University of Oxford
Department of Social Policy and Intervention
Oxford Centre for Population Research: Working Paper #60
Effect of children composition on
the sex of next birth in the context
of low fertility in rural China
Stuart Basten, Ph.D.
Department of Social Policy and Intervention
University of Oxford
[email protected]
Quanbao Jiang, Ph.D.
Institute for Population and Development Studies
Xi'an Jiaotong University (China)
Ying Li
Institute for Population and Development Studies
Xi'an Jiaotong University (China)
Xiujun Tai, Associate Professor
School of Economics and Management
Shanxi Normal University (China)
July 2013
1
Abstract
China currently has the most skewed national sex ratio at birth [SRB] in the
world. In the paper, we use the data from China’s 2001 National Family
Planning and Reproductive Health Survey and employ hierarchical models
to study how macro factors (mainly fertility policy) and micro factors
(mainly fertility intention and sex composition of children already born)
affect the sex of next birth. We find that fertility policy exerts a significant
effect on the sex of next birth, but this effect is intertwined by sex
composition of children already born. For those having had a son (or sons),
the policy exert no effect, but for those with only daughters, the effect is
significant. Furthermore, fertility intention, independent from fertility policy,
has a significant effect on the sex of next birth.
Keywords: fertility policy, fertility intention, sex ratio at birth, children
composition
2
1. INTRODUCTION AND BACKGROUND
1.1 Sex Ratios at Birth in East and South East Asia
Skewed sex ratios at birth [SRB], with excesses of males, have been a
feature of numerous Asian countries in recent years. Currently, sites of
skewed SRB include Korea, Taiwan, Hong Kong, India and Vietnam
(Guilmoto, 2009, Guilmoto et al., 2009, Basten and Verropoulou, 2012).
Furthermore, emerging patterns are developing in Western Asian countries
such as Azerbaijan, Georgia and Armenia as well as in the Balkans (Meslé et
al., 2007). A complex and interconnected set of drivers of skewed SRBs –
defined as above the ‘natural’ sex ratio at birth of 103 to 107 male births per
100 females – include easy access to fetal screening and a preference for
sons, especially in the context of weak and changing welfare support
systems (Lipatov et al., 2008). These become increasingly important in the
context of societal level fertility decline to small family sizes (~2 or fewer
children) (Park and Cho, 1995).
In the past decade, however, evidence from the People’s Republic of China
has consistently shown most skewed SRB globally (Poston and Zhang, 2009,
UNPD, 2011).Since 1980s more boys have been born than girls annually due
to the sharp decline of the fertility level (Poston and Glover, 2006; Poston et
al., 2011) and the near continuous increase of the sex ratio at birth has been
3
one of the most remarkable structural changes of Chinese population during
the past two decades(Yuan and Shi, 2005). According to the 2010 Census,
during the period November 1st 2009 to October 31st 2010, there were
13,836,187 births nationally, of which 7,487,489 were male and 6,348,698
were female (Basten 2012). This results in an overall national SRB of 117.94
(95% C.I. =117.81-118.60). This is marginally lower than the SRB reported
by Zhu, Lu and Hesketh (2009) for the 2005 Intercensal survey [SRB: 119;
95% C.I. =119-120]. In the 1990 and 2000 Censuses, the National SRB was
111.45 and 119.92 respectively (National Bureau of Statistics, 1993; PCO,
2002). Despite this, it should be noted that previous studies have rightly
identified measurement difficulties in identifying the ‘true’ Chinese SRB at
both the national and provincial level. Reasons for this include general issues
in census and survey quality and explicit under-reporting of (illegal) female
births (Goodkind, 2011).
There are, however, wide regional differences. Anhui, Fujian, Hainan,
Hubei, Hunan, Jiangxi, Guangxi, Guizhou and Guangdong each report SRBs
above 120, while Tibet and Xinjiang are around the global average SRB at
around 106. The extremely high SRBs reported in 2005 for Jiangxi (137) and
Anhui (132) are similarly high in the 2010 Census (123 and 129
respectively), while Shaanxi saw a notable decline from 134 to 115. As we
shall discuss shortly, there is an important policy dimension to these
4
divergent regional trends.
Such a skewed sex ratio can have both macro- and micro-level
consequences of a generation of excess males regarding lower fertility, more
rapid ageing, difficulties in partnership formation and shifts in household
saving and spending (Guilmoto, 2010, Jiang et al., 2011, Qian, 2008).
