Internet Use and Fertility Intention in China
Sho Komatsu
Asian Growth Research InstituteWorking Paper Series Vol. 2024-09
March 2024

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Asian Growth Research InstituteInternet Use and Fertility Intention in China

Sho Komatsu
Asian Growth Research Institute

Abstract
China’s fertility rate has been on a downward trend since the adoption of the one-childpolicy in 1979. Internet use is among the promising technological changes that may solve
low fertility issue by income effect and improving work-life balance which promotes
fertility intention. However, Internet use may also reduce fertility intention by cultivating
modern value of fertility and substitution effect. Therefore, whether Internet use improveor hinder fertility intentions must be empirically tested. As no studies have examined
impact of Internet use on fertility intention addressing unobserved heterogeneity, thisstudy uses panel data and instrumental variables method to fill the gap in the literature.
Using two-wave panel data from the 2014 and 2018 China Family Panel Studies, thisstudy investigates the impact of Internet use on fertility intention. This study finds that
Internet use improves fertility intention. Moreover, while the frequency of Internet use
does not improve fertility intention, attitude toward the Internet does. Furthermore, there
are significant differences by age, marital status, gender, hukou, and educational
background. Finally, Internet use affects fertility intention through higher householdincome.

Keywords: Internet use; Fertility intention; China; Panel data
JEL Classification Codes: C23; J13

1.

Introduction

China’s fertility rate has been on a downward trend since the adoption of the one-childpolicy in 1979. For nearly two decades, China’s fertility has been below the level that1

allows the population to replace itself in the long run 1 (Zheng, Cai, Wang, & Gu, 2009).
Total fertility rate (TFR) has decreased from 6.385 in 1965 to 2.309 in 1990, and further
to 1.665 in 2015. According to the latest data from the Seventh National Census, TFR
reached 1.3 in 2020, already at a low level. China has already fallen into the low fertility
trap (Yan et al., 2021). Low fertility rate is considered to be a major contributor to thedecline in the working-age population and population aging (Liu, Xing, & Zhang, 2020).
Despite the gradual relaxation of the birth control policy in recent years 2, the expected
increase in the fertility rate has not yet been observed (Chen, 2016; Mu, 2017; Song,
2016; Zhong, 2016). After the issue of the “universal two-child” policy, the fertility rateincreased by 12.95‰ and 12.43‰ in 2016 and 2017, respectively. However, the followup impetus of the policy was insufficient and played a limited role in boosting fertility
behavior. The birth rates in 2018 and 2019 decreased by 1.17‰ and 1.63‰, comparedwith the 2011–2015 period.
China’s low fertility issue could be due to low fertility intention. The soaring
education costs, uncertainty about the future, and inadequate child-rearing environments
could hinder the desire to give birth. As a preliminary step to increasing the number ofbirths and birth rate, it is necessary to create a society in which people actively want tohave and raise children. Moreover, fertility intention has long been regarded as a predictorof fertility behavior (Schoen et al., 1999; Jiang et al., 2016). Unlike fertility behavior,fertility intention is observable for not only women of reproductive age but also men,
middle or advanced age class, and singles. Fertility behavior may be affected by fertility
intention of surrounding people and society. Therefore, it is essential for China toinvestigate the factors that promote or inhibit fertility intention as China has joined thegroup of below-replacement-fertility countries in the world.
Internet use is among the promising technological changes that may solve this low
fertility issue improving work-life balance and increasing household income (income
effect). However, Internet use may also reduce fertility intention cultivating more modern
1

Under modern low-mortality conditions, which typify China, replacement-level fertility isconventionally defined as a total fertility rate (TFR) of 2.1 births per woman.
2 The Chinese government passed the “selective two-child” policy and the “universal two-child”
policy, respectively, in 2013 and 2015.
2

value of fertility and increasing opportunity costs of having children (substitution effect).
Therefore, in this study, the impact of Internet use on fertility intention is to be empiricallytested. This study finds that Internet use has a significantly positive impact on fertility
intention. This study contributes to the existing literature in four ways. First, using twowave longitudinal data, this study addresses heterogeneity and other endogeneity
problems together in the context of China for the first time. Second, this study compares
the effects of Internet use by gender, age groups, marital status, urban and rural household
registers (hukou), and educational background heterogeneous groups. Third, while macro
approach to the effects of Internet use on fertility intention appears in the literature, micro
and a combination of both, as in this study, are few 3. Fourth, this study investigates theeffects of Internet use on fertility intention through the mediation effect model and four
mediators (household income, working hours, values regarding education attainment,
getting married, and housework hours) to improve understanding of the mechanism of
the examined relationship.

2.

