More internet, more babies? The impact of internet use on fertility behaviors among urban women in China

More internet, more babies? 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The impact of internet use on fertility behaviors among urban women in China Hejun Gu, Ying Xiang, Ling Yin, Jia Li, Yanjiao Xia This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6542950/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract We examine the influence of the internet on the fertility behaviors of urban women aged 15–49 years in China using data from the 2014–2020 China Family Panel Studies. We find that internet usage is significantly and positively associated with an increased likelihood of having a second child among urban women of childbearing age. The impact of internet usage on fertility behavior is heterogeneous across regions as well as household registration and education level of women. Workplace flexibility and entrepreneurship may serve as possible mechanisms through which internet usage contributes to women’s fertility behaviors of having a second child. Social science/Science technology and society Social science/Sociology Internet use Fertility behavior Urban China Figures Figure 1 Figure 2 1. Introduction China's birth rate has decreased substantially in the last few decades, falling below the population replacement rate. The total fertility rate dropped from 6 in 1970 to 1.3 in 2021, and the number of newborns declined to 10.62 million, with nine provinces experiencing negative population growth (He et al., 2019 ; Morgan et al., 2009 ; World Bank, 2023). This persistently low birth rate is expected to result in population aging and decline, posing significant challenges for Chinese society. While the family planning policy introduced in the 1980s is often cited as a contributor to the low birth rate, the issue has persisted despite subsequent policy adjustments (Basten & Jiang, 2014 ). Importantly, the urban fertility rate is lower than the national and rural rates, and the gap between urban and rural fertility levels has continued to widen (Yang et al., 2023 ). The 2020 census yearbook revealed an alarming decline in the total fertility rate (TFR) in both rural (1.543) and urban (1.118) regions, raising concerns among policymakers and scholars about the demographic, economic, and social implications. Concurrently, China has experienced rapid development of the digital economy, particularly the internet, which has profoundly shaped individuals, families, and society (Nie et al., 2017 ). As of 2022, China had over 1 billion internet users, with a penetration rate of 73%, indicating the internet has become an essential part of daily life for many Chinese people (Nie et al., 2021 ; Zhao, 2006 ). The rapid development of the digital economy in China has had a significant impact on fertility behaviors, particularly in urban areas, where the total fertility rate has declined the most in the past decades(Zhang et al., 2021 ). Thus, we aim to examine the impact of internet usage on fertility behaviors among urban women of reproductive age in China using data from the most recent four waves of China Family Panel Studies (CFPS). This study contributes to the literature in the following aspects. First, by distinguishing between the fertility behavior of having a first or second child, the findings of our study will expand the empirical evidence on the impact of internet usage on different types of fertility behaviors. Second, by analyzing the heterogeneity in the impact of internet use across different subgroups, this study provides evidence to support the development of targeted government policies. Third, existing studies have primarily examined the impact of the internet on fertility behaviors from three key perspectives: information dissemination, work-family balance, and marriage. Going beyond previous studies, we considered the differences in the nature of the first and second children in the Chinese context and investigated the different mechanisms through which internet usage affects different fertility behaviors. The remainder of this study proceeds as follows. Section 2 reviews related literature and illustrates the conceptual framework of this study. Section 3 explains the data and methods. Section 4 reports empirical results and section 5 concludes. 2. Literature Review and Conceptual Framework 2.1 Literature review Previous studies on the impact of internet use on fertility behaviors have primarily focused on developed countries, which have been plagued by low fertility rates since the 1950s (Adsera, 2005 ; The ESHRE Capri Workshop Group, 2010 ). Gleeson et al. ( 2019 ) indicate that people's perceptions and decisions regarding fertility are significantly shaped by their online experiences and exposures. For instance, prior studies have found that internet access enables infertile couples to more readily obtain relevant information, better manage reproductive health conditions, and ultimately enhance their fertility outcomes (Gulec Satir & Kavlak, 2017 ; Weissman et al., 2000 ). Similarly, Guldi & Herbst ( 2017 ) carried out an empirical analysis based on pertinent data from the Federal Communications Commission (FCC) Form 477 and discovered that broadband connectivity enhances information access and lowers the rate of teenage pregnancies. However, the relationship between internet use and fertility appears to be nuanced and contingent on various individual and contextual factors. For example, Billari et al. ( 2019 ) analyzed panel data from Germany and found that internet access can increase the fertility rate of highly educated women aged 25–45 by promoting a balance between family and work. The current decade has witnessed a persistent decline in China's fertility rate, prompting growing scholarly interest in investigating the underlying factors. Chinese researchers have increasingly focused on the potential influence of the rapid expansion of internet access and usage. A recent study by Nie et al. ( 2023 ), utilizing data from the 2014–2018 China Family Panel Studies (CFPS), delved into the relationship between internet use and actual fertility behaviors among Chinese women. Their results indicate that internet usage significantly reduced the number of children born to mothers in rural areas while exhibiting no discernible impact on their urban counterparts. Similarly, Ning et al. ( 2022 ) used data from the 2017 China General Social Survey (CGSS) to explore the impact of new media consumption on the fertility intentions of Chinese women of reproductive age. Their analysis suggests that increased new media usage is associated with an increased likelihood of negative social interactions, which in turn exerts a detrimental influence on fertility intention. The existing research has predominantly examined the impact of internet usage on infertility treatment and its influence on family fertility behavior. In the Chinese context, most studies have centered on the adverse effects of internet usage on women's mental well-being due to exposure to negative news, subsequently leading to negative impacts on their fertility behavior. However, limited attention has been given to the potential positive implications of internet usage on women's employment and income. We investigate how internet use enhances fertility behavior by increasing women's income and providing greater flexibility in managing their time. Additionally, it seeks to analyze the varying effects on families with one child versus those with two children, aiming to bridge the existing research gap in this area. 2.2 Conceptual framework In this section, we sketch out a conceptual framework for understanding the impact of internet use on fertility behavior in the Chinese context. Given that internet use may affect the intentions to have a first or second child through different mechanisms, we focus on a different set of channels that have received attention in existing literature, respectively. Fertility theory points out that couples usually decide the number of children under the dual constraints of time and income and seek to maximize the family's overall utility; that is, the level of fertility depends on a tradeoff between the income effect and the substitution effect of fertility demands (Becker, 1960 ). Following the classic Becker fertility model (Becker, 1960 ), we also consider children as durable goods. In the Chinese context, we further consider the first child as a necessary good while the second child or above is a luxury good. Regarding the first child, several key contextual factors within the Chinese societal landscape merit consideration. First, the multifaceted evolution of China's society, economy, and prevailing mindset since the inception of the reform and opening-up policy in 1978 has ushered in a partial liberation of traditional norms and values. While women have been increasingly encouraged to participate in the labor market, the deeply rooted Confucian ideology continues to exert a significant influence on Chinese family structures and dynamics (Zhang, 2006 ). The prevalent belief that bearing at least one child is a familial duty and honor to perpetuate the ancestral lineage remains a prominent feature of the traditional Chinese family culture, particularly in less developed regions. Second, the past decades have not witnessed a strong tendency toward voluntary childlessness in China (Sobotka, 2021 ; Yu & Xie, 2022 ). Although there is growing tolerance, especially among young women born after 1995, towards the decision to remain childless, the proportion of childlessness in China is comparatively lower when compared with Western countries and neighboring nations like Japan and South Korea (Yu & Xie, 2022 ). Consequently, we consider it reasonable to view the first child as a necessity good within the Chinese context. The existing empirical observations within the Chinese context suggest that married families in China continue to exhibit a significant inclination towards having a first child, with a rare voluntary decision to abstain from parenthood. The perceived importance of the first child within the family unit is remarkably high, leading to resilience against external influences on the decision-making process. Therefore, we propose the following hypothesis: Hypothesis 1 The impact of internet usage is limited on having a first child. In contrast, families tend to carefully consider both time and income constraints when deciding to have a second child. Regarding time constraints, the fertility process, including pregnancy, childbirth, lactation, and child-rearing, imposes significant time pressures that contribute to low fertility rates (Hank & Kreyenfeld, 2003 ). Studies have found that many women choose to forgo or delay childbearing due to these time constraints, as the time required for childcare reduces their available work time (Eibich & Siedler, 2020 ; Greenhaus & Beutell, 1985 ). Moreover, the lack of accessible childcare facilities, especially for children under three years of age, exacerbates the time pressure faced by women of reproductive age in China (National Health Commission, 2021 ). Concurrently, the high labor force participation rate of urban women of reproductive age in China (World Bank, 2017) creates a considerable dilemma between having children and maintaining employment (Maurer-Fazio et al., 2011 ). The opportunity cost of childbearing is relatively high for these women, as they must take time out of the workforce to care for their children, leading to a negative correlation between fertility and female employment (Angrist & Evans, 1996 ; Bailey, 2006 ; D. E. Bloom et al., 2009 ; Gu et al., 2021 ). However, the flexibility offered by internet-enabled employment can potentially alleviate the time constraints faced by women of reproductive age (Joona, 2017 ; Semykina, 2018 ). By enabling remote work and more flexible work arrangements, the internet can promote overall employment of women of childbearing age, create more self-employment opportunities, and allow for better work-family balance (N. Bloom et al., 2015 ; Weinberg, 2000 ). Consequently, the possibility of having a second child may correspondingly increase for urban women of reproductive age. Thus, we propose the second hypothesis as follows: Hypothesis 2 internet usage will increase the likelihood of having a second child among urban women of reproductive age through promoting flexible employment. The decision to have a second child is often more financially constrained compared to the decision to have a first child. Existing literature suggests that the primary motivations for having a first child tend to be social and normative, such as the desire to "carry on the family name" (Yu & Xie, 2022 ). In contrast, women of reproductive age tend to make more economically rational decisions when considering a second child, focusing on practical factors like the additional costs involved (Becker & Lewis, 1973 ; Liu et al., 2020 ). The marginal opportunity cost of a second child is higher than that of a first child, increasing the family's economic burden (Galor, 2005 ; Waldfogel, 1997 ). Consequently, most people choose to have a second child only when their income is considerably larger and their basic living needs have been met (Zhu & Hong, 2022 ). The use of the internet can increase the likelihood of women having a second child by promoting female entrepreneurship that raises incomes. The internet can help women overcome traditional entrepreneurial challenges, access new business opportunities in female-dominated industries, and start businesses with lower investment requirements (Audretsch et al., 2015 ; Brush et al., 2009 ; Mack et al., 2017 ; Tong et al., 2021 ). This suggests that increased internet use may positively impact second-child birth rates by empowering women economically. On this basis, we propose the third hypothesis as follows: Hypothesis 3 Internet usage will increase the likelihood of having a second child among urban women of reproductive age by increasing entrepreneurship. 