Considering the squeeze on the marriage market, unlike other sites of
skewed sex ratios where brides from (usually) poorer countries have ‘filled
the gap’, in China the excess males are likely to be the most destitute from
the poorest regions and, hence, unlikely to be successful in the cross-border
marriage market (Das Gupta et al., 2010). This has led some to suggest that
there could be shifts in sexual attitudes and behavior (Yang et al.,
forthcoming), increases in both adverse psycho-social outcomes (Zhou et al.,
2011) as well as potential increases in crime and disorder (Hudson and Boer,
2002, Hudson and Boer, 2004). [For a useful review of such consequences,
see (Poston et al., 2011)].
1.2 What are the underlying causes of high SRBs in East Asia?
Many settings in East and South East Asia – including China – have
been operating under strict family systems of patrilineality, patriarchy and
patrilocality which lead to the dominant position of man in the respects of
5
property
inheritance,
living
arrangement,
family
continuity
and
intra-household rights. Men take more responsibilities than women, as they
have to afford economic and endowment support, carry on the family line,
bring honor and authority to the family, take charge of rituals and so on
(Skinner, 1997; Das Gupta and Li, 1999; Khan and Khanum, 2000; Das
Gupta et al., 2004; Attané, 2005). In this setting, the strong son preference
exists among people who think that having no male heir is the gravest of the
three cardinal offences against filial piety.
This cultural context has held relatively firm in the context of rapid
fertility decline in the region. As Figure 1 demonstrates, rapid fertility
decline has been observed in these sites of higher SRBs, with period Total
Fertility Rates [pTFR] currently well below replacement level. Indeed,
recent studies suggest that the pTFR of China is even lower than the estimate
employed in the latest UN projections at around 1.4 (UNPD, 2011; Zhao and
Chen, 2011). Taiwan and Hong Kong SAR currently have among the lowest
fertility rates in the world (UNPD, 2011). This is fundamentally associated
with skewed SRBs. When the fertility level stays high couples rarely need to
resort to sex-selection technology to have a boy, as they can simply meet
their desired number of boys by having more children. As such, in order to
insure they have a son in a low fertility setting, some couples will seek
assistance from sex-selection technology (Li et al. 2000). In this context, a
6
crucial mediator is the sex composition of the family already achieved (Das
Gupta, 2005).
FIGURE 1 about here
The causes for this rapid fertility decline have been described at length
elsewhere, and include strong economic development, improved access to –
and quality of – education, urbanization, increased female labor participation,
reaction to mortality decline and family planning policies (Caldwell and
Caldwell, 2006). In addition, high costs of living and investment in
children’s education, long working hours, gender inequality in housework,
lack of affordable childcare and low levels of extra-marital childbearing have
been suggested as key factors in keeping fertility low (e.g. Frejka et al 2011).
It has been argued that these issues can become self-reinforcing as
smaller family sizes become normalized and countries fall into the ‘Low
Fertility Trap’, where fertility ideals are lowered and it becomes ever more
difficult for countries to increase their fertility. Across East and South East
Asia, fertility ideals and intentions have indeed altered in reflection of
fertility decline. In Taiwan and Hong Kong, for example, reported ideal
family size among young people is now well below two children (Basten,
2012). Such patterns are also now visible in Thailand where fertility ideals
are now below 2 children (UNFPA, 2012). Furthermore, among educated,
7
urban young women in India, almost one-quarter report an ideal family size
of just one child (Kumari and Parasuraman, 2010). As such, where ideal
family sizes are below two and in the context of widely available
sex-selective technology, intentions regarding the sex composition of the
household are extremely important. Where there is a strong preference for
one child, it may be important to guarantee that this only child is male.
Otherwise, in a two-child ideal, if the first child is female, there is a strong
incentive to ensure that the second child is male.
1.3 Is China special?
Most studies of fertility decline and SRB in East Asia tend to consider
China separately from other territories. The main reasons for this are
generally held to be the enormous size of its population, and the recent
history of proscriptive family planning policies. Recently, however, scholars
are increasingly questioning the central role played by family planning
restrictions in driving down fertility at the macro-level in the period since
1980, others point out the enormous effect of socioeconomic progress (Tien,
1984; Peng, 1989; Du, 2005; Wolf, 1986). Prior to the implementation of the
family planning of rural areas in 1970s, highly-educated women had already
begun to control marital fertility level, which triggered the natural transition
of fertility rate (Lavely and Freedman, 1990). In other words, the same core
8
drivers of fertility decline as seen in other East Asian settings and described
above can, broadly, be seen in China.