Literature review

Although there are several studies regarding influential factors of fertility intention, thesestudies mainly focus on particular population such as urban residents (e.g., Liu & Gong,2020), women (e.g., Zheng et al., 2016; Liu et al., 2020), women attending outpatient
gynecology clinics (e.g., Lau et al., 2018), and within one province (e.g., Liu & Lummaa,
2019; Wei et al., 2018; Zheng et al., 2009). Moreover, studies that focus on fertility
intention among Chinese women in the general population at the national level are still
limited (e.g., Liu et al., 2020; Yan et al., 2021). Although Liu et al. (2020) and Wang etal. (2021) investigate the influential factors of fertility intention using nationally
representative survey project cross-sectional data, to the best of our knowledge, there are
no studies on the influential factors of fertility intention in China using panel data toaddress unobserved heterogeneity. Therefore, this study uses nationally representative
micro panel data.
3

A macro approach examines Internet use as a whole and a micro approach focuses on specific online
activities (Nie et al., 2017).
3

Although policy, institutional, cultural, socioeconomic, and demographic variables are
all important factors that influence fertility outcome (Zheng et al., 2009), many scholars
have focused on the effects of income and social security on fertility intentions (Liu &Gong, 2020) and those of general socioeconomic factors such as household income,hukou, and education attainment. Moreover, while there are some studies on the impact
of Internet use on fertility decisions in developed countries (e.g., Billari et al., 2019), withsome exceptions, related studies in the Chinese context are few. Using German panel data,
Billari et al. (2019) find positive effects of broadband Internet on the fertility of highly
educated women aged 25–45. Using Chinese cross-sectional data, Li et al. (2021)
examine the relations between Internet use and fertility behavior from the perspective ofdigital economy and find that Internet use has a significant negative impact on fertility
behavior as opportunity costs of having children (substitution effect) are more significant
than positive effects by increasing labor income (income effect). A more specificdescription follows. They find that Internet use increases labor income, suggesting
income effect; Internet use decreases working hours, suggesting that Internet use helps
workers to be more productive and gives them more time to choose their “work-family”
allocation. Although Internet use has no significant effect on the availability of work,
workers who use the Internet frequently are more likely to be self-employed and flexibly
employed. Internet use reduces women’s physical disadvantage in the labor market andincreases women's employment opportunities and income, which increases women’s
opportunity cost of having children and reduces fertility through the substitution effect.
Thus, both income and substitution effects are present, but the substitution effect is moresignificant. Closely related to this study, using Chinese cross-sectional data, Wang et al.
(2021) investigate the impact of Internet use on fertility intention and find that individuals
who use the Internet and the more frequently they use it have lower fertility intention.
Further research finds that reproductive experience and intergenerational digital divide
proxied by difference in average educational attainment between individuals and theirparents have negative moderating effects. However, they use cross-sectional data, whichcannot fully control for unobserved characteristics. Therefore, this study can provide new
and rich evidence using panel data.
42.1 Channels of influence of Internet use on fertility intention
It can be assumed that Internet use has both positive and negative effects on fertility
intention. As a related study, Billari et al. (2019) discuss three potential mechanisms of
the Internet on fertility: information, marriage, and work–family balance. Although they
focus on fertility behavior, the potential mechanisms can also be applied to fertility
intention. Regarding first mechanism, the Internet provides access to information about
contraceptive behaviors and about the possible life course consequences of the choice tobecome a parent. Regarding second mechanism, the Internet has been described as ‘thenew social intermediary in the search for mates’ (Rosenfeld & Thomas, 2012). As Bellou(2015) states, the Internet may decrease search costs and increase the rate of partnershipoffers. Regarding third mechanism, high-speed Internet may affect labor force
participation and work-family balance (Wajcman, 2015; Dettling, 2017), thereby enabling
individuals to balance work and parenting more easily. Moreover, several studies haveinvestigated the impact of Internet use (access) on household income (earnings) and findthat Internet use increases household income (e.g., DiMaggio & Bonikowski, 2008;
Chang & Just, 2009; Chang, Lambert, & Mishra, 2008; Ma, Nie, Zhang, & Renwick,2020). Therefore, it is implied that there is income effect. Moreover, Wang et al. (2021)
discuss three potential mechanisms of the Internet on fertility intention: information cost,childbearing history, and Intergenerational “digital divide”. Regarding first mechanism
which can further exacerbate individual feelings of anxiety and trust in others. Regarding
second mechanism, extreme discussion on the Internet reflects the general anxiety about
having a child and heightens the anxiety about having another child. Regarding third
mechanism, large intergenerational “digital divide” leads to more family relationshipconflicts for individuals who use the Internet frequently, which in turn may inhibit theirfertility intention. Other than income and information effects, Li et al. (2021) findsubstitution effect through increases in opportunity costs of having children. Based on theabove arguments, this study discusses following five potential mechanisms of Internet
use on fertility intention: information, marriage, work-family balance, income, andsubstitution effects.
Regarding positive effects, marriage, work-family balance, and income effects
5

can be described as follows.
Transition to marriage effect. The arrival of the Internet has partly displaced traditional
pathways of forming partnership, thus Internet use may affect the transition to marriage,and more generally, the likelihood of finding a partner to become the parent of a joint
child (Rosenfeld & Thomas, 2012). Bellou (2015) states that the Internet has the potential
to reduce search frictions allowing individuals to identify faster more available options
and finds that the Internet has contributed to more marriages. It is implied that transition
to marriage increases fertility intention.
Work–family balance effect. High-speed Internet use leads to increase in labor force
participation and it is suggested that home Internet facilitates work-family balance(Dettling, 2017), thereby allowing individuals to reconcile work and parenthood moreeasily (Billari et al., 2019). Billari et al. (2019) state that by allowing individuals to work
remotely, access to high-speed Internet can reduce commuting times, reduce absenteeism,and increase productivity and find that the Internet increases the likelihood of working
from home, encourages part-time work, and reduces working hours. Dettling (2017) andBillari et al. (2019) state that telework (the Internet) can also reduce childcare costs andallow individuals to spend more time with their families, reducing the nonpecuniary costsof working. It is implied that work-family balance through telework increases fertility
intention.
Income effect. Several studies have investigated the impact of Internet use (access) on
household income (earnings) (e.g., DiMaggio & Bonikowski, 2008; Chang & Just, 2009;
Chang, Lambert, & Mishra, 2008; Ma, Nie, Zhang, & Renwick, 2019). Internet use
increases human capital, which improves productivity, which leads to higher income.
Moreover, as Bloom et al. (2015) provide experimental evidence that telework can
improve worker productivity, telework might also increase wages. Internet use may alsointervene in the earnings determination process by promoting the expansion and use ofsocial networks (Lin, 2001). According to Butz and Ward (1979), higher male wages raisehousehold income and increase demand for children. As increase in the female wage rate
also brings about an increase in income, if the income effect (the effect of an increase in
the number of births due to an increase in the income level through an increase in wages)
6

