ECONOMICS DISSERTATION COVER SHEET
STUDENT ID: 2693268
DATE: 18th July
NAME OF SUPERVISOR: Dr Otto Lenhart
DISSERTATION TITLE: Does Income Gap Affect the Fertility Intention? — Evidence
from China
WORD COUNT: 13247
PROGRAMME OF STUDY: Economic Development, MSc
Adam Smith Business School
West Quadrangle, Gilbert Scott Building, Glasgow, G12 8QQ, Scotland UK
Telephone: +44 (0)141 330 3993 Facsimile: +44 (0)141 330 4939
Email: business-school@glasgow.ac.uk
The University of Glasgow, charity number SC004401
Table of Contents
Abstract ...................................................................................................................... 4
1. Introduction ........................................................................................................... 5
2. Literature Review .................................................................................................. 8
2.1 Fertility Intention ................................................................................................ 8
2.2 Income Gap...................................................................................................... 13
2.3 China Context .................................................................................................. 18
3. Theoretical Model and Mechanism ........................................................................... 20
3.1 Basic Model ..................................................................................................... 21
3.2 Mechanism Analysis ......................................................................................... 22
4. Data and Empirical Model ....................................................................................... 23
4.1 Data Resources and Data Processing ................................................................... 23
4.2 Empirical Model ............................................................................................... 30
5. Econometric Result and Analysis ............................................................................. 31
5.1Baseline Regression Result ................................................................................. 31
5.2Panel Data estimate analysis ............................................................................... 39
6. Robustness Test and Heterogeneity Analysis ............................................................. 42
6.1 Robustness Test ................................................................................................ 42
6.2 Heterogeneity Analysis ...................................................................................... 46
6.2.1 Spatial heterogeneity analysis....................................................................... 47
6.2.2Family heterogeneity analysis ....................................................................... 51
7. Conclusion and Policy Suggestion ............................................................................ 55
Reference ................................................................................................................. 57
2
List of Tables
Table 1. Descriptive statistics of variables................................................................... 28
Table 2. Baseline Regression Result ............................................................................ 33
Table 3.Average Marginal Effect ................................................................................ 38
Table 4. Fix Effect Model and Randomize Effect Model ............................................. 41
Table 5. Average Marginal Effect of Fixed Effect Model ............................................. 41
Table 6.Sub-sample robustness tests ........................................................................... 44
Table 7.AME of subsamples ....................................................................................... 45
Table 8.Differences in fertility intentions under spatial heterogeneity ........................ 49
Table 9.Average Marginal Effect of different regions ................................................. 50
Table 10.Differences in fertility intentions by education level ..................................... 52
Table 11.Average Marginal Effect of Education ......................................................... 52
Table 12. Logit estimation and AME of income level .................................................. 54
3
Does Income Gap Affect the Fertility
Intention? — Evidence from China
Abstract
The current stage of accelerated population ageing and uneven income distribution
plague China's development and micro-economic decision-making by households. This
paper uses Chinese General Social Survey (CGSS) data from 2010 to 2017 to delve into
the impact of changes in social income disparity at the macro level on micro household
fertility intentions by estimating a linear probability model, logit model compared with
a Probit model. The empirical results show that the widening income gap significantly
inhibits households' fertility intentions. At the same time, there are significant regional
and micro-level differences in this effect. In terms of regional differences, the impact
on the western region is significantly smaller than on the central and eastern regions.
At the household level, differences in the level of education and income of families
affect their ability to cope with the widening income gap. Finally, the policy
implications of the above findings are that redistributive income policies are actively
being improved to reduce income inequality. Also, promote coordinated regional
development to reduce disparities between regions.
4
1. Introduction
First of all, it is a brief review of China's demographic policy changes. In terms of total
population and history, China has always been considered a traditionally large country
in the aspect of the population. That is why China introduced the one-child policy in
1978 and it contributed to a great decline in the total population but it was not the sole
driving force. Social atmosphere, economic, and cultural change also brought about this
decline with the implementation of the one-child policy and continue to keep China’s
fertility rate low(Zhou and Guo, 2020). However, with the demographic dividend
disappearing and aging population, in 2013 and 2015, the government launched the
conditional two-child policy and universal two-child policy respectively. Moreover, the
government permitted a three-child policy in 2021. This ongoing series of population
policy changes, the rapid shift from strict restrictions to full encouragement, has drawn
much interest from both academics and the general public regarding whether the
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Chinese are willing to have a second child.
Secondly, it is about China's demographic situation and future trends. In 2017, the
United Nations already mentioned that China's population is expected to show an
"inverted V" reversal by the end of the 21st century. China will be in a state of
accelerated decline with a low fertility rate. Meanwhile, the National Bureau of
Statistics also gives similar data that in 2018, 15.27 million people were born in
China, a decrease of 2.01 million from the previous year and a record low since 1962.
This is a record low since 1962. The number and share of the working-age population
fell for the seventh consecutive year, by 27.71 million in seven years. The number and
share of the working-age population fell for the seventh consecutive year, by
2,771,000 in seven years. What’s worse, according to the 7th Census in 2021, the
decline in births is a general trend, with 11 provinces already experiencing negative
population growth. From the data above, it is apparent that the universal two-child
policy has been met with a lukewarm response, with fertility behavior not showing the
expected buildup and the population dependency ratio continuing to rise due to the
ageing of fewer children. The increasing dependency ratio due to the ageing of the
population and the hesitancy of women of childbearing age after the relaxation of the
childbirth policy has led to a serious demographic crisis in China.
On the other hand, it is about the income gap problem. Although China's economy has
been growing at a high rate that has caught the world's attention since the turn of the
century, the problems of widening income disparity and declining consumption levels
over the same period have come to the fore and have attracted the attention of all sectors
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of society. Although the latest authoritative public data are not yet available, the World
Bank's 2005 development report shows that China's Gini coefficient has exceeded the
0.4 thresholds and is in the lower middle of the global scale. Furthermore, when we talk
about the income gap in China, we usually include four kinds of income gap: urbanrural income gap, regional income gap, industrial income gap, and property income gap.
In recent years, despite the decline in the number of low-income groups as a result of
poverty reduction policies, what more noteworthy is the widening impact of the wealthincome gap. Some scholars estimate that the Gini coefficient of wealth has continued
to rise from 0.599 in 2000 to 0.711 in 2015 and on the meanwhile, the uneven allocation
of real estate and financial assets to household assets is the main reason for the widening
gap in wealth accumulation (Yuan et al, 2020). Thus, it is also crucial to study the
impact of income inequality on society as a whole and people’s decision-making.
In summary, we further proposed our research question: Does the income gap affect
fertility intention? And the contribution of this paper is as follow: First, from the
statement above, we can know that fertility intentions and income inequality are hot
topics in Chinese society today, and a study combining these two can be relevant and
guidance for policy development. Second, at present, research on income disparity in
China has mainly focused on the measurement of income disparity, the analysis of the
causes of urban-rural income disparity, or the impact of a single factor on income
disparity. There is a lack of analysis of the spillover effects of income disparity in terms
of the transmission mechanism of the impact of income disparity fluctuations. Thirdly,
the empirical results were further analyses for individual heterogeneity, including
regional differences, urban-rural differences, and et.al
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The whole structures of this paper are as followed: section 2 is the literature review,
and it will shortly discuss related literature in the fertility intention area and income gap
area. Section 3 is mechanism analysis, it will discuss how income disparity specifically
affects people's fertility decisions and intentions through literature and theoretical
model. Section 4 is the data and empirical model, this part will introduce data sources,
data collation process, basic descriptive statistics and empirical model setting for this
paper.
Section 5 is baseline regression, it will analysis of basic empirical results, and
endogeneity issues with the method of panel data, which indicate that the widening
income gap has had a significant dampening effect on people's willingness to have
children. Section 6 is a robustness check and heterogeneity analysis. Likewise, not only
are the findings in the benchmark regressions robust and valid but geographical
differences in the impact of income disparities and micro-level differences in
households are also found. Section7 is the conclusion and shortcomings.
