Normative Fertility Attitudes in Urban China: Evidence from a Factorial Survey Experiment

Normative Fertility Attitudes in Urban China: Evidence from a Factorial Survey Experiment | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Normative Fertility Attitudes in Urban China: Evidence from a Factorial Survey Experiment Jia Miao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9097806/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Persistently declining fertility has become a major demographic concern in China. While existing research has examined the role of economic resources and family policies in shaping fertility intentions, less attention has been paid to how gender inequality within families influences normative evaluations of childbearing. This study examines how family conditions, particularly the division of domestic labor, shape fertility norms in Beijing, Shanghai, Guangzhou, and Shenzhen. The analysis draws on data from the Four Metropolises Urban Neighborhood Survey, which incorporates a factorial survey experiment that randomly presents respondents with hypothetical couples whose family conditions vary across several dimensions. Ordered logistic regression results show that respondents evaluate couples with more egalitarian divisions of household labor, greater financial resources, and greater childcare support as better positioned for childbearing. Women respond more strongly than men to equal housework arrangements, and these gender differences are especially pronounced among college-educated respondents. These findings highlight the importance of gender inequality within families in shaping fertility-related norms and suggest that policies aimed at raising fertility in highly developed urban areas may need to address not only economic constraints but also persistent gender inequalities within households. normative fertility attitudes ideal number of children factorial experimental design Chinese metropolises Figures Figure 1 Figure 2 Introduction Persistently low fertility has become one of the most pressing demographic challenges in many advanced societies (Morgan and Taylor 2006 ). In several East Asian societies, including mainland China, Japan, and South Korea, fertility rates have declined to extremely low levels over the past two decades (Cheng 2020 ). The total fertility rate of mainland China fell to 1.3 in 2020 and has continued to decline (National Bureau of Statistics of China 2020 ), with fertility levels in large metropolitan areas even lower than the national average. Shanghai, for example, reported a total fertility rate of only 0.7 in 2022 (Shanghai Municipal Health Commission 2023 ). These trends have raised growing policy concerns regarding rapid population aging, shrinking labor forces, and long-term economic sustainability. In response, governments across East Asia have introduced a range of policy initiatives aimed at encouraging childbearing. These measures typically focus on reducing the economic costs of raising children, such as through financial incentives, and facilitating work-family reconciliation, for example by expanding childcare services (Gauthier and Gietel-Basten 2025 ). Despite increasing policy attention to fertility support, fertility rates in many highly developed urban areas remain persistently low. This raises an important question for both researchers and policymakers: what social conditions shape people’s perceptions of whether couples are well positioned to have children in contemporary societies? One influential explanation for this persistent gap between policy efforts and fertility outcomes highlights the role of gender inequality within families. The gender revolution framework argues that fertility tends to decline when gender equality advances more rapidly in the public sphere, such as in education and employment, than in the private sphere of household labor and caregiving (McDonald 2000 ; Goldscheider, Bernhardt, and Lappegård 2015 ). In many East Asian societies, women’s educational attainment and labor force participation have increased dramatically, yet domestic responsibilities remain highly gendered (Cheng 2020 ). As a result, many women continue to face a “second shift” of housework and childcare after returning home from paid work. Under such circumstances, the prospect of having children may be perceived as particularly burdensome, especially among highly educated women with demanding careers in large cities. A growing body of research has examined the impact of gender inequality in the division of domestic labor on fertility intentions and behaviors in Western societies (McDonald 2000 ; Mills et al. 2008 ; Goldscheider, Bernhardt, and Brandén 2013 ; Sullivan, Billari, and Altintas 2014 ; Begall and Hiekel 2025 ; Goldin 2025 ). However, relatively little empirical work has examined how gender inequality within families shapes normative evaluations of childbearing, particularly among Chinese populations. Moreover, household labor arrangements are often closely intertwined with other family conditions, such as household income and access to childcare support. Because these factors tend to co-occur in real-life family settings, traditional observational studies face difficulties in disentangling their independent effects To address this empirical and methodological gap, this study examines how Chinese respondents evaluate under what circumstances a couple “should” have a certain number of children, with particular attention to gender inequality within families. Normative judgments are important because they reflect broader fertility norms and may influence the perceived legitimacy and effectiveness of family policies. This study employs a factorial survey experiment embedded in a large-scale survey conducted in four major Chinese metropolises: Beijing, Shanghai, Guangzhou, and Shenzhen. Respondents were randomly presented with hypothetical couples whose family conditions varied along several dimensions, including division of housework, household income, wife’s education, grandparental childcare support, and access to public childcare services. Respondents were then asked to evaluate the ideal number of children for the described couple. By experimentally varying these conditions, the design allows us to identify how individuals evaluate whether couples should have children under different family circumstances while isolating the independent effects of each factor. Specifically, this study addresses three questions. First, how do key family conditions, such as division of housework, household socioeconomic conditions, and the availability of childcare support, affect people’s assessments of the ideal number of children for a couple? Second, do men and women evaluate these fertility conditions differently? Third, do these gender differences vary by education level, particularly among highly educated individuals facing greater opportunity costs of childbearing? By addressing these questions using a factorial survey experiment, the study provides new evidence on how gender inequality in the division of domestic labor shapes fertility-related norms in low-fertility urban contexts. This study contributes to research on fertility and family policy in three ways. First, it shifts attention from individuals’ own fertility intentions to normative evaluations of fertility conditions, capturing how people assess whether childbearing is appropriate or feasible under specific family circumstances. By asking respondents to evaluate hypothetical couples presented in standardized scenarios, the study isolates the effects of specific family conditions while reducing the influence of unobserved individual characteristics that often shape self-reported fertility attitudes. Second, the study advances the literature on low fertility by examining gender inequality within families as a key mechanism shaping fertility-related norms. While existing research has widely discussed the role of gender equity in fertility decline, empirical studies rarely isolate the impact of the division of domestic labor on how people evaluate the desirability of childbearing. The study further investigates whether the importance individuals attach to household labor arrangements varies by educational attainment. By highlighting both the role of the division of domestic labor and its heterogeneous effects across social groups, the analysis links micro-level gender dynamics within families to broader patterns of low fertility in highly developed urban context. Third, the study contributes to policy debates on low fertility by highlighting the importance of gender relations within families. Much of the existing policy discussion focuses on economic and time constraints, emphasizing financial incentives, housing support, or the expansion of childcare services. While such measures may alleviate some practical barriers to childbearing, they may be insufficient if gender inequalities in household labor persist. By drawing attention to how the division of domestic labor shapes normative evaluations of childbearing, this study suggests that policies aimed at addressing fertility decline need to move beyond economic resources alone and consider the broader gendered organization of family life. Family Conditions, Gender Inequality, and Fertility Norms Family conditions and fertility norms This study focuses on fertility norms, referring to socially shared expectations about the ideal number of children, or ideal family size couples should have. The ideal number of children can be understood as an abstract concept formed through family socialization and subsequently adjusted in response to situation-specific constraints (Karabchuk et al. 2022 ). Evaluations of the ideal family size depends on perceptions of whether couples possess the resources and conditions considered appropriate for raising children. Demographers have long relied on measures of ideal family size to capture underlying fertility norms and long-term reproductive preferences (Behrman 2025). Accordingly, a growing body of research has examined the social and economic conditions that shape individuals’ evaluations of the ideal number of children (Hirschman 1994 ; Behrman and Rosenzweig 2002 ; Torr and Short 2004 ; Bachrach and Morgan 2013 ; Balbo et al. 2013 ). The present study examines several key family conditions that may influence how individuals evaluate the ideal number of children. Besides the division of domestic labor, these include household income, women’s education, and the availability of childcare support, all of which are closely related to the organization of housework and caregiving within families. Together, these conditions reflect the core resources required for raising children, including financial means, time, human capital, and caregiving capacity. They also correspond to two major domains of family policy aimed at supporting families and encouraging childbearing: financial assistance and policies that facilitate work-family reconciliation (Gauthier and Gietel-Basten 2025 ). Household income and childcare provision primarily relate to the direct costs of childrearing, whereas women’s education and the division of domestic labor are closely associated with the indirect and opportunity costs of caregiving. Economic resources play an important role in shaping normative fertility attitudes. According to the economic theory of fertility, parents weigh the costs and benefits of childbearing when deciding how many children to have (Becker et al. 1990 ). As societies undergo economic development and demographic transition, the economic value of children declines while the costs associated with investing in children’s “quality”, such as education and health, increase (Morgan and Taylor 2006 ). Under such circumstances, families often prioritize investing more resources in fewer children. Households with higher incomes may therefore be perceived as better able to afford the financial costs associated with raising children and providing high-quality upbringing. Empirical studies have consistently documented a positive association between family income and fertility ideals or intentions (Yu et al. 2023 ). Mother’s education may also influence how individuals evaluate the desirability of childbearing. Educational attainment represents an important form of human capital that shapes parenting practices, expectations about childrearing, and family investments in children (Augustine 2017 ). Women’s education is also closely linked to household socioeconomic resources, as highly educated women are more likely to partner with similarly educated and higher-earning spouses (Oppenheimer 1994 ; Mills et al. 2008 ). These factors may lead individuals to view highly educated mothers as better positioned to raise children. At the same time, higher levels of education increase the opportunity costs of childbearing, as career interruptions associated with motherhood can result in the well-documented “motherhood wage penalty” (Budig and England 2001 ). As a result, respondents may also interpret a mother’s educational attainment as reflecting potential costs of childbearing. Taken together, a mother’s education may serve as a signal of both family resources and the potential opportunity costs associated with childrearing. In addition, childcare support also plays a critical role in shaping evaluation of ideal family size by reducing the time and career trade-offs associated with childrearing. When childcare responsibilities can be partially outsourced to formal childcare services or shared with family members, the burden of caregiving becomes more manageable for parents (Schultz 1997 ). Empirical research across different societies has shown that the availability of formal childcare services (e.g., subsidized or publicly provided childcare) and informal childcare arrangement (e.g., support from grandparents) can increase individuals’ desired or intended number of children (Ji et al. 2015 ; Pronzato 2017 ). At the macro level, cross-national studies suggest that countries where childcare responsibilities are more strongly supported by public institutions tend to maintain higher fertility levels than societies where childcare responsibilities remain primarily within families (Balbo et al. 2013 ). In the Chinese context, recent studies also document positive associations between childcare provision and fertility intentions (Yu, Shen, and Xie 2023 ; Chen and Hu 2025 ). In sum, individuals’ evaluations of the ideal number of children depend on their perceptions of whether couples possess the resources and support