exceeds the substitution effect (the effect of a decrease in the number of births due to anincrease in the cost of raising children), the number of births will rather increase (Adachi& Nakazato, 2017). This income effect on fertility is observed in existing studies (e.g.,
Takayama et al., 2000). It is implied that household income increase fertility intention.
Regarding negative effect, substitution and information effects can be describedas follows.
Substitution effect. Substitution effect is the effect of an increase in the wage rate on
childbearing through an increase in the cost of raising children. According to Butz andWard (1979), substitution effect is the negative effect of higher wages for married womenon the demand for children. Internet use reduces fertility through the substitution effect.
According to time allocation theory, individuals allocate their time rationally among paid
labor, unpaid household work, and leisure to maximize utility. Internet use reduces
women’s physical disadvantage in the labor market and increases women’s employment
opportunities and income, which makes the opportunity cost of having children higher
for women, and women devote more time to paid work, such as part-time work, while
squeezing out time for having children, reducing fertility through the substitution effect(Li et al., 2021).
Information effect. The Internet provides unprecedented access to information about
contraceptive behavior and the possible life-course consequences of the choice to becomea parent. Moreover, the Internet may increase the quality and quantity of information
available on childcare costs and benefits, as well as on health and sexual practices. (Billari
et al., 2019). Similarly, Li et al. (2021) state that the Internet provides general knowledge
about contraception and what life might be like after becoming a parent, and it can also
spread the cost of parenting through interactive communication, which may cause“fertility panic” among urban population who worry about whether they have enoughbudget and energy to have children. Moreover, the Internet may weaken traditional values
held towards childbearing such as ‘bringing up children for the old age’, ‘predominanceof men over women’, and ‘more children, more happiness’ to some extent through theabove information channels. Therefore, it is implied that information obtained throughthe Internet changes value of fertility to less traditional one, and thus decreases fertility
7intention. Furthermore, Wang et al. (2021) state that the Internet brings information costs
through information overload, information conflict, and information narrowing, which
can further exacerbate individual feelings of anxiety and trust in others, reduce fertility
utility, and thus affect individual fertility intention negatively. On the other hand, Guldi
and Herbst (2017) state that increased availability of information affects the teen birthrate, but in a potentially ambiguous direction. They find that the Internet has decreasing
effect on fertility at younger ages possibly through information mechanism: changes insexual activity, contraception use, abortion, or some combination. However, thisdecreasing effect may be reversed at older ages, if information seeking and social learning
are tied to the proactive achievement of higher desired fertility at later ages (Billari et al.,2019).

3.

Methodology

3.1 ModelThis study uses a fixed effects (FE) model, a fixed effects ordered logit model, and arandom effects ordered logit model to address the heterogeneity problem. Moreover, thisstudy also uses a fixed-effects Poisson model. The fixed effects Poisson model is a fixedeffects model used when the explained variable is not a continuous quantity but a variable
that takes discrete values and is not a negative number (count data). Cluster-robust
standard errors 4 are used to determine the correlation of individual errors. The model isexpressed by Eq. (1):(1)
𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖 = 𝑎𝑎 + 𝛽𝛽𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖𝑖𝑖 + 𝛾𝛾𝑋𝑋𝑖𝑖𝑖𝑖 + 𝑣𝑣𝑖𝑖 + 𝑢𝑢𝑖𝑖𝑖𝑖 ,where i denotes the individual; t denotes year; 𝐹𝐹𝐹𝐹 is the indicator of fertility intention.
𝐼𝐼𝐼𝐼𝐼𝐼 is the indicator of Internet use, which is a key independent variable; 𝑋𝑋 is a set ofcontrol variables; 𝛽𝛽 and 𝛾𝛾 are the coefficient of variables; 𝑣𝑣𝑖𝑖 denotes an individual-

specific time-invariant factor, and 𝑢𝑢𝑖𝑖𝑖𝑖 denotes an idiosyncratic error.

