang Lieming
(fanglieming@126.com)
Academic editor: V. Faminsky | Received 7 March 2022 | Accepted 18 August 2022 | Published 5 October 2022
Citation: Lieming, F., Shatalova, E., & Kalabikhina, I. (2022). Determinants of regional fertility in China during the
first years of reaching below-replacement fertility. BRICS Journal of Economics, 3(3), 101–127. https://doi.org/10.3897/
brics-econ.3.e83259
Abstract
China reached a stable below-replacement fertility in the middle of 1990s. The turn of this century
saw the population development gap in various regions across China expanding, the total
fertility rate (TFR) shrinking and remaining at a relatively lower level with the passage of time.
Based on China’s ocial statistics, the authors analyzed the characteristics of the total fertility
rate at each stage of the population policy adjustment (1970s, 1982-2013, after 2013), in particular,
in the regional aspect. The sub-stage of 1995-2010 – the rst years of below-replacement fertility –
were considered closely in sense of determinants of regional gaps in fertility. With the help of
quantitative analysis, it can be proved that regional per capita GDP (wealth level) has signicant
links with fertility rate. The higher the per capita GDP, the lower the fertility rate. The authors
concluded that the regional total fertility rates and per capita GDP were inversely related, and per
capita GDP was the factor that had the greatest correlation with the regional total fertility rate. To
increase the regional fertility rate, it is necessary not only to relax the family planning policy, but
also to implement incentive policies related to human fertility and to strengthen social, economic,
demographic, and cultural constructions.
Keywords
Determinant of fertility, total fertility rate, below-replacement fertility, per capita GDP, population
policies, China.
JEL: J11, J13, R12, Y10.
Copyright Lieming Fang, Ekaterina Shatalova, Irina Kalabikhina. This is an open access article distributed under the terms of the Creative Commons
Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author
and source are credited.
BRICS JOURNAL OF ECONOMICS
DOI 10.3897/brics-econ.3.e83259
2022 Volume 3 Number 3
Lieming Fang, Ekaterina Shatalova, Irina Kalabikhina
102
Introduction
Population is one of the primary and most signicant issues in society. Long-term
balanced growth of population maers a lot to the development of a country and a nation,
as well as to the political, economic and social stability of the country. A country with
below-replacement fertility, as usual, cannot have growth of population without a high
level of replacement migration. The study of factors aecting the regional fertility
in such a country helps to understand the drivers of changes in fertility.
The family planning policy in China from 1971 to 1979 had the maximum
eect on the decline in fertility. The total fertility rate (TFR) dropped from
5.7 to 2.6 children per woman. This was due to the fact that the family planning policy
was weakened at the beginning of the demographic transition. Educated women
reduced the fertility even earlier (Lavely & Freedman, 1990).
During the long-term “one family - one child” policy from 1982 to 2013, the TFR
slowly decreased from 2.6 to 1.6 children per woman. The inuence of this policy
on fertility also takes place, but its strength corresponds to the period of completion
of the demographic transition. Our research interest relates to the period when
the TFR decreased to below-replacement fertility. This special period is important
for understanding the factors contributing to the decline in fertility on the basis
of regional data. The demographic transition is coming to an end, several types
of policies have accelerated the transition. Fertility decreased below-replacement
level at the beginning of 1990s. What other factors are acting at this historical moment
to reduce fertility below the replacement level in the regional context? The dierences
in the regional fertility levels were still strong.
In the People’s Republic of China (hereinafter referred to as “the PRC” or
“China”), regional population growth is uneven (Krasova & Bao, 2016) and this
situation has become increasingly signicant in recent decades (Kalabikhina et al.,
2020). The regional gap in the population development trends is related to such factors
as social, economic, political, cultural and environmental. Before China implemented
economic openness and reform in 1978, the Chinese government had encouraged
childbirth. The country was in a period of planned economy. There were related
political, social and cultural factors that encouraged childbirth. During this period,
China’s fertility rate was at a relatively high level. Since 1971, especially since 1982,
China has been implementing a strict family planning policy. During this period,
China’s total fertility rate has been declining year by year and is currently at a relatively
low level. Although China has been pursuing a “two-child policy” since 2013,
the eect of increasing fertility is not obvious. After China’s reform and opening up in
1978, the income divide between the western region, the eastern coast and middle
China widened (Bazhenova, 2010). Since 1980, China’s Gini coecient has been on the
rise. According to the data of the International Monetary Fund and the National
Bureau of Statistics, China’s Gini coecient in 2016 was 46.5%, ranking 28th in the
world, having increased by 15% during the period of 1990-2008 (Jain-Chandra et al.,
2017). Hence, economic development has a close connection with population growth.
Determinants of regional fertility in China during the rst years... 103
At the turn of this century, China’s uneven social and economic growth (Appendix
B, Figures B3, B4) increases the gap in population growth between various regions.
Dierences in China’s population reproduction and selement entwine national
economic development and spatial distribution of wealth.
This paper analyzes the impact of demographic and economic determinants on the
regional total fertility rate in China at the turn of this century. We are conducting
a statistical analysis of China’s total fertility rate dynamics since the inception
of a strong demographic policy in the 1970s until the end of the 2010s. Then we consider
the period 1995-2010 more closely. This is the period when the total fertility rate has
steadily declined to below-replacement fertility. However, the regional total fertility
rates were still clearly dierent in 1995. The regional dynamics of the total fertility rate
was characterized by convergence in 1995-2010. Of interest is a set of factors that could
inuence the dierentiation of regional fertility in this period. The paper also discusses
the factors that resulted in dierent fertility rates in China at the turn of this century
and the impact of local economic development on fertility. Then a conclusion is drawn
and related suggestions (including a discussion of population policy) are put forward.
1. Literature review
One of the main issues when choosing explanatory variables for fertility in the regions
of China — which has a stronger impact — is family planning policies or social
and economic factors. Since 1971, a family planning policy has been carried out; then,
since 1982, the country has been implementing a demographic policy “one family –
one child,” limiting fertility, which the PRC authorities proclaimed due to fears that
the rapidly growing population would exhaust the land, water and energy resources
of the country, hinder economic development and lead to poverty and underdevelopment
of the PRC. The presence of a long-running anti-natalist policy expands the list
of fertility factors, which, aside from a political impact, is one of the most complex
processes in factor modelling. The decline in the birth rate in China is generally
recognized as a result of a combination of socio-economic development and family
planning policies (Winckler, 2002). The two forces interacted, most likely reinforcing
one another: the level of social and economic development could be a precondition
for family planning programs; fertility policies, in turn, could further strengthen
existing social and family norms. Both socio-economic changes and population control
policies have played a fundamental role in the decline of fertility in China, and in
those places where family planning was most stringent, there have also been profound
changes in the socio-economic structure (Tien, 1984).
A similar conclusion is made by Poston and Gu, who considered 28 regional entities
in China using the country’s 1982 census data (Poston & Gu, 1987). They believe that
China’s decline in fertility should not be seen solely as a result of population control
policies. Skinner and co-authors, using the hierarchical regional space (HRS) spatial
approach to study fertility change in China in the 1990s, highlight the following
Lieming Fang, Ekaterina Shatalova, Irina Kalabikhina
104
factors of regional fertility dierentiation: levels of socio-economic development,
implementation of family planning policies, changes in traditional family norms,
and dissemination of abortion technologies selective on gender basis (Skinner et al.,
2000).