3. Data and Methods 3.1 Data Household survey data used in this study came from the CFPS released by the Institute of Social Science Survey, Peking University. The CFPS has collected nationally representative survey data every two years since 2010. This study derived data from four recent waves of the survey, from 2014 to 2020 rounds, as the survey began to collect information about internet usage in 2014. The survey covers 25 provinces/municipalities/ autonomous regions and consists of four basic types: the community survey, the family survey, the adult survey, and the children’s survey. According to the WHO definition of women of childbearing age, this study focused on the influence of the internet on fertility behaviors of urban women aged 15–49 years. After screening the marital status of women, the database contains married urban women aged 17–49. In this study, data relating to internet use and individual and spouse characteristics came from the adult survey; fertility behavior data came from the family member relationship database. Data including household income, household size, and household assets, were extracted from the household economic database. First, we matched the adult questionnaire and family member relation database according to each individual’s code and checked the family economic database according to the family code. Third, a range of other factors may influence women's reproductive behaviors including personal characteristics, such as age, education level, agricultural household registration, health, and working status. In addition, a series of family characteristics such as the logarithm of family income, family size, housing property rights, household income, and expense was also included. In addition, this study also controlled for the province and year-fixed effects. Table 1 displays summary statistics. 3.2 Empirical strategy In this study, we aim to examine the impact of internet usage on the fertility behaviors of married women aged 15–49 years in the urban areas of China. The baseline regression model of this paper is as follows: Fertility ijt = β 0 + β 1 Internet ijt +∑ Controls ijt + Province j + Year t + ε ijt (1) where Fertility ijt is the fertility behavior of woman i from province j interviewed in year t . We use the birth information of each child in the family member relationship database to determine whether the woman had given birth to any child preceding the interview and the total number of children she has ever born. Internet ijt is the main variable of interest representing internet usage of women i from province j in year t . We measure internet usage based on the following two questions: (1) "whether you use the computer to access the internet?"; (2) and "whether you access the internet on mobile devices such as mobile phones?" following previous literature (Nie et al. 2023). If a woman used either computers or mobile devices to access the internet, we code Internet ijt as equal to 1, and 0 otherwise. Controls ijt represents an array of control variables at the household and individual levels. Household characteristics include the logarithm of family income, family size, and a dummy variable for house ownership. Individual characteristics comprise age, education level, place of household registration, health status, and employment status. Province j and Year t denote province- and year-fixed effects, respectively. ε ijt is the error term. Even though we control for province- and year-fixed effects, as well as a series of household and individual characteristics in Eq. (1), endogeneity may also exist due to other confounding factors. For instance, provinces with a higher internet usage rate are likely to differ from those with a lower internet usage rate systematically. Internet usage may have been driven by both observable and unobservable characteristics that also affect the fertility behavior of women, such as wealth, and urbanization. We address the non-random assignment of internet access using an instrumental-variable approach. Specifically, we use the proportion of Internet access by other people (excluding the respondent themselves) in respondent’s district or county as an instrument variable of internet usage. First, an individual's Internet use behavior is often influenced by their immediate surroundings. Specifically, the Internet usage situation of other residents in the same county or district can indirectly reflect the local network infrastructure conditions and affect an individual's Internet use behavior through the "peer effect". Additionally, Internet penetration rate in an area should not directly affect the respondent's fertility behavior. We denote the proportion by Internet_proportion, and estimate the following first-stage equation: Internet ijt = β 0 + β 1 Internet_proportion +∑ Controls ijt + Province j + Year t + ε ijt (2) 4. Results 4.1 Baseline Results Table 2 displays the estimated results of Eq. (1). In all specifications, we cluster the standard error at the provincial level, and control for province- and year-fixed effects. We find that internet usage is significantly associated with an increased likelihood of having a child by 3.31% among urban women in China. As discussed earlier, the influence of internet usage may have differential effects on having a first or second child, thus we further differential women who have given birth to any child into two categories. The estimated results in columns (2) and (3) of Table 2 showed that internet usage is significantly associated with an increased possibility of having a second child by 2.07%, but does not influence having the first child. Table 2 Baseline results (1) (2) (3) Having a child Having a first child Having a second child Internet 0.0331** 0.0124 0.0207*** (0.0125) (0.0120) (0.0074) Controls Yes Yes Yes Province FE Yes Yes Yes Year FE Yes Yes Yes Observations 8,286 8,286 8,286 R-squared 0.1785 0.1130 0.1004 Note: Standard errors in parentheses are clustered at the provincial level. *, **, and *** demonstrate significance at the 10%, 5%, and 1% levels, respectively. 4.2 Two-stage Least Squares (2SLS) results As discussed in section 3.2 , other confounding factors may bias our estimate of the influence of internet usage on fertility behavior. We display the estimation results from the second-stage equation with the internet_proportion as an instrument for internet usage in panel A of Table 3 . After controlling for the non-random placement of internet usage, it remains significantly positively associated with the likelihood of having a child or a second child. The size of the estimated coefficient is larger than those from OLS, which may reflect (i) that the internet usage is not randomly allocated in terms of ex-ante fertility behavior, with the internet being more likely to be found in locations where the fertility rate is lower and (ii) heterogeneity in the overall effect, as IV estimates are local average treatment effect. We further explore the heterogeneous effect in the section below. The outcomes of the first-stage estimation shown in Panel B of Table 3 reveal that there is a significant and positive association between the instrument and internet usage of women. The first-stage F test results reject the possibility of a weak instrument. Table 3 2SLS estimation results Variable Having a child Having a first child Having a second child Panel A: Second-stage estimation Internet use 0.1299* 0.0241 0.1058** (0.0713) (0.0565) (0.0524) Panel B: First-stage estimation Internet_proportion 0.2845*** 0.2845*** 0.2845*** (0.0229) (0.0229) (0.0229) Controls Yes Yes Yes Province FE Yes Yes Yes Year FE Yes Yes Yes Kleibergen–Paap F statistic 214.34 214.34 214.34 Observations 7620 7620 7620 Note: Standard errors in parentheses are clustered at the provincial level. *, **, and *** demonstrate significance at the 10%, 5%, and 1% levels, respectively. Since certain counties/districts contained only a single respondent in our sample (rendering the instrumental variable undefined), we ultimately retained 7,620 valid observations for the two-stage least squares (2SLS) estimation. 4.3 Robustness Checks 4.3.1 Alternative definition of internet use In our baseline model, we define internet usage based on whether the women use computers or mobile devices to access the internet. However, with the development of information technology and the introduction of 5G technology, an increasing number of people use mobile devices, which are convenient to carry, to surf the internet. As this use of mobile devices better reflects contemporary internet use, we redefine internet usage as those who access the internet through mobile devices. The Panel A of Table 4 displays the estimated results which are similar to the baseline results shown in Table 2 . 4.3.2 Alternative time window China introduced the one-child policy in the early 1980s, which ended in 2013 when the selective two-child policy was introduced (Zhai & Jin, 2023 ). Previous studies have shown that the selective two-child policy in China failed to have a significant impact on individuals’ fertility intentions (Zeng & Hesketh, 2016 ), while the universal two-child policy introduced in 2015 significantly affected fertility behavior among women. Thus, we limit our sample to the most recent three waves of CFPS since 2016, during which the two-child policy was fully implemented in China. The estimated results presented in panel B of Table 4 is consistent with those shown in Table 2 . 4.3.3 Alternative definition of reproductive age In our baseline estimation, we define reproductive age as between 15 and 49 years old. However, women aged over 45 years are less likely to get pregnant and give birth, and it is not common to give birth before reaching the legal marriage age for Chinese women (Sugai et al., 2023 ). We redefined the childbearing age as between 20 to 45 years old, and re-estimated Eq. (1). The results shown in panel C of Table 4 reveal that there is a significant positive correlation between internet usage and reproductive behavior among urban women in China. 