systems necessary to raise children. When families are perceived to have sufficient financial resources, supportive childcare arrangements, and favorable caregiving conditions, childbearing is more likely to be viewed as feasible and desirable. However, some family characteristics, such as mothers’ education, may signal both greater resources and higher opportunity costs, making their expected influence less clear. Based on the preceding discussion, the following hypothesis is proposed: Hypothesis 1 Couples described as having more favorable family conditions, including higher income and greater childcare support, will be evaluated as having a higher ideal number of children. Gender inequality within families and fertility norms While economic resources and childcare support shape the feasibility of childbearing, a growing body of research highlights the importance of gender inequality within families in shaping fertility outcomes. The gender revolution framework argues that fertility patterns are closely linked to the degree of gender equality in both the public and private spheres (McDonald 2000 ; Goldscheider, Bernhardt, and Lappegård 2015 ). In many societies, women’s educational attainment and labor force participation have increased substantially over recent decades, yet domestic labor and childcare responsibilities remain disproportionately concentrated on women. When gender equality advances more rapidly in the public sphere than in the private sphere, women may face increasing work-family conflicts that discourage childbearing (Goldin 2025 ). Goldscheider, Bernhardt, and Lappegård ( 2015 ) conceptualize this process as a two-stage gender revolution. In the first stage, women’s entry into the labor market leads to delays in marriage and parenthood as women invest in education and career development. In the second stage, greater gender equality within the household, particularly increased male participation in housework and childcare, facilitates a reconciliation between work and family roles and may contribute to higher fertility. Under such conditions, a more egalitarian division of domestic labor can reduce women’s caregiving burden and increase the perceived feasibility of having children. Empirical studies provide substantial support for this argument. At the aggregate level, research shows that fertility declines more rapidly in societies where domestic responsibilities remain highly unequal between men and women (McDonald 2000 ; Goldscheider, Bernhardt, and Brandén 2013 ; Sullivan, Billari, and Altintas 2014 ). Drawing on historical data from twelve countries, Goldin ( 2025 ) similarly finds that fertility rates decline more sharply in societies where household and caregiving responsibilities are less equally shared between partners. At the individual level, studies from Western countries show that a more equitable division of household labor is associated with earlier transitions to parenthood and stronger fertility intentions (Torr and Short 2004 ; Mills et al. 2008 ; Zhou and Kan 2019 ). Recent research in Nordic countries further demonstrates that a satisfactory division of household labor significantly increases fertility intentions, particularly among individuals who hold gender-egalitarian attitudes (Begall and Hiekel 2025 ). The pace of the gender revolution, however, varies across societies. In East Asian contexts, gender equality in the public sphere has progressed more rapidly than equality within the family (Sullivan, Billari, and Altintas 2014 ). Despite substantial gains in women’s education and employment, women in East Asia continue to perform the majority of housework and childcare (Kan and Zhou 2022 ). Comparative research on mainland China, Japan, South Korea, and Taiwan shows that women bear the bulk of domestic responsibilities in these societies (Kan and Hertog 2017 ). Under such conditions, the unequal distribution of domestic labor may increase the perceived costs of childbearing and contribute to persistently low fertility in the region (Brinton and Oh 2019 ). Because women disproportionately bear the time and opportunity costs of childbearing and childrearing (Torr and Short 2004 ), they may be particularly sensitive to family conditions that affect the distribution of caregiving labor. When evaluating whether couples should have children, women may place greater importance on the division of domestic labor. These gendered experiences may therefore lead men and women to evaluate the desirability of childbearing under different family conditions in distinct ways. Based on the preceding discussion, I derive the following hypothesis: Hypothesis 2 Couples described as having an equal division of housework will be evaluated as having a higher ideal number of children, with this effect being stronger among women than among men. Education and stratified gender responses Potential differences in the normative fertility attitudes between men and women may vary across education levels. First, the costs and rewards of parenthood can be more divergent for those with higher education. Many professional and academic careers demand long hours and intense commitment for success, making it challenging for highly educated individuals to balance work and parenthood (Coltrane 2004 ). Second, higher education often exposes women to more liberal ideas and raises their expectations for gender equality (Kane 1995 ). More highly educated women are more inclined to advocate for an equal division of family responsibilities and hold less traditional views regarding the importance and permanence of marriage and childbearing (Koropeckyj-Cox and Pendell 2007 ). Third, individuals with higher education are generally more aware of the challenges associated with balancing work and parenthood, as well as the potential risks involved including economic insecurity. This heightened awareness may make them more sensitive to family conditions such as childcare availability and the division of domestic labor, influencing their fertility decisions (Koropeckyj-Cox and Pendell 2007 ). The educational gradients in gender gaps regarding fertility attitudes have been empirically examined in a limited number of studies. For instance, researchers find that the gender difference in attitudes about childlessness is greater among people with college degrees than their less-educated counterparts in the U.S. (Koropeckyj-Cox and Pendell 2007 ). The positive impacts of childcare-sharing with men on fertility intentions are particularly salient among tertiary-educated wives in Taiwan, who often exhibit greater economic empowerment and higher expectations for gender equality at home (Cheng and Hsu 2020 ). In sum, these findings suggest that educational attainment may amplify gender differences in fertility-related evaluations. Highly educated women may be particularly responsive to family conditions that reduce the burden of caregiving or facilitate work-family reconciliation. In contrast, such gender differences may be less pronounced among individuals with lower levels of education, for whom the opportunity costs associated with childbearing are typically smaller. Building on this discussion, I propose the following hypothesis: Hypothesis 3 Gender differences in the evaluation of fertility conditions will be more pronounced among college-educated respondents than among those without a college degree. Data, Measurements, and Methods Data This study uses data from the Four Metropolises Urban Neighborhood Survey conducted in Beijing, Shanghai, Guangzhou, and Shenzhen, a mobile phone survey implemented by Shanghai University from October 25th to November 27th, 2021. According to the National Bureau of Statistics of China, the penetration of mobile phones was 178.43% in Beijing, 171.99% in Shanghai, and 123.30% in Guangdong province, in which both Guangzhou and Shenzhen are located (National Bureau of Statistics of China 2020 ) by 2020 1 , indicating the feasibility of gaining a representative sample using a mobile random-digit-dialing (RDD). This survey used a sample framework provided by the three major mobile network operators in China: China Mobile, China Telecom, and China Unicom. The three operators owned approximately 1.64 billion mobile phone subscribers, comprising nearly 100 percent of China’s national market share by 2021 (Ministry of Industry and Information Technology of the People’s Republic of China 2023). The raw mobile RDD sample was created based on respondents’ geographical location identified by the first seven digits of a Chinese mobile phone number, which are known as a number’s “series block.” The last four digits of a mobile phone number within all series blocks were randomly generated by the survey system. The research team then screened all numbers in the database and removed the disconnected numbers from the sample list. Mobile phone numbers in each city’s final sample list were selected using a simple random sampling method. To verify eligibility, respondents were asked whether they were permanent residents (i.e., whether they had lived in the survey cities for at least six months). The numbers of respondents in Shanghai, Beijing, Guangzhou, and Shenzhen are 3,616, 1,586, 1,343, and 1,460, respectively. This study restricts the respondents to those aged 15 to 49, corresponding to women’s reproductive age. The normative fertility attitudes of people within this age range are the most significant for policy implications. After removing cases with missing values for dependent or independent variables (3.98 percent of all cases), the analytical sample consists of 5,709 respondents. Factorial survey experimental design Over the past two decades, survey experiments have become an increasingly influential methodological innovation in social science research (Mize and Manago 2022 ). By embedding experimental designs into large-scale surveys, these methods enable researchers to estimate causal effects while retaining the advantages of large sample sizes, such as enhanced external validity and the ability to conduct cross-group comparisons (Schachter and Weisshaar 2025). Survey experiments are ideal for addressing questions about the causes of differences in attitudes, perceptions, or beliefs, the outcomes that are well-suited for measurement in a survey (Schaeffer and Dykema 2020 ). In the field of family studies, survey experiments have gained growing attention over the past decade, especially in research on normative beliefs, gender roles, and fertility intentions (Doan, Quadlin, and Khanna 2024 ). Recent studies have applied these methods to explore topics such as fertility intentions (Lappegård et al. 2022 ) and the desired number of children (Marshall and Shepherd 2018 ). Within the broader category of survey experiments, the factorial survey has become a particularly valuable method for investigating the social norms and cognitive evaluations that shape fertility-related attitudes. Unlike traditional surveys that ask respondents to report their own intentions or attitudes in the abstract, factorial surveys present systematically varied, realistic hypothetical scenarios (vignettes) and ask respondents to evaluate them (Wallander 2009 ). This design enables researchers to isolate both the independent and interactive effects of multiple factors on respondents’ judgments, offering a more nuanced understanding of how fertility attitudes are formed. The factorial survey helps overcome several limitations of traditional approaches. First, people are not always fully aware of certain factors’ impacts on their judgments and are thus incapable of explicating such impacts when asked about them. Using standardized vignettes in which selected factors believed to influence the judgment are simultaneously manipulated, the factorial survey experimental design can uncover the structures of complex judgments (Jasso 2006 ). Second, because respondents are randomly assigned to different versions of the vignettes, the design effectively eliminates selection bias and improves internal validity, which is often a concern in observational studies (Wallander 2009 ). Third, factorial surveys mitigate social desirability bias by asking respondents to evaluate hypothetical others, rather than disclose personal attitudes or intentions (Schachter and Weisshaar 2025). Fourth, by employing a population-representative sample, this approach addresses a common limitation of laboratory experiments: the lack of external validity (Auspurg and Hinz 2015 ). Several studies have employed factorial experiments to examine fertility intentions and attitudes in both Asian and Western contexts. These studies have primarily focused on how factors such as parental employment status and family-friendly policies (Yu et al. 2023 ; Guetto, Alderotti, and Vignoli 2025 ; Chen and Hu 2025 ), and political efficacy (Cheung, Lui, and Mu 2024 ) influence ideal family size and fertility intentions. Following the factorial survey experimental method (Jasso and Opp 1997 ; Jasso 2006 ), and based on the abovementioned empirical evidence, this study focuses on the five determinants of fertility attitudes: family income level (low vs. medium vs. high), wife’s education (with vs. without a college degree), division of housework (husband rarely doing housework vs. husband does at least half of housework), grandparenting (grandparents can provide vs. cannot provide childcare), and free public childcare services (yes vs. no). A complete factorial design (one dimension with three levels and four with two levels) yields 48 (3*2*2*2*2) vignettes. The detailed vignette dimensions and levels are listed in Table 1 . Table 1 Vignette dimensions and levels 1 Dimensions (Variables) Levels (Values) Vignette text Family income level 0 Low income 1 Medium income 2 High income 2 Wife’s education 0 Without a college degree 1 With a college degree 3 Husband’s housework contribution 0 Husband rarely does housework 1 Husband does half of housework 4 Grandparenting 0 Grandparents cannot provide childcare 1 Grandparents can provide childcare 5 Public childcare service 0 Free public childcare service is unavailable 1 Free public childcare service is available [Table 1 About Here] The survey asked the respondents what the ideal number of children would be for the described couple. The research team presented one randomly selected vignette to each respondent to avoid possible respondent fatigue. This setup also prevents respondents from comparing vignettes and thus reduces their tendency to provide a socially desirable answer. An example of the exact wording of the vignettes is listed in