Other researchers acknowledge that the declining TFR in China correlated with
increasing incomes even before the one-child policy was implemented (Birdsall &
Jamison, 1983). Also, researchers claim that in China, society’s acceptance of the
politically-sanctioned family size follows the gradient of socio-economic development:
high acceptance rates are observed in most urban, industrialized regions while
the lowest are observed among women living in poorer and less urbanized regions
(Merli & Smith, 2002). Using data from the 2000 census, Cai (2010) compares
the fertility levels at the district level of two provinces, Jiangsu and Zhejiang, which
have similar levels of economic development but have dierent policies in the eld
of fertility. Cai nds that while the birth policy rules vary widely enough between these
two provinces, the county-level fertility rates are still very similar. The author concludes
that even if the decline in fertility was initiated by harsh government intervention,
in recent years the persistently low fertility trend is mainly due to socio-economic
factors rather than state intervention. In addition, many studies have shown that
population policy accounted for only a small proportion of fertility variations and most
of the decline in fertility was caused by an increase in the level of women’s education
and household income, as well as a shift in the concentration of labor from agriculture
to industrial production and services (Schul & Zeng, 1995; McElroy & Yang, 2000).
On the one hand, decentralization of the demographic policy (Goodkind, 2011; Short &
Zhai, 1999) suggests the impact of this factor on the regional fertility dierentiation,
on the other hand, the growing number of exclusions and exceptions for a number
of population groups (Gu et al., 2007; Peng, 2008) allows us to focus on socio-economic
factors of fertility.
Note that fertility is aected not only by demographic, but also by social policy,
such as scal policy in the eld of education and social protection (He et al., 2016),
and distribution of funds for the education system (Chen & Miao, 2019).
The close relationship of urbanization with demographic changes has aracted
some aention (Ge, 2015) when searching for fertility factors. It was noted that
many countries have experienced demographic transition and have also achieved
signicant progress in urbanization (Sato & Yamamoto, 2005). Industrialization
and urbanization, in turn, increase the cost of raising children and the involvement
of women in the labor market, as well as promote the ideals of a small family,
which ultimately aects reproductive motivation (Birdsall, 1983; Tien, 1984). It was
in cities that demographic policy was more assertive. Urban centers were also centers
of propaganda campaigns. In addition, health care and fertility control were more
available in cities, and it was also easier to control the reproductive behavior of the
population due to more compact living in urban areas compared to rural areas.
All these factors together led to a more eective birth restriction in the cities of China
(Bazhenova, 2010).
Determinants of regional fertility in China during the rst years... 105
Even before the birth control policies began, urbanization and education were
negatively correlated with fertility in marriage (Lavely & Freedman, 1990).
It is also important to note the spatial dierences in the decline in infant mortality
as an explanatory factor of dierences in fertility: in regions where infant mortality
is lower, the intervals between births are greater and the total fertility is lower (Zhang,
1990). In urban areas, infant mortality is generally lower, which, in turn, aects
the decline in the desired number of children in the family. Thus, fertility reduction
factors are more eective in urban areas, the level of education of a woman, her social
status and activity denitely aect her reproductive behavior (Bazhenova, 2010).
In general, higher incomes contribute to the reduction of infant mortality due to
improved nutritional standards and contribute to the reduction of fertility by increasing
the market value of women’s time and, therefore, the alternative cost of childbearing.
In addition, China’s rapid economic growth and globalization led to the emergence
of the idea of a “small family” among a signicant part of the population of the PRC
(Zheng et al., 2016).
Migration to cities reduces the likelihood of having children (Liang, 2018).
A woman’s migration status refers to whether she is a migrant or not, as well as her
membership in the hukou household registration system (Guo & Gu, 2014). Thus,
in 2005, non-migrant women with rural hukou (1.64 on average) had the highest TFR,
and non-migrant women with urban hukou (Guo & Gu, 2014) had the lowest. In 2010,
this trend continued. At the same time, in 2010, the highest coecient for one child
was observed among migrants living in the city, and for three children and above –
in rural areas.
Economic factors often adversely aect the TFR. The literature indicates that,
for example, there is a signicant negative impact of rising real estate prices on TFR
(Ge & Zhang, 2019). The high level of economic development, high level of education
and autonomy for women (Wang et al., 2015) and high level of human development
index (income, education and life expectancy) of the regions of China (Tao et al., 2017)
negatively aected fertility in these regions.
However, an ethnographic study conducted by Zhang (Zhang, 2007) nds an inverse
relationship between the TFR and the economic development level. In areas with high
levels of economic development, auent families tend to have more children because
they can pay nes, while relatively poor rural families tend to have only one child
due to fears of high costs.
Socio-cultural factors also maer. The link between gender equality and fertility
is becoming unsustainable. The classical relationship is that the higher the level
of education of women, the lower fertility rates. However, in modern Chinese society,
gender equality (women’s higher education and equal levels of education of spouses)
can increase the likelihood of having a child (Zhao, 2019). Social interaction and the
spread of cultural norms in relation to fertility are also important in addition to social
and economic development (Bongaarts & Watkins, 1996). Increasingly, evidence
suggests that the spread of ideas, norms and behaviors may not fully correspond
to the spatial structures of socio-economic characteristics. Compared to rapid economic
Lieming Fang, Ekaterina Shatalova, Irina Kalabikhina
106
development and institutional change, changes in culture and norms can be slow.
As a result, consideration of fertility reduction and dierentiation factors includes both
socio-economic and socio-cultural factors. Peng (2008), for example, draws aention
to the dynamics of the implementation of local birth control policies in the context
of common family norms: places with strong patrilineal norms (measured by family
networks and the presence of ancestral temples – a temple or ancestral hall, also called
a genealogical temple, is a Chinese temple dedicated to deied ancestors and forefathers
of lineages or families in traditional Chinese religion) are particularly resistant to the
anti-natalist family planning policies and show a high TFR despite such policies.
However, Hou Lee (Hou, 2018) believes that reproductive seings change
depending on economic changes and/or family planning policies and play a secondary
role in fertility dynamics.
The general conclusion from the above-mentioned literature is that socio-economic,
socio-cultural, and demographic factors aect the regional total fertility rates during
the entire period of decline in the TFR. The same story was investigated during the period
of 1970th family planning or the 1982-2013 “only one child” policy. Population policies
maer but the role of the previous ones may prevail. We are interested in fertility
determinant in the below-replacement fertility period at the rst 15 years. Assessing
the impact of various factors on fertility in China in 1995-2010, we will focus on socio-
economic and demographic factors, including economic development, urbanization,
women’s education level, and mortality rates. This period is characterized by stable
birth control policies. These were the last years of a strict demographic policy in China.
The regional total fertility rates were still clearly dierent in 1995, and the regional
dynamics of the total fertility rate was characterized by convergence in 1995-2010
within the framework of a steady below-replacement fertility after 1995.
2. Research methods and research data
2.1. Research methods
Literature analysis, which is the collection, sorting and screening of literature,
was adopted to help comprehensively analyze the research topic and ensure
the correctness of the literature. Thus, it is possible to identify the factors that aect
the total fertility rate throughout China. This paper determines the stages of fertility
changes using comparative statistical analysis comparing the changes in the Chinese
regional TFRs. The basic econometric modeling method was adopted to make
assumptions on the linking determinants of the regional total fertility rate (GDP per
capita, urban population ratio, female education level, life expectancy at birth) at the
turn of the century when population policy was stable and the TFR fell to a below-
replacement level. The results were veried using regression analysis. Signicant
determinants linking regional fertility rates were analyzed using literature analysis,
Determinants of regional fertility in China during the rst years... 107
data comparison, and basic econometric modeling to draw a conclusion and identify
countermeasures.
2.2. Research data
The information was compiled by data of the Population Division of the United Nations,
the World Bank, the Statistical Yearbooks of the People’s Republic of China: The World
Bank Open Data,1 China Statistical Yearbooks,2 Fertility Estimates for provinces
in China, 1975-2000, National Bureau of Statistics of China & East-West Center USA,
2007.3
3. Transformation of the regional fertility rate in China
Currently, China has an exceptionally low total fertility rate (TFR) — the national
average is 1.6-1.7 (1994-2020). The TFR was very high throughout the 1960s, with
a maximum of 6.385 births per woman in 1965 (Figure 1). This rate began to decline
sharply in the 1970s and by the time the one-child policy was implemented, it was
2.747 births per woman (1979). The coecient continued to decline further, in the 1990s
it fell to values below the level of population reproduction, which is 2.1. Consistently,
the demographic model of the PRC has transformed from high rates of birth (as well
as mortality and population growth) to low.