4.3.4 Alternate estimation method Given that fertility behaviors are measured using binary variables in this study, we further use the Probit and Logit regression models to estimate the influence of internet usage on fertility behavior following previous literature (MacPhail & Dong, 2007 ). The estimated results are shown in Panels D and F of Table 4 , showing similar results to the baseline results shown in Table 2 . Table 4 Robustness test (1) (2) (3) Having a child Having a first child Having a second child Panel A: Alternative definition of internet use Internet use 0.0309** 0.0106 0.0203*** (0.0114) (0.0110) (0.0073) Observations 8,286 8,286 8,286 R-squared 0.1784 0.1130 0.1004 Panel B: Alternative time window Internet use 0.0328** -0.0027 0.0355*** (0.0128) (0.0128) (0.0099) Observations 6,016 6,016 6,016 R-squared 0.1759 0.1051 0.1068 Panel C: Alternative definition of reproductive age Internet use 0.0422*** 0.0158 0.0263*** (0.0149) (0.0144) (0.0092) Observations 7,096 7,096 7,096 R-squared 0.1661 0.1138 0.0991 Panel D: Alternate estimation method, logit model Internet use 0.0436*** 0.0245* 0.0233*** (0.0109) (0.0137) (0.0084) Observations 8,278 8,278 8,128 R-squared 0.2401 0.2598 0.1852 Panel E: Alternate estimation method, probit model Internet use 0.0388*** 0.0219* 0.0209*** (0.0102) (0.0127) (0.0077) Observations 8,278 8,278 8,128 R-squared 0.2458 0.2615 0.1936 Controls Yes Yes Yes Province FE Yes Yes Yes Year FE Yes Yes Yes Note: Standard errors in parentheses are clustered at the provincial level. *, **, and *** demonstrate significance at the 10%, 5%, and 1% levels, respectively. We control for household and individual characteristics, province-, and year-fixed effects in all specifications. 4.4 Heterogeneity Analysis Considering that the impact of internet usage may affect fertility behavior differently among different sub-groups of women, we further conduct a series of heterogeneity analyses. 4.4.1 By household registration A notable feature in the urban areas of China is that the allocation of public services has been regulated by the household registration system (the Hukou system), under which people with agricultural and non-agricultural hukou enjoy different social welfare and benefits (Dong et al., 2023 ). Thus, we differentiate the household registration of women into agricultural and non-agricultural hukou and re-estimate Eq. (1). The estimated results in Table 5 show that internet usage has no significant influence on whether to have a first child among both agricultural and non-agricultural hukou holders. In terms of whether or not to have a second child, internet usage has a significantly positive effect on second births among the two groups of women. Table 5 Heterogeneity analysis by Hukou (1) (2) (3) (4) (5) (6) Having a child Having a first child Having a second child Agricultural Non-agricultural Agricultural Non-agricultural Agricultural Non-agricultural Internet use 0.0440*** 0.0276** 0.0189 0.0080 0.0250* 0.0196*** (0.0158) (0.0111) (0.0150) (0.0098) (0.0132) (0.0067) Controls Yes Yes Yes Yes Yes Yes Province FE Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes Observations 4,007 4,279 4,007 4,279 4,007 4,279 R-squared 0.1799 0.1695 0.1100 0.1239 0.1008 0.0964 Note: Standard errors in parentheses are clustered at the provincial level. *, **, and *** demonstrate significance at the 10%, 5%, and 1% levels, respectively. We control for household and individual characteristics, province-, and year-fixed effects in all specifications. 4.4.2 By education level The existing literature has highlighted the heterogeneity in fertility outcomes across different socioeconomic statuses (Bollen et al., 2001 ; Lim, 2021 ). Thus, we further investigate whether the impact of internet usage on the fertility behaviors of urban women varies according to their educational attainment. We categorized those with post-secondary degrees or above as the high education group, while those with less than post-secondary education formed the low education group. The estimated results summarized in Table 6 revealed that internet usage has significantly increased the likelihood of urban women having a first and second child among women with a lower education level. Table 6 Heterogeneity analysis by education level (1) (2) (3) (4) (5) (6) Having a child Having a first child Having a second child High Low High Low High Low Internet use 0.0069 0.0448*** 0.0261 0.0207* -0.0191 0.0242*** (0.0303) (0.0116) (0.0300) (0.0113) (0.0220) (0.0085) Controls Yes Yes Yes Yes Yes Yes Province FE Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes Observations 2,763 5,523 2,763 5,523 2,763 5,523 R-squared 0.1408 0.2071 0.1160 0.1176 0.0911 0.1127 Note: Standard errors in parentheses are clustered at the provincial level. *, **, and *** demonstrate significance at the 10%, 5%, and 1% levels, respectively. We control for household and individual characteristics, province-, and year-fixed effects in all specifications. 4.4.3 By region Given that regional disparities exist in China in terms of fertility behaviors due to economic and cultural reasons, (Li et al., 2024 ), We divided the sample into four regions, namely East, Central, West, and Northeast according to the classification criteria defined by the National Bureau of Statistics of China, and display the estimated results in Fig. 2 . The results reveal distinct patterns across women in different regions. Internet utilization exhibited a non-significant and even negative relationship with the reproductive behaviors of women of childbearing age in the Northeast region. For the decision to have a first child, the positive effect of internet usage was statistically significant only in the Central region, with no discernible impact observed in the other regions. However, when examining the choice to have a second child, the influence of internet use was most pronounced, and statistically significant at the 5% level, for women of childbearing age residing in the Western regions of China. 4.5 Mechanism Analysis The baseline regression analysis and subsequent robustness checks have revealed that the impact of internet usage on having a second child is more pronounced among urban women of childbearing age. Building on these initial findings, the current section delves deeper into the potential mechanisms underpinning this observed relationship, with a particular focus on the roles of flexible employment and entrepreneurship. The analysis centers on the subpopulation of urban women of childbearing age who have already given birth to one child and are thus likely to have a second child. 4.5.1 Workplace flexibility In CFPS, respondents were asked to report the primary location of their employment, with the following response options: (1) outdoor, (2) workshop, (3) office, (4) home, (5) other indoor workplaces, (6) in transportation, and (7) other workplaces. We make use of the above question to measure workplace flexibility and assign a value of 1 to women who reported their workplace to be their home, other indoor settings, or other locations beyond the traditional office environment. All other respondents were coded as 0, reflecting an office-based employment arrangement. The regression results presented in Column (1) of Table 8 shed light on the relationship between internet usage and the likelihood of engaging in flexible employment among urban women in China. The findings indicate that internet utilization significantly promotes the adoption of flexible work arrangements. 4.5.2 Entrepreneurship Another mechanism linking internet usage to the fertility behaviors of urban women lies in entrepreneurship. The extant literature has suggested that working women often limit their family size to avoid compromising their career advancement opportunities (Ning et al., 2022 ). In contrast, self-employment and entrepreneurial activities may afford greater schedule flexibility and reduced risk of job loss, thereby potentially enabling these women to more readily pursue their fertility goals. To capture the entrepreneurial activities of the respondents, we leverage a relevant question about the primary occupation of women. If the women worked in private enterprises or were self-employed, we define entrepreneurial as equal to one, and 0 if the primary occupation of the women was agricultural production and management, non-farm employment, etc. The regression results presented in Column (2) of Table 8 illuminate the relationship between internet usage and the likelihood of urban women of childbearing age engaging in entrepreneurial activities. The findings indicate that internet utilization significantly promotes entrepreneurship among women with one child. Table 8 Potential mechanisms (1) (2) Flexible employment Entrepreneurship Internet use 0.0804*** (0.0122) 0.0304** (0.0137) Controls Yes Yes Province FE Yes Yes Year FE Yes Yes Observations 6,802 6,802 R-squared 0.0834 0.0419 Note: Standard errors in parentheses are clustered at the provincial level. *, **, and *** demonstrate significance at the 10%, 5%, and 1% levels, respectively. We control for household and individual characteristics, province-, and year-fixed effects in all specifications. 5. Discussion and Conclusions While prior research has examined the relationship between internet usage and fertility intentions, limited attention has been paid to understanding its impacts on actual fertility behaviors. To address this gap, we leverage data from the CFPS to explore the effects of internet utilization on the fertility behaviors of urban, married women of reproductive age in China. The findings indicate that internet usage significantly increases the likelihood of childbirth among urban women in China, with a particularly pronounced effect on the decision to have a second child. To unpack the potential mechanisms underlying these relationships, the analysis delves into two key pathways. First, the internet has a positive impact on the decision to have a second child by promoting flexible employment which relaxed time constraints faced by women. Second, internet use increases the income of women of reproductive age by enabling their entrepreneurship, which positively impacts their decision to have a second child. We also examined whether the impact of internet use on fertility behaviors differed across sub-groups of women. Interestingly, results showed that internet usage has a larger positive effect on having a second child in urban women with agricultural hukou and low education. This may be due to higher internet usage by women from non-agricultural households than women from agricultural households. Moreover, women from non-agricultural households or those with higher education levels in urban areas usually have a higher standard of living, pursue a more stable life, and are less likely to start their businesses; hence, internet usage appears to have less impact on them. Finally, internet usage has a larger impact on having a first child in the central region, while in the western region, it significantly increases the likelihood of having a second child. This study makes several valuable contributions to the existing literature on the impact of internet use on fertility behaviors. However, the paper also acknowledges several limitations, which could be addressed in future research to further expand the knowledge base in this domain. First, the CFPS collects data on urban women of childbearing age every two years, limiting the ability to observe the persistent effects of internet use on fertility behaviors. The restriction of the sample to married women further reduces the representativeness of the analysis. Second, while the study examines two key mechanisms underlying the relationship between internet use and fertility behaviors, there are likely to be additional factors at play. Future studies could explore a more comprehensive set of mechanisms to enhance the understanding of this complex phenomenon. Third, the CFPS data has a considerable number of missing values for control variables, such as personal income, which are relevant to the fertility decisions of individual women. The inability to account for these factors may introduce potential biases in the analysis. Finally, the current study focuses on three specific fertility behaviors, but there are various other dimensions of fertility that could be examined, for instance, the birth intervals. Future research expanding the scope of fertility-related outcomes would contribute to a more holistic understanding of the internet's impact in this context. Declarations Data availability The datasets generated and analyzed for this study can be found in the CFPS repository. Please see http://www.isss.pku.edu.cn/cfps/ for more details, further inquiries can be directed to the corresponding author. 