2. Literature Review
This section has three sub-chapters. The main sections include a review of existing
research in the area of fertility intentions and income inequality and a presentation of
findings from China-specific studies.
2.1 Fertility Intention
Low fertility has been a central concern of demographers and policymakers in Europe
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and East Asia over the past few decades. A large literature has explored the causes of
fertility decline in the post-industrial era and the possible reasons for cross-country
differences. The existing research literature on this topic can be divided into three main
branches.
The earliest literature on the microeconomic analysis of family fertility is proposed by
Becker (1960). He discussed in the context of the utility maximization theory of family
reproductive decision-making. Then based on Becker’s theory, the first type of main
research is about housing prices and housing wealth affect the fertility intention.
Dettling and Kearney (2014) indicate that houses as a kind of micro commodities, their
own price changes can have income and substitution effects on households' budget
constraints. Kauir Atalay et al. (2021) use the Australian housing market with resident
households as a sample, it is found that an increase in overall house prices, in turn, has
a more pronounced wealth effect, particularly for loan holders. An increase in overall
housing values increases households' propensity to have children. Similar research in
America also shows that for each $1 increase in housing equity For every $1 increase
in housing equity, parents receive an average of 25 - 30 cents in benefits, and it was
found that Young parents use these gains to pay for childcare-related expenses (Mian
and Sufi, 2009). In contrast, Yi (2008) handle with Hong Kong data and prove that there
is a long-term effect of rising house prices on fertility, with each 1% increase in the
house price index significantly reducing the total fertility rate by 0.45%. On the other
hand, there are also some studies that suggest that the rising house price does have
significant differences in fertility intention between the individuals who own a house
and those who do not. Like Dettling and Kearney (2014) by using data from
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metropolitan statistical areas to analyze house price changes and household fertility that
short-term increases in house prices lead to lower fertility rates among the unhoused
and a net increase in the fertility rate of those who do own a home.
Besides, the second one which is most related to fertility intention, is gender role and
gender inequality. The relative early discussion about gender inequality is gender
preference, most usually, a preference for sons— which is common in many regions of
the world. And this phenomenon lead to “the missing women” (Sen, 1990). To research
the relationship between the gender preference and fertility intention, Ralentine et al.
(2022) draw Southeast Asia and Latin America as an example to find how gender
preference adjusts fertility intention. They find that it will finally leads to one boy and
one girl in Latin America. Moreover, nowadays researchers provide a variety of
measurement of gender inequality. For instance, Anneli et al. (2011) use gender altitude
and family life as the measurements of the gender inequality and it draws an inverted
U curve between fertility intention and gender inequality, which means both traditional
and equal males are willing to have a baby. Similarly, Coke (2003) points out that the
willingness of the whole family to have a second child is increased when the father has
a strong role in the care of the first child. What’s more, Mary et al. (2018) also provide
cross-country qualitative comparative analysis evidence to prove this topic. They select
Japan, Spain, America, and Sweden as research objects and use work conditions and
family conflict to measure gender inequality. They found that a lack of support for
women’s roles will eventually lead to a low fertility intention. It is not unique that a
supportive environment for families can be beneficial for women’s fertility intention.
If policy can provide a more equal environment for gender roles, women will be willing
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to have a second child (Soo-Yeon, 2017). The same, a panel study of 21 European
countries shows that family policies that favor closing the gender gap in income are
positively associated with increased fertility intentions for both first and second
children (Tommy and Sunnee, 2014).
Thirdly, it combines demographic and socioeconomic characteristics with fertility
intention. And it mainly includes age, ethnicity, education, income, regional context,
and so on. First of all, from an individual perspective, age always plays a key role in
fertility intentions and behaviors, and rising age has a significant dampening effect on
fertility intentions (Morgan, 1982) because age represents the objective condition of
being able to have children safely. Other studies also support that although women
without children suffer much stronger social pressure, they still will decrease their
expectations for a baby (Aizen and Kloabs, 2013). Secondly, ethnicity is always
considered to play a role in people's willingness to have children, along with social
norms, culture, and beliefs (John, 1982). Sarah and Morgan (2007) discuss How
religion affects fertility intentions in the United States and it provides a mechanism that
religious schemas often incorporate attitudes and values about family-related behavior,
which the more devout believers will have higher fertility intention (Edgell, 2003).
Then the third part is the most important factor, education, and human capital. In the
early literature, education is usually considered to have an inverse relationship with
fertility intentions (Bongaarts, 1997). Because early researches believe that highly
educated women will prefer children quality rather than the amount of children (Becker
and Lewis, 1973). Whether there are national or regional differences in this educational
impact is also an area of concern for scholars. For example, Melanie and Harper (2019)
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the influence of education on fertility intention has significant differences around SubSaharan Africa. Or in Pakistan, Samia and Bashir (2013) think that educational
attainment influences women's contraceptive decisions and thus family reproductive
decisions. But, in recent years, some empirical researches have challenged this
traditional view. Testa (2014) find that there is a positive relationship between education
and fertility intention in 27 European countries where the birth rate of the whole society
is low. On the meanwhile, high-level of education can guarantee better job prospect,
which makes female more confidence to have a baby (Dupray and Pailhé, 2018).
Moreover, women who have a high level of education can be able to handle with job
strain and family conflict when they want to have a baby (Katia and Melinda, 2010).
Fourth, it is the income and job factor. Work and income levels determine a family's
stability and ability to raise children. It is traditional to find that household income
positively affects both actual and preferred fertility rates (Moav, 2005). However, the
positive impact of this income is offset by the level of female education. Besides,
employment uncertainty is also a factor that affects fertility intention (Doris et al, 2016).
And it offers a variety of pathways that uncertainty influences fertility intention. Such
as, Kravadal (2002) indicates that the effect of employment uncertainty on fertility may
differ between partners. Often, employment uncertainty generates high opportunity
costs in terms of forgone career promotions or salary increases among highly educated
populations and therefore results in delayed parenthood and fewer births in this group
(Kravadal and Rindfuss, 2008). Fifth, it is regional context and fertility intention.
Especially, differences between urban and rural areas are most concerned. Historically,
fertility rates have started to decline earlier in cities and more rapidly than in rural areas.
Capitals and larger cities usually have lower fertility rates than rural areas (Kulu et al,
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2007). Early on it was a decomposition study of urban-rural differences. Berahard and
Isebella (2019) through the gap among realization and intention discuss how the
regional context affects the fertility intention. They found that Delay in childbirth is
more pronounced in urban than rural areas due to expectations of employment and
promotion.
In addition to the three branches of literature mentioned above, there are also a number
of new studies that are methodologically innovative and are beginning to introduce
experimental approaches to the analysis of fertility intentions. For instance, Maximilian
et al (2022) make a field experiment in Kenya to explain how descriptions of future
uncertainty affect women's fertility intentions.
Compared to existing literature, when previous literature has explored the impact on
fertility intentions from an economic perspective, it has mostly been measured in terms
of direct income or assets, and wealth. This means that the measure is directly on the
individual micro-household. However, as some of the more recent literature also points
out, people's fertility behavior, as a planned behavior, is usually also influenced by their
own expectations of the future. In turn, families' expectations of the future are usually
based on social information. Therefore, the main contribution of this paper is to use the
income gap of the population as a research perspective to explore, from a macro social
development perspective, how people's fertility intentions would be affected if a society
becomes more or less equitable.
2.2 Income Gap
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The income gap has been a hot topic in academia related to income distribution. As we
mentioned above, existing research on income disparities can be divided into
measurement and causal analysis categories. The former is mainly based on the
discussion and improvement of the measure, while the latter is a study of the causes
and effects of income disparity.