In accordance with the regional division of the PRC, we calculated the averages
of the TFR for 8 regions for the period from 1975 to 2010. In 1975, the region of East
China had the lowest rate, in 2010, it was North-East China. The initial signicant
spread of regional indicators narrowed down over time to a range between 0.74 and 1.44
in 2010 (Figure 2).
One of the most aected regions due to population policies is the Northeast, where
the TFR fell from 3-3.5 children per woman in 1975 to 0.5-0.7 in 2010 (China Statistical
Yearbook, 2011). According to ocial statistics, already in 2005, an average of 49.5%
of the population under the age of 30 were the only children in the family.
The change in the fertility rate in China can be divided into three stages in accordance
with the change of population policy: the rst stage, before 1978; the second stage, 1978-
2012; the third stage, 2013-present.
1) The rst stage, the 1970s. 1950s and 1960s were marked by economic recovery,
social stability, improvement of people’s living standards and medical service
in China, and the population of China grew rapidly from 540 million in the early years
of the founding of the Republic of China to 830 million in 1970. Until the late 1970s,
China’s economy followed a planned economic policy. Under the system of “low salary
1
hps://data.worldbank.org/
2
hp://www.stats.gov.cn/english/Statisticaldata/AnnualData/
3
hps://www.eastwestcenter.org/publications/fertility-estimates-provinces-china-1975-2000
Lieming Fang, Ekaterina Shatalova, Irina Kalabikhina
108
and good welfare,” the cost of raising children in China was relatively low. Fertility
was encouraged by the government and illegal abortion was strictly prohibited (Peng,
1997). The Chinese Marriage Law of 1950 stipulates that the minimum age of marriage
is 20 for men and 18 for women. During this period, although the total fertility rate
in China declined year by year, it was at a relatively high level.
Figure 1. Fertility rate, total (births per woman) – China, 1960-2019. Source: Compiled by the authors
based on World Bank Database.
Figure 2. Dynamics of the TFR in 8 regions of China in 1975-2010. Source: Compiled by the authors
based on Fertility Estimates for provinces in China, 1975-2000. National Bureau of Statistics of China.
China Statistical Yearbook 2011.
Determinants of regional fertility in China during the rst years... 109
Table 1. Fertility rates in China and its regions
1971 1975 1985 1995 2000 2010
All China 5,426 3,86 2,13 1,52 1,41 1,18
Beijing 2,991 2,04 1,17 1,06 0,97 0,71
Tianjin 3,061 2,59 1,22 1,13 1,05 0,91
Hebei 5,006 3,05 2,17 1,42 1,46 1,31
Shanxi 5,797 3,99 2,49 1,9 1,61 1,1
Inner Mongolia 5,414 4,25 2,35 1,37 1,18 1,06
Liaoning 4,036 2,96 1,22 1,16 1,09 0,73
Jilin 5,348 3,29 1,65 1,1 1,04 0,75
Heilongjiang 5,202 3,69 1,71 1,12 1,05 0,74
Shanghai 1,960 1,67 1 1,06 1,06 0,73
Jiangsu 4,054 2,84 1,57 1,23 1,12 1,04
Zhejiang 4,388 2,96 1,62 1,37 1,29 1,02
Anhui 6,168 4,07 2,52 1,62 1,4 1,49
Fujian 6,340 4,21 2,66 1,65 1,36 1,12
Jiangxi 6,359 6,13 2,9 1,71 1,61 1,29
Shandong 5,510 3,54 1,91 1,27 1,33 1,17
Henan 5,926 4,26 2,19 1,47 1,54 1,3
Hubei 5,773 3,81 2,28 1,45 1,27 1,35
Hunan 5,777 4,32 2,28 1,34 1,45 1,42
Guangdong 5,460 3,92 2,76 2,2 1,6 1,08
Guangxi 5,936 5,03 3,56 1,84 1,71 1,8
Hainan -4,02 3,02 2,32 1,81 1,51
Chongqing -4,79 1,6 1,42 1,26 1,18
Sichuan 6,348 4,68 1,68 1,44 1,3 1,09
Guizhou 6,789 7,1 3,43 2,27 2,11 1,75
Yunnan 6,013 5,63 3,74 1,97 1,92 1,41
Tibet - 5 3,74 2,41 2,06 1,05
Shaanxi 5,229 3,68 2,55 1,7 1,39 1,06
Gansu 6,387 3,95 2,49 2,03 1,61 1,28
Qinghai 5,238 5,54 2,85 1,82 1,68 1,37
Ningxia 6,001 5,29 2,87 2,15 1,81 1,36
Xingang 5,754 5,25 3,9 1,85 1,73 1,52
Source: Female Fertility in China - A 1/1000 Population Survey, compiled by the China Population Information Center,
New World Press, 1988 (for 1971). National Bureau of Statistics of China. China Statistical Yearbook 2011 (for 1975,
1985, 1995, 2000, 2010).
Lieming Fang, Ekaterina Shatalova, Irina Kalabikhina
110
2) The second stage, 1982-2013. China has been promoting the family planning
policy since the 1970s. In 1982, the “only one child” family planning policy was continued
as China’s main national policy. With each married couple having one child, the trend
of rapid population growth was eectively brought under control. After the late 1970s,
China adopted a policy of reform and opening up, and the Chinese economy moved
from a planned economy to a market economy, which increased the cost of raising
children. The Marriage Law of 1980 raised the minimum age of marriage to 22 for men
and 20 for women, and the government advocated “late marriage and late childbearing.”
At this stage, the regional total fertility rate was declining year by year.
We can single out a sub-stage of fertility development after the mid-1990s. Since
the mid-1990s, the TFR has decreased to below-replacement fertility level. It was
relatively stable during the next 15 years (see Figure 1). Regional total fertility rates
were still clearly dierent in 1995, while the regional dynamics of total fertility rates
were characterized by convergence (see Figure 2).
Let’s look at the fertility rate by province in 1995 (Figure 3) and 2010 (Figure 4).
Figure 3. China, Total Fertility Rate by Province, 1995. Source: Compiled by the authors based
on China Statistical Yearbook 1996.
Determinants of regional fertility in China during the rst years... 111
In 1995, the most worrying situation was observed in North-East China, as well
as in Beijing, Tianjin and Shanghai, where the total fertility rate did not exceed 1.2.
By 2015, this indicator fell below 1 in these administrative divisions. It is important
to note that already in 1995, almost nowhere in the PRC was there a level of the
TFR sucient for population reproduction, which is equal to 2.1. The TFR above this
value was observed in only 5 administrative subdivisions out of 31 — the provinces
of Guangdong, Hainan, Guizhou, as well as Tibet and Xinjiang. And by 2015, the PRC
completely lacked a level of TFR sucient for simple population reproduction.
3) The third stage, 2013-present. On November 15, 2013, the Third Plenary Session
of the 18th CPC Central Commiee issued the “Decision of the Central Commiee
of the Communist Party of China on Several Major Issues Concerning Comprehensively
Deepening Reforms” and the “selective two-child” policy (each couple is allowed
to give birth to two children, if one of the parents is an only child) was implemented.
On October 29, 2015, the Fifth Plenary Session of the 18th CPC Central Commiee
Figure 4. China, Total Fertility Rate by Province, 2010. Source: Compiled by the authors based
on China Statistical Yearbook 2011.
Lieming Fang, Ekaterina Shatalova, Irina Kalabikhina
112
decided to fully implement the “universal two-child” policy (each couple is allowed
to give birth to two children).