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Feminist Economics , 13 (3–4), 93–124. https://doi.org/10.1080/13545700701439457 Maurer-Fazio, M., Connelly, R., Chen, L., & Tang, L. (2011). Childcare, Eldercare, and Labor Force Participation of Married Women in Urban China, 1982–2000. Journal of Human Resources , 46 (2), 261–294. https://doi.org/10.3368/jhr.46.2.261 Morgan, S. P., Zhigang, G., & Hayford, S. R. (2009). China’s Below‐Replacement Fertility: Recent Trends and Future Prospects. Population and Development Review , 35 (3), 605–629. https://doi.org/10.1111/j.1728-4457.2009.00298.x National Health Commission. (2021). National Health Commission . Nie, P., Li, Y., Zhang, N., Sun, X., Xin, B., & Wang, Y. (2021). The change and correlates of healthy ageing among Chinese older adults: findings from the China health and retirement longitudinal study. BMC Geriatrics , 21 (1), 78. https://doi.org/10.1186/s12877-021-02026-y Nie, P., Peng, X., & Luo, T. (2023). Internet use and fertility behavior among reproductive-age women in China. China Economic Review , 77 , 101903. https://doi.org/10.1016/j.chieco.2022.101903 Nie, P., Sousa-Poza, A., & Nimrod, G. (2017). Internet Use and Subjective Well-Being in China. Social Indicators Research , 132 (1), 489–516. https://doi.org/10.1007/s11205-015-1227-8 Ning, C., Wu, J., Ye, Y., Yang, N., Pei, H., & Gao, H. (2022). How Media Use Influences the Fertility Intentions Among Chinese Women of Reproductive Age: A Perspective of Social Trust. Frontiers in Public Health , 10 . https://doi.org/10.3389/fpubh.2022.882009 Semykina, A. (2018). Self‐employment among women: Do children matter more than we previously thought? Journal of Applied Econometrics , 33 (3), 416–434. https://doi.org/10.1002/jae.2596 Sobotka, T. (2021). Un tiers des femmes d‘Asie de l’Est resteront sans enfant. Population & Sociétés , N° 595 (11), 1–4. https://doi.org/10.3917/popsoc.595.0001 Sugai, S., Nishijima, K., Haino, K., & Yoshihara, K. (2023). Pregnancy outcomes at maternal age over 45 years: a systematic review and meta-analysis. American Journal of Obstetrics & Gynecology MFM , 5 (4), 100885. https://doi.org/10.1016/j.ajogmf.2023.100885 The ESHRE Capri Workshop Group. (2010). Europe the continent with the lowest fertility. Human Reproduction Update , 16 (6), 590–602. https://doi.org/10.1093/humupd/dmq023 Tong, Q., Chu, C.-Y., Zhou, D., & Feng, Y. (2021). Does Internet Connectedness Disconnect Marriage? A Micro Empirical Analysis. Social Indicators Research , 158 (1), 143–176. https://doi.org/10.1007/s11205-021-02686-8 Waldfogel, J. (1997). The Effect of Children on Women’s Wages. American Sociological Review , 62 (2), 209. https://doi.org/10.2307/2657300 Weinberg, B. A. (2000). Computer Use and the Demand for Female Workers. ILR Review , 53 (2), 290–308. https://doi.org/10.1177/001979390005300206 Weissman, A., Gotlieb, L., Ward, S., Greenblatt, E., & Casper, R. F. (2000). Use of the Internet by infertile couples. Fertility and Sterility , 73 (6), 1179–1182. https://doi.org/10.1016/S0015-0282(00)00515-X World Bank data. (2017). Labor force participation rate, female (% of female population ages 15-64) - China . World Bank data. (2023). Fertility rate, total (births per woman) . Yang, Y., He, R., Zhang, N., & Li, L. (2023). Second-Child Fertility Intentions among Urban Women in China: A Systematic Review and Meta-Analysis. International Journal of Environmental Research and Public Health , 20 (4), 3744. https://doi.org/10.3390/ijerph20043744 Yu, J., & Xie, Y. (2022). Is there a Chinese pattern of the second demographic transition? China Population and Development Studies , 6 (3), 237–266. https://doi.org/10.1007/s42379-022-00113-0 Zeng, Y., & Hesketh, T. (2016). The effects of China’s universal two-child policy. The Lancet , 388 (10054), 1930–1938. https://doi.org/10.1016/S0140-6736(16)31405-2 Zhai, Z., & Jin, G. (2023). China’s family planning policy and fertility transition. Chinese Journal of Sociology , 9 (4), 479–496. https://doi.org/10.1177/2057150X231205773 Zhang, W. (2006). Child Adoption in Contemporary Rural China. Journal of Family Issues , 27 (3), 301–340. https://doi.org/10.1177/0192513X05283096 Zhang, W., Zhao, S., Wan, X., & Yao, Y. (2021). Study on the effect of digital economy on high-quality economic development in China. PLOS ONE , 16 (9), e0257365. https://doi.org/10.1371/journal.pone.0257365 Zhao, S. (2006). The Internet and the Transformation of the Reality of Everyday Life: Toward a New Analytic Stance in Sociology. Sociological Inquiry , 76 (4), 458–474. https://doi.org/10.1111/j.1475-682X.2006.00166.x Zhu, W., & Hong, X. (2022). Are Chinese Parents Willing to Have a Second Child? Investigation on the Ideal and Realistic Fertility Willingness of Different Income Family. Early Education and Development , 33 (3), 375–390. https://doi.org/10.1080/10409289.2021.1955581 Table 1 Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6542950","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":470977481,"identity":"aa87df46-f912-4d35-84f9-61b130d9dec4","order_by":0,"name":"Hejun Gu","email":"","orcid":"","institution":"Nanjing University of Information Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Hejun","middleName":"","lastName":"Gu","suffix":""},{"id":470977482,"identity":"48e2984d-c9c2-4d31-8b7d-120bf3852197","order_by":1,"name":"Ying Xiang","email":"","orcid":"","institution":"Nanjing University of Information Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Xiang","suffix":""},{"id":470977483,"identity":"62f1c570-eeda-42a0-8f04-05bcef6aa1b0","order_by":2,"name":"Ling Yin","email":"","orcid":"","institution":"Nanjing University of Information Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Ling","middleName":"","lastName":"Yin","suffix":""},{"id":470977484,"identity":"bfde03a2-eeea-4961-b819-e9c268dfd078","order_by":3,"name":"Jia Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAnElEQVRIiWNgGAWjYFAC5sYDEhUScvIkaGFsOCBxxsLYsIEkLYxtFYkMB4jVwC99sOGA5TyJBMYG5oePbhCjRbIvseGA5DaJPHYGNmPjHGK0GJxhBGspZmzgYZMmQcscCaBdpGlpIEWLZA8okI9JGBs2E+sXfh7mg48laurk5NmbHz4mSgsIMEuASWKVgwDjB1JUj4JRMApGwcgDADKsLnPtDTd6AAAAAElFTkSuQmCC","orcid":"","institution":"Nanjing University of Information Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Jia","middleName":"","lastName":"Li","suffix":""},{"id":470977485,"identity":"c5204bfb-0c15-41c3-a5d7-dbc7dc9a99a4","order_by":4,"name":"Yanjiao Xia","email":"","orcid":"","institution":"Nanjing University of Information Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yanjiao","middleName":"","lastName":"Xia","suffix":""}],"badges":[],"createdAt":"2025-04-28 01:53:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6542950/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6542950/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84779644,"identity":"fb3b10d1-7356-432f-bf31-e171bbb7456b","added_by":"auto","created_at":"2025-06-17 09:23:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":29216,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConceptual framework\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe figure is created by the authors.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6542950/v1/7064f81f1035dbf1162d22da.png"},{"id":84779638,"identity":"1ab2e38c-24ee-40a9-aa83-de3c566c63c0","added_by":"auto","created_at":"2025-06-17 09:23:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":25741,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHeterogeneity analysis by region\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStandard errors in parentheses are clustered at the provincial level. *, **, and *** demonstrate significance at the 10%, 5%, and 1% levels, respectively. We control for household and individual characteristics, province-, and year-fixed effects in all specifications.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6542950/v1/f180b65ea8bf09b312e95d77.png"},{"id":84781108,"identity":"9b5580da-174c-4746-bce2-fc4b7f13d876","added_by":"auto","created_at":"2025-06-17 09:31:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1060935,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6542950/v1/69f3be7e-b7e6-4224-8cc1-71263de07d4c.pdf"},{"id":84779647,"identity":"201e66af-741f-4c40-b2e8-7798d1702873","added_by":"auto","created_at":"2025-06-17 09:23:13","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17823,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6542950/v1/8fb170d6ef46657305f189ff.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"More internet, more babies? The impact of internet use on fertility behaviors among urban women in China","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eChina's birth rate has decreased substantially in the last few decades, falling below the population replacement rate. The total fertility rate dropped from 6 in 1970 to 1.3 in 2021, and the number of newborns declined to 10.62\u0026nbsp;million, with nine provinces experiencing negative population growth (He et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Morgan et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; World Bank, 2023). This persistently low birth rate is expected to result in population aging and decline, posing significant challenges for Chinese society.\u003c/p\u003e \u003cp\u003eWhile the family planning policy introduced in the 1980s is often cited as a contributor to the low birth rate, the issue has persisted despite subsequent policy adjustments (Basten \u0026amp; Jiang, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Importantly, the urban fertility rate is lower than the national and rural rates, and the gap between urban and rural fertility levels has continued to widen (Yang et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The 2020 census yearbook revealed an alarming decline in the total fertility rate (TFR) in both rural (1.543) and urban (1.118) regions, raising concerns among policymakers and scholars about the demographic, economic, and social implications.\u003c/p\u003e \u003cp\u003eConcurrently, China has experienced rapid development of the digital economy, particularly the internet, which has profoundly shaped individuals, families, and society (Nie et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). As of 2022, China had over 1\u0026nbsp;billion internet users, with a penetration rate of 73%, indicating the internet has become an essential part of daily life for many Chinese people (Nie et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhao, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The rapid development of the digital economy in China has had a significant impact on fertility behaviors, particularly in urban areas, where the total fertility rate has declined the most in the past decades(Zhang et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Thus, we aim to examine the impact of internet usage on fertility behaviors among urban women of reproductive age in China using data from the most recent four waves of China Family Panel Studies (CFPS).\u003c/p\u003e \u003cp\u003eThis study contributes to the literature in the following aspects. First, by distinguishing between the fertility behavior of having a first or second child, the findings of our study will expand the empirical evidence on the impact of internet usage on different types of fertility behaviors. Second, by analyzing the heterogeneity in the impact of internet use across different subgroups, this study provides evidence to support the development of targeted government policies. Third, existing studies have primarily examined the impact of the internet on fertility behaviors from three key perspectives: information dissemination, work-family balance, and marriage. Going beyond previous studies, we considered the differences in the nature of the first and second children in the Chinese context and investigated the different mechanisms through which internet usage affects different fertility behaviors.\u003c/p\u003e \u003cp\u003eThe remainder of this study proceeds as follows. Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reviews related literature and illustrates the conceptual framework of this study. Section \u003cspan refid=\"Sec5\" class=\"InternalRef\"\u003e3\u003c/span\u003e explains the data and methods. Section \u003cspan refid=\"Sec8\" class=\"InternalRef\"\u003e4\u003c/span\u003e reports empirical results and section \u003cspan refid=\"Sec23\" class=\"InternalRef\"\u003e5\u003c/span\u003e concludes.