First of all, it is about the selection of the measurement of the income gap. The study
of income disparity indicators has its roots in Ricardo's theory of factor income
distribution (Richardo, 1817). Then Pareto (1895) was a pioneer in this field at the
micro level using statistical methods. Over a few decades, Gini challenged Pareto’s
research, and finally proposed the Gini coefficient (Gini, 1914). The coefficient remains
an important indicator of income disparity today. Besides, the Gini coefficient is a
grouping of the total population by level of income. It is a measure of income disparity,
but not an accurate measure of urban-rural income disparity. Therefore, Thail (1972)
extended and presented another index named Generalized Entropy. Moreover, the
definition of the Gini coefficient is also expanding and is no longer limited to a
discussion of the net income range. Household property, between industries and
financial disparities, can all be measured by the Gini coefficient (Molina, 2018).
Secondly, it is about the causality research in the income gap area and here this section
will mainly review the urban-rural income gap. Since the Gini coefficient was used to
measure income distribution inequality in the 1950s, the inverted "U" curve proposed
by Kuznets has been the focus of income distribution research. However, the Kuznets
curve is, after all, only an empirical theorem and has been constantly questioned by
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scholars due to the lack of a more rigorous theoretical explanation. In summary, the
available research can be stated at followed three branches.
The first concerns the relationship between the urban-rural income gap and economic
growth. Barro(1991, 1997, 2000) argues that conditional convergence due to
diminishing returns to physical and human capital accumulation is negatively correlated
with the real output when potential output is constant, and positively correlated with
potential output when real output is constant. Income disparity affects the potential
output by affecting the factors of production, which in turn affects the rate of economic
growth and the level of output per capita. Further Galor and Moav (2004) argue that the
accumulation of human capital, which is the main engine of economic growth, replaces
the accumulation of physical capital during the transition period of economic
development thus changing the impact of income on economic growth. In other words,
the accumulation of physical capital is the main source of economic growth in the early
stages of economic development or when the economy is at a low level, which means
an adequate income gap is good for economic growth. Meanwhile, the income gap
widening constraints on increased investment in human capital by those on low incomes
when there is a high level of GDP per capita. Whether the income gap is good for
economic growth, it is the main point of argument. For example. Alesina and Rodrik
(1994) use two stages least square empirical method to inspect the relationship between
the urban-rural income gap and economic growth based on the theoretical model, and
they find that there is a stable negative relationship between them. It is not the only one,
Persson and Tabellini (1994) examine the relationship between urban-rural income
disparity and economic growth from the perspective of the mechanism of policy change,
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arguing that urban-rural income disparity is detrimental to improving labor productivity
and, in turn, hurts the economy. Frank (2005) found that income The gap has a
significant negative impact on economic growth and there is a long term dynamic
equilibrium between the two. In contrast, it is also argued that a widening income gap
between rural and urban areas can contribute to economic growth. Such as government
expenditure in CES model can motivate economic growth through the income gap (Li
and Zou, 1998). Foreb (2008) used panel data and robustness check to find that a
widening income gap can be beneficial for economic growth in a short run. In addition,
other scholars’ researches have found that the relationship between urban-rural income
disparity and economic growth is not invariant.
Secondly, it’s about the trend of the income gap. Loewy and Papell (1996) provided an
analysis of the stability of regional income disparities in the US and found that three of
the eight regions studied showed random walk. Saranties and Stewart (1999) treat
twenty OCED countries as examples and calculate their consumption to income ratio,
they also found that income gap is not stable. Strazicich et al. (2004) found that during
the Second World War, income levels in many countries were randomly unstable, and
that each country had significant points of sudden change in relative income. Tunali
and Yilanci (2010) test the income per capita from 1950 to 2006, and they indicate that
most countries in the Middle East and North Africa Income disparities continue to
widen in most countries in the Middle East and North Africa.
Thirdly, it’s about the factors that affect the income gap. The mainstream view sees
historical legacy and government systems as factors influencing the widening of the
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urban-rural income gap. For instance, Sicular et.al (2007) consider that the urban-rural
dichotomy is an important factor in the widening income gap. Similarly, Jedweb et al.
(2017) explain that urban expansion in the developing world has been dramatic with a
more serious income gap. There are also some scholars who believe that resource
endowment and economic development are the causes of the widening urban-rural
income gap. Like Douglass (1998) believe that the uneven distribution of resources and
market mechanisms are influencing the widening of the income gap between urban and
rural areas. Blanchard and Giavazzi (2003) interpret that in an imperfectly competitive
market structure, the urban-rural income gap is influenced by the factor shares between
urban and rural areas by constructing a general equilibrium model. Moreover, the
creation of a financial structure and the improvement of financial levels are also
important influences on the widening of the income gap. It also has two sides with the
income gap. On the one hand, the poor can use credit for human capital The poor can
invest in human capital through credit, which facilitates the training of vocational skills
and the creation of new businesses, and the rapid rise in income of the lower strata of
the population, thereby reducing Income inequality (Banerjee and Newman, 1993). On
the other hand, When the financial system is inadequate, information asymmetries, and
transaction costs. The existence of information asymmetries and transaction costs
discourage the poor, who lack collateral, credit history and social connections, from
gaining equal access to credit, and the rich have an overwhelming advantage in access
to financial resources. The access of the rich to financial resources is overwhelmingly
dominant and the problem of income inequality grows (Greenwood and Jovanovic,
1990). Furthermore, educational resources, globalization and other factors are also
perspectives for studying income gap.
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This paper draws on the literature on income inequality mainly in terms of the
calculation of an index for measuring inequality, which will be discussed in Section 4.
The other aspect is to clarify the impact of income disparity on the economy
2.3 China Context
This section presents the main studies on fertility and income disparities in China and
analyses the shortcomings of the existing literature and the lessons learned from this
paper.
Overall, research on fertility intentions in China continues to follow the three main
categories described above. First of all, Chinese scholars also focus on gender roles and
gender inequality in China. Considering China is characterized by a low fertility
intention, a strong preference for sons, as well as a stringent birth control policy, Jiang
et.al (2015) select Shaanxi province as the sample and they find that different from
Latin America, in China, Women with son preference turn to sex-selective abortion to
ensure that their first child is a son, thus reducing the likelihood of a second child and
decreasing the fertility rate rather than continuing to have children until there is a boy.
There is also an alternative perspective. Guo and Zhou (2020) use Blinder-Oaxaca to
decompose the difference on fertility intention between urban and rural female. Liu
et.al (2021) create a connection between the Internet usage frequency and women’s
fertility. This study showed that the more frequently females use the Internet, the less
likely women are willing to have children with controlling other variables. They
interpret Internet brings cultural shock to people. Because the more frequently female
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use the Internet, the more they disagree with traditional Chinese gender roles, where
"men work outside the home to support the family while women stay at home to take
care of the family". Also, some researches have focused more deeply on the impact of
women's family-of-origin experiences on fertility intentions. Manyu Lan (2021) gives
a conclusion that the number of siblings in childhood, the level of education of the
parents and the stability of the parent’s marriage all influence women's perceptions of
marriage and therefore their willingness to have children. Meanwhile, women with
better socio-economic status have higher fertility intentions. Though there is some
literature studying housing and fertility intention, it is not very much. Song et.al (2017)
depressing effect is the dominant effect on the fertility intention when housing price
rises in China.
Besides, demographic and socioeconomic characteristics in China are also concerned
by scholars. For example, elder women in Jiangsu Province tend to give up fertility
intentions of having two children (Luo and Mao, 2014). And China is different with
foreign countries, there is no ethnicity influence between Han group and non-Han group
on fertility intention (Huang et al, 2016). In education aspect, Cui and Zhang (2020)
combine women’s education, mental health of pregnancy age women and fertility
intention. They found that highly educated women were less likely to develop mental
health problems during pregnancy and therefore more willing to have children. Given
the vast regional differences in social and economic conditions, fertility intentions may
differ across regions in China. Especially, regional inequality in economic development
in China is prominent, with marked gaps around different areas. According to recent
paper, the per capita fertility intention in the eastern province of Jiangsu is 1.8 children,
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while the per capita fertility intention in Shaanxi is 1.4 children (Jiang et al, 2016). This
significant geographical variation is also a point worth analyzing.