Second, although the FE model can address the heterogeneity problem, there

may also exist other endogeneity problems, such as the inverse causality problem (e.g.,
an individual who has higher fertility intention is likely to use the Internet). Individuals
4

The standard errors are clustered at individual level.
8

who use the Internet may have systematically different characteristics from non-Internet
users, i.e., there may be self-selection. If those differences are unobserved but correlated
to the outcome variable of fertility intention, then Internet use dummy is endogenous.
Some previous literature (e.g., Chang & Just, 2009; Hübler & Hartje, 2016) state that theInternet use variable is potentially endogenous because a household decides whether to
use the broadband Internet service. Based on the above, this study uses the instrumental
variables (IV) method to address this endogeneity problem, as expressed by Eqs. (3)–(5):(3)
𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖𝑖𝑖 = 𝑎𝑎 + 𝛽𝛽𝑧𝑧 𝑍𝑍𝑖𝑖𝑖𝑖 + 𝛾𝛾𝛾𝛾𝑖𝑖𝑖𝑖 + 𝜇𝜇𝑖𝑖𝑖𝑖 ,� 𝑖𝑖𝑖𝑖 + 𝛾𝛾𝛾𝛾𝑖𝑖𝑖𝑖 + 𝜀𝜀𝑖𝑖𝑖𝑖 ,(4)
𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖 = 𝑎𝑎 + 𝛽𝛽𝐼𝐼𝐼𝐼𝐼𝐼 𝐼𝐼𝐼𝐼𝐼𝐼(5)
𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐(𝑍𝑍, ɛ) = 0, and 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐(𝑍𝑍, 𝜇𝜇) ≠ 0,
where 𝐼𝐼𝐼𝐼𝐼𝐼 is the probability of Internet use and 𝑍𝑍 represents the IV.

This study also estimates an interaction term to focus on the group difference (e.g., marital
status, gender, hukou) in the effect of Internet use on fertility intention, as shown in Eq.
(6) –(9):(6)
𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖𝑖𝑖 = 𝑎𝑎 + 𝛽𝛽𝑧𝑧 𝑍𝑍𝑖𝑖𝑖𝑖 + 𝛾𝛾𝛾𝛾𝑖𝑖𝑖𝑖 + 𝜇𝜇𝑖𝑖𝑖𝑖 ,(7)
𝐼𝐼𝐼𝐼𝐼𝐼 ∗ 𝐺𝐺𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖 = 𝑎𝑎 + 𝛽𝛽𝑧𝑧 𝑍𝑍𝑖𝑖𝑖𝑖 + 𝛽𝛽𝑧𝑧 𝑍𝑍 ∗ 𝐺𝐺𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖 + 𝛾𝛾𝛾𝛾𝑖𝑖𝑖𝑖 + 𝜇𝜇𝑖𝑖𝑖𝑖 ,𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖 = 𝑎𝑎 + 𝛽𝛽1 𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖𝑖𝑖 + 𝛽𝛽2 𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝑖𝑖𝑖𝑖 +𝛽𝛽3 𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖𝑖𝑖 × 𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝑖𝑖𝑖𝑖 + 𝛾𝛾𝑋𝑋𝑖𝑖𝑖𝑖 + (8)𝑣𝑣𝑖𝑖 + 𝑢𝑢𝑖𝑖𝑖𝑖 ,(9)𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐(𝑍𝑍, ɛ) = 0, and 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐(𝑍𝑍, 𝜇𝜇) ≠ 0,𝛽𝛽3 denotes the coefficient of the interaction term Internet use × group variables, whichindicates group differences in the impact of Internet use on an individual’s fertility
intention. In heterogeneous analysis, we especially focus on the coefficient of 𝛽𝛽3.

Finally, this study uses the mediation model to investigate the channels of the influenceof Internet use on fertility intention, as expressed by Eqs. (10)–(12).
(10)
𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖 = 𝛽𝛽0 + 𝛽𝛽1 𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖𝑖𝑖 +𝛽𝛽2 𝑋𝑋𝑖𝑖𝑖𝑖 + 𝜀𝜀𝑖𝑖 ,𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖 = 𝛽𝛽0′ + 𝛽𝛽1′ 𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖𝑖𝑖 + 𝛽𝛽2′ 𝑋𝑋𝑖𝑖𝑖𝑖 + 𝜀𝜀𝑖𝑖′ , and𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖 = 𝛽𝛽0′′ + 𝛽𝛽1′′ 𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖 + 𝛽𝛽2′′ 𝑋𝑋𝑖𝑖 + 𝛽𝛽3 𝑀𝑀𝑀𝑀𝑖𝑖 + 𝜀𝜀𝑖𝑖′′ ,

(11)
(12)

where 𝑀𝑀𝑀𝑀 represents mediator variables. When 𝛽𝛽1′ and 𝛽𝛽3 are statistically significant,as well as 𝛽𝛽1′′ changes (e.g., become smaller) compared with 𝛽𝛽1, we can say that Internet
use affects fertility intention, possibly through mediator 𝑀𝑀𝑀𝑀. The indirect effect is 𝛽𝛽2′×
𝛽𝛽3.
3.2 Data and variables