The age structure and dependency ratio of the population since 2008 are as follows
(Table 2):
Table 2. Demographic structure in Chinа, 2008-2017
Index 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008
Total population
at the end of
the year
(10 thousand)
139 008 138 271 137 462 136 782 136 072 135 404 134 735 134 091 133 450 132 802
Population aged
0-14 years
(10 thousand)
23 348 23 008 22 715 22 558 22 329 22 287 22 164 22 259 24 659 25 166
Population aged
15-64 years
(10 thousand)
99 829 100 260 100 361 100 469 100 582 100 403 100 283 99 938 97 484 96 680
Population aged
65 years and older
(10 thousand)
15 831 15 003 14 386 13 755 13 161 12 714 12 288 11 894 11 307 10 956
Total Dependency
Ratio (%)
39.2 37.9 37.0 36.2 35.3 34.9 34.4 34.2 36.9 37.4
Children’s
Dependency Ratio
(%)
23.4 22.9 22.6 22.5 22.2 22.2 22.1 22.3 25.3 26.0
Aged
Dependency Ratio
(%)
15.9 15.0 14.3 13.7 13.1 12.7 12.3 11.9 11.6 11.3
Source: Data from the China National Bureau of Statistics. Data for 2015 make up 1% of the sample survey data; 2010
is the year of the national census; and other data is 0.1% of the sample survey data of each province in China.
As can be seen from the Table 2, the population aged 0-14 years increased after
the implementation of the “two-child” policy in 2013, but the increase is small, which
indicates that the population policy plays a certain role in the growth of the child
population. The aged dependency ratio is constantly increasing, and the population
ageing is worsening.
The “two-child” policy was carried out in China from 2013 to August 20, 2021.
However, there were no incentive policies related to the population policy and the
cost of raising children remained relatively high. During this period, the “two-child”
policy played a certain role in the growth of the child population, but the eect
was insignicant. The total fertility rate in all regions of China remained relatively low.
On August 20, 2021, the Chinese government amended the Law on Population
and Family Planning, which advocates marriage and childbirth at appropriate age and
promotes good prenatal and postnatal care. According to the amended law, each couple
is allowed to have up to three children.
Determinants of regional fertility in China during the rst years... 113
4. Research model and hypotheses
The next step of our research is a close examination of the determinants of the regional
dierence of the TFRs in the rst 15 years of below-replacement fertility.
Indicators such as gross regional product (GRP) per capita, proportion of urban
population, proportion of women with higher education, life expectancy at birth,
net migration rate in the region, aged dependency ratio and infant mortality rates
were chosen to build an econometric model. After multicollinearity tests, the model
was implemented with explanatory variables such as per capita GRP; the proportion
of women with higher education; life expectancy at birth; the proportion of urban
population. We can emphasize that we now have data on the regional population policy.
Both population policy and socio-cultural determinants are included in unexplained
residuals in the model. The dependent variable is the total fertility rate.
The authors put forward the following hypotheses:
1) The growth of GRP per capita has a negative impact on the TFR;
2) An increase in the proportion of the urban population has a negative impact
on the TFR;
3) An increase in the proportion of women with higher education has a negative
impact on the TFR;
4) An increase in life expectancy at birth has a negative impact on the TFR.
To build quantitative models, data were collected on 31 regional subdivisions of the
PRC at the provincial level for 3 periods: 1995, 2000 and 2010. The sample includes
all regions of the PRC, except for special autonomous regions (Macau and Hong
Kong), as well as Taiwan. The source of all data is the Statistical Yearbook of the
People’s Republic of China released by the National Bureau of Statistics of the PRC.
Table 3 presents the indicators in the nal database.
Basic regression model:
l
nTFRr GRPPCrLEr FEDUCr URBr REGr e
=+ ++ +++
aa aa a
01 23 5
.
Table 3. Descriptive statistics of dependent variables
Indicator (units) Medium Standard deviation Minimum Maximum Symbol in models
Gross regional
product per capita
(RMB)
7545.9 16 183 15 411 74 573
GRPPC
Life expectancy
at birth (years) 71.93 3.772 62.01 80.26 LE
Proportion of women
with tertiary
education among
the total number
of women over 18
years of age (%)
6.83 5.61 0.05 34.93
FEDUC
Lieming Fang, Ekaterina Shatalova, Irina Kalabikhina
114
Each model also uses dummy variables for 7 regions of the PRC (North-East China
is taken as the base comparison region) for a more detailed assessment of regional
dierentiation. The dummy variables added correspond to the regional subdivision
described in Appendix 1 (for the 11th Five-Year Period 2006-2010).
All models used heteroscedasticity-resistant (robust) standard errors.
5. Data analysis and results
Table 4 presents the results of estimating regressions of the total fertility rate variable
for all regressors using the pooled OLS approach.
Table 3. Continued
Indicator (units) Medium Standard deviation Minimum Maximum Symbol in models
Proportion of urban
population (%) 41.31 17.48 13.45 89.3 URB
Dummy variable
of the region:
Northeast China
(base variable)
Northern Coast
of China
Eastern Coast
of China
Southern Coast
of China
Northwest China
Southwest China
Yellow River Delta
Yange River Delta
reg_2
reg_3
reg_4
reg_5
reg_6
reg_7
reg_8
Table 4. Results of regressions evaluation (robust standard errors are given in parentheses below
the coecients)
Dependent variable: ln_TFR
Variables (1) (2) (3) (4)
const 1.546** 0.1444** 3.549** 0.4313**
(0.1761) (0.03376) (0.4323) (0.06092)
l_GRPPC -0.1696**
(0.01852)
FEDUC -0.02676**
(0.003157)
Determinants of regional fertility in China during the rst years... 115
With the dependent variable ln_TFR, the following modelling results are obtained:
with per capita GRP growth of 1%, the TFR decreases by 0.17%
if the proportion of women with higher education increases by 1%, the TFR
decreases by 2.68%
if life expectancy at birth increases by 1 year, the TFR decreases by 4.9%
with an increase in the proportion of urban population by 1%, the TFR de-
creases by 1%.
Fixed and random eects models for each dependent variable were also tested.
After conducting the linear constraint test, the Breusch−Pagan test and the Hausmann
test in a model with the GRP (this time without dummy variables), we conclude that
the xed-eects model is beer suited to describe the relationship between variables.
So, we make a choice in its favor (the variable per capita GRP is signicant at the 1%
level). Accordingly, the presence of individual characteristics in regional fertility,
constant in time (Appendix C, Table C1), is conrmed. However, among the rest of the
models (with women’s education, life expectancy or urban population), the random
Table 4. Continued
Dependent variable: ln_TFR
Variables (1) (2) (3) (4)
LE -0.04901**
(0.005882)
URB -0.009496**
(0.001038)
reg_2 0.2191** 0.3163** 0.2459** 0.2146**
(0.05960) (0.04594) (0.05493) (0.04149)
reg_3 0.2270** 0.2196** 0.2573** 0.2368**
(0.04623) (0.04953) (0.04205) (0.06581)
reg_4 0.5143** 0.4568** 0.5131** 0.4802**
(0.06481) (0.06574) (0.08764) (0.08335)
reg_5 0.4711** 0.5291** 0.2939** 0.3758**
(0.03777) (0.04699) (0.06914) (0.05048)
reg_6 0.4021** 0.4301** 0.3465** 0.3173**
(0.08092) (0.08946) (0.07965) (0.08722)
reg_7 0.2927** 0.3312** 0.2734** 0.2245**
(0.04286) (0.03907) (0.06438) (0.04649)
reg_8 0.3354** 0.3469** 0.3214** 0.2602**
(0.02980) (0.02962) (0.03679) (0.03845)
n 93 93 93 93
R20.7672 0.7079 0.7299 0.6986
lnL 57.77 47.22 50.85 45.75
Note: *, **, *** denotes signicance at 10%, 5% and 1% respectively.
Lieming Fang, Ekaterina Shatalova, Irina Kalabikhina
116
eects model is preferred (Appendix C, Tables C2, C3, C4), i.e. the probability that
missing variables are one of the constituent errors.