Fig. 1 . [Figure 1 About Here] Measurements and Methods The outcome variable is the ideal number of children specified by respondents for described couples under specified conditions. Responses with a value greater than 3 are coded as 3 because less than 0.5 percent of respondents reported that the ideal number of children of described couples would be 4 or more. The key independent variables are the five vignette-level variables: family income level, wife’s education, division of housework, whether the grandparent could help with childcare, and whether free public childcare services are available. This study controls for a set of respondents’ demographic and socioeconomic characteristics that are assumed to be relevant to fertility attitudes, including age, gender, education level, marital status, number of children, resident registration status ( hukou ), employment status, homeownership, and family income. City fixed effects are also included to account for unobserved contextual differences across cities, such as economic conditions, local family policies, and prevailing fertility norms that may shape fertility attitudes. Because normative fertility attitudes are measured on an ordered scale, the main analyses employ ordered logit models. As a robustness check, I also estimate multinomial logit models that relax the proportional odds assumption and allow the effects of covariates to vary across outcome categories. The substantive conclusions remain similar across model specifications. Because the ordered logit model provides a more parsimonious specification in the presence of interaction terms, I present these models as the main results and use multinomial logit models as a robustness check. Empirical Results Descriptive statistics Table 2 shows the descriptive statistics of the analytical sample. The average ideal number of children for the couples described in the vignettes is 1.24. Additionally, 12.7% of respondents suggest that the couple in the vignette should not have any children under any circumstances. Only 3.5% believe that the ideal number of children should be three or more, despite the introduction of the Three-child policy on May 31, 2021, five months before the interviews took place. Younger, more educated, and unmarried respondents are more likely to report a smaller ideal family size (see Appendix 2). Gender differences are evident, as men report more positive fertility attitudes, with an average ideal number of children of 1.3, compared to 1.15 reported by women. Table 2 Descriptive statistics of analytical sample: Four Metropolises Urban Neighborhood Survey Variables All N = 5,709 Females N = 2,143 Males N = 3,566 Average ideal number of children 1.24 (0.71) 1.15 (0.69) 1.30 (0.72) Ideal number of children 0 12.72% 14.98% 11.36% 1 53.69% 56.84% 51.79% 2 30.11% 26.13% 32.50% 3 3.49% 2.05% 4.35% Age 31.55 (7.97) 31.23 (8.24) 31.74 (7.81) Female (Yes = 1) 37.48% College education (Yes = 1) 53.20% 58.80% 49.83% Local hukou (Yes = 1) 46.38% 52.59% 42.65% Married (Yes = 1) 52.47% 55.30% 52.13% Have children (Yes = 1) 44.67% 44.98% 44.48% Employed (Yes = 1) 86.35% 80.59% 89.82% Homeownership (Yes = 1) 48.87% 54.78% 45.32% Family income level 1 = less than 100k 17.73% 18.20% 17.44% 2 = 100k-200k 26.43% 27.25% 25.94% 3 = 200k-300k 17.87% 16.94% 18.42% 4 = 300k-500k 14.24% 14.56% 14.05% 5 = more than 500k 14.68% 12.41% 16.04% 6 = refusal 9.06% 10.64% 8.10% City Beijing 19.44% 20.67% 18.70% Shanghai 44.98% 47.04% 43.75% Shenzhen 21.81% 18.48% 23.81% Guangzhou 13.77% 13.81% 13.74% Note: Standard deviations in parentheses. [Table 2 About Here] Figure 2 shows the ideal number of children across different vignette dimensions. Without adjusting for covariates, respondents report larger ideal family sizes for couples with more favorable family conditions. When the husband shares at least half of the housework, the reported ideal family size increases from 1.14 to 1.35. When the couple is described as having a high income, the reported ideal family size rises to 1.44, compared with 1.05 for a low-income couple. Informal childcare support (grandparental help) and formal childcare support (public childcare services) increase the reported ideal number of children to 1.38 and 1.40, respectively. By contrast, respondents’ evaluations do not vary significantly by whether the mother holds a college degree. [Figure 2 About Here] Family conditions and normative fertility attitudes in Chinese metropolises Ordered logit models are used to estimate the effects of vignette-level characteristics on respondents’ normative fertility attitudes and to examine whether these effects vary by gender. Table 3 presents the estimated coefficients. Positive coefficients indicate that the vignette characteristics are associated with more pronatalist evaluations of the hypothetical couple. Table 3 Ordered logit coefficients of normative fertility attitudes on vignettes and socioeconomic characteristics Treatment variables Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Housework division (ref.=husband rarely does housework) Husband does half of housework 0.690*** 0.546*** 0.691*** 0.690*** 0.689*** 0.691*** (0.053) (0.068) (0.053) (0.053) (0.053) (0.053) Family income (ref.=low-income family) Medium-income family 0.613*** 0.611*** 0.702*** 0.613*** 0.615*** 0.614*** (0.064) (0.064) (0.085) (0.064) (0.064) (0.064) High-income family 1.230*** 1.228*** 1.283*** 1.230*** 1.234*** 1.230*** (0.067) (0.067) (0.085) (0.067) (0.067) (0.067) Wife’s education (ref.=without a college degree) With a college degree 0.038 0.034 0.040 0.046 0.039 0.038 (0.053) (0.053) (0.053) (0.068) (0.053) (0.053) Grandparenting (ref.= Grandparents cannot provide childcare) Grandparents can provide childcare 0.912*** 0.915*** 0.914*** 0.912*** 1.006*** 0.912*** (0.054) (0.054) (0.054) (0.054) (0.070) (0.054) Free public childcare service (ref.= Unavailable) Available 0.948*** 0.946*** 0.949*** 0.948*** 0.949*** 0.991*** (0.054) (0.054) (0.054) (0.054) (0.054) (0.069) Treatment variables * Female Equal division of housework * Female 0.384*** (0.108) Medium-income family * Female -0.229 (0.129) High-income family * Female -0.139 (0.134) Wife has a college degree * Female -0.020 (0.108) Grandparenting * Female -0.249* (0.108) Free public childcare service * Female -0.114 (0.107) Covariates Age 0.017*** 0.017*** 0.017*** 0.017*** 0.017*** 0.017*** (0.005) (0.005) (0.005) (0.005) (0.005) (0.005) Female (Yes = 1) -0.347*** -0.543*** -0.224* -0.337*** -0.222** -0.290*** (0.055) (0.082) (0.092) (0.078) (0.077) (0.079) College degree (Yes = 1) -0.334*** -0.333*** -0.337*** -0.334*** -0.336*** -0.334*** (0.060) (0.060) (0.060) (0.060) (0.060) (0.060) Local hukou (Yes = 1) -0.291*** -0.293*** -0.293*** -0.291*** -0.294*** -0.291*** (0.065) (0.065) (0.065) (0.065) (0.065) (0.065) Married (Yes = 1) -0.080 -0.079 -0.082 -0.079 -0.083 -0.083 (0.101) (0.101) (0.101) (0.101) (0.101) (0.101) Having Children (Yes = 1) 0.290** 0.289** 0.292** 0.290** 0.292** 0.293** (0.104) (0.104) (0.104) (0.104) (0.104) (0.104) Employed (Yes = 1) -0.186* -0.180* -0.190* -0.186* -0.192* -0.187* (0.087) (0.087) (0.087) (0.087) (0.087) (0.087) Observations 5,709 5,709 5,709 5,709 5,709 5,709 Pseudo R 2 0.110 0.111 0.111 0.110 0.111 0.111 Note: Robust standard errors in parentheses. *** p < 0.001, ** p < 0.01, * p < 0.05. Covariates included but not shown in the table include family income, homeownership, and city fixed effects. Model 1 in Table 3 shows that family income significantly shapes normative fertility attitudes. Among the vignette characteristics, the division of housework between spouses emerges as an important factor shaping respondents’ fertility evaluations. When the husband in the vignette is described as sharing housework equally with the wife, respondents express significantly more positive fertility attitudes toward the couple. Predicted probabilities indicate that the likelihood of reporting an ideal family size of two increases from 24.7% when the husband rarely performs housework to 35.3% when housework is equally divided. Compared with low-income families, respondents believe that couples with medium or high incomes should have more children. In substantive terms, the predicted probability that respondents report an ideal family size of two increases from 20.5% under the low-income vignette to 39.7% when the couple is described as having a high income, holding other characteristics constant. Childcare support within or outside the family also significantly enhances pronatalist attitudes. Both the availability of grandparental childcare and the provision of free public childcare services are strongly associated with more favorable fertility evaluations. When grandparents are available to help with childcare, the predicted probability that respondents report an ideal family size of two increases from 22.3% to 37.1%. Similarly, when free public childcare services are available, the probability of reporting an ideal family size of two increases by approximately 15 percentage points. These results provide support for Hypothesis 1 . In contrast, the educational attainment of the wife described in the vignette does not significantly influence respondents’ fertility attitudes. The coefficient for wives having a college degree is small and statistically insignificant, suggesting that respondents do not perceive women’s higher education as incompatible with childbearing in these metropolitan contexts. The results also reveal significant associations between respondents’ individual characteristics and the conditional ideal number of children. Men and older respondents are more likely than women and younger individuals to believe that the fictitious couples described in the vignettes should have more children. Respondents with higher socioeconomic status indicated by college education and employment tend to report lower conditional ideal numbers of children than their counterparts. This pattern is consistent with prior research suggesting that individuals with higher education and more prestigious occupations are more likely to perceive the opportunity costs associated with childbearing (Budig and England 2001 ). Moreover, respondents with a local hukou tend to assign lower ideal numbers of children to the couples described in the vignettes than migrants, despite their better access to the social welfare system. One possible explanation is that many migrants originate from less urbanized areas where traditional family norms remain stronger and the perceived opportunity costs of childbearing are lower. Models 2 through 6 examine whether the effects of vignette-level characteristics differ between men and women. Model 2 indicates that the positive association between equal housework division and fertility attitudes is significantly stronger among women than among men. Although women generally express less pronatalist attitudes than men, the gender gap narrows substantially when the husband in the vignette is described as sharing housework equally. Predicted probabilities suggest that equal housework division increases the probability of reporting an ideal family size of two by 13.4 percentage points for women, compared with 8.6 percentage points for men, suggesting that gender equality in household labor is particularly salient for women’s fertility evaluations. Model 5 shows that the positive association between grandparental childcare support and fertility attitudes is weaker among women than among men. In other words, although both men and women report more favorable fertility attitudes when grandparents can provide childcare, the increase is significantly smaller among women. Taken together, these findings support Hypothesis 2 . No statistically significant gender differences are found in the effects of family income, wife’s education, or the availability of public childcare services. The results are robust to alternative model specifications using multinomial logit models (see Appendix Table A3). [Table 3 About Here] To further explore whether gender differences in normative fertility attitudes vary by education level, I divide respondents into two groups: those with a college degree and those without. Ordered logit models are estimated separately for each group, including interaction terms between gender and the vignette characteristics. The results are presented in Table 4 . Table 4 Ordered logit coefficients of normative fertility attitudes on vignettes and socioeconomic characteristics: By gender and education level Model 1 Model 2 Model 3 Model 4 Model 5 College College College College College Yes No Yes No Yes No Yes No Yes No Treatment variables * Female Equal division of housework * Female 0.578*** 0.106 (0.146) (0.162) Medium-income family * Female -0.074 -0.400* (0.173) (0.195) High-income family * Female -0.042 -0.299 (0.183) (0.198) Wife has a college degree * Female -0.021 -0.006 (0.146) (0.161) Grandparenting * Female -0.357* -0.116 (0.147) (0.161) Free public childcare * Female -0.142 -0.112 (0.145) (0.160) Observations 3,037 2,672 3,037 2,672 3,037 2,672 3,037 2,672 3,037 2,672 Adj. R-squared 0.121 0.093 0.119 0.093 0.119 0.093 0.120 0.093 0.119 0.093 Note: Robust standard errors in parentheses. *** p < 0.001, ** p < 0.01, * p < 0.05. All treatment variable and covariates are included but not shown in the table. The findings indicate that gender differences in the effects of housework division and grandparental support are concentrated among college-educated respondents. Among respondents with a college degree, women report significantly more pronatalist attitudes than men when the husband in the vignette is described as sharing housework equally (Model 1). By contrast, when grandparents are described as available to provide childcare, college-educated women report less pronatalist attitudes than their male counterparts (Model 4). This suggests that although extended-family support can alleviate childcare burdens, it does not increase normative fertility attitudes among highly educated women to the same extent as it does among men. These results provide support for Hypothesis 3 . Among respondents without a college education, however, the interaction effects between gender and the vignette characteristics are not statistically significant, indicating that men and women respond similarly to these contextual conditions. One exception is family income: women respond less positively than men when the vignette couple is described as having a medium income rather than a low income. This pattern may reflect gender differences in perceived economic constraints