Furthermore, similar patterns in reported ideal family size and fertility
intentions can be seen in China relative to other East Asian settings. During
the past two decades, the ideal children number of rural and urban residents
has indeed decreased in China. In 1987, Whyte and Gu reported Mean Ideal
Family Size [MIFS] in six urban settings [1983-85] of 1.50-1.81 with an
outlier from Zhejiang Province at 1.15. In 10 rural areas for 1982-1985, the
range was 1.56-2.49, with a mean of 1.98. According to the 2001 national
family planning/reproductive health survey data, for example, the ideal
children number of women of childbearing age is 1.70 (Qiao and Ren, 2006).
Indeed, in a recent meta-review of survey of mean ideal family size (MIFS)
in China, Basten (2012) found a MIFS range in urban areas of 1.0-1.5
children per woman, and 1.2 to 1.8 in rural areas. Again, strong provincial
level differences exist, with particularly low MIFS reported for the biggest
metropolises (Merli and Morgan 2011; Basten 2010). Even taking into
account possible under-reporting owing to the desire to give a ‘politically
correct response’, these figures represent, in the context of sub-replacement
fertility intentions found elsewhere in East Asia, demonstrate a strong
preference for one- or two-child families – an important predictor of skewed
SRB.
9
Despite these regional similarities in terms of the context of fertility
decline China is clearly an outlier in two ways – firstly, in terms of having a
proscriptive family planning regime, and secondly in terms of having a
significantly higher SRB than other territories in Asia. As mentioned earlier,
there are clear differentials within China regarding the overall SRB by
province. The latest evidence from the 2010 Census is replicated in Table 1.
Clearly, in some provinces such as Anhui, Fujian and Hainan, the SRB is
exceptionally high. Indeed, the provincial distribution of SRBs has led a
number of scholars to draw a link between the different provincial level
family planning regimes and the various SRBs reported (Zhu et al., 2009,
Zhou et al., 2012, Ding and Hesketh, 2006).
TABLE 1 about here
Since the early 1980s, the Chinese government has carried out strict
family planning policies. In order to show their organizational capability and
political enthusiasm, local officials were eager to achieve specific goals in
the short term, and would even adopt some forceful measures which were
directly acted on the women physically (Short et al., 2000; White 2006). In
rural areas, as peasants have little deposits and no pensions, the need to be
supported by their children in old age was strong, as was the traditional
preference for larger families with sons. This meant that in the early stages
10
of the implementation of the family planning restrictions a number of
obstacles to acceptance were found, especially in rural areas (Kane and Choi,
1999). To ease up the conflicts, in 1984 the government changed its original
policy to the policy of ‘1.5 children’ in rural areas: if the first birth of a
couple is a girl, they are permitted to have a second child (Hesketh and Zhu,
1997; Zeng, 2007). Under the guidance of the general principles of central
authorities, the family planning policies are mainly programmed and carried
out by provincial governments. In order to adapt to the regional difference
considering social, economic and cultural elements, policies across the
country are flexible. Currently a ‘one-child family’ policy is in force in urban
areas while in rural areas different provinces implement different policies. In
Beijing, Tianjin, Shanghai, Chongqing, Jiangsu, Sichuan, for example, a
strict ‘one-child policy’ is upheld. Meanwhile, couples living in the rural
areas of Hainan, Ningxia, Qinghai, Yunnan, Xinjiang are allowed to have
two children. In rural areas of other provinces, if their first child is a
daughter, they are admitted to have the other child (Gu et al., 2007) under
the so-called ‘1.5-child policy’.
Indeed, in the 1980s the SRB of China rose gradually, accompanying
the strict implementation of family planning policies and the spread of sex
selection technology. Using difference-in-differences (DD) estimation and
China’s census data, Li et al. (2011) estimated that enforcement of the strict
birth control policy led to 4.4 extra boys per 100 girls in the 1980s,
11
accounting for about 94% of the total increase in the SRB during this period.
For the 1991-2005 birth cohorts, the birth control policy resulted in about 7.0
extra boys per 100 girls, accounting for about 57% and 54% of the total
increase in sex ratios for the 1991-2000 and 2001-2005 birth cohorts,
respectively. Based on the longitudinal China Health and Nutrition Survey,
Yang (2006) adopted the normal regression method to show that family
planning policies exert influences on the proportion of boys in the
community. Besides the fixed effect model was also used to prove that the
proportion changes with the variation of types of family planning policies
and implementing tactics.