On the other hand, China’s income gap problem is around two main perspectives, which
are measurement and urban-rural income gap. From 1995 to 2002, Yue et al (2007)
measure the income gap and make a decomposing through Blinder-Oaxaca. They find
that income gap increase over time, despite consideration of price adjustments. The
existing literature mainly analyses income distribution at the national level as a whole,
and lacks research on the differences and similarities that exist between different
regions. Moreover, in the studies on the factors affecting the urban-rural income gap,
there is no shortage of scholars who focus on the factors influencing the urban-rural
income gap from a macro perspective. However, there is a lack of analysis of the
characteristics of the urban-rural income gap itself.
To sum up, with China as the subject of study, we expect to combine income disparity
with fertility intentions, it explores whether the level of income inequality in society as
a whole affects people's fertility intentions and provides a portrayal of the transmission
mechanism. It also further analyses whether there are regional and household micro
level differences in this channel of influence in China.
3. Theoretical Model and Mechanism
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This section will provide a logical analysis and theoretical model of the mechanism that
how income gap affects other factors and then influence people’s fertility intention
indirectly. Then we will further propose the research hypothesis for this paper.
3.1 Basic Model
According to the Becker’s Child Quantity Quality Alternative Model, we suppose that
the net cost of raising a baby for the household is positive and the child is a durable
good. And there is a relationship between changes in the quality of children and the cost
of parenting. Furthermore, assuming that there are no technological advances in the
household, the cost minimization is achieved by adjusting the choice of child quality,
quantity and other consumption.
Then we have the cost function of household in short run:
TC  TVC( n,q, z )  TFC  ( i )
Of which, TC is the total cost in short run, TVC is the total variable cost in short run,
and TFC represents the total fixed cost in short run on the meanwhile, n,q,z stand for
the children number, children quality and the other consumption respectively.
Also the budget constrain of the household can be written as follow:
Y  nWn  qWq  zWz  ( ii )
Of which, Y is the total income of the household, Wn is the price of children n, Wq
means the price of children quality q and Wz represents the price of other consumption.
Thus, with a certain budget constraint Y, there is a conflict between the child's choice
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of quantity and quality. If the quality of children q is higher, then under a budget
constraint Y the number of children Wn and the marginal cost MCn are consequently
lower for a given budget constraint Y, at which point the household reduces the number
of children. Based on the assumption above, we can further speculate that when income
gap becomes larger, people will turn for preventive saving, which eventually lead to the
reducing of household budgets and family will have less children.
3.2 Mechanism Analysis
This subsection will give the further mechanism analysis based on the literature and
propose our research hypothesis. First of all, the classical theory tells that the
relationship between income inequality and the consumption emphasizes that if the
propensity to consume decreases with income, then from the macro level, the rising
inequality will reduce the consumption then influence the economic growth. In other
words, if the inequality keep rising, the family will turn to save money and smooth their
consumption curve. Parenthetically, most studies conclude that income disparity creates
a significant disincentive to micro household consumption. For examples, Aguiar and
Bils (2011) discover that the consumption inequalities predicted by saving behavior
were found to be largely caused by income disparities. Jin et.al (2011) use data from
the 1997-2006 China Urban Household Survey, the study found that even after
controlling for household income, income disparity still has a negative impact on
household education expenditure and has a positive impact on household saving.
Likewise, if we think this logic from the household fertility intention area, the larger
income inequality means less confidence with household income, which make family
to reduce their fertility consumption and have weaker fertility intention. Because
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parents might be worried about the extra burden and stress of raising a child on the
family or the inability to provide a good environment and future for your child.
Hence, we propose the first hypothesis of this paper:
HP1: The larger income inequality will depress family fertility consumption, and
eventually discourage family fertility intention.
On the other hand, reference the 3.1 section, it shows that Families trade-off between
quantity and quality when making fertility decisions based on budget constraints.
Responsible parents who not only want to provide their children with good living and
educational conditions now, but who will also continue to invest in the future. Hence,
the micro-level sample of households provides factors that vary between households,
and our second hypothesis of this paper:
HP2: Education and income level differences among households will lead to
different response to the income gap impact on the fertility intention.
And this hypothesis will be tested in the heterogeneity analysis section.
4. Data and Empirical Model
4.1 Data Resources and Data Processing
This section will focus on the data sources used in this paper as well as the measurement
of inequality data and the processing of the raw data.
23
(1) Chinese General Social Survey
In order to obtain data on fertility intentions, this paper selected the 2010, 2012, 2013,
2015 and 2017 Chinese General Social Surveys (CGSS). Started in 2003, the CGSS is
the first national, comprehensive and continuous academic survey project in China. The
CGSS systematically and comprehensively collects data from society, communities,
households and individuals at multiple levels to summarize trends in social change.
Meanwhile, CGSS provide specific personal characteristics, family characteristics and
regional information, which is convenient to facilitate the construction of rich and
sufficient control variables in this paper.
Next it is the data processing for this paper: First of all, to construct the explanatory
variable, fertility intention, of this paper, referencing to available literature, we use the
number of children expected to have as the key measurement of our independent
variable. In particular, this variable is generated from the questionnaire “If there were
no policy restrictions, how many children would you like to have?” The ideal situation
would take the values of the dummy variables of not wanting children and wanting
more than 1 child. However, in practice, as the proportion of the sample that answered
“do not want to have a child” was too low. The dummy variable was set to 0 and the
label “low fertility intention” was assigned to them. Also, we combine “want to have 2
children” and “want to have 3 or more children” were grouped together and the dummy
variable was set to 1, and give the label “high fertility intention”.
Secondly, it is the set of a series of control variables. In terms of the selection of control
24
variables, the paper follows the previous literature review section and set control
variables in three directions: housing, gender roles, and demographic characteristics. In
the housing area, according to “whether you own more than one house”, we generate
dummy variable house, which 0 represents “less than one (including one)” and 1
represents “more than two”. Furthermore, taking “whether you own car” into account,
set dummy variable Car. 0 means family has no cars or 1 means family has cars. And
based on the self-assessment of income levels, we divide the households into two types,
one is “Below average”, the other is “above and equal to the average”. Then we use
these three variables together to measure the economic characteristics of households.
In accordance with the literature section, in order to take into account the impact of the
gender equality perspective, through the survey “Do you agree that - men put their
careers first and women put their families first”, we can received the equality variable.
The last parts of control variables is related to the demographic characteristics. In this
regard we take into account, age, gender, political affiliation, Hukou system, ethnicity,
religion, education, personal income, health, working hours, and social security. Of
which, in terms of age, we took an age-appropriate sample of 17-50 year olds as for
robustness check. On the other hand, to identify the education influence, the 13
education levels of the questionnaire were divided into four categories of variables:
“never had any education” is assigned the value of 0, then “private school, literacy
class”, “primary school” and “junior high school” were combined into “primary
education level” and assigned a value of 1 , then “vocational high school”, “general
high school”, “secondary school” and “technical school” are combined as “secondary
education level” and assigned a value of 2. The “university college (adult higher
education)”, “University college (formal higher education)”, “University bachelor's
25
degree (adult higher education)” and “University bachelor's degree (formal higher
education)” are combined as “advanced education” and assign a value of 3. Finally,
“Postgraduate and above” is assigned a value of 4. The remaining control variables are
described below: political affiliation includes “Normal” and “Communist party
member”. Hukou system is used to distinguish the rural areas, which 0 means “nonrural” and 1 represents “rural”. The ethnicity dummy variable is set to 1 for Han Chinese
and 0 for other ethnicities. The social security dummy variable measures whether
people are covered by pension insurance, where 1 means they are covered and 0 means
they are not. Working hours and household income are continuous variables, measuring
the number of hours worked in a week and total income after tax in a year respectively,
and make logarithmisation of two variables.