associated with childbearing among less-educated respondents. [Table 4 About Here] Discussion and Conclusion This study examines how family conditions shape normative fertility attitudes in urban China using a factorial survey experiment, paying particular attention to gender inequality in the division of labor within families. The results show that respondents evaluate couples as better positioned to have children when they possess more egalitarian household arrangements, greater economic resources, and more childcare support. In particular, the division of housework emerges as a key factor shaping pronatalist fertility norms. Couples described as sharing housework equally are perceived as more suitable for childbearing, and this effect is significantly stronger among women than among men. Moreover, gender differences in these evaluations are especially pronounced among college-educated respondents. Together, these findings highlight the importance of gender inequality within families in shaping fertility-related norms in contemporary urban China. These results contribute to the broader literature on the relationship between gender equality and fertility. Consistent with the two-stage gender revolution framework, this study shows that in the most socioeconomically developed Chinese cities, women attach greater importance to equality in the private sphere as they gain more equal opportunities in the public sphere. Compared with their male counterparts, women are more likely to value an equal division of housework when evaluating the ideal family size for the vignette couples, suggesting that gender inequality in household labor remains a salient concern. This pattern is consistent with the persistence of the “second shift” among mothers. Even when childcare responsibilities can be partially outsourced to extended family members or formal institutions, mothers often remain the primary caregivers upon returning home from work (Short et al. 2002 ). These findings suggest that increasing the availability of childcare services alone may not be sufficient to enhance fertility intentions in China. Persistent gender inequality within households may continue to shape how individuals evaluate the feasibility of childbearing. The findings regarding grandparental childcare support further highlight the gendered nature of family and fertility norms. While extended-family support is often considered an important resource for young parents in China, its perceived benefits vary between men and women. Chinese grandparents frequently play an active role in the daily upbringing of their grandchildren and often co-reside with young couples. This arrangement allows parents to participate in the workforce without the financial burden of high-cost childcare services and also shapes family dynamics (Miao and Wu 2021 ). However, because childcare responsibilities remain unequally distributed within families, mothers are typically more directly involved in coordinating childcare and interacting with grandparents. As a result, mothers are also more likely to experience disagreements or conflicts with grandparents, particularly in mother-daughter-in-law relationships (Song and Zhang 2012 ). Consistent with this pattern, the results of this study suggest that men and women may perceive the benefits of grandparental childcare differently. Because husbands often bear fewer childcare responsibilities, they may be less directly exposed to intergenerational caregiving conflicts, which may in turn lead them to evaluate grandparental childcare support more positively than women. The results also highlight the role of educational stratification in shaping fertility-related evaluations. Gender differences in the perceived desirability of childbearing conditions are more pronounced among college-educated respondents. Highly educated women often face greater opportunity costs associated with childbearing, including potential career interruptions and income penalties related to motherhood (Budig and England 2001 ). At the same time, highly educated urban women in China frequently encounter demanding work environments while still bearing primary responsibility for domestic tasks. Under these circumstances, the perceived feasibility of childbearing may depend strongly on whether family arrangements allow women to reconcile work and family roles. The stronger gender differences observed among college-educated respondents suggest that tensions between career aspirations and unequal household labor may play a particularly important role in shaping fertility norms among highly educated urban populations. The findings of this study offer valuable insights into understanding the persistently low fertility rates observed in many East Asian societies, such as Japan, South Korea, Hong Kong, and Taiwan. Previous research has primarily concentrated on the impact of economic and institutional factors, including industrialization, educational expansion, family planning programs, and social welfare policies aimed at supporting families (Cheng 2020 ). In response to these factors, governments in the region have introduced a variety of pro-natalist policies, such as extended paid parental leave, tax incentives for families with children, and expanded access to free childcare. While it is premature to fully evaluate the long-term effectiveness of these recent policies, ongoing declines in both marriage and fertility rates suggest that additional measures are necessary to stimulate fertility intentions (Cheng 2020 ). Research has highlighted the significant empowerment of women in East Asian societies through education and employment over the past several decades. However, the persistence of traditional social norms and the relatively low level of men’s contributions to household labor have created a mismatch that reduces women’s incentives to marry and have children (Kan et al. 2022 ). Thus, while policies aimed at alleviating the economic burdens of families are important, shifts in gender norms and ideologies may be central to addressing the region’s ultra-low fertility levels. The results of this study offer important policy implications for the design and implementation of demographic and family policies aimed at addressing the challenges posed by low fertility rates in China and similar contexts. First, the study finds that the average conditional ideal number of children among adults in the four surveyed cities is 1.24, which is higher than the actual fertility rates observed in three of the four cities studied. This gap suggests that extremely low fertility in these metropolitan areas may not solely reflect a lack of desire for children, but may also be shaped by constraints that limit individuals’ ability to realize their fertility preferences. In other words, many adults may still view having children as desirable under more favorable family and social conditions. This finding indicates that supportive policy interventions still have the potential to influence fertility outcomes in highly developed urban contexts. Second, the findings suggest that efforts to encourage childbearing may need to move beyond financial incentives and childcare expansion to address gender inequality within families. Policies that promote greater involvement of fathers in childcare and domestic work may play an important role in shaping fertility decisions. Prior research shows that men’s participation in housework and childcare is more likely when social policies explicitly encourage or require such engagement (Goldin 2025 ). For example, parental leave policies that reserve nontransferable leave specifically for fathers, such as the “father quotas” implemented in several Nordic countries have been shown to increase fathers’ involvement in childcare (Tamm 2019 ). Similarly, workplace practices that support work-family balance for both men and women, including flexible working arrangements may encourage fathers to participate more actively in caregiving (Kuang, Perelli-Harris, and Berrington 2025 ). Together, these policy approaches may help reduce the unequal caregiving burdens faced by mothers and promote broader cultural shifts that normalize men’s participation in caregiving. Third, while support from grandparents is a crucial source of childcare for dual-income families in large Chinese cities, the impacts of grandparenting on family dynamics warrant further attention. The findings of this study suggest that men and women may perceive the benefits of grandparental childcare differently, reflecting the gendered nature of caregiving responsibilities within families. Because mothers are more directly involved in childcare and daily interactions with grandparents, they may be more likely to experience tensions or conflicts, particularly in mother-daughter-in-law relationships. In this context, husbands may play an important role in mediating intergenerational disagreements and facilitating communication between their spouses and parents. Community-based family education programs and parenting workshops, such as those organized through community parenting schools in some large Chinese cities, may help improve grandparenting practices and promote constructive conflict resolution within families. Such initiatives may help maintain harmonious family relationships and enhance the effectiveness of intergenerational childcare support. This study has two main limitations. First, the data were collected through a mobile phone survey. Although mobile phone penetration is high in the studied cities, individuals with lower levels of education and income may have been less likely to complete the survey because of time constraints. As a result, these groups are underrepresented in the sample. While this potential sampling bias does not undermine the causal inference, it does limit the generalizability of the findings to the broader population. Second, the vignettes were presented to respondents via phone calls, which required a certain level of cognitive effort to process the information and provide answers. While the analytical sample comprises relatively young individuals, which helps mitigate this concern, future research could employ self-administered or mixed-mode survey designs to reduce cognitive burden and enhance response accuracy. Despite these limitations, this study is the first study to investigate the normative fertility attitudes among Chinese individuals in first-tier cities. By experimentally varying key attributes of hypothetical couples, the study isolates how individuals evaluate the desirability of childbearing under different circumstances, offering new insight into the social foundations of fertility norms. In conclusion, this study shows that normative evaluations of childbearing are strongly shaped by family conditions, particularly the division of domestic labor. The results indicate that gender inequality within households remains an important factor influencing fertility norms in urban China and may help explain persistently low fertility in East Asian societies. More broadly, the study provides insights into how fertility norms evolve in societies where rapid economic transformation challenges traditional family values and gender norms, leaving individuals limited time to navigate the tensions between tradition, modernity, and changing gender roles. These insights have implications for the design of targeted, forward-looking family policies aimed at supporting individuals and couples in achieving their fertility goals. Declarations Author Contribution Jia Miao is the sole author of this article and was responsible for the study conception and design, data analysis, interpretation of the results, and preparation of the manuscript. Acknowledgement The data collection for the Four Metropolises Urban Neighborhood Survey was conducted by the School of Sociology and Political Science and the Center for Data and Urban Science (CENDUS) at Shanghai University. I express my gratitude to Wei Chen, Zhiming Sheng, Xiulin Sun, and Jianpin Wang for for their efforts in organizing the data collection, and to Junjie Du and Xinyang Tang for their research assistance. Data Availability The data used in this study are from the Four Metropolises Urban Neighborhood Survey conducted by the School of Sociology and Political Science and the Center for Data and Urban Science (CENDUS) at Shanghai University. Due to data licensing restrictions, the dataset is not publicly available. Researchers may request access to the data from the data custodians at Shanghai University, subject to approval. References Augustine, J. M. (2017). Increased educational attainment among US mothers and their children’s academic expectations. 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Social Science Research , 38 (3), 505–520. Yu, J., Shen, X., & Xie, Y. (2023). Economic resources, childcare services, and son preference: a conjoint analysis of fertility potential in China. China Population and Development Studies , 7 (4), 383–417. Zhou, M., & Kan, M. Y. (2019). A new family equilibrium? Changing dynamics between the gender division of labor and fertility in Great Britain, 1991–2017. Demographic Research , 40(50). Footnotes The penetration of mobile phones is expressed as the ratio of number of SIM cards to the total population, therefore, it can exceed 100% if the number of SIM cards in one place is higher than the actual population size. 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-9097806","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":625489605,"identity":"73f39697-3f44-42f8-a670-ad204b20e989","order_by":0,"name":"Jia Miao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIiWNgGAWjYBACxgbmAwckDGwgPB7itLAlPrCoSCNBC1CZsUHFmcMkaGGedsZM4mbb+TyDGwmMD962EeOw2WllkjPbbhcDtTAbziVOS/I2acm224kbbiSwSfMSpyXBTPpv2zmQFvbfRGpJMTaQOHMAbAszkVrSEh9IVCQnzjzzsFlyzjkitBjOTgZFpV1i3/Hkgx/elBGjpQHKUDjA2IBHHRKQhzOI1DAKRsEoGAUjEAAA67E9jCjsfIsAAAAASUVORK5CYII=","orcid":"","institution":"New York University Shanghai","correspondingAuthor":true,"prefix":"","firstName":"Jia","middleName":"","lastName":"Miao","suffix":""}],"badges":[],"createdAt":"2026-03-11 19:54:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9097806/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9097806/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107705901,"identity":"957af6fd-4a0a-4c1e-b482-e7be979ad7ad","added_by":"auto","created_at":"2026-04-24 09:15:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":46766,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExample vignette (translated version)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9097806/v1/da5e0d2540ddda60971335f9.png"},{"id":107603066,"identity":"062e7cb3-5907-4491-9439-6655d31a1a8b","added_by":"auto","created_at":"2026-04-23 07:06:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":53591,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMean ideal number of children assigned to the vignette couple: By vignette dimensions\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9097806/v1/71dc664edc33df5ced24cea3.png"},{"id":107711290,"identity":"cde9dc28-5048-4a7c-b103-041528c5e722","added_by":"auto","created_at":"2026-04-24 09:45:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":787245,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9097806/v1/6310c395-6463-4631-a52e-cc949145ffc2.pdf"},{"id":107603065,"identity":"15b8bf02-f233-47ed-903f-691290393d4b","added_by":"auto","created_at":"2026-04-23 07:06:52","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":22182,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-9097806/v1/3ef9d971c3a34fd49f68d7bd.