This study uses data from the China Family Panel Studies (CFPS), a national longitudinalsurvey project that collects data at the individual, family, and community levels launched9in 2010 by the Institute of Social Science Survey of Peking University, China. The
baseline national survey was launched in the 25 provinces/municipalities/autonomous
regions, and it successfully interviewed members of 14,960 households. A total of 33,600
adults and 8,990 youths were interviewed (Xie & Hu, 2014). The CFPS has five waves
from 2010 to 2018; this study uses the two-wave longitudinal survey data (2014 and 2018)
because only these two waves have questionnaire items on fertility intention. The samplesof CFPS are 37,147 (2014) and 37,354 (2018). This study selects samples aged 16 years
and over and exclude missing values.
3.2.1 Dependent variable
The dependent variable is the fertility intention. Arnocky et al. (2012) define individual
fertility intention as the desire to have or not to have children, or the desire for a specificnumber of children over the lifespan. As with existing studies (e.g., Liu et al., 2020; Meng
& Lyu, 2021; Zheng et al., 2016; Zhu & Hong, 2021), this study uses ideal number ofchildren as an indicator of fertility intention. Ideal number of children is based on thequestion “What’s your ideal number of child.”
3.2.2 Key independent variable
The key independent variable is (i) the dummy variable for Internet use (1 = “used” and0 = “did not use”) based on the question “Do you use Internet,” and (ii) hours of Internet
use. Moreover, attitude toward the Internet captured by asking respondents the degree ofimportance of the Internet for collecting information (5 = “very important” and 1 = “veryunimportant”).
3.2.3 Control variables
Referring to previous studies, this study employed the following control variables: (1)education attainment (years of schooling); (2) age and age squared term; (3) sex (maledummy); (4) self-reported health status dummy variables (excellent, very good, good, fair,and poor); (5) marital status (having married dummy); (6) hukou (urban hukou dummy);(7) employment status (working dummy); (8) residence (urban area dummy); (9) numberof family members; (10) absolute income (household income and its squared term); (11)public pension enrollment, (12) public medical insurance enrollment, (13) the provincial
dummy, and (14) year dummy variables.
103.2.4 Instrument variable
Two types of IVs have been used in previous studies on Internet use and fertility: (i)Internet penetration rate in a province (Li et al., 2021; Wang et al., 2021); (ii) distance ofa household residence from the main distribution frame (MDF) (Billari et al., 2019). This
study performed a set of tests to select an appropriate IV. As the test results of endogenous
variables and weak identification show that the number of base stations of mobile phones
is a more appropriate IV, this study uses it in the analysis. This study also uses the Internet
penetration rate in a province as an IV, but this instrument did not pass a weakidentification test. The second condition for a valid IV, the exclusion restriction, isdifficult to test. To address this problem, this study adopts the recent Conley et al. (2012)
bounds approach. Although our IV plausibly satisfies the exclusion restrictions, we reportresults from the Conley et al. (2012) bounds approach, under the assumption that the
instruments may violate the exclusion restrictions. It specifies a support of possible valuesfor 𝜃𝜃𝑗𝑗 ∊ [-δ, +δ], where δ > 0 can be arbitrarily small. Under this assumption, we
estimate the lower and upper bounds for the estimation of 𝛽𝛽1. Data on the number of basestations of mobile phones in a province are obtained from the China Statistical Yearbook.
Tables 1 provides the summary statistics of the variables included in the analysis.
Table 1 Near here
4.

Results

4.1 The impact of Internet use on fertility intention
Table 2 presents the results estimated using a FE model, a fixed effects ordered logit
model, a random effects ordered logit model, a fixed-effects Poisson model, and a FE-IV
model. While columns (1), (3), (5), (7), and (9) do not control for family-level variables,
columns (2), (4), (6), (8), and (10) do.
Table 2 Near here
The coefficient of Internet use is statistically significant and positive based on a
FE model, a fixed effects ordered logit model, a random effects ordered logit model, anda fixed effects Poisson model. The results are robust in a FE-IV model. Using Conley etal. (2012) approach, even under the condition that our IV violates the exclusion restriction,11

our results are still robust 5 . These results suggest that addressing both individual
heterogeneity and endogeneity problems together, Internet use statistically significant
positively affects fertility intention. In other words, these results imply that Internet use
significantly increases ideal number of children. Calculating the marginal effects of fixed
effects ordered logit model and those of random effects ordered logit model, the results
show that those who use the Internet increases their fertility intention to have threechildren by 0.005-0.006 and 0.003-0.004 units, respectively. The results are consistent
with Billari et al. (2019) which find that broadband Internet has a positive impact on
fertility behavior in Germany. On the other hand, the results are different from Wang etal. (2021), which is the only study on the impact of Internet use on fertility intention in
the Chinese context. However, it is not surprising as Wang et al. (2021) do not address
individual heterogeneity problem using different data source: cross-sectional data from
Chinese General Social Survey.
Next, this study examines the impact of the frequency of Internet use and attitude
toward the Internet for further enrichment of the findings. While the frequency of Internet
use is non-significant 6, attitude toward the Internet is statistically significant and positivein all models. Therefore, it is implied that those who think that information gathering via
the Internet is more important will substantially increase the number of ideal children.
The results are shown in Table 3.
Table 3 Near here
4.2 Heterogeneity analysis
Third, this study explores the heterogeneous effects of Internet use using
subsample analysis. Regarding the heterogeneous group, there are significant differencesby age, marital status, gender, hukou, and educational background. The results are shownin Table 4 (age and gender), Table 5 (hukou), and Table 6 (education and marital status).