Finally, after collating the above variables and eliminating missing values, matching
individual IDs, household IDs with community IDs and combining the samples from
2010, 2013 and 2017 to obtain 3 periods balanced panel data, where the samples from
2012and 2015 would be used for robustness check. Then the final output is the
following descriptive statistics table (Table 1)
(2) China Urban Statistics Yearbook
The dependent variable in this paper, income gap, cannot directly get data from National
Bureau of Statistics. Therefore, we need to calculate the Gini coefficient to measure the
inequality. According to the definition, if the population is divided into N equal groups,
which each group is an equal share of the total and the mean value of the income of the
26
corresponding sub-groups is known yi, then the Gini coefficient is calculated as:
G=
N N
1
  | yi  y j | （i ）
2  N 2 i 1 j 1
Of which, G is Gini coefficient,  is the expected value of total income of each subgroup, N
is the observation samples. But there are some problems with the equation (i) for the reason that the
data do not appear in equal groups for a variety of reasons. So based on Thomas et al (2000), they
provide a formula for calculating the Gini coefficient for non-equal groups is proposed, and the
statistical yearbooks of the various provinces in China again suffer from inconsistencies in income
groupings. This non-equivalent grouping is therefore a more appropriate calculation for this paper.
Based on the Lorenz Curve, we can have the formula follow:
1 n 1
G= [ 2  ( Pj  W j )]
n i 1
（ ii）
In this formula, The ratio of the cumulative income of the population from group 1 to
group i to the total income of the entire population is W, and the cumulative population
to total population ratio from group 1 to group i is P. Equation (ii) avoid the difficult
problem of equal and unequal groupings, and group people only by their income, if the
number of people in each group and their income are known, the Gini coefficient can
be calculated.
Therefore, this paper calculates the Gini coefficient of population income in Stata based
on the surveyed population income data in China Statistical Yearbook in 2010, 2012,
2013, 2015 and 2017, combined with Equation (2). And we output descriptive statistics
table as follow (Table 1)
27
Table 1. Descriptive statistics of variables
Variable Name
Freq.
Percent
Fertility Intention
Variable Type
Dummy Variable
low fertility intention
2464
23.38
high feritility intention
8075
76.62
Gender
Dummy Variable
Male
5252
49.83
Female
5287
50.17
Ethnicity
Dummy Variable
Han
9704
92.08
Other
835
7.92
Religion Belief
Dummy variable
No religious belief
9457
89.73
Have religious belief
1082
10.27
Education Experience
Ordered categorical
variables
No education
1210
11.49
Primary education
5297
50.30
Secondary education
2060
19.56
28
Advanced education
1848
17.55
Postgraduate
115
1.09
Political Affiliation
Dummy variable
Normal
8785
83.36
Communist
1754
16.64
Health Status
Dummy variable
Health
6183
58.69
Not health
4352
41.31
Hukou System
Dummy variable
Non-rural
5267
49.98
Rural
5272
50.02
Gender Role
Dummy variable
Inequality
4346
41.32
Equality
6173
58.68
Income Level
Dummy variable
Below average
4145
39.33
Above and equal to
6394
60.67
average
29
House
Dummy variable
Less than one (include
8962
85.04
1577
14.96
one)
More than one
Family Assets
Dummy variable
No car
8497
80.7
With car
2032
19.3
Variable
Obs
Mean
Std.Dev.
Min
Max
age
10539
49.381
15.702
17
101
Working hours
9057
32.737
28.348
0
168
Family income
10539
69122.12
211000
0
9930000
Gini
10561
0.3
0.1
0.078
.544
4.2 Empirical Model
To test the relation between the income gap and people’s fertility intention, we set
empirical model as followed:
Fertilityi ,t  0  1 ln( Ginii ,s ,t )   2 householdi ,t eco  3equalityi ,t   4 Demographici ,t   i ,t
Fertilityi ,t represents the fertility intention of sample i in the t time. ln( Ginii ,s ,t ) is the
30
logarithmic form of the Gini coefficient for the province s in which the sample is located,
and the logarithmic form can explain the impact of percentage change in Gini
coefficient on fertility intention of the population. householdi ,t eco , equaltiy i,t , and
Demographici ,t are the series of control variable described in 4.1 section. Of which,
householdi ,t eco includes housing, Car, and income level. equaltiyi ,t includes gender
equality and Demographici ,t contains age, gender, political affiliation, Hukou system,
ethnicity, religion, education, family income, health, working hours, and social security.
 i ,t is the error term. In addition, Fertilityi ,t is the dummy variable, thus we will use
probit model and logit model to estimate the econometric function. 1 is the core
problem of this paper, and it reflects how the variation of income gap affect the fertility
intention.
5. Econometric Result and Analysis
5.1Baseline Regression Result
Fertilityi ,t is dummy variable, so probit and logit are the main estimate methods. But,
Ai, Norton, and Berry (2010) point out that the significance of the coefficients of the
interaction terms in the non-linear model is neither a sufficient nor a necessary
condition for the interaction between variables. In contrast, the interpretation of the
coefficients of the interaction terms in the linear model is intuitive. Thus, here we test
31
and compare the linear probability model, logit model and probit model, and to find
whether differences. The regression equations are estimated separately and the results
display in Table 2 below.
(1)
(2)
(3)
LPM
logit
probit
VARIABLES
fertility
fertility
fertility
lnGini
-0.127***
-0.756***
-0.442***
(0.016)
(0.097)
(0.057)
-0.002
-0.014
-0.009
(0.009)
(0.054)
(0.031)
0.005***
0.031***
0.018***
(0.000)
(0.002)
(0.001)
0.011
0.096
0.055
(0.016)
(0.107)
(0.060)
0.042***
0.288***
0.152***
(0.014)
(0.097)
(0.055)
0.008
0.055
0.030
(0.007)
(0.039)
(0.023)
0.046***
0.260***
0.151***
(0.013)
(0.077)
(0.044)
0.005
0.025
0.018
(0.010)
(0.058)
(0.034)
Gender
Age
Ethnicity
Religion
Education
Political affiliation
Health
32
Hukou
0.130***
0.754***
0.441***
(0.012)
(0.065)
(0.038)
0.053***
0.297***
0.176***
(0.010)
(0.055)
(0.032)
0.000
0.001
0.001
(0.000)
(0.001)
(0.001)
0.013
0.075
0.047
(0.010)
(0.059)
(0.034)
0.015***
0.087***
0.049**
(0.005)
(0.033)
(0.019)
0.024**
0.142**
0.085**
(0.010)
(0.057)
(0.033)
0.025*
0.142*
0.080*
(0.013)
(0.073)
(0.043)
0.020
0.121
0.076*
(0.013)
(0.076)
(0.044)
0.030
-3.023***
-1.729***
(0.071)
(0.436)
(0.254)
Observations
8,868
8,868
8,868
R-squared
0.067
Gender equality
Working
Pensions
Family income
Income level
Car
House
Constant
Table 2. Baseline Regression Result
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
33
Before explaining the coefficient of each regression, we will test the model setting first.
To test if the logit model and probit model are set up correctly, in this paper, we compare
the logit and probit under ordinary standard error and robust standard error, respectively.
And it shows that the ordinary standard error and robust standard error are very close,
which indicates that there is no need to worry about the setting problems. Secondly, we
will compare the percent correctly predict (PCP) between the logit model and probit
model. In Stata we can get the PCP is 75.9% in probit model and 74.7% in logit model,
both two are very similar. Overall there is no significant difference among these two
models, and they are equivalent.
Then we can move forward focusing coefficient. Here we explain the column (2) and
column (3) at first. As we mention above, coefficients in logit and probit are only
significant in terms of changes in direction, not in terms of specific changes in quantity.