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Normative Fertility Attitudes in Urban China: Evidence from a Factorial Survey Experiment","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePersistently low fertility has become one of the most pressing demographic challenges in many advanced societies (Morgan and Taylor \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). In several East Asian societies, including mainland China, Japan, and South Korea, fertility rates have declined to extremely low levels over the past two decades (Cheng \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The total fertility rate of mainland China fell to 1.3 in 2020 and has continued to decline (National Bureau of Statistics of China \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), with fertility levels in large metropolitan areas even lower than the national average. Shanghai, for example, reported a total fertility rate of only 0.7 in 2022 (Shanghai Municipal Health Commission \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These trends have raised growing policy concerns regarding rapid population aging, shrinking labor forces, and long-term economic sustainability.\u003c/p\u003e \u003cp\u003eIn response, governments across East Asia have introduced a range of policy initiatives aimed at encouraging childbearing. These measures typically focus on reducing the economic costs of raising children, such as through financial incentives, and facilitating work-family reconciliation, for example by expanding childcare services (Gauthier and Gietel-Basten \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Despite increasing policy attention to fertility support, fertility rates in many highly developed urban areas remain persistently low. This raises an important question for both researchers and policymakers: what social conditions shape people\u0026rsquo;s perceptions of whether couples are well positioned to have children in contemporary societies?\u003c/p\u003e \u003cp\u003eOne influential explanation for this persistent gap between policy efforts and fertility outcomes highlights the role of gender inequality within families. The gender revolution framework argues that fertility tends to decline when gender equality advances more rapidly in the public sphere, such as in education and employment, than in the private sphere of household labor and caregiving (McDonald \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Goldscheider, Bernhardt, and Lappeg\u0026aring;rd \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In many East Asian societies, women\u0026rsquo;s educational attainment and labor force participation have increased dramatically, yet domestic responsibilities remain highly gendered (Cheng \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). As a result, many women continue to face a \u0026ldquo;second shift\u0026rdquo; of housework and childcare after returning home from paid work. Under such circumstances, the prospect of having children may be perceived as particularly burdensome, especially among highly educated women with demanding careers in large cities.\u003c/p\u003e \u003cp\u003eA growing body of research has examined the impact of gender inequality in the division of domestic labor on fertility intentions and behaviors in Western societies (McDonald \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Mills et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Goldscheider, Bernhardt, and Brand\u0026eacute;n \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Sullivan, Billari, and Altintas \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Begall and Hiekel \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Goldin \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, relatively little empirical work has examined how gender inequality within families shapes normative evaluations of childbearing, particularly among Chinese populations. Moreover, household labor arrangements are often closely intertwined with other family conditions, such as household income and access to childcare support. Because these factors tend to co-occur in real-life family settings, traditional observational studies face difficulties in disentangling their independent effects\u003c/p\u003e \u003cp\u003eTo address this empirical and methodological gap, this study examines how Chinese respondents evaluate under what circumstances a couple \u0026ldquo;should\u0026rdquo; have a certain number of children, with particular attention to gender inequality within families. Normative judgments are important because they reflect broader fertility norms and may influence the perceived legitimacy and effectiveness of family policies. This study employs a factorial survey experiment embedded in a large-scale survey conducted in four major Chinese metropolises: Beijing, Shanghai, Guangzhou, and Shenzhen. Respondents were randomly presented with hypothetical couples whose family conditions varied along several dimensions, including division of housework, household income, wife\u0026rsquo;s education, grandparental childcare support, and access to public childcare services. Respondents were then asked to evaluate the ideal number of children for the described couple. By experimentally varying these conditions, the design allows us to identify how individuals evaluate whether couples should have children under different family circumstances while isolating the independent effects of each factor.\u003c/p\u003e \u003cp\u003eSpecifically, this study addresses three questions. First, how do key family conditions, such as division of housework, household socioeconomic conditions, and the availability of childcare support, affect people\u0026rsquo;s assessments of the ideal number of children for a couple? Second, do men and women evaluate these fertility conditions differently? Third, do these gender differences vary by education level, particularly among highly educated individuals facing greater opportunity costs of childbearing? By addressing these questions using a factorial survey experiment, the study provides new evidence on how gender inequality in the division of domestic labor shapes fertility-related norms in low-fertility urban contexts.\u003c/p\u003e \u003cp\u003eThis study contributes to research on fertility and family policy in three ways. First, it shifts attention from individuals\u0026rsquo; own fertility intentions to normative evaluations of fertility conditions, capturing how people assess whether childbearing is appropriate or feasible under specific family circumstances. By asking respondents to evaluate hypothetical couples presented in standardized scenarios, the study isolates the effects of specific family conditions while reducing the influence of unobserved individual characteristics that often shape self-reported fertility attitudes.\u003c/p\u003e \u003cp\u003eSecond, the study advances the literature on low fertility by examining gender inequality within families as a key mechanism shaping fertility-related norms. While existing research has widely discussed the role of gender equity in fertility decline, empirical studies rarely isolate the impact of the division of domestic labor on how people evaluate the desirability of childbearing. The study further investigates whether the importance individuals attach to household labor arrangements varies by educational attainment. By highlighting both the role of the division of domestic labor and its heterogeneous effects across social groups, the analysis links micro-level gender dynamics within families to broader patterns of low fertility in highly developed urban context.\u003c/p\u003e \u003cp\u003eThird, the study contributes to policy debates on low fertility by highlighting the importance of gender relations within families. Much of the existing policy discussion focuses on economic and time constraints, emphasizing financial incentives, housing support, or the expansion of childcare services. While such measures may alleviate some practical barriers to childbearing, they may be insufficient if gender inequalities in household labor persist. By drawing attention to how the division of domestic labor shapes normative evaluations of childbearing, this study suggests that policies aimed at addressing fertility decline need to move beyond economic resources alone and consider the broader gendered organization of family life.\u003c/p\u003e\n\u003ch3\u003eFamily Conditions, Gender Inequality, and Fertility Norms\u003c/h3\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eFamily conditions and fertility norms\u003c/h2\u003e \u003cp\u003eThis study focuses on fertility norms, referring to socially shared expectations about the ideal number of children, or ideal family size couples should have. The ideal number of children can be understood as an abstract concept formed through family socialization and subsequently adjusted in response to situation-specific constraints (Karabchuk et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Evaluations of the ideal family size depends on perceptions of whether couples possess the resources and conditions considered appropriate for raising children. Demographers have long relied on measures of ideal family size to capture underlying fertility norms and long-term reproductive preferences (Behrman 2025). Accordingly, a growing body of research has examined the social and economic conditions that shape individuals\u0026rsquo; evaluations of the ideal number of children (Hirschman \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Behrman and Rosenzweig \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Torr and Short \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Bachrach and Morgan \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Balbo et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe present study examines several key family conditions that may influence how individuals evaluate the ideal number of children. Besides the division of domestic labor, these include household income, women\u0026rsquo;s education, and the availability of childcare support, all of which are closely related to the organization of housework and caregiving within families. Together, these conditions reflect the core resources required for raising children, including financial means, time, human capital, and caregiving capacity. They also correspond to two major domains of family policy aimed at supporting families and encouraging childbearing: financial assistance and policies that facilitate work-family reconciliation (Gauthier and Gietel-Basten \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Household income and childcare provision primarily relate to the direct costs of childrearing, whereas women\u0026rsquo;s education and the division of domestic labor are closely associated with the indirect and opportunity costs of caregiving.\u003c/p\u003e \u003cp\u003eEconomic resources play an important role in shaping normative fertility attitudes. According to the economic theory of fertility, parents weigh the costs and benefits of childbearing when deciding how many children to have (Becker et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). As societies undergo economic development and demographic transition, the economic value of children declines while the costs associated with investing in children\u0026rsquo;s \u0026ldquo;quality\u0026rdquo;, such as education and health, increase (Morgan and Taylor \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Under such circumstances, families often prioritize investing more resources in fewer children. Households with higher incomes may therefore be perceived as better able to afford the financial costs associated with raising children and providing high-quality upbringing. Empirical studies have consistently documented a positive association between family income and fertility ideals or intentions (Yu et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMother\u0026rsquo;s education may also influence how individuals evaluate the desirability of childbearing. Educational attainment represents an important form of human capital that shapes parenting practices, expectations about childrearing, and family investments in children (Augustine \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Women\u0026rsquo;s education is also closely linked to household socioeconomic resources, as highly educated women are more likely to partner with similarly educated and higher-earning spouses (Oppenheimer \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Mills et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). These factors may lead individuals to view highly educated mothers as better positioned to raise children. At the same time, higher levels of education increase the opportunity costs of childbearing, as career interruptions associated with motherhood can result in the well-documented \u0026ldquo;motherhood wage penalty\u0026rdquo; (Budig and England \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). As a result, respondents may also interpret a mother\u0026rsquo;s educational attainment as reflecting potential costs of childbearing. Taken together, a mother\u0026rsquo;s education may serve as a signal of both family resources and the potential opportunity costs associated with childrearing.