\u003c/p\u003e"},{"header":"2. Literature Review and Conceptual Framework","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Literature review\u003c/h2\u003e \u003cp\u003ePrevious studies on the impact of internet use on fertility behaviors have primarily focused on developed countries, which have been plagued by low fertility rates since the 1950s (Adsera, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; The ESHRE Capri Workshop Group, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Gleeson et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) indicate that people's perceptions and decisions regarding fertility are significantly shaped by their online experiences and exposures. For instance, prior studies have found that internet access enables infertile couples to more readily obtain relevant information, better manage reproductive health conditions, and ultimately enhance their fertility outcomes (Gulec Satir \u0026amp; Kavlak, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Weissman et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Similarly, Guldi \u0026amp; Herbst (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) carried out an empirical analysis based on pertinent data from the Federal Communications Commission (FCC) Form 477 and discovered that broadband connectivity enhances information access and lowers the rate of teenage pregnancies. However, the relationship between internet use and fertility appears to be nuanced and contingent on various individual and contextual factors. For example, Billari et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) analyzed panel data from Germany and found that internet access can increase the fertility rate of highly educated women aged 25\u0026ndash;45 by promoting a balance between family and work.\u003c/p\u003e \u003cp\u003eThe current decade has witnessed a persistent decline in China's fertility rate, prompting growing scholarly interest in investigating the underlying factors. Chinese researchers have increasingly focused on the potential influence of the rapid expansion of internet access and usage. A recent study by Nie et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), utilizing data from the 2014\u0026ndash;2018 China Family Panel Studies (CFPS), delved into the relationship between internet use and actual fertility behaviors among Chinese women. Their results indicate that internet usage significantly reduced the number of children born to mothers in rural areas while exhibiting no discernible impact on their urban counterparts. Similarly, Ning et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) used data from the 2017 China General Social Survey (CGSS) to explore the impact of new media consumption on the fertility intentions of Chinese women of reproductive age. Their analysis suggests that increased new media usage is associated with an increased likelihood of negative social interactions, which in turn exerts a detrimental influence on fertility intention.\u003c/p\u003e \u003cp\u003eThe existing research has predominantly examined the impact of internet usage on infertility treatment and its influence on family fertility behavior. In the Chinese context, most studies have centered on the adverse effects of internet usage on women's mental well-being due to exposure to negative news, subsequently leading to negative impacts on their fertility behavior. However, limited attention has been given to the potential positive implications of internet usage on women's employment and income. We investigate how internet use enhances fertility behavior by increasing women's income and providing greater flexibility in managing their time. Additionally, it seeks to analyze the varying effects on families with one child versus those with two children, aiming to bridge the existing research gap in this area.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Conceptual framework\u003c/h2\u003e \u003cp\u003eIn this section, we sketch out a conceptual framework for understanding the impact of internet use on fertility behavior in the Chinese context. Given that internet use may affect the intentions to have a first or second child through different mechanisms, we focus on a different set of channels that have received attention in existing literature, respectively.\u003c/p\u003e \u003cp\u003eFertility theory points out that couples usually decide the number of children under the dual constraints of time and income and seek to maximize the family's overall utility; that is, the level of fertility depends on a tradeoff between the income effect and the substitution effect of fertility demands (Becker, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1960\u003c/span\u003e). Following the classic Becker fertility model (Becker, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1960\u003c/span\u003e), we also consider children as durable goods. In the Chinese context, we further consider the first child as a necessary good while the second child or above is a luxury good.\u003c/p\u003e \u003cp\u003eRegarding the first child, several key contextual factors within the Chinese societal landscape merit consideration. First, the multifaceted evolution of China's society, economy, and prevailing mindset since the inception of the reform and opening-up policy in 1978 has ushered in a partial liberation of traditional norms and values. While women have been increasingly encouraged to participate in the labor market, the deeply rooted Confucian ideology continues to exert a significant influence on Chinese family structures and dynamics (Zhang, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The prevalent belief that bearing at least one child is a familial duty and honor to perpetuate the ancestral lineage remains a prominent feature of the traditional Chinese family culture, particularly in less developed regions. Second, the past decades have not witnessed a strong tendency toward voluntary childlessness in China (Sobotka, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yu \u0026amp; Xie, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Although there is growing tolerance, especially among young women born after 1995, towards the decision to remain childless, the proportion of childlessness in China is comparatively lower when compared with Western countries and neighboring nations like Japan and South Korea (Yu \u0026amp; Xie, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Consequently, we consider it reasonable to view the first child as a necessity good within the Chinese context.\u003c/p\u003e \u003cp\u003eThe existing empirical observations within the Chinese context suggest that married families in China continue to exhibit a significant inclination towards having a first child, with a rare voluntary decision to abstain from parenthood. The perceived importance of the first child within the family unit is remarkably high, leading to resilience against external influences on the decision-making process. Therefore, we propose the following hypothesis:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 1\u003c/strong\u003e \u003cp\u003eThe impact of internet usage is limited on having a first child.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eIn contrast, families tend to carefully consider both time and income constraints when deciding to have a second child. Regarding time constraints, the fertility process, including pregnancy, childbirth, lactation, and child-rearing, imposes significant time pressures that contribute to low fertility rates (Hank \u0026amp; Kreyenfeld, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Studies have found that many women choose to forgo or delay childbearing due to these time constraints, as the time required for childcare reduces their available work time (Eibich \u0026amp; Siedler, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Greenhaus \u0026amp; Beutell, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1985\u003c/span\u003e). Moreover, the lack of accessible childcare facilities, especially for children under three years of age, exacerbates the time pressure faced by women of reproductive age in China (National Health Commission, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConcurrently, the high labor force participation rate of urban women of reproductive age in China (World Bank, 2017) creates a considerable dilemma between having children and maintaining employment (Maurer-Fazio et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The opportunity cost of childbearing is relatively high for these women, as they must take time out of the workforce to care for their children, leading to a negative correlation between fertility and female employment (Angrist \u0026amp; Evans, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Bailey, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; D. E. Bloom et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Gu et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, the flexibility offered by internet-enabled employment can potentially alleviate the time constraints faced by women of reproductive age (Joona, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Semykina, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). By enabling remote work and more flexible work arrangements, the internet can promote overall employment of women of childbearing age, create more self-employment opportunities, and allow for better work-family balance (N. Bloom et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Weinberg, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Consequently, the possibility of having a second child may correspondingly increase for urban women of reproductive age. Thus, we propose the second hypothesis as follows:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 2\u003c/strong\u003e \u003cp\u003einternet usage will increase the likelihood of having a second child among urban women of reproductive age through promoting flexible employment.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe decision to have a second child is often more financially constrained compared to the decision to have a first child. Existing literature suggests that the primary motivations for having a first child tend to be social and normative, such as the desire to \"carry on the family name\" (Yu \u0026amp; Xie, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In contrast, women of reproductive age tend to make more economically rational decisions when considering a second child, focusing on practical factors like the additional costs involved (Becker \u0026amp; Lewis, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1973\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The marginal opportunity cost of a second child is higher than that of a first child, increasing the family's economic burden (Galor, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Waldfogel, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Consequently, most people choose to have a second child only when their income is considerably larger and their basic living needs have been met (Zhu \u0026amp; Hong, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe use of the internet can increase the likelihood of women having a second child by promoting female entrepreneurship that raises incomes. The internet can help women overcome traditional entrepreneurial challenges, access new business opportunities in female-dominated industries, and start businesses with lower investment requirements (Audretsch et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Brush et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Mack et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Tong et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This suggests that increased internet use may positively impact second-child birth rates by empowering women economically. On this basis, we propose the third hypothesis as follows:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 3\u003c/strong\u003e \u003cp\u003eInternet usage will increase the likelihood of having a second child among urban women of reproductive age by increasing entrepreneurship.\u003c/p\u003e \u003c/p\u003e "},{"header":"3. Data and Methods","content":"\u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003e3.1 Data\u003c/h2\u003e\n \u003cp\u003eHousehold survey data used in this study came from the CFPS released by the Institute of Social Science Survey, Peking University. The CFPS has collected nationally representative survey data every two years since 2010. This study derived data from four recent waves of the survey, from 2014 to 2020 rounds, as the survey began to collect information about internet usage in 2014. The survey covers 25 provinces/municipalities/ autonomous regions and consists of four basic types: the community survey, the family survey, the adult survey, and the children’s survey.