Hence, Table2 shows that both in probit and logit model, there is a negative relation
between the increase in the Gini coefficient and fertility intentions and they are both
significant at 1% confidence level. In other words, the larger income gap will depress
people’s fertility intention. Except for the core variable, the rest of control variables are
also interesting, though some of them might not significant.
Firstly, in this regression analysis, there is no apparent difference in gender aspect,
which means both females and males suffered the impact when the income gap in
society becomes wider. Secondly, unlike foreign samples, in China, the older the family,
the higher the willingness to have children. One of possible explanation is aging issue.
Due to the measurement about fertility intention, the focus is on willingness rather than
34
ability. Moreover, in China, lots of family trust they raise the children for their old age
in advanced. As a result, the elder Chinese families are, the more they want to have
more children to solve their retirement problems. Thirdly, likewise Huang et al (2016),
there is no ethnicity affect between Han group and non-Han group. Nevertheless
religious belief and political affiliation show significant. It means having religious
beliefs or being a member of the Communist Party increases people's willingness to
have children. This is relatively easy to understand: Party members need to be more
proactive in responding to policies. Other demographic control variables like health and
Hukou also display positive relation, which means healthy people have higher fertility
intentions and non-rural household have higher fertility intention. Non-farm households
have a better level of economic income than rural households and therefore have a
higher propensity to have children. In the gender equality aspect, the coefficient shows
that a more equal gender perspective will increase people's willingness to have children.
As for the household economic characteristics variables, these variables all prove that
higher overall household economic levels correspond to higher fertility intentions. In
addition to the control variable listed above, there are also some control variables show
result beyond our expectation. For instance, working measures the weekly working
hours is not significant in the table 1, which is contradicted with previous finding (Katia
and Melinda, 2010). The traditional view is that longer working hours mean higher
work stress, which may reduce people's willingness to have children. But, on the one
hand the previous literature measured this on an individual basis, and on the other hand,
in terms of family structure, most Chinese families still maintain a division of labor
where the man is responsible for work and the woman is the housewife. This means
that due to the division of labor in the household, the pressure of working hours is
35
shared, which is the reason that the control variable of working hours is not significant
in this paper. Likewise the pensions variable gives similar result. Some literatures
believe that better social security, such as health insurance and pension insurance, can
help reduce financial stress and make people more willing to have children. However,
this conclusion seems like that it is not suitable. That might because pension insurance
itself is a current investment to protect the future, and the purchase of pension insurance
will, to a certain extent, reduce the household's current budgetary constraints and thus
influence fertility decisions. Finally, although education variable is not significant in
the Table1, we will not discuss the education until the heterogeneity section.
To further research the impact of specific changes in the degree of income disparity on
fertility intentions and compare with the LPM , we output the average marginal effects
of the logit and probit models as below Table3.
(1)
(2)
VARIABLES
Logit margins
Probit margins
lnGini
-0.131***
-0.133***
(0.0167)
(0.0170)
-0.00240
-0.00270
(0.00934)
(0.00943)
0.00530***
0.00541***
(0.000386)
(0.000391)
0.0162
0.0162
Gender
Age
Ethnicity
36
Religion
Education
Political
Health
Hukou
Equality
Working
Pensions
Family income
Income level
Car
House
(0.0176)
(0.0175)
0.0466***
0.0435***
(0.0146)
(0.0148)
0.00949
0.00909
(0.00680)
(0.00693)
0.0428***
0.0436***
(0.0120)
(0.0123)
0.00432
0.00543
(0.0101)
(0.0102)
0.131***
0.132***
(0.0113)
(0.0114)
0.0521***
0.0534***
(0.00973)
(0.00980)
0.000249
0.000262
(0.000181)
(0.000182)
0.0130
0.0143
(0.0103)
(0.0104)
0.0150***
0.0146**
(0.00565)
(0.00569)
0.0247**
0.0257**
(0.00996)
(0.0101)
0.0239**
0.0236*
(0.0120)
(0.0123)
0.0205
0.0224*
37
Observations
(0.0125)
(0.0127)
8,868
8,868
Table 3.Average Marginal Effect
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
A comparison of the results in Table 3 with the results of the LPM estimates in column
1 of Table 1 shows that, the coefficient of the core variable, Gini reports similar values
like -0.127,-0.131 and -0.133 in three models. Hence, we can explain such value in a
more detailed way: in these three models, with the income gap 1% higher, there are
12.7%, 13.1% and 13.3% probability that people will decrease their fertility willingness.
Likewise, control variables can also be stated as followed: compared with normal
people, the party member will have 4.28% higher fertility intention. In addition, people
with non-rural Hukou also have 13.1% higher than people with rural Hukou. And those
with a more equal gender perspective also show a 5.21% higher willingness to have
children compared with those have inequality gender view.
Overall, comparison of the coefficients of LPM, logit and probit shows that in terms of
estimation effectiveness, there is no significant difference between these three
estimation methods in this article, and they all yielding consistent conclusions that:
(1) Increasing income gap shows a negative relationship with fertility intentions
(2) For every 1% increase in the Gini coefficient, the fertility intention will decrease
13% approximately.
(3) In terms of control variables, it shows a unique variation based on Chinese reality
38
5.2Panel Data estimate analysis
The estimation of the panel data all base on the different assumption of the  i ,t . The
basic idea is as followed: First, consider whether there is individual effect, if not, we
will apply the pool regression. If there is individual effect, we can handle it with the
randomize effect model and fixed effect model. And to ensure the accurate model
selection, we estimated the fixed effects model and the random effects model separately
and performed the Hausman test on both and report them in Table 4 below
(1)
(2)
Fixed Effect
Randomize Effect
VARIABLES
fertility
fertility
lnGini
-0.932***
-0.780***
(0.134)
(0.102)
-0.035
-0.013
(0.073)
(0.055)
0.034***
0.031***
(0.003)
(0.002)
0.033
0.095
(0.140)
(0.109)
0.174
0.291***
Gender
Age
Ethnicity
Religion
39
Education
Political
Health
Hukou
Gender equality
working
Pensions
Family income
Income level
Car
House
Constant
(0.125)
(0.097)
0.061
0.055
(0.054)
(0.041)
0.187*
0.269***
(0.102)
(0.077)
0.037
0.024
(0.081)
(0.060)
0.750***
0.769***
(0.089)
(0.068)
0.340***
0.305***
(0.073)
(0.056)
0.001
0.001
(0.001)
(0.001)
0.068
0.077
(0.080)
(0.061)
0.056
0.075**
(0.045)
(0.034)
0.113
0.147**
(0.078)
(0.059)
0.202**
0.147**
(0.096)
(0.073)
0.108
0.124
(0.106)
(0.078)
-2.958***
40
(0.453)
Observations
4,191
8,868
Number of id
1,575
3,510
Table 4. Fix Effect Model and Randomize Effect Model
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
According to the result of Hausman test in Stata, it believes that the estimated
coefficients are significantly different between the two models, so the fixed effects
model is the better choice to eliminate the effect of individual effects. Moreover, we
look back at table4 column (1) the coefficient of the Gini is still significant at 1%
confidence level and also perform same negative relation with the fertility intention.
Similarly, to further observe the effect of income disparity on fertility intention after
excluding individual effects, this paper reports the average marginal effect under fixed
effects. (Table 5)
(1)
VARIABLES
Fixed Effect margins
lnGini
-0.013*
(0.0068)
Observations
4191
Table 5. Average Marginal Effect of Fixed Effect Model
41
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
After sample censoring, with the removal of individual effects, table 5 shows that the
average marginal effect in the benchmark regression may be overestimated. In the fixed
effects model, the relationship between income disparity and fertility intentions can be
explained as follows: a 10% increase in the Gini coefficient would result in a 13% drop
in fertility intentions.