\u003c/p\u003e \u003cp\u003eIn addition, childcare support also plays a critical role in shaping evaluation of ideal family size by reducing the time and career trade-offs associated with childrearing. When childcare responsibilities can be partially outsourced to formal childcare services or shared with family members, the burden of caregiving becomes more manageable for parents (Schultz \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Empirical research across different societies has shown that the availability of formal childcare services (e.g., subsidized or publicly provided childcare) and informal childcare arrangement (e.g., support from grandparents) can increase individuals\u0026rsquo; desired or intended number of children (Ji et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Pronzato \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). At the macro level, cross-national studies suggest that countries where childcare responsibilities are more strongly supported by public institutions tend to maintain higher fertility levels than societies where childcare responsibilities remain primarily within families (Balbo et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In the Chinese context, recent studies also document positive associations between childcare provision and fertility intentions (Yu, Shen, and Xie \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Chen and Hu \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn sum, individuals\u0026rsquo; evaluations of the ideal number of children depend on their perceptions of whether couples possess the resources and support systems necessary to raise children. When families are perceived to have sufficient financial resources, supportive childcare arrangements, and favorable caregiving conditions, childbearing is more likely to be viewed as feasible and desirable. However, some family characteristics, such as mothers\u0026rsquo; education, may signal both greater resources and higher opportunity costs, making their expected influence less clear.\u003c/p\u003e \u003cp\u003eBased on the preceding discussion, the following hypothesis is proposed:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 1\u003c/strong\u003e \u003cp\u003eCouples described as having more favorable family conditions, including higher income and greater childcare support, will be evaluated as having a higher ideal number of children.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGender inequality within families and fertility norms\u003c/h3\u003e\n\u003cp\u003eWhile economic resources and childcare support shape the feasibility of childbearing, a growing body of research highlights the importance of gender inequality within families in shaping fertility outcomes. The gender revolution framework argues that fertility patterns are closely linked to the degree of gender equality in both the public and private spheres (McDonald \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Goldscheider, Bernhardt, and Lappeg\u0026aring;rd \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In many societies, women\u0026rsquo;s educational attainment and labor force participation have increased substantially over recent decades, yet domestic labor and childcare responsibilities remain disproportionately concentrated on women. When gender equality advances more rapidly in the public sphere than in the private sphere, women may face increasing work-family conflicts that discourage childbearing (Goldin \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGoldscheider, Bernhardt, and Lappeg\u0026aring;rd (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) conceptualize this process as a two-stage gender revolution. In the first stage, women\u0026rsquo;s entry into the labor market leads to delays in marriage and parenthood as women invest in education and career development. In the second stage, greater gender equality within the household, particularly increased male participation in housework and childcare, facilitates a reconciliation between work and family roles and may contribute to higher fertility. Under such conditions, a more egalitarian division of domestic labor can reduce women\u0026rsquo;s caregiving burden and increase the perceived feasibility of having children.\u003c/p\u003e \u003cp\u003eEmpirical studies provide substantial support for this argument. At the aggregate level, research shows that fertility declines more rapidly in societies where domestic responsibilities remain highly unequal between men and women (McDonald \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Goldscheider, Bernhardt, and Brand\u0026eacute;n \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Sullivan, Billari, and Altintas \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Drawing on historical data from twelve countries, Goldin (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) similarly finds that fertility rates decline more sharply in societies where household and caregiving responsibilities are less equally shared between partners. At the individual level, studies from Western countries show that a more equitable division of household labor is associated with earlier transitions to parenthood and stronger fertility intentions (Torr and Short \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Mills et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Zhou and Kan \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Recent research in Nordic countries further demonstrates that a satisfactory division of household labor significantly increases fertility intentions, particularly among individuals who hold gender-egalitarian attitudes (Begall and Hiekel \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe pace of the gender revolution, however, varies across societies. In East Asian contexts, gender equality in the public sphere has progressed more rapidly than equality within the family (Sullivan, Billari, and Altintas \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Despite substantial gains in women\u0026rsquo;s education and employment, women in East Asia continue to perform the majority of housework and childcare (Kan and Zhou \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Comparative research on mainland China, Japan, South Korea, and Taiwan shows that women bear the bulk of domestic responsibilities in these societies (Kan and Hertog \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Under such conditions, the unequal distribution of domestic labor may increase the perceived costs of childbearing and contribute to persistently low fertility in the region (Brinton and Oh \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBecause women disproportionately bear the time and opportunity costs of childbearing and childrearing (Torr and Short \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), they may be particularly sensitive to family conditions that affect the distribution of caregiving labor. When evaluating whether couples should have children, women may place greater importance on the division of domestic labor. These gendered experiences may therefore lead men and women to evaluate the desirability of childbearing under different family conditions in distinct ways. Based on the preceding discussion, I derive the following hypothesis:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 2\u003c/strong\u003e \u003cp\u003eCouples described as having an equal division of housework will be evaluated as having a higher ideal number of children, with this effect being stronger among women than among men.\u003c/p\u003e \u003c/p\u003e\n\u003ch3\u003eEducation and stratified gender responses\u003c/h3\u003e\n\u003cp\u003ePotential differences in the normative fertility attitudes between men and women may vary across education levels. First, the costs and rewards of parenthood can be more divergent for those with higher education. Many professional and academic careers demand long hours and intense commitment for success, making it challenging for highly educated individuals to balance work and parenthood (Coltrane \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Second, higher education often exposes women to more liberal ideas and raises their expectations for gender equality (Kane \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). More highly educated women are more inclined to advocate for an equal division of family responsibilities and hold less traditional views regarding the importance and permanence of marriage and childbearing (Koropeckyj-Cox and Pendell \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Third, individuals with higher education are generally more aware of the challenges associated with balancing work and parenthood, as well as the potential risks involved including economic insecurity. This heightened awareness may make them more sensitive to family conditions such as childcare availability and the division of domestic labor, influencing their fertility decisions (Koropeckyj-Cox and Pendell \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe educational gradients in gender gaps regarding fertility attitudes have been empirically examined in a limited number of studies. For instance, researchers find that the gender difference in attitudes about childlessness is greater among people with college degrees than their less-educated counterparts in the U.S. (Koropeckyj-Cox and Pendell \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The positive impacts of childcare-sharing with men on fertility intentions are particularly salient among tertiary-educated wives in Taiwan, who often exhibit greater economic empowerment and higher expectations for gender equality at home (Cheng and Hsu \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn sum, these findings suggest that educational attainment may amplify gender differences in fertility-related evaluations. Highly educated women may be particularly responsive to family conditions that reduce the burden of caregiving or facilitate work-family reconciliation. In contrast, such gender differences may be less pronounced among individuals with lower levels of education, for whom the opportunity costs associated with childbearing are typically smaller. Building on this discussion, I propose the following hypothesis:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 3\u003c/strong\u003e \u003cp\u003eGender differences in the evaluation of fertility conditions will be more pronounced among college-educated respondents than among those without a college degree.\u003c/p\u003e \u003c/p\u003e\n"},{"header":"Data, Measurements, and Methods","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eData\u003c/h2\u003e \u003cp\u003eThis study uses data from the Four Metropolises Urban Neighborhood Survey conducted in Beijing, Shanghai, Guangzhou, and Shenzhen, a mobile phone survey implemented by Shanghai University from October 25th to November 27th, 2021. According to the National Bureau of Statistics of China, the penetration of mobile phones was 178.43% in Beijing, 171.99% in Shanghai, and 123.30% in Guangdong province, in which both Guangzhou and Shenzhen are located (National Bureau of Statistics of China \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) by 2020\u003csup\u003e1\u003c/sup\u003e, indicating the feasibility of gaining a representative sample using a mobile random-digit-dialing (RDD). This survey used a sample framework provided by the three major mobile network operators in China: China Mobile, China Telecom, and China Unicom. The three operators owned approximately 1.64\u0026nbsp;billion mobile phone subscribers, comprising nearly 100 percent of China\u0026rsquo;s national market share by 2021 (Ministry of Industry and Information Technology of the People\u0026rsquo;s Republic of China 2023).\u003c/p\u003e \u003cp\u003eThe raw mobile RDD sample was created based on respondents\u0026rsquo; geographical location identified by the first seven digits of a Chinese mobile phone number, which are known as a number\u0026rsquo;s \u0026ldquo;series block.\u0026rdquo; The last four digits of a mobile phone number within all series blocks were randomly generated by the survey system. The research team then screened all numbers in the database and removed the disconnected numbers from the sample list. Mobile phone numbers in each city\u0026rsquo;s final sample list were selected using a simple random sampling method. To verify eligibility, respondents were asked whether they were permanent residents (i.e., whether they had lived in the survey cities for at least six months). The numbers of respondents in Shanghai, Beijing, Guangzhou, and Shenzhen are 3,616, 1,586, 1,343, and 1,460, respectively.\u003c/p\u003e \u003cp\u003eThis study restricts the respondents to those aged 15 to 49, corresponding to women\u0026rsquo;s reproductive age. The normative fertility attitudes of people within this age range are the most significant for policy implications. After removing cases with missing values for dependent or independent variables (3.98 percent of all cases), the analytical sample consists of 5,709 respondents.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eFactorial survey experimental design\u003c/h2\u003e \u003cp\u003eOver the past two decades, survey experiments have become an increasingly influential methodological innovation in social science research (Mize and Manago \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). By embedding experimental designs into large-scale surveys, these methods enable researchers to estimate causal effects while retaining the advantages of large sample sizes, such as enhanced external validity and the ability to conduct cross-group comparisons (Schachter and Weisshaar 2025). Survey experiments are ideal for addressing questions about the causes of differences in attitudes, perceptions, or beliefs, the outcomes that are well-suited for measurement in a survey (Schaeffer and Dykema \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In the field of family studies, survey experiments have gained growing attention over the past decade, especially in research on normative beliefs, gender roles, and fertility intentions (Doan, Quadlin, and Khanna \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Recent studies have applied these methods to explore topics such as fertility intentions (Lappeg\u0026aring;rd et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and the desired number of children (Marshall and Shepherd \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWithin the broader category of survey experiments, the factorial survey has become a particularly valuable method for investigating the social norms and cognitive evaluations that shape fertility-related attitudes. Unlike traditional surveys that ask respondents to report their own intentions or attitudes in the abstract, factorial surveys present systematically varied, realistic hypothetical scenarios (vignettes) and ask respondents to evaluate them (Wallander \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). This design enables researchers to isolate both the independent and interactive effects of multiple factors on respondents\u0026rsquo; judgments, offering a more nuanced understanding of how fertility attitudes are formed.