Some studies have used hierarchical models to examine the macro- and
micro-level influences of Chinese family planning policy on SRB.
Combining the one-per- thousand national census data of 2000 and data of
regional fertility policies of 1999, Guo (2007) applied hierarchical models to
analyze what factors will influence the imbalance of sex ratio and the results
showed that the number and gender of children would affect sex ratio as well
as some social characters. As to family planning policies, it not only made an
impact on sex ratio directly, but could exert an influence by interacting with
other factors. Based on the 2005 national 1% population survey data, Yang
(2012) again used hierarchical models to analyze how family planning
policies interacted with individual characters (like educational attainment) to
influence SRB.
12
A crucial, if understudied element within the SRB literature, is influence
of parity and sex composition of children already born (Poston et al., 2011).
Indeed, studies from South Korea as well as South Asia have identified this
this as a key determinant of the application of sex-selective technology (Das
Gupta 2005). Table 2 reproduces data from the 1990 and 2000 census which
shows the importance of examining SRB by sex composition of existing
children. Here, the sex ratio at parity two, for example, ranges from 107.5
where the first child is a boy through to 190.0 where the first child is a girl.
This demonstrates the clear importance of maximizing the opportunity to
secure a son at the point of second birth.
TABLE 2 about here
It is clear, therefore, that sex composition of existing children will
influence the gender of next birth. Recent studies have analyzed how
macro-level elements and individual children composition make an impact
on the gender of next birth, as Guo (2007) and Yang (2012) have shown by
adopting hierarchical models. However, these studies only employ Census
data without concerning individual fertility intention and desired number of
children.
1.3 Rationale
13
From the review of the literature presented so far, it is clear that there are
both similarities and differences between China and elsewhere in East Asia
regarding the context and levels of skewed SRB. Strong similarities exist
concerning some of the underlying drivers behind fertility decline with
economic development a constant theme in the literature. Indeed, Yuan and
Shi (2005) suggest that it was not the implementation of the family planning
policies that led to the higher level of the sex ratio at birth, but rather the
traditional ideology of son preference, the present economic situation, the
accessibility of prenatal sex determination and sex-selection technology, as
well as the willingness to have fewer children.
Despite this, the clear differences in the scale of the skewed SRB in
China means that there is, indeed, something ‘special’ about China which
needs to be identified and analyzed in its own right. As we have suggested, a
number of studies have argued that this key differential is the presence of a
proscriptive family planning policy limiting the number of children which
couples are legally entitled to have. However, this policy interacts with a
wide array of other factors which shape childbearing and, ultimately, the
SRB.
In Figure 2, we present a simplified model of the relationships discussed
in the paper so far. At the top is a selection of some of the key drivers in
shaping fertility ideals (and ultimately actual fertility) as derived from the
14
literature. These include, but are not limited to, economic development;
urbanization/modernization of society; improvements in quality and access
to education; changing gender roles including female participation in the
labor force; changes in the mortality context as proposed in demographic
transition theory and, in the context of certain countries, son preference. The
model also ignores the potentially self-reinforcing effect of low fertility upon
ideals, intentions and actual fertility. The model is further simplified in terms
of the relationship between fertility intentions and actual fertility. Of course,
there is a large number of mediating social, economic, and biological issues.
The point, however, is to emphasize the importance of proscriptive family
planning regulations in China relative to other settings. Here, too,
differentials in policy regulations within China are key. In urban areas, for
example, the strict one-child policy formulation places much tighter
restrictions on the relationship between intentions and actual fertility. In rural
areas, meanwhile, the presence of the 1.5 child policy and two child policy
allows for a greater degree of flexibility. Finally, the lower part of the model
attempts to demonstrate the interaction between son preference, access to
sex-selective technology and sex composition of children already born in
terms of shaping actual fertility and, ultimately, both overall and
parity-specific SRBs.
FIGURE 2 about here
15
Therefore, utilizing data from the 2001 National Family Planning and
Reproductive Health survey, this paper analyzes the influence of children
composition on the sex of next birth in the context of the low fertility levels
extant in China in the 1990s. In this study, we focus exclusively upon rural
households simply because of the increased flexibility in family planning
restrictions in these settings. In urban areas, where the ‘one child policy’ is
more strictly and universally enforced, looking for prior births is extremely
difficult as the number of second-born children are very low. Couples who
are both only-children in urban areas are, indeed, now entitled to apply to
have a second child. However, owing to the generational effect the number
of such couples in urban areas in 2001 was relatively limited. Different from
the studies already performed, this paper not only takes macro-level policies
into consideration but also considered micro-level factors such as individual
fertility intention. We find that in China in the late 1990s, both sex
composition of children already born and individual fertility intention
exerted great influence on the gender of next birth while family planning
policies had relatively little influence. This is a novel finding compared to
previous studies.