The estimated bounds are reported for 95 percent confidence intervals in Table 9, with δ = 0.0001,
0.0005, 0.001, 0.005, and 0.01. The reported results are from FE-IV estimators. The results show thatthe estimated bounds do not vary significantly with the value of δ. Moreover, none of the 95 percent
confidence intervals contain zero.
6 These results are not listed in the paper owing to the limited space; they are, however, available uponrequest.
12
5

Based on a FE model, significant positive impact is observed among all age groups, male,those who are married, have rural hukou, and graduated from high school or below.
Moreover, based on a FE-IV model, significant positive impact is observed among age
group under 49 years old, female, those who are married, have rural hukou, and graduatedfrom high school or below. It is implied that age group under 49 years old tend to use theInternet more frequently and get more information, thus significant positive impact isobserved. Moreover, the significant positive impact of Internet use among those who aremarried indicates that childbirth and child rearing are perceived as more realistic options,and that the positive information effect of Internet use is more pronounced. Furthermore,significant positive impact of Internet use observed among those who have rural hukou
or graduated from high school or below indicates that Internet use may reinforce
traditional value of fertility such as ‘more children, more happiness’ for those who live inrural areas or less educated.
Furthermore, the results of heterogeneous analysis on the effects of Internet use
on fertility intention are shown in Table 7. The regression coefficient of the interaction
term of Internet use and hukou status is estimated to be negative (-0.066) and that ofInternet use is estimated to be positive (0.047) based on a FE model. Although theregression coefficients of Internet use are positive in Column (1), given the urban hukou
residents dummy, dFI/dINT = 0.047-0.066*1 = -0.019, Internet use impact on fertility
intention of urban hukou residents is negative. However, the regression coefficient of theinteraction term of Internet use and hukou status is estimated to be negative (-0.570) andthat of Internet use is estimated to be positive (1.411) based on a FE-IV model. Therefore,
while it is implied that Internet use decreases fertility intention of those who with urban
hukou, addressing endogeneity issues, Internet use increases fertility intention of urban
hukou residents. On the other hand, the coefficient of the interaction term of Internet use
and marital status, gender, and educational background is non-significant, respectively.
4.3 Channels of the influence of Internet use on fertility intention
Finally, this study investigates the mechanism of the effect of Internet use on
fertility intention based on the mediation effect model. The results are shown in Table 8.
Five potential effects moderators (household income, working hours, values regarding13

educational attainment, marital status, and housework hours) are used. One channel in themechanism of relationship between Internet use and fertility intention—increases inhousehold income—are found to be significant. The indirect effect coefficient of Internet
use to increase household income to then promote fertility intention is 0.112×-0.008,
which is approximately -0.001. It is implied that positive impact of Internet use on fertility
intention decreases due to negative mediating impact of household income on fertility
intention. The results are consistent with related existing studies showing positive effectsof computer use, smartphone use, and Internet use on wage and household income (e.g.,
Krueger, 1993; Ma, Grafton, & Renwick, 2020; Mao, Zeng, & Hu, 2018; Chang & Just,
2009) and those of showing negative effects of household income on fertility intention(e.g., Pan & Ning, 2010; Liu & Gong, 2020).

5.

Conclusions and policy implications

Using the data from the CFPS 2014 and 2018 to address individual heterogeneity andother endogeneity problems simultaneously, this study investigates the impact of Internet
use on fertility intention and clarified the mechanisms. The main conclusions are as
follows: First, the impact of Internet use on fertility intention is statistically significant
and positive based on a FE model, fixed effects ordered logit model, a random effects
ordered logit model, and a fixed-effects Poisson model. The results are robust as a FE–IVmodel show that, when addressing heterogeneity and other endogeneity problems,Internet use positively affects fertility intention. Robustness checks using attitude toward
the Internet measured as the degree of importance of the Internet for collecting
information confirm these conclusions. The results are consistent with Billari et al. (2019),
but not with Wang et al. (2021), the only study using cross-sectional data, which showsthat Internet use negatively affects fertility intention. The findings in this study indicatethat there is an analysis bias when using cross-sectional data. In this study, we provide
evidence on the causal association between Internet use and fertility intention based on
two-wave panel data analyses.
Second, regarding the heterogeneous group, there is a significant difference by
age cohort, marital status, hukou status, and educational attainment. There exists a
14

positive relationship between Internet use and fertility intention among the younger
generation whose age are under 49, those who are married, those who have rural hukou,
and those who graduated from high school or below.
Third, the influence of Internet use on fertility intention is mediated byhousehold income effect channel. Internet use increases household income which
negatively affects fertility intention. The results are consistent with existing studies
showing that Internet use increases household income (e.g., Ma et al., 2020) andhousehold income decreases fertility intention (e.g., Liu & Gong, 2020).
The policy implications from these empirical results can be considered asfollows: It is found that Internet use positively affects fertility intention. Therefore, online
media policies to control or decrease negative information on childcare (i.e., soaring
education costs and fiercely competitive university entrance examinations) on the Internet
platform should be discussed. It is also suggested that the Chinese government increases
positive information on childcare (i.e., expansion of parental leave, child allowance) andadvertisement (i.e., pronatalism 7) on the Internet platform.
It is also found that although Internet use positively affects fertility intention, it
also increases household income which decreases fertility intention. Policymakers mustthen develop strategies to support telework and working from home to improve fertility
intention. It is also suggested for policymakers to expand education expenditure andestablish more childcare facilities to reduce opportunity costs of having children. Given
that Internet use is shown to greatly increase fertility intention of those who are married,
have rural hukou, graduated from high school or below, expanding services to thesegroups may increase the overall fertility intention of nations. It should be noted that
attitude toward Internet use has significant positive impact on fertility intention,
indicating the need for better public awareness on the Internet as a useful source of
information on childbirth and child care.
The limitations of this study are as follows. First, although this study used the
FE and IV methods to address individual heterogeneity and other endogeneity problems
7

In Yueyang city, Hunan, the statistical department has published recommendations for birthincentives.
15that were not addressed simultaneously in previous studies, empirical studies of the causal
relationship between Internet use and fertility intention based on other methods (e.g., thequasi-experiment method) are future issues to be addressed. Second, although this study
investigated the mechanisms of Internet use on fertility intention using four mediators,
we encourage more research on discovering and analyzing other possible mechanisms,
once richer data is available.