\u003c/p\u003e \u003cp\u003eThe factorial survey helps overcome several limitations of traditional approaches. First, people are not always fully aware of certain factors\u0026rsquo; impacts on their judgments and are thus incapable of explicating such impacts when asked about them. Using standardized vignettes in which selected factors believed to influence the judgment are simultaneously manipulated, the factorial survey experimental design can uncover the structures of complex judgments (Jasso \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Second, because respondents are randomly assigned to different versions of the vignettes, the design effectively eliminates selection bias and improves internal validity, which is often a concern in observational studies (Wallander \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Third, factorial surveys mitigate social desirability bias by asking respondents to evaluate hypothetical others, rather than disclose personal attitudes or intentions (Schachter and Weisshaar 2025). Fourth, by employing a population-representative sample, this approach addresses a common limitation of laboratory experiments: the lack of external validity (Auspurg and Hinz \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral studies have employed factorial experiments to examine fertility intentions and attitudes in both Asian and Western contexts. These studies have primarily focused on how factors such as parental employment status and family-friendly policies (Yu et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Guetto, Alderotti, and Vignoli \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Chen and Hu \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and political efficacy (Cheung, Lui, and Mu \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) influence ideal family size and fertility intentions.\u003c/p\u003e \u003cp\u003eFollowing the factorial survey experimental method (Jasso and Opp \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Jasso \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), and based on the abovementioned empirical evidence, this study focuses on the five determinants of fertility attitudes: family income level (low vs. medium vs. high), wife\u0026rsquo;s education (with vs. without a college degree), division of housework (husband rarely doing housework vs. husband does at least half of housework), grandparenting (grandparents can provide vs. cannot provide childcare), and free public childcare services (yes vs. no). A complete factorial design (one dimension with three levels and four with two levels) yields 48 (3*2*2*2*2) vignettes. The detailed vignette dimensions and levels are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVignette dimensions and levels\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=\"char\" char=\".\" 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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDimensions (Variables)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLevels (Values)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVignette text\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFamily income level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow income\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=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedium income\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=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh income\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWife\u0026rsquo;s education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWithout a college degree\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=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWith a college degree\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHusband\u0026rsquo;s housework contribution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHusband rarely does housework\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=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHusband does half of housework\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrandparenting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGrandparents cannot provide childcare\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=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGrandparents can provide childcare\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePublic childcare service\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFree public childcare service is unavailable\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=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFree public childcare service is available\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e About Here]\u003c/p\u003e \u003cp\u003eThe survey asked the respondents what the ideal number of children would be for the described couple. The research team presented one randomly selected vignette to each respondent to avoid possible respondent fatigue. This setup also prevents respondents from comparing vignettes and thus reduces their tendency to provide a socially desirable answer. An example of the exact wording of the vignettes is listed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e[Figure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e About Here]\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasurements and Methods\u003c/h3\u003e\n\u003cp\u003eThe outcome variable is the ideal number of children specified by respondents for described couples under specified conditions. Responses with a value greater than 3 are coded as 3 because less than 0.5 percent of respondents reported that the ideal number of children of described couples would be 4 or more.\u003c/p\u003e \u003cp\u003eThe key independent variables are the five vignette-level variables: family income level, wife\u0026rsquo;s education, division of housework, whether the grandparent could help with childcare, and whether free public childcare services are available. This study controls for a set of respondents\u0026rsquo; demographic and socioeconomic characteristics that are assumed to be relevant to fertility attitudes, including age, gender, education level, marital status, number of children, resident registration status (\u003cem\u003ehukou\u003c/em\u003e), employment status, homeownership, and family income. City fixed effects are also included to account for unobserved contextual differences across cities, such as economic conditions, local family policies, and prevailing fertility norms that may shape fertility attitudes.\u003c/p\u003e \u003cp\u003eBecause normative fertility attitudes are measured on an ordered scale, the main analyses employ ordered logit models. As a robustness check, I also estimate multinomial logit models that relax the proportional odds assumption and allow the effects of covariates to vary across outcome categories. The substantive conclusions remain similar across model specifications. Because the ordered logit model provides a more parsimonious specification in the presence of interaction terms, I present these models as the main results and use multinomial logit models as a robustness check.\u003c/p\u003e"},{"header":"Empirical Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive statistics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the descriptive statistics of the analytical sample. The average ideal number of children for the couples described in the vignettes is 1.24. Additionally, 12.7% of respondents suggest that the couple in the vignette should not have any children under any circumstances. Only 3.5% believe that the ideal number of children should be three or more, despite the introduction of the Three-child policy on May 31, 2021, five months before the interviews took place. Younger, more educated, and unmarried respondents are more likely to report a smaller ideal family size (see Appendix 2). Gender differences are evident, as men report more positive fertility attitudes, with an average ideal number of children of 1.3, compared to 1.15 reported by women.\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\u003eDescriptive statistics of analytical sample: Four Metropolises Urban Neighborhood Survey\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;5,709\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemales\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;2,143\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMales\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;3,566\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage ideal number of children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.24 (0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.15 (0.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.30 (0.72)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIdeal number of children\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\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.72%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.98%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.36%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e53.69%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56.84%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51.79%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30.11%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.13%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.50%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.49%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.05%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.35%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31.55 (7.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.23 (8.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.74 (7.81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale (Yes\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37.48%\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\u003eCollege education (Yes\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e53.20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58.80%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49.83%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocal \u003cem\u003ehukou\u003c/em\u003e (Yes\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46.38%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52.59%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42.65%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried (Yes\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52.47%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55.30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52.13%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHave children (Yes\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44.67%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.98%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.48%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployed (Yes\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e86.35%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80.59%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e89.82%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHomeownership (Yes\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48.87%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.78%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45.32%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily income level\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\u003e1\u0026thinsp;=\u0026thinsp;less than 100k\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.73%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.44%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u0026thinsp;=\u0026thinsp;100k-200k\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26.43%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.94%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u0026thinsp;=\u0026thinsp;200k-300k\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.87%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.94%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.42%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u0026thinsp;=\u0026thinsp;300k-500k\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.24%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.56%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.05%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u0026thinsp;=\u0026thinsp;more than 500k\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.68%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.41%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.04%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u0026thinsp;=\u0026thinsp;refusal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.06%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.64%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.10%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity\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\u003eBeijing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19.44%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.67%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.70%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShanghai\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44.98%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47.04%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43.75%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShenzhen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.81%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.48%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.81%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGuangzhou\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.77%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.81%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.74%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: Standard deviations in parentheses.