6. Robustness Test and Heterogeneity Analysis
6.1 Robustness Test
This section begins with a series of robustness tests on the analysis above. First of all,
following the description in section 4.1, the 2012 and 2015 samples are recombined in
this paper as the regression samples for the robustness test. The sample from 2012-2015
was used as a time subsample to test whether the relationship between income disparity
and fertility intention was consistent with the previous findings. Again, as mentioned
in section 4.1, age is an important influence on fertility intentions, and thus whether the
effect of income gap on fertility intentions is as described in the previous section is the
second direction of the robustness test when faced with different age groups. So here
we select 18 years old to 50 years old, which are suitable ages for having a baby, as the
robustness tests for age subsamples. Furthermore, given the gender division of labor
and cultural environment in China, women are under the double pressure of working
for a salary and having offspring. We therefore need to identify whether the effect on
women is misallocated to men in the overall sample estimates. As a result, we apply
42
gender subsamples as the third way to make the robustness test.
Separate regression estimates for the time subsample, the age subsample and the gender
subsample were used as robustness tests for this paper. And we uniformed use logit
model to estimate the result, then output Table6 as the robustness result.
(1)
(2)
(3)
(4)
Time
Age
Gender
Gender
2012-2015
18-50
Male
Female
VARIABLES
fertility
fertility
fertility
fertility
lnGini
-0.656***
-0.445*
-0.739**
-0.483*
(0.214)
(0.232)
(0.294)
(0.319)
0.094
0.068
(0.095)
(0.106)
Gender
Age
0.024***
(0.004)
Ethnicity
Religion
Education
Political
0.027
0.016
-0.365
0.498*
(0.176)
(0.197)
(0.233)
(0.287)
0.400**
0.348*
0.405*
0.375
(0.172)
(0.193)
(0.234)
(0.256)
0.079
-0.056
0.022
-0.124
(0.070)
(0.077)
(0.093)
(0.100)
-0.156
-0.124
0.019
-0.297
43
Health
Hukou
Gender equality
Working
Pensions
Family income
Income level
Car
House
Constant
Observations
(0.132)
(0.153)
(0.165)
(0.230)
0.051
0.117
0.059
0.317*
(0.105)
(0.120)
(0.135)
(0.162)
0.825***
0.596***
0.930***
0.554***
(0.112)
(0.125)
(0.148)
(0.178)
0.359***
0.446***
0.465***
0.185
(0.095)
(0.107)
(0.124)
(0.151)
-0.002
-0.002
-0.002
-0.007*
(0.002)
(0.003)
(0.003)
(0.004)
0.240**
0.195*
0.224*
0.526***
(0.102)
(0.111)
(0.135)
(0.149)
0.053
0.077
-0.069
0.087
(0.062)
(0.078)
(0.085)
(0.090)
0.018
0.052
0.155
-0.150
(0.102)
(0.115)
(0.135)
(0.156)
-0.011
0.070
-0.129
0.116
(0.132)
(0.143)
(0.175)
(0.207)
0.200
0.225
0.179
0.308
(0.133)
(0.148)
(0.172)
(0.214)
-2.017**
-0.805
0.092
-0.371
(0.828)
(0.948)
(1.064)
(1.124)
3,444
2,353
1,969
1,475
Table 6.Sub-sample robustness tests
44
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
From the table 6 above, we can discover that the Gini coefficient is significant among
the robustness test in general. In all three subsamples, they all indicate that the larger
income gap will lead to the decreasing of the fertility intention. Likewise, we also
calculate the average marginal effect of each subsamples group and list as table 7 below.
(1)
(2)
(3)
(4)
VARIABLES
2012-2015
Age(18-50)
Male
Female
lnGini
-0.092*
-0.072*
-0.103*
-0.068
(0.030)
(0.037)
(0.041)
(0.041)
3444
2353
1969
1475
Observations
Table 7.AME of subsamples
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
From the table 7, we can learn that the overall estimate is worse than the baseline
regression results, but this is due to the decline in sample size versus classification.
Moreover, the marginal effect of income gap shows that in the column (1) 1% change
in the Gini coefficient will result in 9.2% decreasing probability in having children.
Then in the column (2) the fluctuations in the income gap will lead to the 7.2%
decreasing in the fertility intention among the 18 years old to 50 years old group. In the
average marginal effect level, the robustness check also give a strong proof that the
45
widen income gap can depress the fertility intention of the population though it might
have some specific values differences. However, tests on the gender subsample showed
different results. Compared with the column (3) and column (4), the results of this paper
show that the impact of the widening income gap is greater for men than for women.
This is not quite the same as the results of some studies with foreign samples. In studies
that have been conducted in foreign countries, gender-specific surveys have shown that
changes in some independent variables (like housing price, working conditions) should
have a greater impact on women than on men. Because women are the mainstay of
childbirth. But, in the table7, males perform a stronger depressing effects compared
with females. This might be explained as followed: In China, it is difficult for women
to combine childbirth and work at the same time. As a result, when women want to have
children or plan to have children, they move away from work to a certain extent or
become housewives. The increase in the income gap therefore has less impact on
women than on men who are still working.
To sum up, the above tests all indicate that the basic conclusions of this paper are robust
to different subsamples.
6.2 Heterogeneity Analysis
Based on the following considerations, this paper will develop a heterogeneity analysis.
First of all, the preceding analysis is based on an overall sample with a national
perspective. However, as we know, the influence might be limited or different through
the regions. Also the level of development of different regions affects their response to
changes in income disparity. Secondly, there are many micro-level differences between
46
households. These differences can be shown as human capital levels and income levels.
These family-level heterogeneities are also the focus of this paper, and a detailed
exploration of such heterogeneities and family responses will also help to inform more
targeted policy proposals.
6.2.1 Spatial heterogeneity analysis
Based on the unbalanced reality of China's regional development, we break through the
constraints of the "smooth economy" perspective of modern economics, and start from
the "block economy" perspective advocated by spatial economy, and from the
perspective of "block economy", i.e. agglomeration economy, as advocated by spatial
economics. This paper seeks to further identify the spatial characteristics of changes in
fertility intentions under the influence of income gap. Therefore, according to
geographic characteristics, we divided the samples into eastern samples, midland
samples and western samples. Then we use logit model to estimate the coefficient and
the result are shown as table below (Table 8).
(1)
(2)
(3)
east
mid
west
VARIABLES
fertility
fertility
fertility
lnGini
-0.728***
-1.184***
-0.621*
(0.183)
(0.195)
(0.343)
-0.000
-0.042
-0.200
Gender
47
Age
Ethnicity
Religion
Education
Political
Health
Hukou
Gender equality
Working
Pensions
Family income
Income level
(0.079)
(0.105)
(0.150)
0.028***
0.033***
0.031***
(0.003)
(0.004)
(0.007)
0.340
0.523*
0.105
(0.270)
(0.298)
(0.206)
0.396***
0.106
0.219
(0.148)
(0.193)
(0.263)
0.068
0.095
0.012
(0.056)
(0.077)
(0.110)
0.278***
-0.001
0.222
(0.103)
(0.141)
(0.217)
-0.054
-0.088
0.278*
(0.087)
(0.108)
(0.160)
0.975***
0.741***
0.881***
(0.112)
(0.128)
(0.182)
0.193**
0.180*
0.597***
(0.079)
(0.108)
(0.150)
0.001
0.003
0.002
(0.002)
(0.002)
(0.003)
-0.125
-0.095
0.155
(0.100)
(0.124)
(0.150)
0.035
0.068
0.179**
(0.052)
(0.067)
(0.087)
0.122
0.160
0.199
48
Car
House
Constant
Observations
(0.084)
(0.109)
(0.156)
0.134
0.028
0.339
(0.092)
(0.154)
(0.257)
-0.007
-0.008
0.327
(0.101)
(0.138)
(0.251)
-2.060***
-3.264***
-3.891***
(0.688)
(0.892)
(1.197)
3,708
2,567
1,551
Table 8.Differences in fertility intentions under spatial heterogeneity
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
From the table8 we can learn that whatever in each region, it always obey the basic
relation that there is a negative effect on fertility intention with the larger income gap.