2. DATA AND METHODS
The data come from the 2001 National Family Planning and
16
Reproductive Health Survey performed by the National State Population and
Family Planning Commission in 2001. The method of three-stage stratified
sampling was adopted in the survey and a proportion sampling method was
conducted in each stage according to the given probability. First, the country
was divided into 31 regions. In the first stage, counties, districts and cities(all
addressed as counties in the following) were selected from 31 provinces; in
the second stage, three to four villages, towns or streets (all addressed as
villages in the following) were selected from each chosen county; in the
third stage, a village group were selected from each chosen village. Finally,
approximate 1,000 villages and urban community groups were selected
which contained 180,000 people including 45,000 women of child-bearing
age aged from 15 to 49. There are community questionnaires, household
questionnaires and individual questionnaires. The community questionnaire
fall into two categories: one questionnaire for townships and one for village
family planning service providers. 47,043 households were interviewed,
including 39,586 women of child-bearing age.
In the individual questionnaire, there is a retrospective question for
childbearing-age women about their pregnancy history. To this question, the
interviewee is required to report the end year and month and result of every
pregnancy. The result falls into the following categories, live male birth, live
female birth, induced abortion, natural abortion, still birth. Here, we analyse
only the last reported births of mothers. As this paper aims to explore how
17
the children composition of rural households exerts influence on the sex of
next birth, data pertaining to urban residents was omitted, and only part of
information on conceiving and childbearing in the individual questionnaire
and economic information in the village questionnaire is used. Given that
provincial family planning policies were adopted after 1990, especially in
rural areas (Pang et al., 2008), we further confine the individual samples to
the Chinese rural women at childbearing age with live babies in the latest
birth delivery during 1990 to the end of this survey.
In the sample, there are 13,300 childbearing-age women in rural areas
in which 2,087 women live in areas implementing a one-child policy
(15.69%); 10,237 women in 1.5-child policy areas (76.97%); and 976
women in 2-child policy areas (7.34%). The mean income per capita of these
villages is about 2,029 Yuan. By the end of survey, the proportion of women
whose ages are under 27 at the time of last birth is 71%. 6,072 women were
childless; 2,092 had one boy; 2,997 women had one daughter; 583 women
had a daughter and a son; 284 women had two sons; 614 women had two
daughters and 658 women had at least three children. There are 27.18% of
the samples expecting to have one child; 64.00% expecting to have two
children and 5.70%expecting to have three children.
As previous studies largely ascribe the rise of sex ratio at birth in 1990s
18
to the implementation of fertility policies, this ignores the sex selection
pressure brought by the expected number of children (intentions). Although
some studies have differentiated influences made by macroeconomic policy
and family decisions, with the aggravation of regional development
difference after 1990, it’s more likely to yield a deviated result by using
models that failed to differentiate data structures making it difficult to
identify which layer should be cause for the rise of sex ratio at birth. This
paper adopts hierarchical linear model to handle this problem.
In order to examine how fertility policies and children composition of
child-bearing age women exert influences on the sex of next birth, firstly, we
build a null-model for the sex of the next birth, the results of which show
that there is significant difference among villages. This suggests that the use
of a hierarchical linear model is necessary. Secondly, without considering the
variables of the second level, a random regression equation was established,
which only focused on the variables of the individual level. The regression
results show that most variables and intercept of the individual level are
significantly different among villages. As such it is necessary to establish a
further model for these significant slopes and intercept in the second level. In
sum, we can explore the effect of individual variables on dependent variables,
as well as the effect and its mechanism of economic level and fertility policy.
The Logistic model of the effect on next birth sex selection was
19
established as the following:
log
p
=B0 j +B1 j X ij +eij
1-p
(1)
In equation (1), p is the probability for the next birth to be a live son;
x is the personal feature; i is the individual code; and j is the community
code. Considering the data structure and the notable difference of personal
data in different communities, the intercept and slopes in equation (1) are
established as follows:
B0 j =C00  C01Z j +u0 j
(2)
B1 j =C10  C11Z j
(3)
In equation (2), Z j is the variables at village level, which is measured with
“net per capita income of 2000 in the village”, “whether as the 1.5 children
policy region”, “whether as the two children policy region” and so on. C00 ,
C01 , C10 and C11 are variables to be evaluated. We treat u0 j as a random
variable and assume that it takes a normal distribution N(0, σij)
.