16

Tables
Table 1 Summary statistics
Variable

Obs

Fertility intention

48,035

2.063

0.844

0

12

Internet use

48,232

0.384

0.486

0

1

Internet hours (hours)

19,59012.387

11.891

0

168

Importance of Internet as communication path

48,161

2.398

1.605

1

5

Household income (RMB)

52,500 59273.840 80261.770

0 4270560

Household income per capita (RMB)

52,085 20393.140 42767.950

0 3300000

Individual income (RMB)

24,300 22333.530 31070.860

0840000Age54,30147.77916.711

16100

Gender

54,308

0.496

0.500

0

1

Urban hukou

50,735

0.255

0.436

0

1

Marital status

47,416

0.878

0.327

0

1

Health status

54,040

2.966

1.247

1

5

Education

54,221

2.221

1.348

0

8

Urban residence

51,352

0.471

0.499

0

1

Working

49,310

0.766

0.423

0

1

Public medical insurance

50,350

0.922

0.268

0

1

17

Mean

Std. dev.

Min

Max

Public pension participation

39,058

0.630

0.483

0

1

Family size

53,821

4.289

1.992

1

21

The number of base stations of mobile phones

54,303177639.6

125000.2 25000

559000

Table 2 Estimation results of the effect of Internet use on fertility intention
FE(1)

FEologit(2)(3)

REologit(4)(6)

Fertility intention

(7)

FE-IV
(8)

(10)

Fertility intention

Internet use

0.033**

0.034**

0.248***

0.253***

0.125***

0.152***

0.018**

0.018**

1.677***

1.697***(0.014)(0.014)(0.083)(0.083)(0.044)(0.044)(0.007)(0.007)(0.420)(0.425)

Individual variables

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Family variables

No

Yes

No

Yes

No

Yes

No

Yes

Yes

Yes

Province fixed effects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Year fixed effects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

1.149***

1.135***(0.230)(0.231)

Observations

31,651

31,651

9,244

9,244

31,651

31,651

24,69024,69031,651

31,651

Number of pid/panel id

19,294

19,294

3811

3811

19,294

19,294

12,345

12,345

19,294

19,29418

Fertility intention(9)

VARIABLES

Constant

Fertility intention(5)

FEpoisson

Fertility intention

Within R-squared

0.015

0.015

Between R-squared

0.061

0.063

0.006

0.008

Overall R-squared

0.043

0.045

0.002

0.003

Log likelihood-3065.054-3063.326

Pseudo R-squared

0.043

0.044

-26483.509-26297.405-12505.132-12504.987

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1
Table 3 Estimation results of the effect of attitude toward the Internet on fertility intention
FE(1)
VARIABLES

FEologit(2)

Fertility intention(3)

REologit(4)

Fertility intention(5)

FEpoisson(6)

Fertility intention

(7)

FE-IV
(8)

Fertility intention

(9)

(10)

Fertility intention

Importance of Internet as communication path
Very important

0.038**

0.038**

0.256**

0.259***

0.170***

0.187***

0.020**

0.020**(0.019)(0.019)(0.100)(0.100)(0.054)(0.054)(0.009)(0.009)

Importance of Internet as communication path

0.648***

0.651***(0.189)(0.189)Individual variables

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Family variables

No

Yes

No

Yes

No

Yes

No

Yes

Yes

Yes

19

Province fixed effects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Year fixed effects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

1.178***

1.165***

0.029

0.010

(0.229)(0.230)(0.492)(0.494)

Observations

31,63031,630

Number of pid

19,285

19,285

Within R-squared

0.015

0.015

Between R-squared

0.061

Overall R-squared

0.042

Constant

9,238

9,238

31,63031,63024,666

24,666

31,63031,63019,285

19,285

12,333

12,333

19,285

19,285

0.063

0.002

0.002

0.044

0.002

0.002

Log likelihood-3063.034-3061.556

Pseudo R-squared

0.043

0.044

-26461.696-26277.117-12493.302-12493.173

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1
Table 4 Heterogeneous effects of Internet use on fertility intention (age and gender)age<=49(1)
VARIABLES

Internet use(2)FE

0.029*

age>=50(3)

female
(4)

(5)

FE

0.030*

0.110***

0.109***

male
(6)

(7)

FE

0.013

age<=49
(8)

FE

0.014

0.054**

20

0.054**

(9)

(10)
FE-IV

1.868***

1.895***

female
(11)

(12)
FE-IV

1.313***

1.345***

(0.017)(0.017)(0.041)(0.041)(0.018)(0.018)(0.022)(0.022)(0.622)(0.630)(0.418)(0.428)