The results suggest that when selecting GRP per capita as a variable of interest,
the total fertility rate in Region 4 (Southern Coast of China) is 51% higher compared
to Northeast China with a 95% probability. The fertility rate in Region 5 (Northwest
China), all other things being equal, is 47% higher compared to Northeast China with
a 95% probability. The total fertility rate in Region 8 (Yange River Delta) is 33% higher
compared to Northeast China with a 95% probability. The North and East Coast regions
of China showed a smaller spread. The results obtained support our arguments about
signicant regional inequality in fertility.
Preference of random eects for models with women’s education, life expectancy
or urban population suggests that there are no unambiguous paerns between regional
fertility and regional indicators. A more complete set of socioeconomic and sociocultural
factors is needed to identify a dependency. However, it can be argued that wealth levels
are an important factor aecting the PRC’s regional inequality in fertility. Based on the
assumption that the current fertility trends remain unchanged, one can conclude that
a 1% increase in GRP per capita will reduce the existing regional total fertility rates
by 0.17%. The presence of xed eects for the model with per capita GRP conrms
the dependence of fertility on the regional distribution of wealth.
6. Conclusion and discussion
In conclusion, we would like to stress the limitations of our model. The rst point
is the quality of our data. Our dependent variables – the regional TFR – was extracted
from a 1% sample survey data. Estimations of the level of fertility in the provinces
vary in dierent sources (i.e., the 1975 TFRs of China Population Information Center
and National Bureau of Statistics of China). In each case, we selected data from
the National Bureau of Statistics of China. The other data were collected from 1%
sample survey data, national census data, vital statistics, 0.1% of the sample survey
data of each province in China.
The second point is our quantitative model. Our regression is simple and closer
to correlation analysis. We do not have a strategy to avoid endogeneity (reverse
causality). We didn’t even have the opportunity to take lagged variables because of the
data we used (we have only three points over a 15-year period). Thus, we can discuss
the links between variables, but not the factors of regional fertility.
Given these limitations, we will focus on some results. We would like to present
some points of discussion on fertility determinants in China, especially during the rst
decades of below-replacement fertility.
1) In 1970s, with China implementing the policy of reform and opening up,
the country underwent a transformation from a planned economy to a market economy.
Until 1978, when China carried out a policy of low salary and high welfare (Huang
Aihe, the Long Evolution of Chinese Welfare, China Newsweek, December 25, 2006),
Determinants of regional fertility in China during the rst years... 117
the childrearing cost was relatively low. From 1960 to 1978, fertility of the Chinese
population maintained a relatively high level (see Figure 1). After the reform and opening
up, China began to control population growth. The results of the study are based
on data for the period from 1995 to 2010. During this period, China implemented a strict
family planning policy “a couple with only one child.” As seen from the comparison
of China’s population fertility rate chart (see Figure 1) and China’s per capita GDP chart
(Figure 5), after 1978, the relationship between China’s population fertility rate and per
capita GDP is reversed, and the relationship between China’s population fertility rate
and per capita GDP before 1978 is also roughly reversed. The actual data show that
the relationship between population fertility rate and per capita GDP is reversed, as we
think, regardless of the strictly controlled family planning policy or the not-so-strictly-
controlled population policy.
Figure 5. Per capita GPD, China, 1960-2020. Source: Compiled by the authors based on World Bank
Database. hps://data.worldbank.org/indicator/NY.GDP.PCAP.CD?locations=CN&start=1960&en
d=2020&view=chart
2) The result of the research is consistent with data at the regional level.
It reveals that per capita GDP is an important factor for the local total fertility rate.
Table 9 shows per capita GDP in each region at the end of the study period. According
to Table 9 and Figure 3, a region with a high per capita GDP normally has a low total
fertility rate; and vice versa. Northeast China registers the lowest rate and Tibet –
comparatively low. Such trends aribute to many reasons. In general, the results of the
research are in line with the data obtained.
Lieming Fang, Ekaterina Shatalova, Irina Kalabikhina
118
Low fertility rate in Northeast China. When China moved from its planned economy
to a market economy after reform and opening-up, industry in Northeast China
encountered huge challenges. In the 1990s, when a large number of enterprises
and factories shut down, some people immigrated to developed areas along
the eastern coast. With the eective implementation of family planning policy,
the country’s population growth was slow (Gu & Jia, 2015). Mostly young people
of reproductive age migrated in search of work. According to the sixth National Census,
the net population outow in Liaoning province, Jilin province and Heilongjiang
province was about 2 million, and the total fertility rate there was the lowest in 2010.
Given the data, per capita GDP in these provinces is high, but the total fertility rate
is low, which perfectly conrms the correctness of the research result.
Fertility rate in Tibet. Several factors brought about low per capita GDP and
low total fertility rate in Tibet during the past decades. Firstly, urbanization there
is speeding up, and the urbanization rate in 2010 was about 11.5% faster than that
in 1990. Secondly, more and more people are receiving higher and secondary education
(Bureau of statistics in Tibet, sample survey of 1% population on permanent residents
in various regions/cities in 2015 in the Tibet Autonomous Region), to name just a few.
In 1995, per capita GDP in Tibet was low, about 2,332.5 yuan, but the total fertility rate
was high, about 3.32, which proves the correctness of the research results.
3) The results of this research are very convincing due to the fact that economic
growth is associated with a low birth rate through several channels. First, the relative
cost of raising children is growing. Secondly, people in economically developed zones
postpone birth, which is the reason for the decrease in the conventional total fertility
rate. Competition is high, work is a priority. Thirdly, there is a strain on the physical
condition of the body. The higher the per capita GDP, the lower the rate will be, for in
a region with a high per capita GDP, people often need more money to make a living,
which leads to an increase in the child-raising cost. So, they gradually refuse to have
Table 9. Per capita GDP in each province, city and autonomous region in 2010 (Yuan)
Region Year 2010 Region Year 2010 Region Year 2010
Beijing 75,943 Anhui 20,888 Sichuan 21,182
Tianjin 72,994 Fujian 40,025 Guizhou 13,119
Hebei 28,668 Jiangxi 21,253 Yunnan 15,752
Shanxi 26,283 Shandong 41,106 Tibet 17,319
Inner Mongolia 47,347 Henan 24,446 Shaanxi 27,133
Liaoning 42,355 Hubei 27,906 Gansu 16,113
Jilin 31,599 Hunan 24,719 Qinghai 24,115
Heilongjiang 27,076 Guangdong 44,736 Ningxia 26,860
Shanghai 76,074 Guangxi 20,219 Xinjiang 25,034
Jiangsu 52,840 Hainan 23,831
Zhejiang 51,711 Chongqing 27,596
Source: Compiled by the authors based on China Statistical Yearbook 2011.
Determinants of regional fertility in China during the rst years... 119
a baby (Yang, 2020). Also, such a region enjoys sound economic growth, so people
are shouldering so much pressure that the probability of geing pregnant is declining
and the number of spontaneous abortions is rising, which hinder an increase in the
fertility rate (Xu et al., 2016).
4) Related policies can aect the regional total fertility rate. In 1975, when China
was in a period of planned economy, population policies were conducive to the increase
of the total fertility rate. However, cities with high per capita GDP, such as Shanghai,
Beijing and Tianjin, had a low regional total fertility rate (1.67 in Shanghai, nearly
2.0 in Beijing and Tianjin), and other regions with lower per capita GDP had higher
regional total fertility rate, which conrms the direct relationship between regional
per capita GDP and the regional total fertility rate. Besides, the nding that per capita
GDP has the greatest impact on the total fertility rate is also conrmed.
5) The practice of China’s population policy and related policies have enriched
the theory of population development. Before 1978, the population related policies
and social and cultural factors caused an increase in the total fertility rate. The relatively
low cost of raising children, specically, was conducive to the increase of the total
fertility rate. Even though the regional TFR decreased with the increase of per capita
GDP, the regional TFR remained at a relatively high level. In 2013, China adopted
the universal two-child policy. However, due to the lack of an incentive policy,
the child-raising cost is very high and the total fertility rate has not signicantly
increased. The practice of China’s population policy shows that in order to improve
the total fertility rate, not only should a country relax its population policy, but also
adopt incentive policies and strengthen social and cultural construction to reduce
the cost of raising children.