\u003c/p\u003e\n \u003cp\u003eAccording to the WHO definition of women of childbearing age, this study focused on the influence of the internet on fertility behaviors of urban women aged 15–49 years. After screening the marital status of women, the database contains married urban women aged 17–49. In this study, data relating to internet use and individual and spouse characteristics came from the adult survey; fertility behavior data came from the family member relationship database. Data including household income, household size, and household assets, were extracted from the household economic database. First, we matched the adult questionnaire and family member relation database according to each individual’s code and checked the family economic database according to the family code.\u003c/p\u003e\n \u003cp\u003eThird, a range of other factors may influence women's reproductive behaviors including personal characteristics, such as age, education level, agricultural household registration, health, and working status. In addition, a series of family characteristics such as the logarithm of family income, family size, housing property rights, household income, and expense was also included. In addition, this study also controlled for the province and year-fixed effects. Table 1 displays summary statistics.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003e3.2 Empirical strategy\u003c/h2\u003e\n \u003cp\u003eIn this study, we aim to examine the impact of internet usage on the fertility behaviors of married women aged 15–49 years in the urban areas of China. The baseline regression model of this paper is as follows:\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eFertility\u003c/em\u003e \u003csub\u003e\u0026nbsp;\u003cem\u003eijt\u003c/em\u003e\u0026nbsp;\u003c/sub\u003e = \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e + \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003cem\u003eInternet\u003c/em\u003e\u003csub\u003e\u003cem\u003eijt\u003c/em\u003e\u003c/sub\u003e +∑\u003cem\u003eControls\u003c/em\u003e\u003csub\u003e\u003cem\u003eijt\u003c/em\u003e\u003c/sub\u003e +\u003cem\u003eProvince\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e+ \u003cem\u003eYear\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e + \u003cem\u003eε\u003c/em\u003e\u003csub\u003e\u003cem\u003eijt\u003c/em\u003e\u003c/sub\u003e (1)\u003c/p\u003e\n \u003cp\u003ewhere \u003cem\u003eFertility\u003c/em\u003e\u003csub\u003e\u003cem\u003eijt\u003c/em\u003e\u003c/sub\u003e is the fertility behavior of woman \u003cem\u003ei\u003c/em\u003e from province \u003cem\u003ej\u003c/em\u003e interviewed in year \u003cem\u003et\u003c/em\u003e. We use the birth information of each child in the family member relationship database to determine whether the woman had given birth to any child preceding the interview and the total number of children she has ever born. \u003cem\u003eInternet\u003c/em\u003e\u003csub\u003e\u003cem\u003eijt\u003c/em\u003e\u003c/sub\u003e is the main variable of interest representing internet usage of women \u003cem\u003ei\u003c/em\u003e from province \u003cem\u003ej\u003c/em\u003e in year \u003cem\u003et\u003c/em\u003e. We measure internet usage based on the following two questions: (1) \"whether you use the computer to access the internet?\"; (2) and \"whether you access the internet on mobile devices such as mobile phones?\" following previous literature (Nie et al. 2023). If a woman used either computers or mobile devices to access the internet, we code \u003cem\u003eInternet\u003c/em\u003e\u003csub\u003e\u003cem\u003eijt\u003c/em\u003e\u003c/sub\u003e as equal to 1, and 0 otherwise. \u003cem\u003eControls\u003c/em\u003e\u003csub\u003e\u003cem\u003eijt\u003c/em\u003e\u003c/sub\u003e represents an array of control variables at the household and individual levels. Household characteristics include the logarithm of family income, family size, and a dummy variable for house ownership. Individual characteristics comprise age, education level, place of household registration, health status, and employment status. \u003cem\u003eProvince\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eYear\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e denote province- and year-fixed effects, respectively. \u003cem\u003eε\u003c/em\u003e\u003csub\u003e\u003cem\u003eijt\u003c/em\u003e\u003c/sub\u003e is the error term.\u003c/p\u003e\n \u003cp\u003eEven though we control for province- and year-fixed effects, as well as a series of household and individual characteristics in Eq.\u0026nbsp;(1), endogeneity may also exist due to other confounding factors. For instance, provinces with a higher internet usage rate are likely to differ from those with a lower internet usage rate systematically. Internet usage may have been driven by both observable and unobservable characteristics that also affect the fertility behavior of women, such as wealth, and urbanization. We address the non-random assignment of internet access using an instrumental-variable approach. Specifically, we use the proportion of Internet access by other people (excluding the respondent themselves) in respondent’s district or county as an instrument variable of internet usage. First, an individual's Internet use behavior is often influenced by their immediate surroundings. Specifically, the Internet usage situation of other residents in the same county or district can indirectly reflect the local network infrastructure conditions and affect an individual's Internet use behavior through the \"peer effect\". Additionally, Internet penetration rate in an area should not directly affect the respondent's fertility behavior. We denote the proportion by Internet_proportion, and estimate the following first-stage equation:\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eInternet\u003c/em\u003e \u003csub\u003e\u0026nbsp;\u003cem\u003eijt\u003c/em\u003e\u0026nbsp;\u003c/sub\u003e = \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e + \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003cem\u003eInternet_proportion\u003c/em\u003e +∑\u003cem\u003eControls\u003c/em\u003e\u003csub\u003e\u003cem\u003eijt\u003c/em\u003e\u003c/sub\u003e +\u003cem\u003eProvince\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e+ \u003cem\u003eYear\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e + \u003cem\u003eε\u003c/em\u003e\u003csub\u003e\u003cem\u003eijt\u003c/em\u003e\u003c/sub\u003e (2)\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Baseline Results\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e displays the estimated results of Eq.\u0026nbsp;(1). In all specifications, we cluster the standard error at the provincial level, and control for province- and year-fixed effects. We find that internet usage is significantly associated with an increased likelihood of having a child by 3.31% among urban women in China. As discussed earlier, the influence of internet usage may have differential effects on having a first or second child, thus we further differential women who have given birth to any child into two categories. The estimated results in columns (2) and (3) of Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e showed that internet usage is significantly associated with an increased possibility of having a second child by 2.07%, but does not influence having the first child.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHaving a child\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHaving a first child\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHaving a second child\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInternet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0331**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0207***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0125)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0120)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0074)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProvince FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8,286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8,286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8,286\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: Standard errors in parentheses are clustered at the provincial level. *, **, and *** demonstrate significance at the 10%, 5%, and 1% levels, respectively.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Two-stage Least Squares (2SLS) results\u003c/h2\u003e \u003cp\u003eAs discussed in section \u003cspan refid=\"Sec7\" class=\"InternalRef\"\u003e3.2\u003c/span\u003e, other confounding factors may bias our estimate of the influence of internet usage on fertility behavior. We display the estimation results from the second-stage equation with the internet_proportion as an instrument for internet usage in panel A of Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. After controlling for the non-random placement of internet usage, it remains significantly positively associated with the likelihood of having a child or a second child. The size of the estimated coefficient is larger than those from OLS, which may reflect (i) that the internet usage is not randomly allocated in terms of ex-ante fertility behavior, with the internet being more likely to be found in locations where the fertility rate is lower and (ii) heterogeneity in the overall effect, as IV estimates are local average treatment effect. We further explore the heterogeneous effect in the section below.\u003c/p\u003e \u003cp\u003eThe outcomes of the first-stage estimation shown in Panel B of Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e reveal that there is a significant and positive association between the instrument and internet usage of women. The first-stage F test results reject the possibility of a weak instrument.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e2SLS estimation results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHaving a child\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHaving a first child\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHaving a second child\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePanel A: Second-stage estimation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInternet use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1299*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1058**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0713)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0565)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0524)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePanel B: First-stage estimation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInternet_proportion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2845***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2845***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2845***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0229)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0229)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0229)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProvince FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKleibergen\u0026ndash;Paap F statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e214.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e214.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e214.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7620\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: Standard errors in parentheses are clustered at the provincial level. *, **, and *** demonstrate significance at the 10%, 5%, and 1% levels, respectively. Since certain counties/districts contained only a single respondent in our sample (rendering the instrumental variable undefined), we ultimately retained 7,620 valid observations for the two-stage least squares (2SLS) estimation.