The definitions, assignments and descriptive statistics of the model
variables are shown in Table 3.
TABLE 3 about here
20
3. RESULTS
Table 4 shows the estimated coefficients of the multilevel model. The
direction and intensity of these coefficients represent the impact of
independent variables of various levels on the dependent variable. Model 1
presents the main effects before interactions are added, while Model 2
presents the main effects without controls for (subjective) fertility intentions.
Model 3 provides the results of the initial interpretation model, and the
Model 4 is the final interpretation by phasing out those not statistically
significant interactive effects. The strategy of identifying interactions is
based on theoretical considerations derived from previous studies (Yang
2011, Guo 2007, Yang 2006). The software we use to analyze the data is
HLM 6.02.
TABLE 4 about here
From Table 4, we can examine how the different children composition
and the fertility policy affect family sex selection. Compared to the one-child
policy, the 1.5-child policy has a significant impact on the gender of the next
child. However, the effect of the two-child policy on the gender of the next
child is not significant. From Model 4, we can see that comparing to the
family without any child, the family that already has one boy lowers the
odds of having another son by 15.5% (e-0.169-1);the family that already has
one girl can lift the odds of having another son by 65% (e0.500-1);the family
21
that has one boy and one girl can lift the odds by 20.5% (e0.186-1);the family
that already has two boys can lower the odds by 49% (e-0.674-1) and the
family that already has two girls can lift the odds by 97% (e0.680-1). In other
words, if a family has only a son or sons, then a girl is preferred. If a family
has only a girl or girls, then a son is preferred. When a family has already
had sons and daughters, under the traditional ideology of “more sons, more
blessing”, the sex selection toward the next child tends to be a boy.
Moreover, from Model 3, we find that compared to the family without
any children, the family that already has one boy, or one boy and one girl, or
two boys is unlikely to be affected by the village-level variable (i.e. fertility
policy and average income) when deciding the gender of next birth. But
families with only girls will be affected by fertility policy. According to
Model 4, comparing to one-child policy areas, in the areas where the 1.5
child policy is applied, the more girls the woman has had, the more likely
she tends to choose a boy for the next birth, whereas in the areas applying
the fertility policy of two children, the odds of having a boy is lower.
Compared to the one-child policy areas in the 1.5 child policy areas under
the condition of one girl being born, the odds increases by 74% (e0.555-1), and
with two girls, the odds increases by 106% (e0.721-1). While, with one girl,
the odds are lower by around 47% (e-0.634-1), and with two girls, the odds is
again lower at about 64% (e-1.034-1) in the two-child policy areas. The
22
fertility policy interaction with existing children position plays a significant
role in the gender of next birth. Moreover, the results also show that the
1.5-child family planning policy strengthens sex selection behavior to a
certain extent, while the two-child policy loosened the sex selection
pressure.
The confusing fact is that, according to Model 4, for the parents already
with three children or more, the odds of expecting another boy increases
markedly and is higher than those with one girl and two girls. The data show
that under the condition of having three children or more, the odds of giving
birth to a boy increases by 144% (e0.893-1). This may indicate the random
choice in the gender, but if it is already a large family, they tend to choose a
boy in order to achieve ideal family structure. It is also evidence that
reproductive behavior at this level is more related fertility intention than
policy.
Indeed, for women of child-bearing age, their fertility intention plays an
important role in reproductive behavior. In Model 3, independent variables at
village level are not significantly correlated with fertility intention, which
can be interpreted as the effect of fertility intention on the birth meaning that
sex would not be affected by fertility policy and economic level. Some
studies point out during the early stage of fertility transition in China,
23
fertility intentions will be greatly influenced by fertility policy and further
serve to restrict people’s reproductive behavior. While, the effect of policy
on fertility intention is weakened in the context of low fertility, for most
people it appears to be simply a deterrent (Yang, 2011). In the survey
analyzed here, the fertility intention for more than 90% of the interviewees is
one or two children. With low fertility level and low fertility intention,
Model 4 indicates that fertility intention of child-bearing age woman plays a
more significant role in the reproductive behavior, especially for sex
selection of next birth; the decline of fertility intention can raise the odds of
having a son for the next birth.