6) Socio-cultural and economic factors can be more signicant than population
policy. For example, the population policy of China towards ethnic minorities does not
contribute to an increase in their total fertility rates. In Guangxi Autonomous Region,
per capita GDP is quite low but the fertility rate is comparatively high, which is hardly
the result of the local policies towards ethnic minorities. There are 56 ethnic groups
in China, of which 55 groups, with the exception of the Han nationality representing
the overwhelming majority of the population, are always viewed as ethnic minorities.
During the time when China carried out the one-child policy, every family from ethnic
groups was allowed to have two children. The fertility rates of Guangxi autonomous
region and Gansu province are presented in Table 10.
Table 10. Fertility rates of Guangxi Autonomous Region and Gansu Province in 1995-1996
Region Total fertility rate Population of ethnic
minorities (thousand)
Total population
(thousand)
Proportion of ethnic minorities
in the total population
Gansu >2 2,275.8 24,255.6 9.38%
Guangxi 1.8 -2 17,784 45,274 39.28%
Source: Compiled by the authors based on China Statistical Yearbook.
The data in Table 10 show that the proportion of ethnic minorities in the total
population of Guangxi Autonomous Region was greater than that of Gansu Province,
Lieming Fang, Ekaterina Shatalova, Irina Kalabikhina
120
while the fertility rate in Guangxi was lower than that in Gansu Province. This shows
that the two-child policy for ethnic minorities failed to improve the fertility rate
of ethnic minorities when China was carrying out the one-child policy. The number
of ethnic minorities in Southwest China is large, and the high fertility rate there is not
caused by the two-child policy for ethnic minorities, but by the low per capita GDP in
Southwest China.
7) The regional fertility rate aects the balanced development of the
region’s population, as well as national security and social stability. As China’s economy
continues to develop, people are striving for wealth and a beer life. At present, due to
the low fertility rate in China, a complete relaxation of family planning policy is greatly
needed. Besides, an incentive policy in the eld of fertility should be implemented
to reduce birth and rearing costs. What’s more, social and cultural construction should
be reinforced. Special aention should be paid to the areas of low fertility to increase
fertility, promote balanced development of the population, and ensure national security
and social stability.
At the turn of the century, the period of stable below-replacement fertility,
the regional fertility dierentiation in the PRC is still relatively high. The leaders
in terms of infertility are Southwest China (Yunnan, Guizhou and Guangxi provinces)
and Northwest China (Xinjiang).
The authors analyze a number of socio-economic and demographic determinants
in the last part of the family planning period in China at the turn of the century
and draw the following conclusions. The regional fertility rate and per capita GDP are
inversely related, and per capita GDP is the factor that has the greatest inuence on the
regional total fertility rate. According to the relevant literature, this rule also applies
to China’s fertility-promotion period before 1978.
In order to improve the regional fertility rate, it is necessary not only to relax
the modern family-planning policy, but also to implement incentive policies related
to human fertility, as well as to strengthen social, economic, demographic, and cultural
structures.
Acknowledgments
The paper is funded by the following projects: The 2013 Province level excellent course
“Labor Economics” in Shandong Province China (2013BK386); Project granted by the
Training Program of Shandong Youth University of Political Science, China (2016).
References
Bazhenova, E. S. (2010). 1 300 000 000. Naselenie Kitaya: Strategiya razvitiya i demogracheskoj politiki
(1300000000. Population of China: Strategy for Development and Demographic Policy). Forum.
Birdsall, N., & Jamison, D. (1983). Income and other factors inuencing fertility in China. Population
and Development Review, 9(4), 651-675.
Determinants of regional fertility in China during the rst years... 121
Birdsall, N., & Jamison, D. (1983). Income and other factors inuencing fertility in China. Population
and Development Review, 9(4), 651-675.
Bongaarts, J., & Watkins, S. C. (1996) Social interactions and contemporary fertility transitions.
Population and Development Review, 22(4), 639-682.
Cai, Y. (2010). China’s below-replacement fertility: Government policy or socioeconomic
development? Population and Development Review, 36(3), 419-440.
Chen, Y., & Miao, G. (2019). Distribution of resources for education and fertility. Jiangsu Science,
3, 97-102.
Coale, A. J., & Chen, S. Li. (1987). Basic data on fertility in the provinces of China, 1940-1982. Studies
in Family Planning. East-West Center, Population Institute, 1, 308.
Dyson, T., & Murphy, M. (1985). The onset of fertility transition. Population and Development Review,
11(3), 399-440.
Ge, Y.-X. (2015). How the urbanization aects fertility rate in China?: A study based on spatial
panel data model. Population Journal, 3, 88-101.
Ge, Y., & Zhang, X. (2019). The eect of housing price on family fertility decision in China.
Population Research, 3, 52-62.
Goodkind, D. (2011). Child underreporting, fertility, and sex ratio imbalance in China. Demography,
48(1), 291–316.
Gu, B., Wang, F., Guo, Z., & Zhang, E. (2007). China’s local and national fertility policies at the
end of the Twentieth Century. Population and Development Review, 33(1), 129-148.
Gu, Guofeng & Jia, Zhanhua. (2015). Population and Development. Study on the Evolution
Characteristics and Formation Mechanism of the Population distribution in Northeast China, 6, 38-94.
Guo, Z., & Gu, B. (2014). China’s low fertility: Evidence from the 2010 census. INED Population
Studies, Springer, Dordrecht, 3. hps://doi.org/10.1007/978-94-017-8987-5_2
He, Y., Lin, Y., & Zhang, L. (2016). The eect of public education investment and social security
on fertility rate and education level. Nankai Economic Studies, 3, 133-153.
Hou, L. (2018). Analysis of the cause of the prolonged low birth rate in the Northeast region
of China. Demographic Journal, 2, 96 -104.
Islam, N. (2009). Resurgent China: Issues for future. In N. Islam (Ed.), Resurgent China. Palgrave
Macmillan.
Jain-Chandra, S., Khor, N., Mano, R., Schauer, J., Wingender, & P., Zhuang, J. (2017). Inequality
in China – Trends, drivers and policy remedies. IMF Working Paper No. WP/18/127.
Kalabikhina, I., Shatalova, E., & Lieming, F. Demographic situation in China: Convergence
or divergence? BRICS Journal of Economics. 2020,1(1), 81–101. hp://doi.org/10.38050/2712-
7508-2020-6
Krasova, E. V., & Bao, G. (2016). Issledovanie demogracheskoj dierenciacii v kontekste
ekonomicheskogo razvitiya Kitaya i ego regionov v XXI veke. Azimut Nauchnyh Issledovanij:
Ekonomika I Upravlenie, 5(3), 126-129 (Research of demographic dierentiation in the context
of economic development of China and its regions in the XXI century. Azimuth of Scientic
Research: Economics and Management, 5(3), 126-129).
Lan, M., & Kuang, Y. (2016). The impact of women’s education, workforce experience, and the
one child policy on fertility in China: A census study in Guangdong, China. Springerplus, 5(1),
1708.
Lieming Fang, Ekaterina Shatalova, Irina Kalabikhina
122
Lavely, W., & Freedman, R. (1990). The origins of the Chinese fertility decline. Demography, 27(3),
357-367.
Liang, T.-G. (2018). The impact of rural urban migration on the birth rate in China. Population
of South China, 1, 30-47.
McElroy, M., & Yang, D. (2000). Carrots and sticks: Fertility eects of China’s population policies.
American Economic Review, 90(2), 389-392.
Merli, M. G., & Smith, H. L. (2002). Has the Chinese family planning policy been successful
in changing fertility preferences? Demography, 39(3), 557-572.
Peng, P. (1997). China Family Planning Encyclopedia. China Population Publishing House.
Poston, D. L. Jr., & Gu B. (1987). Socioeconomic development, family planning, and fertility
in China. Demography, 24(4), 531-551.