And the coefficient of income gap all perform significant in general. Hence, we provide
average marginal effect of logit model to study the differences in detailed and the
marginal effect display in table9.
VARIABLES
lnGini
(1)
(2)
(3)
East
Midland
West
-0.140***
-0.183***
-0.076*
(0.0352)
(0.0308)
(0.042)
49
Observations
3,708
2567
1551
Table 9.Average Marginal Effect of different regions
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Through table9, we can find that there are obvious regional differences. For example,
the midland area shows highest reaction, which 1% change in Gini coefficient can result
in 18.3% depressing fertility intention. On the meanwhile, the western area is the least
reaction area. Eastern area is similar to the national average level (13%). This
phenomenon is undoubtedly related to the differences in the level of economic
development of the various regions.
The two main reasons for this regional variation are the following: economic
development and population movements. For these two reasons, we focus first on the
Western region. Firstly, the overall level of development in the western region lags
behind that of the eastern and midland regions, which means that the impact of the
widening income gap in the western region is not all negative, in other words, the
impact of the widening income gap in the west is less than that of the eastern and
midland regions (referring to the Kuznets Hypothesis). Next, what comes with poor
economic development is population loss. In order to make a living, the population of
the West will move more towards the East and midland regions where the economic
level is better, and middle aged and elder people are left. For these kinds of people, they
prefer offspring rather than earing more money. And this is consistent with finding in
5.1 section. Then it’s easy to understand eastern and midland region. These two regions
50
have higher depressing effect first for the reason that better economic development level.
Larger income gap will give people more pressure and they will be more possible to
give up having a baby. Likewise, the depressing effect is particularly pronounced in the
eastern and midland regions, which have a high in-migration population and where
young new arrivals have no savings or assets to cope with the impact of the widening
income gap.
6.2.2Family heterogeneity analysis
This sub-section will discuss how households handle with changes in income gap from
the perspective of household heterogeneity, both in terms of the level of human capital
and the level of household income
(1) Heterogeneity in the level of human capital
Following the education variables in the previous section, household heads were
divided into sub-samples at different levels of human capital and their responses to
changes in income disparity were explored separately. And the estimate result of four
levels of education samples display as followed table 10.
(1)
(2)
(3)
(4)
No education
primary
secondary
advance
VARIABLES
fertility
fertility
fertility
fertility
lnGini
0.085
-0.938***
-0.685***
-0.854***
51
Constant
Observations
(0.431)
(0.149)
(0.187)
(0.202)
-1.451
-3.148***
-2.985***
-4.637***
(1.553)
(0.632)
(0.972)
(1.035)
919
4,434
1,749
1,775
Table 10.Differences in fertility intentions by education level
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Compared with other three levels of the education, the column (1) in table 10 shows
apparently different. Only sample with no education experience provide a positive
relation between income gap and negative relationship at all other education levels.
Then further report the average marginal effects of each education levels in table 11.
VARIABLES
lnGini
Observations
(1)
(2)
(3)
(4)
No education
Primary
Secondary
Advanced
0.079*
-0.136***
-0.143***
-0.176***
(0.0352)
(0.023)
(0.039)
(0.041)
919
4434
1749
1775
Table 11.Average Marginal Effect of Education
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
From the column (1) in the table 11, people with no education seem like getting stuck
52
in a wired circle: The worse their living are, the more willing they are to have children.
Fortunately, this is just a minority and with the rising of the education level, the
depressing effect of the income gap shows more serious. It might be explained as:
Families invest in human capital for better income levels instead of having more
children. Meanwhile, higher education level means later entry into labor market, which
implies less working experience and less saving. Therefore, they have no ability to
response to the income gap changes. Furthermore, another possible explanation is that
the low level of human capital makes it difficult for women to bear the cost of raising
two or more children. Women with primary education are more likely to appreciate the
importance of human capital, i.e. the quality of their children, and invest more in their
children's education, thus discouraging them from having children. And this might
explain why depressing effect is more significant among the higher education level
groups.
(2) Heterogeneity of income levels
In section 5.1, we already know that Families with better economic status also have
higher fertility intentions. But what is the response of households to changes in income
disparity at different income levels? Hence we make heterogeneity analysis here. The
household sample was divided into below average, and above average (including
average). Then report the logit estimation result and average marginal effects as
followed.
(1)
(2)
(3)
(4)
Below
Above
Below
Above
53
VARIABLES
fertility
fertility
AME
AME
lnGini
-0.791***
-0.740***
-0.147***
-0.103***
(0.157)
(0.124)
(0.026)
(0.020)
3,476
5,392
3476
5392
Observations
Table 12. Logit estimation and AME of income level
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
According to the column (1) and column (2) in table 12, we can easily discover that
income gap still shows a negative impact on fertility intention although we make a
distinction on income level. It seems like that regardless of income level, they will
receive the negative impact of the widening income gap. However, do households at
lower and higher income levels receive the same impact when faced with shocks?
Absolutely not. Column (3) and column (4) provides detailed marginal effect on two
different income levels. It shows that for the family belongs to low income level, the 1%
change in income gap can make them reduce 14.7% probability on having children. On
the contrast, this effect is only 10.3% decreasing for the family with higher income
level. Actually, this kind of impact is quite similar to the “Housing wealth effect”
(Dettling and Kearney, 2014), which means Households with multiple properties are
better able to cope with rising house prices. Likewise, this difference can be explained
as followed: compared with low income level household, richer family income level
have more preserved assets and being able to offset some of the impact of the widening
income gap because they are already above the average income level. In contrast,
54
households at lower income levels are left with heavier pressures and costs of living as
the income gap widens, with the lacking of the effective means of spreading the risk.
Meanwhile, regression above also indicate that the continued widening of the income
gap is not a good thing for the whole society, no matter what level of income a
household has.
7. Conclusion and Policy Suggestion
This paper uses the data of Chinese General Social Survey from 2010 to 2017 to study
the impact of changes in income gap on the fertility intention, and discuss the
endogenous problem through the fixed effect model. Furthermore, by the three subsamples, we also make the robustness test to ensure the regression result is stable and
consistent. Finally, this paper provide heterogeneity analysis of spatial and household
dimension. Through the above analysis, we have following key findings:
(1) Increasing income gap shows a negative relationship with fertility intentions, and
for every 1% increase in the Gini coefficient, the fertility intention will decrease 13%
approximately. Meanwhile, some control variables like age show a unique variation
based on Chinese reality.
(2)In term of spatial level, it reflects the impact of income gap changes has clear
difference among eastern, midland and western region. And the possible reason might
be economic development and population movements.
(3) Heterogeneity analysis at the family level indicates that rising education level does
55
help to break the wired circle that the worse their living are, the more willing they are
to have children. However, education levels do not directly handle with the effects of
changes in income disparities. Moreover, although a widening income gap is a bad thing
for society, families with higher income levels are better able to response with the shock
of the income gap on fertility intentions.
Based on the above findings, this paper makes the following policy suggestions:
(1) Improve redistributive income policies and strive to ensure that income disparities
are in a reasonable range. Reduce the negative impact on the policy of encouraging
childbirth due to the widening of the income gap.
(2) Balanced regional development to reduce the gap between the economic
development levels in the East, Middle and West. At the same time, helping the western
region to solve the problem of net population outflow
As a concluding remark, this paper also has some disadvantages: First of all, for the
measurement of income gap, this paper mainly uses the Gini coefficient to measure the
income inequality. However there are many other ways to measure inequality, and
whether the conclusions of this paper still hold under other measures is a direction that
can be remedied in the future. Meanwhile, the paper lacks analysis of urban-rural
differences, in particular the question of whether the impact of changes in the urbanrural income gap is consistent for urban and rural samples. Finally, on the endogenous
problem, the instrumental variables approach to are not better explored due to data and
methodological limitations.
56
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