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Robustness Checks\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e4.3.1 Alternative definition of internet use\u003c/h2\u003e \u003cp\u003eIn our baseline model, we define internet usage based on whether the women use computers or mobile devices to access the internet. However, with the development of information technology and the introduction of 5G technology, an increasing number of people use mobile devices, which are convenient to carry, to surf the internet. As this use of mobile devices better reflects contemporary internet use, we redefine internet usage as those who access the internet through mobile devices. The Panel A of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e displays the estimated results which are similar to the baseline results shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e4.3.2 Alternative time window\u003c/h2\u003e \u003cp\u003eChina introduced the one-child policy in the early 1980s, which ended in 2013 when the selective two-child policy was introduced (Zhai \u0026amp; Jin, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Previous studies have shown that the selective two-child policy in China failed to have a significant impact on individuals\u0026rsquo; fertility intentions (Zeng \u0026amp; Hesketh, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), while the universal two-child policy introduced in 2015 significantly affected fertility behavior among women. Thus, we limit our sample to the most recent three waves of CFPS since 2016, during which the two-child policy was fully implemented in China. The estimated results presented in panel B of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e is consistent with those shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e4.3.3 Alternative definition of reproductive age\u003c/h2\u003e \u003cp\u003eIn our baseline estimation, we define reproductive age as between 15 and 49 years old. However, women aged over 45 years are less likely to get pregnant and give birth, and it is not common to give birth before reaching the legal marriage age for Chinese women (Sugai et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). We redefined the childbearing age as between 20 to 45 years old, and re-estimated Eq.\u0026nbsp;(1). The results shown in panel C of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e reveal that there is a significant positive correlation between internet usage and reproductive behavior among urban women in China.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e4.3.4 Alternate estimation method\u003c/h2\u003e \u003cp\u003eGiven that fertility behaviors are measured using binary variables in this study, we further use the Probit and Logit regression models to estimate the influence of internet usage on fertility behavior following previous literature (MacPhail \u0026amp; Dong, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The estimated results are shown in Panels D and F of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, showing similar results to the baseline results shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRobustness test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHaving a child\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHaving a first child\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHaving a second child\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePanel A: Alternative definition of internet use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInternet use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0309**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0203***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0114)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0110)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0073)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8,286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8,286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8,286\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePanel B: Alternative time window\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInternet use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0328**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0355***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0128)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0128)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0099)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6,016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003ePanel C: Alternative definition of reproductive age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInternet use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0422***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0263***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0149)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0144)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0092)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7,096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7,096\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0991\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePanel D: Alternate estimation method, logit model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInternet use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0436***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0245*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0233***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0137)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0084)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8,278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8,278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8,128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1852\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePanel E: Alternate estimation method, probit model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInternet use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0388***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0219*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0209***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0102)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0127)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0077)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8,278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8,278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8,128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1936\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProvince FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: Standard errors in parentheses are clustered at the provincial level. *, **, and *** demonstrate significance at the 10%, 5%, and 1% levels, respectively. We control for household and individual characteristics, province-, and year-fixed effects in all specifications.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Heterogeneity Analysis\u003c/h2\u003e \u003cp\u003eConsidering that the impact of internet usage may affect fertility behavior differently among different sub-groups of women, we further conduct a series of heterogeneity analyses.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e4.4.1 By household registration\u003c/h2\u003e \u003cp\u003eA notable feature in the urban areas of China is that the allocation of public services has been regulated by the household registration system (the \u003cem\u003eHukou\u003c/em\u003e system), under which people with agricultural and non-agricultural hukou enjoy different social welfare and benefits (Dong et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Thus, we differentiate the household registration of women into agricultural and non-agricultural \u003cem\u003ehukou\u003c/em\u003e and re-estimate Eq.\u0026nbsp;(1). The estimated results in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e show that internet usage has no significant influence on whether to have a first child among both agricultural and non-agricultural \u003cem\u003ehukou\u003c/em\u003e holders. In terms of whether or not to have a second child, internet usage has a significantly positive effect on second births among the two groups of women.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHeterogeneity analysis by Hukou\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eHaving a child\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eHaving a first child\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eHaving a second child\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgricultural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-agricultural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAgricultural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-agricultural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAgricultural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNon-agricultural\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInternet use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0440***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0276**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0250*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0196***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0158)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0111)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0150)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0098)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.0132)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.0067)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProvince FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4,007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4,279\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0964\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: Standard errors in parentheses are clustered at the provincial level. *, **, and *** demonstrate significance at the 10%, 5%, and 1% levels, respectively. We control for household and individual characteristics, province-, and year-fixed effects in all specifications.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e4.4.2 By education level\u003c/h2\u003e \u003cp\u003eThe existing literature has highlighted the heterogeneity in fertility outcomes across different socioeconomic statuses (Bollen et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Lim, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Thus, we further investigate whether the impact of internet usage on the fertility behaviors of urban women varies according to their educational attainment. We categorized those with post-secondary degrees or above as the high education group, while those with less than post-secondary education formed the low education group. The estimated results summarized in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e revealed that internet usage has significantly increased the likelihood of urban women having a first and second child among women with a lower education level.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHeterogeneity analysis by education level\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eHaving a child\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eHaving a first child\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eHaving a second child\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInternet use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0448***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0207*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0242***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0303)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0116)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0300)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0113)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.0220)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.0085)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProvince FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5,523\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1127\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: Standard errors in parentheses are clustered at the provincial level. *, **, and *** demonstrate significance at the 10%, 5%, and 1% levels, respectively. We control for household and individual characteristics, province-, and year-fixed effects in all specifications.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e4.4.3 By region\u003c/h2\u003e \u003cp\u003eGiven that regional disparities exist in China in terms of fertility behaviors due to economic and cultural reasons, (Li et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), We divided the sample into four regions, namely East, Central, West, and Northeast according to the classification criteria defined by the National Bureau of Statistics of China, and display the estimated results in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The results reveal distinct patterns across women in different regions. Internet utilization exhibited a non-significant and even negative relationship with the reproductive behaviors of women of childbearing age in the Northeast region.