Sato, Y., & Kazuhiro, Y. (2005). Population concentration, urbanization, and demographic
transition. Journal of Urban Economics, 58(1) 45-61.
Schul, T., & Zeng, Y. (1995). Fertility of rural China: Eects of local family planning and health
programs. Journal of Population Economics, 8(4), 329-350.
Skinner, G.W., Henderson, M., & Jianhua, Y. (2000). China’s fertility transition through regional
space: Using GIS and census data for a spatial analysis of historical demography. Soc Sci Hist,
24(3), 613-652.
Susan, E., & Fengying, Zhai. (1999). Looking locally at China’s one-child policy. Studies in Family
Planning, 29(4), 373-387.
Tao, T., Jin, G., & Yang, F. (2017). Re-examining China’s provincial socioeconomic development
and fertility change. Population Research, 6, 33-43.
Tien, H. (1984). Induced fertility transition: Impact of population planning on socio-economic
change in the People’s Republic of China. Population Studies, 38(3), 385-400.
Wang, L.-J., Liang, K., & Peng, Yu. (2015). The countywide dierences of China’s total fertility rate
and an empirical study of its inuencing factors. Population Journal, 3, 16-25.
Weeks, J. R. (2008). Population: An introduction to concepts and issues, 10
th
ed. San Diego State
University, Thomson Wadsworth.
Winckler, E. A. (2002). Chinese reproductive policy at the turn of the millennium: Dynamic
stability. Population and Development Review, 28(3), 379-588.
Xu, Jihong, Peng, Zuoqi & Ma, Xu. (2016). Investigation on the basic characteristics, mental
pressure and pregnancy result of oating childbearing-age women. China Journal of Family
Planning, 2, 90-93.
Yang, Ningcong. (2020). Research review of population fertility intention inuencing factors under
the background of universal two-child policy. Chongqing Social Sciences, 1, 94-105.
Zhang, H. (2007). From resisting to “embracing?” the one-child rule: Understanding new fertility
trends in a Central China village. The China Quarterly, 192, 855-875.
Zhang, J. (1990). Mortality and fertility: How large is the direct child replacement eect in China?
Journal of Population Economics, 3(4), 303-314.
Zhao, M. (2019). Fertility intentions for a second child in China: Female educational aainment
and assortative marriage. Population Journal, 3, 16-27.
Zheng, Y., Yuan, J., & Xu, T. (2016). Socio-economic status and fertility intentions among Chinese
women with one child. Human Fertility, 19(1), 43-47.
Determinants of regional fertility in China during the rst years... 123
Appendix А. Regional division of the PRC
In total, there are 34 provincial administrative subdivisions in China, including
23 provinces, 5 autonomous regions and 4 central subordination cities, as well
as 2 special administrative regions Aomen (Macau) and Xianggang (Hong Kong).
Aomen, Xianggang and Taiwan Province are usually not included in the national
census (Appendix A, Figure A1). Thus, the national census of China only includes 31
subdivisions.
Figure A1. China, Provincial-Level Administrative Divisions: Provinces, Autonomous Regions,
Direct-Controlled Municipalities, Special Administrative Regions. Source: Compiled by the authors.
In order to implement the strategy of coordinated regional development,
it is necessary to create in China a corresponding multilevel system and a framework
of regional division to facilitate the implementation of targeted regional policies.
According to China’s 11th Five-Year Plan, the country is divided into eight regions:
1) North-East China: Liaoning, Jilin, Heilongjiang;
2) Northern Coast of China: Beijing, Tianjin, Hebei, Shandong;
3) Eastern Coast of China: Shanghai, Jiangsu, Zhejiang;
4) Southern Coast of China: Fujian, Guangdong, Hainan;
5) Northwest China: Gansu, Qinghai, Ningxia, Xinjiang, Tibet;
6) Southwest China: Chongqing, Sichuan, Guizhou, Yunnan, Guangxi;
7) Yange River Delta: Hubei, Anhui, Jiangxi, Hunan;
8) Yellow River Delta: Shanxi, Inner Mongolia, Henan, Shaanxi.
Lieming Fang, Ekaterina Shatalova, Irina Kalabikhina
124
Appendix B. The ratio of urban/rural resident’s income
to the urban/rural national average, China, 1998, 2017
Figure A2. Regional division of the PRC. Source: Compiled by the authors.
Note: The administrative subdivision is based on China’s 11
th
Five-Year Plan.
Figure B3. The ratio of urban/rural resident’s income to the urban/rural national average, China,
2017. Source: Compiled by the authors based on China Statistical Yearbook 2018.
Determinants of regional fertility in China during the rst years... 125
Appendix C. Testing OLS, xed and random eects models
for some dependent variable. Total fertility rate. China
Figure B4. The ratio of urban/ rural resident’s income to the urban/rural national average, China,
1998. Source: Compiled according to China Statistical Yearbook 1999.
Table C1. Total fertility rate (TFR) and gross regional product (GRP) per capita, pooled ordinary
least squares (OLS), xed and random eects models
Dependent variable: ln_TFR
Variables (1) pooled OLS (2) xed eects model (3) random eects model
const 2.158** 1.712** 1.816**
(0.1961) (0.1737) (0.1756)
l_GRPPC -0.2012** -0.1526** -0.1640**
(0.02121) (0.01891) (0.01875)
n 93 93 93
R20.4957 0.6432 -
Breusch-Pagan test χ 2 (1) = 31.8249; р-value = 1.68712e-008 < 0.01 => random eects model is beer
than pooled OLS
Hausman test χ 2 (1) = 19.0675; р-value = 1.26174e-005 < 0.01 => xed eects model is beer
than random eects model
Robust test for diering
group intercepts
Welch F(30, 21.9)=28.9513; р-value= P(F(30, 21.9)>28.9513)=7.39417e-012 < 0.01 =>
xed eects model is beer than pooled OLS
Note: *, **, *** denotes signicance at 10%, 5% and 1% respectively. Breusch-Pagan test. H0: Variance of observation –
specic errors = 0. Hausman test. H0: pooled OLS estimates are consistent. Robust test for diering group intercepts.
H0: The groups have common constants.
Lieming Fang, Ekaterina Shatalova, Irina Kalabikhina
126
Table C2. Total fertility rate (TFR) and proportion of women with higher education (WEDUC),
pooled OLS, xed and random eects models
Dependent variable: ln_TFR
Variables (1) pooled OLS (2) xed eects model (3) random eects model
const 0.5264** 0.5434** 0.5364**
(0.04451) (0.03381) (0.04382)
WEDUC -0.03166** -0.03415** -0.03313**
(0.004571) (0.004951) (0.004458)
n 93 93 93
R20.4290 0.5080 -
Breusch-Pagan test χ 2 (1) = 30.2508; р-value = 3.79628e-008 < 0.01 => random eects model
is beer than pooled OLS
Hausman test χ 2 (1) = 0.454167; р-value = 0.500363 > 0.01=> random eects model is beer
than xed eects model
Robust test for diering
group intercepts
Welch F(30, 21.9)= 23.3285; р-value = P(F(30, 21.9) > 23.3285) = 7.1344e-011<
0.01 => xed eects model is beer than pooled OLS
Note: *, **, *** denotes signicance at 10%, 5% and 1% respectively.
Table C3. Total fertility rate (TFR) and life expectancy at birth (LE), pooled OLS, xed and random
eects models
Dependent variable: ln_TFR
Variables (1) pooled OLS (2) xed eects model (3) random eects model
const 4.108** 4.253** 4.201**
(0.03304) (0.4795) (0.05410)
LE -0.05280** -0.05482** -0.03313**
(0.004706) (0.006667) (0.004458)
n 93 93 93
R20.5402 0.6378 -
Breusch-Pagan test χ 2 (1) = 34.4173; р-value = 4.44755e-009 < 0.01 => random eects model
is beer than pooled OLS
Hausman test χ 2 (1) = 0.0816182; р-value = 0.775116 > 0.01=> random eects model is beer
than xed eects model
Robust test for diering
group intercepts
Welch F(30, 21.9)= 6.55366; р-value = P(F(30, 22.0) > 6.55366) = 1.24597e-005 <
0.01 => xed eects model is beer than pooled OLS
Note: *, **, *** denotes signicance at 10%, 5% and 1% respectively.