4. DISCUSSION AND CONCLUSION
Chinese couples are now having fewer children than decades ago, but
son preference is still prevalent (Poston et al., 2011). Transformation of
society and population failed to alter the traditional preference for sons with
‘boy and girl’ the ideal family composition followed by boy only (Liu, 2002).
Yuan and Shi (2005) have pointed out in Asian countries, no matter what the
level of social and economic development is, the idea of preference for boys
over girls is widely accepted. As a result, in the background of low fertility
level, people with lower fertility intention will raise the odds of bearing a
boy.
24
The reason that the family planning policies can lead to the high level of
sex ratio at birth is that deeply influenced by thousands years of Confucian
culture along with the rapid decline of fertility and strong son preference
(Kim,1997; Park and Cho, 1995). As Chinese fertility level stayed at a high
level before the 1970s, the chances of bearing a son were high (Pison,2004).
Indeed, when the average number of child born is 6 per woman, the
probability of having no son is below 2%; however when the number falls to
one or two, this probability may rise to 50% (Poston et al., 2011).
Imbalanced sex ratio at birth is correlated with both son preference and the
decline of fertility intention and level (Yang, 2006). Fertility policies still can
make some influences on fertility behaviors, but as socioeconomic
developments occur their effects can wane. This study suggests that against
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25
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31
Table 1: Sex ratio at birth by province, November 1st 2009 to
October 31st 2010
2005
2009/10
95% Confidence
SRB
Interval
Total births
Male
Female
13,836,187
7,487,489
6,348,698
119
117.94
117.81
118.06
Beijing
115,987
60,617
55,370
114
109.48
108.22
110.74
Tianjin
81,977
43,614
38,363
114
113.69
112.14
115.26
Hebei
882,233
471,659
410,574
120
114.88
114.40
115.36
Shanxi
347,160
182,022
165,138
116
110.22
109.49
110.96
Inner Mongolia
204,920
108,305
96,615
114
112.10
111.13
113.08
Liaoning
264,854
138,844
126,010
113
110.18
109.35
111.03
Jilin
177,304
93,345
83,959
113
111.18
110.15
112.22
Heilongjiang
248,961
131,752
117,209
109
112.41
111.53
113.30
Shanghai
128,518
67,651
60,867
117
111.15
109.94
112.37
Jiangsu
685,177
368,321
316,856
125
116.24
115.69
116.80
Zhejiang
444,178
240,545
203,633
114
118.13
117.43
118.83
Anhui
751,580
422,863
328,717
131
128.64
128.06
129.23
Fujian
401,732
223,652
178,080
122
125.59
124.81
126.37
Jiangxi
580,921
320,227
260,694
129
122.84
122.20
123.47
Shandong
982,207
534,555
447,652
114
119.41
118.94
119.89
Henan
1,044,716
564,972
479,744
122
117.77
117.31
118.22
Hubei
592,626
328,171
264,455
128
124.09
123.46
124.73
Hunan
796,975
439,956
357,019
122
123.23
122.69
123.78
1,083,819
591,941
491,878
119
120.34
119.89
120.80
Guangxi
715,349
394,108
321,241
121
122.68
122.11
123.26
Hainan
121,636
67,644
53,992
123
125.29
123.88
126.71
National
SRB
8
North
Northeast
East
Central
Guangdong
32
Southwest
Chongqing
263,380
139,442
123,938
112
112.51
111.65
113.37
Sichuan
743,017
391,946
351,071
115
111.64
111.14
112.15
Guizhou
481,717
264,848
216,869
128
122.12
121.43
122.82
Yunnan
564,346
297,855
266,491
113
111.77
111.19
112.35
Tibet
45,731
23,597
22,134
102
106.61
104.67
108.58
Shaanxi
335,229
179,549
155,680
134
115.33
114.55
116.12
Gansu
278,921
150,604
128,317
116
117.37
116.50
118.25
Qinghai
72,363
38,293
34,070
117
112.40
110.77
114.05
Ningxia
76,000
40,462
35,538
107
113.86
112.24
115.49
Xinjiang
322,653
166,129
156,524
105
106.14
105.41
106.87
Northwest
Table 2: Sex ratios at birth by sex composition of existing children
Sex ratio at birth of next birth
Existing children
1990 census
2000 census
None
105.6
105.5
1 son
101.4
107.3