Individual variables

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Family variables

No

Yes

No

Yes

No

Yes

No

Yes

No

Yes

No

Yes

Province fixed effects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Year fixed effects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Observations

21,45721,457

10,194

10,194

15,826

15,826

15,825

15,825

21,45721,457

15,826

15,826

Number of pid

13,555

13,555

7,553

7,553

9,544

9,544

9,762

9,762

13,555

13,555

9,544

9,544

Within R-squared

0.012

0.014

0.042

0.042

0.026

0.026

0.012

0.012

Between R-squared

0.021

0.030

0.001

0.001

0.081

0.084

0.027

0.028

0.015

0.013

0.037

0.041

Overall R-squared

0.017

0.025

0.002

0.002

0.057

0.059

0.017

0.017

0.010

0.008

0.019

0.022

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

21

Table 5 Heterogeneous effects of Internet use on fertility intention (hukou)
Rural hukou

Urban hukou

Rural hukou

(1)

(3)

(5)

VARIABLES

Internet use

(2)
FE

(4)
FE

(6)
FE-IV

0.032**

0.033**

0.038

0.039

0.733**

0.742**(0.015)(0.015)(0.040)(0.040)(0.344)(0.347)

Individual variables

Yes

Yes

Yes

Yes

Yes

Yes

Family variables

No

Yes

No

Yes

No

Yes

Province fixed effects

Yes

Yes

Yes

Yes

Yes

Yes

Year fixed effects

Yes

Yes

Yes

Yes

Yes

Yes

Observations

24,646

24,646

7,005

7,005

24,646

24,646

Number of pid

15,284

15,284

4,508

4,508

15,284

15,284

Within R-squared

0.020

0.020

0.037

0.038

Between R-squared

0.079

0.080

0.010

0.011

0.060

0.063

Overall R-squared

0.057

0.058

0.007

0.008

0.039

0.042

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

22

Table 6 Heterogeneous effects of Internet use on fertility intention (education and marital status)
High school graduate or below(1)
VARIABLES

Internet use(2)

High school graduate above
(3)

FE

(4)

High school graduate or below
(5)

FE

(6)

not married
(7)

FE-IV

(8)

married(9)FE

married

(10)

(11)

FE

(12)
FE-IV

0.026*

0.027*

0.040

0.029

1.752***

1.778***

0.060

0.061

0.031**

0.031**

1.730***

1.755***(0.014)(0.014)(0.136)(0.136)(0.608)(0.620)(0.079)(0.079)(0.014)(0.014)(0.437)(0.445)

Individual variables

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Family variables

No

Yes

No

Yes

No

Yes

No

Yes

No

Yes

No

Yes

Province fixed effects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Year fixed effects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Observations

28,891

28,891

2,771

2,771

28,891

28,891

2,937

2,937

28,71428,71428,71428,714

Number of pid

18,01518,015

2,300

2,300

18,01518,015

2,273

2,273

17,495

17,495

17,495

17,495

Within R-squared

0.013

0.013

0.122

0.126

0.080

0.080

0.017

0.017

Between R-squared

0.049

0.051

0.010

0.014

0.013

0.015

0.006

0.007

0.036

0.037

0.008

0.011

Overall R-squared

0.035

0.036

0.011

0.014

0.006

0.007

0.011

0.011

0.023

0.024

0.003

0.005

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

23

Table 7 Estimation results of heterogeneous analysis on the effects of Internet use on
fertility intention
(1)

(2)

VARIABLES

FE

FE-IV

Internet use

0.047***

1.411***(0.015)(0.386)-0.066**-0.570***(0.032)(0.122)0.069*

0.268***(0.035)(0.099)Individual variables

Yes

Yes

Family variables

Yes

Yes

Province fixed effects

Yes

Yes

Year fixed effects

Yes

Yes

1.111***

0.579*(0.232)(0.301)

Observations

31,651

31,651

Number of pid

19,294

19,294

Within R-squared

0.016

Between R-squared

0.063

0.024

Overall R-squared

0.044

0.012

Internet use#urban hukou

Urban hukou

Constant

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

24

Table 8 Channels of the influence of Internet use on fertility intention (household income)
(1)

(2)

(3)

VARIABLESln(household income)fertility intention

Internet use

0.112***

0.034**

0.034**(0.029)(0.014)(0.014)-0.008*ln(household income)(0.005)
Individual variables

Yes

Yes

Yes

Family variables

Yes

Yes

Yes

Province fixed effects

Yes

Yes

Yes

Year fixed effects

Yes

Yes

Yes

10.951***

1.077***

1.135***(0.435)(0.223)(0.231)

Observations

31,73332,36331,651

Number of pid

19,31819,494

19,294

Within R-squared

0.053

0.014

0.015

Between R-squared

0.083

0.064

0.063

Overall R-squared

0.067

0.045

0.045

Constant

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

25

Table 9 Bounds for the effect of Internet use on fertility intention
Internet use

95% confidence interval

Provincial Internet penetration rate
Support for possible values of θ=𝜃𝜃1

Lower

Upper

θ∊[-0.0001, +0.0001]

0.957

2.549

θ∊[-0.0005, +0.0005]

0.956

2.551

θ∊[-0.001, +0.001]

0.954

2.554

θ∊[-0.005, +0.005]

0.941

2.574

θ∊[-0.01, +0.01]

0.925

2.600

26

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