Determinants of regional fertility in China during the rst years... 127
Table C4. Total fertility rate (TFR) and proportion of urban population (URB), pooled OLS, xed
and random eects models
Dependent variable: ln_TFR
Variables (1) pooled OLS (2) xed eects model (3) random eects model
const 0.7691** 0.9189** 0.8240**
(0.05588) (0.08942) (0.06222)
URB -0.01110** -0.05482** -0.01243**
(0.001235) (0.002163) (0.002163)
n 93 93 93
R20.5127 0.5121 -
Breusch-Pagan test χ 2 (1) = 20.7117; р-value = 5.339e-006 < 0.01 => random eects model is beer
than pooled OLS
Hausman test χ 2 (1) = 3.13119; р-value = 0.0768076 > 0.01=> random eects model is beer
than xed eects model
Robust test for diering
group intercepts
Welch F(30, 21.9)= 6.27007; р-value = P(F(30, 22.0) > 6.27007) = 1.83744e-005 <
0.01 => xed eects model is beer than pooled OLS
Note: *, **, *** denotes signicance at 10%, 5% and 1% respectively.
ResearchGate has not been able to resolve any citations for this publication.
Full-text available
Article
The impact of women’s education on fertility is of interest to researchers, particularly in China. However, few studies have provided well-founded assessments of how women’s education, workforce experience, and birth control policy jointly affect fertility in China. This study, conducted in Guangdong Province, aimed to analyze how these three factors influenced the timing of births and affected women at different stages of their reproductive lives. We used census data for Guangdong Province (1990, 2000, and 2010) to make cross-sectional age-specific comparisons to examine the effects of women’s education and workforce participation on fertility outcomes under China’s One Child Policy. We found that: (1) under circumstances of low fertility, women tend to have more children with greater educational attainment; (2) the impact of women’s education and workforce experience on fertility varied across age groups, with the effect of education showing a bimodal curve peaking at 25–29 years and 40–44 years, and a workforce experience effect at 25–34 years; and (3) the fertility time-squeeze effect by educational attainment was relatively small, the effect by workforce participation was larger, and the most important effect was birth control policy and its implementation. These results suggest that educational attainment and workforce experience have a substantial effect on women’s fertility, and a tradeoff between them is unavoidable. China’s 2015 birth control policy adjustment should be considered in planning future services to accommodate anticipated increases in the birth rate. More attention should be directed to the causal mechanism (women’s preference and selection effects) behind the factors analyzed in this study.
Full-text available
Chapter
China has been experiencing very fast economic growth for three decades now. As a result, China’s per capita income has increased almost ten-fold from $224 in 1978 to $2,055 in 2007.1 Surpassing other G-8 countries, China has become the second largest economy in the world in Purchasing Power Parity (PPP) terms, and she is already the world’s top exporter country. This fast economic growth has been associated with fundamental changes in China’s institutions. From a centrally planned economy and very egalitarian society, China has become by and large a market-based economy and an unequal society. China has also become more integrated with the rest of the world.
Full-text available
Article
There has been a lack of socioeconomic status (SES) disparity analysis on women in China with only one child, the family planning target population. In 2008, the National Research Institute for Family Planning of China conducted a study investigating the relationship between SES and fertility intentions among 17,093 women in China who already had one child. A questionnaire was used to collect information on SES and fertility intentions, and logistic regression models were used to estimate the odds ratios and 95% CIs of fertility intentions according to SES. Compared with female farmers, women in other occupations intended to have fewer children (p < 0.05). Additionally, compared with women with low educational level (illiterate/primary), women with secondary and postsecondary education intended to have fewer children (p < 0.05) (OR = 0.70; 95% CI: 0.61-0.81 and OR = 0.56; 95% CI 0.47-0.66). A mother's education level was significantly and negatively associated with fertility intentions after adjustment for potential confounders (p < 0.05). Among Chinese women who had one child, the women with higher SES (e.g. higher educational level) had lower fertility intentions. There is an SES disparity in the fertility intention among Chinese women who already have one child. China's policy-makers should consider increasing high SES women's fertility intention.
Article
The purpose of this study is to locate the presence of convergence in the demographic development of Chinese provinces during the end of the demographic transition at the turn of the millennium. We have estimated sigma and beta convergence in fertility, mortality, urbanization, and population ageing basing on the official Chinese statistics for 31 provinces of China. Our results show that the regional convergence in the above indicators has not been sustainable. It was observed only in certain periods, except for the urbanization process. Convergence was accompanied by a catching-up effect in such periods when “lagging” provinces were passing the demographic transition relatively quickly. The paper can serve as a contribution to the regional demographic and economic policy of China, since the issue of the dynamics of the regional demographic development differentiation is the basis for demographic and economic projections and development of local policy measures. The demographic divergence that we discovered in the last decade can determine an obstacle to the sustainable development of the country in the near future.
Chapter
This chapter addressed the patterns of China’s current fertility using data drawn mainly from the 2010 population census. In spite of some remaining uncertainty concerning their accuracy, it appears that all the recent available sources of data nonetheless converge to indicate that China’s fertility dropped below replacement level in the early 1990s, and continued to decline in the 2000s. These converging data therefore challenge the official TFR of 1.8 children per women, regularly used as a reference by Chinese officials.
Article
An analysis of fertility transitions in 69 developing countries since 1960 finds that the relationship between development and pretransitional fertility, the timing of the onset of transitions, and the pace of fertility decline after transition onset deviate substantially from what would be the case if fertility and development, as measured by the Human Development Index, were closely linked. A few noteworthy empirical regularities were identified, including a shifting threshold of development necessary for the onset of transition. This implies that, once a few countries in a region enter the transition, other countries follow sooner than expected. Also, the pace of fertility decline is not related to the pace oi development, as might be expected, but rather to the level of development when the transition began. To explain these findings, the authors propose a key role for social interaction. Social interaction, they suggest, operates at three levels of aggregation. Personal networks connect individuals; national channels of social interaction such as migration and language connect social and territorial communities within a country; and global channels such as trade and international organizations connect nations within the global society. Through these channels, actors at all three levels exchange and evaluate information and ideas, and exert and receive social influence, thus affecting reproductive behavior. Development is important in understanding the timing and pace of fertility change, bur social interaction is likely to have an independent influence on fertility. Given current levels of development and the proliferation of channels of social interaction, it is likely that few countries will fail to experience a fertility transition over the coming three decades.
Article
There is a growing awareness that fertility may undergo a rise before its secular long-term decline. This paper examines available time series on fertility trends in contemporary developing countries, and draws together studies that support the existence of this phenomenon. It argues that predecline rises in fertility have sometimes been substantial, and that they are a usual feature of both historical and contemporary transitions. An analysis of turning points in crude birth rates also suggests that fertility movements in different developing countries have sometimes exhibited considerable similarity in timing. The paper reviews the most likely causes behind fertility increases, and also argues that rising fertility has often made a significant contribution to faster population growth. It concludes by examining implications for matters of population research and policy.
Article
From its initiation in 1979, China's one-child policy has been controversial. Most critiques on the stringent birth control policy in rural China still focus on the resistance framework and there is very little research on whether Chinese peasant families are changing their fertility preferences and behaviours when confronting both the state birth control policy and the rapidly changing social and economic environment. Based on recent ethnographic study in a central China village, this article seeks to explore new fertility trends that indicate the shift from “active resistance against” to “conscious decision for” the one-child limit among rural families. In particular, it discusses the newly emerging social, economic and demographic factors that may have played a role in this fertility shift, and its social implications for the central tenet of son preference in Chinese culture and the norm of child-rearing as a means of securing old age support among rural families.