1 daughter
149.4
190.0
2 sons
74.1
76.5
1 son and 1 daughter
116.4
122.1
2 daughters
224.9
380.6
Source: 1990 census data from Zeng et al. (1993), 2000 census data from Sun (2005)
33
Table 3: Definition and descriptive statistics of the variables
Variable
Definition and measurement
N(%)
Gender of live birth
the gender of last live birth of women of child-bearing
Mean(SD)
0.61(0.49)
age up to the end of the survey
dummy variables: 1= boy, 0= girl
Personal
personal cases: 13300
characteristics
Children composition
reference category:
0 children
1 son
having had only a son:
1 daughter
yes=1, no=0
6,072 (45.7%)
2,092 (15.7%)
0.16(0.36)
having had only a daughter： yes=1, no=0
2,997 (22.5%)
0.23(0.42)
1 son and 1 daughter
having had only a son and a daughter：yes=1, no=0
583 (4.4%)
0.04(0.20)
2 sons
having had only two sons: yes=1, no=0
284 (2.1%)
0.02(0.14)
2 daughters
having had only two daughters: yes=1, no=0
614 (4.6%)
0.05(0.21)
3 children
having had at least three children: yes=1, no=0
658 (4.9%)
0.05(0.22)
Age
woman’s age at the end of the pregnancy
25.85(4.00)
Fertility intention
the ideal number of offspring
1.88(0.88)
Abortion times
First live birth: total abortion times before the live
0.11(0.41)
birth;
Non-first live: abortion times between the latest live
birth and the previous live birth
Education level
Years of Education
Characteristics of
village-level unit: 827
6.36(3.34)
village
Income
net per capita income of 2000
2028.93
(1332.51)
Policy fertility
reference category: 1-child policy
1.5-child policy
1= 1.5-child policy, 0= else
0.77(0.42)
2-child policy
1= 2-child policy, 0= else
0.05(0.22)
TOTAL LIVE
13,300
BIRTHS
(8,143 male;
5,157 female)
Source: 2001 National Family Planning and Reproductive Health Survey (China)
34
35
Table 4: The regression results of the model
Model 1
Fixed effect
intercept
0.453***
fertility intention
Model 2
Model 3
Model 4
0.454***
0.452 ***
0.451***
-0.108***
-0.136
-0.105***
age
-0.033***
-0.033***
-0.006
-0.001
one boy
-0.190**
-0.168*
-0.111
-0.169**
one girl
0.689***
0.717***
0.565**
0.500**
0.226*
0.635
0.186+
one boy and one girl
0.176
+
two boys
-0.677***
-0.646***
-0.739
-0.674***
two girls
1.237***
1.278***
0.767*
0.680+
three children
0.359**
0.421**
1.044*
0.893+
abortion times
-0.046
-0.048
-0.041
-0.041
education level
-0.000
-0.001
-0.001
-0.001
Income
-0.000+
-0.000+
0.000+
0.000+
1.5-child policy
0.111+
0.111+
0.125*
0.125*
2-child policy
-0.146
-0.146
-0.151
-0.151
0.000
0.000+
-0.031+
-0.028
+
0.039+
income*age
1.5-child policy *age
2-child policy *age
0.049
income* fertility intention
0.000
1.5-child
0.033
policy
*fertility
intention
2-child policy *fertility intention
0.183
income*one boy
0.000
1.5-child policy * one boy
0.118
2-child policy * one boy
-0.244
income * one girl
0.000*
0.000
1.5-child policy * one girl
0.573 **
0.555***
2-child policy * one girl
-0.841 **
-0.634*
income * one boy and one girl
0.000
1.5-child policy * one boy and
-0.274
one girl
36
2-child policy * one boy and one
-0.641
girl
income * two boys
0.000
1.5-child policy * two boys
0.299
2-child policy * two boys
-0.188
income * two girls
0.000
0.000
1.5-child policy * two girls
0.747*
0.721*
-1.319**
-1.034*
income * three children
0.000
0.000
1.5-child policy * three children
-0.434
-0.439
-1.973**
-1.587**
2-child policy * two girls
2-child policy * three children
Random effect
Level 2：intercept
0.064***
0.064***
0.067***
0.067***
Level 1：individual
0.984
0.984
0.985
0.984
***p<0.001; **p<0.01; *p<0.05; +p<0.1
37
Figure 1: Period Total Fertility rates of selected sites of higher
SRBs
8.00
7.00
6.00
pTFR
5.00
China
4.00
China, Hong Kong SAR
3.00
Republic of Korea
2.00
Taiwan
Viet Nam
1.00
0
Source: (UN 2012)
38
Figure 2: Simplified model of factors influencing fertility
ideals/intentions, actual fertility and SRBs in China and other
settings
39