\u003c/p\u003e \u003cp\u003eFor the decision to have a first child, the positive effect of internet usage was statistically significant only in the Central region, with no discernible impact observed in the other regions. However, when examining the choice to have a second child, the influence of internet use was most pronounced, and statistically significant at the 5% level, for women of childbearing age residing in the Western regions of China.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Mechanism Analysis\u003c/h2\u003e \u003cp\u003eThe baseline regression analysis and subsequent robustness checks have revealed that the impact of internet usage on having a second child is more pronounced among urban women of childbearing age. Building on these initial findings, the current section delves deeper into the potential mechanisms underpinning this observed relationship, with a particular focus on the roles of flexible employment and entrepreneurship. The analysis centers on the subpopulation of urban women of childbearing age who have already given birth to one child and are thus likely to have a second child.\u003c/p\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e4.5.1 Workplace flexibility\u003c/h2\u003e \u003cp\u003eIn CFPS, respondents were asked to report the primary location of their employment, with the following response options: (1) outdoor, (2) workshop, (3) office, (4) home, (5) other indoor workplaces, (6) in transportation, and (7) other workplaces. We make use of the above question to measure workplace flexibility and assign a value of 1 to women who reported their workplace to be their home, other indoor settings, or other locations beyond the traditional office environment. All other respondents were coded as 0, reflecting an office-based employment arrangement. The regression results presented in Column (1) of Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e8\u003c/span\u003e shed light on the relationship between internet usage and the likelihood of engaging in flexible employment among urban women in China. The findings indicate that internet utilization significantly promotes the adoption of flexible work arrangements.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e4.5.2 Entrepreneurship\u003c/h2\u003e \u003cp\u003eAnother mechanism linking internet usage to the fertility behaviors of urban women lies in entrepreneurship. The extant literature has suggested that working women often limit their family size to avoid compromising their career advancement opportunities (Ning et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In contrast, self-employment and entrepreneurial activities may afford greater schedule flexibility and reduced risk of job loss, thereby potentially enabling these women to more readily pursue their fertility goals.\u003c/p\u003e \u003cp\u003eTo capture the entrepreneurial activities of the respondents, we leverage a relevant question about the primary occupation of women. If the women worked in private enterprises or were self-employed, we define entrepreneurial as equal to one, and 0 if the primary occupation of the women was agricultural production and management, non-farm employment, etc.\u003c/p\u003e \u003cp\u003eThe regression results presented in Column (2) of Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e8\u003c/span\u003e illuminate the relationship between internet usage and the likelihood of urban women of childbearing age engaging in entrepreneurial activities. The findings indicate that internet utilization significantly promotes entrepreneurship among women with one child.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePotential mechanisms\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlexible employment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEntrepreneurship\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInternet use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0804***\u003c/p\u003e \u003cp\u003e(0.0122)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0304**\u003c/p\u003e \u003cp\u003e(0.0137)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProvince FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,802\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0419\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: Standard errors in parentheses are clustered at the provincial level. *, **, and *** demonstrate significance at the 10%, 5%, and 1% levels, respectively. We control for household and individual characteristics, province-, and year-fixed effects in all specifications.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"5. Discussion and Conclusions","content":"\u003cp\u003eWhile prior research has examined the relationship between internet usage and fertility intentions, limited attention has been paid to understanding its impacts on actual fertility behaviors. To address this gap, we leverage data from the CFPS to explore the effects of internet utilization on the fertility behaviors of urban, married women of reproductive age in China.\u003c/p\u003e \u003cp\u003eThe findings indicate that internet usage significantly increases the likelihood of childbirth among urban women in China, with a particularly pronounced effect on the decision to have a second child. To unpack the potential mechanisms underlying these relationships, the analysis delves into two key pathways. First, the internet has a positive impact on the decision to have a second child by promoting flexible employment which relaxed time constraints faced by women. Second, internet use increases the income of women of reproductive age by enabling their entrepreneurship, which positively impacts their decision to have a second child.\u003c/p\u003e \u003cp\u003eWe also examined whether the impact of internet use on fertility behaviors differed across sub-groups of women. Interestingly, results showed that internet usage has a larger positive effect on having a second child in urban women with agricultural \u003cem\u003ehukou\u003c/em\u003e and low education. This may be due to higher internet usage by women from non-agricultural households than women from agricultural households. Moreover, women from non-agricultural households or those with higher education levels in urban areas usually have a higher standard of living, pursue a more stable life, and are less likely to start their businesses; hence, internet usage appears to have less impact on them. Finally, internet usage has a larger impact on having a first child in the central region, while in the western region, it significantly increases the likelihood of having a second child.\u003c/p\u003e \u003cp\u003eThis study makes several valuable contributions to the existing literature on the impact of internet use on fertility behaviors. However, the paper also acknowledges several limitations, which could be addressed in future research to further expand the knowledge base in this domain. First, the CFPS collects data on urban women of childbearing age every two years, limiting the ability to observe the persistent effects of internet use on fertility behaviors. The restriction of the sample to married women further reduces the representativeness of the analysis. Second, while the study examines two key mechanisms underlying the relationship between internet use and fertility behaviors, there are likely to be additional factors at play. Future studies could explore a more comprehensive set of mechanisms to enhance the understanding of this complex phenomenon. Third, the CFPS data has a considerable number of missing values for control variables, such as personal income, which are relevant to the fertility decisions of individual women. The inability to account for these factors may introduce potential biases in the analysis. Finally, the current study focuses on three specific fertility behaviors, but there are various other dimensions of fertility that could be examined, for instance, the birth intervals. Future research expanding the scope of fertility-related outcomes would contribute to a more holistic understanding of the internet's impact in this context.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed for this study can be found in the CFPS repository. Please see http://www.isss.pku.edu.cn/cfps/ for more details, further inquiries can be directed to the corresponding author.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdsera, A. (2005). Vanishing Children: From High Unemployment to Low Fertility in Developed Countries. \u003cem\u003eAmerican Economic Review\u003c/em\u003e, \u003cem\u003e95\u003c/em\u003e(2), 189\u0026ndash;193. https://doi.org/10.1257/000282805774669763\u003c/li\u003e\n\u003cli\u003eAngrist, J., \u0026amp; Evans, W. 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The Internet and the Transformation of the Reality of Everyday Life: Toward a New Analytic Stance in Sociology. \u003cem\u003eSociological Inquiry\u003c/em\u003e, \u003cem\u003e76\u003c/em\u003e(4), 458\u0026ndash;474. https://doi.org/10.1111/j.1475-682X.2006.00166.x\u003c/li\u003e\n\u003cli\u003eZhu, W., \u0026amp; Hong, X. (2022). Are Chinese Parents Willing to Have a Second Child? Investigation on the Ideal and Realistic Fertility Willingness of Different Income Family. \u003cem\u003eEarly Education and Development\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e(3), 375\u0026ndash;390. https://doi.org/10.1080/10409289.2021.1955581\n\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Internet use, Fertility behavior, Urban, China","lastPublishedDoi":"10.21203/rs.3.rs-6542950/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6542950/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe examine the influence of the internet on the fertility behaviors of urban women aged 15\u0026ndash;49 years in China using data from the 2014\u0026ndash;2020 China Family Panel Studies. We find that internet usage is significantly and positively associated with an increased likelihood of having a second child among urban women of childbearing age. The impact of internet usage on fertility behavior is heterogeneous across regions as well as household registration and education level of women. Workplace flexibility and entrepreneurship may serve as possible mechanisms through which internet usage contributes to women\u0026rsquo;s fertility behaviors of having a second child.\u003c/p\u003e","manuscriptTitle":"More internet, more babies? The impact of internet use on fertility behaviors among urban women in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-17 09:23:06","doi":"10.21203/rs.3.rs-6542950/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-03T10:55:48+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-26T14:44:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-26T02:32:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"59093283374592705418587239217191698143","date":"2025-07-30T14:32:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"286631578036681124259753510344116754142","date":"2025-06-28T10:37:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-22T03:51:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"337687838872170050365458748844186452372","date":"2025-06-13T01:36:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-12T15:42:41+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-07T02:51:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-16T13:32:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-16T13:31:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2025-04-28T01:41:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"196d7fb4-32a4-4b8e-9836-f9406430cd2b","owner":[],"postedDate":"June 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":50018988,"name":"Social science/Science technology and society"},{"id":50018989,"name":"Social science/Sociology"}],"tags":[],"updatedAt":"2025-12-03T11:08:44+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-17 09:23:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6542950","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6542950","identity":"rs-6542950","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}