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e About Here]\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the ideal number of children across different vignette dimensions. Without adjusting for covariates, respondents report larger ideal family sizes for couples with more favorable family conditions. When the husband shares at least half of the housework, the reported ideal family size increases from 1.14 to 1.35. When the couple is described as having a high income, the reported ideal family size rises to 1.44, compared with 1.05 for a low-income couple. Informal childcare support (grandparental help) and formal childcare support (public childcare services) increase the reported ideal number of children to 1.38 and 1.40, respectively. By contrast, respondents\u0026rsquo; evaluations do not vary significantly by whether the mother holds a college degree.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e[Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e About Here]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eFamily conditions and normative fertility attitudes in Chinese metropolises\u003c/h2\u003e \u003cp\u003eOrdered logit models are used to estimate the effects of vignette-level characteristics on respondents\u0026rsquo; normative fertility attitudes and to examine whether these effects vary by gender. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the estimated coefficients. Positive coefficients indicate that the vignette characteristics are associated with more pronatalist evaluations of the hypothetical couple.\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\u003eOrdered logit coefficients of normative fertility attitudes on vignettes and socioeconomic characteristics\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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eTreatment variables\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel 5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModel 6\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousework division (ref.=husband rarely does housework)\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 \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHusband does half of housework\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.690***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.546***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.691***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.690***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.689***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.691***\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(0.053)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.068)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.053)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.053)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.053)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.053)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily income (ref.=low-income family)\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 \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium-income family\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.613***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.611***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.702***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.613***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.615***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.614***\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(0.064)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.064)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.085)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.064)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.064)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.064)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-income family\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.230***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.228***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.283***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.230***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.234***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.230***\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(0.067)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.067)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.085)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.067)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.067)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.067)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWife\u0026rsquo;s education (ref.=without a college degree)\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 \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWith a college degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.038\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(0.053)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.053)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.053)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.068)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.053)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.053)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrandparenting (ref.= Grandparents cannot provide childcare)\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 \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrandparents can provide childcare\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.912***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.915***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.914***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.912***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.006***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.912***\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(0.054)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.054)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.054)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.054)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.070)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.054)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFree public childcare service (ref.= Unavailable)\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 \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAvailable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.948***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.946***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.949***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.948***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.949***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.991***\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(0.054)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.054)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.054)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.054)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.054)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.069)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTreatment variables * Female\u003c/b\u003e\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 \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEqual division of housework * Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.384***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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(0.108)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium-income family * Female\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 \u003cp\u003e-0.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.129)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-income family * Female\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 \u003cp\u003e-0.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.134)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWife has a college degree * Female\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 \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.108)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrandparenting * Female\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 \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.249*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.108)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFree public childcare service * Female\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 \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.114\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.107)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCovariates\u003c/b\u003e\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 \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.017***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.017***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.017***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.017***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.017***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.017***\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(0.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.005)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale (Yes\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.347***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.543***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.224*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.337***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.222**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.290***\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(0.055)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.082)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.092)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.078)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.077)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.079)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege degree (Yes\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.334***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.333***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.337***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.334***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.336***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.334***\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(0.060)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.060)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.060)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.060)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.060)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.060)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocal \u003cem\u003ehukou\u003c/em\u003e (Yes\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.291***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.293***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.293***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.291***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.294***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.291***\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(0.065)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.065)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.065)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.065)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.065)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.065)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried (Yes\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.083\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(0.101)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.101)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.101)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.101)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.101)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.101)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHaving Children (Yes\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.290**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.289**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.292**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.290**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.292**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.293**\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(0.104)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.104)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.104)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.104)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.104)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.104)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployed (Yes\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.186*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.180*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.190*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.186*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.192*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.187*\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(0.087)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.087)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.087)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.087)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.087)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.087)\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\u003e5,709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5,709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5,709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5,709\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePseudo R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: Robust standard errors in parentheses. *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Covariates included but not shown in the table include family income, homeownership, and city fixed effects.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eModel 1 in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows that family income significantly shapes normative fertility attitudes. Among the vignette characteristics, the division of housework between spouses emerges as an important factor shaping respondents\u0026rsquo; fertility evaluations. When the husband in the vignette is described as sharing housework equally with the wife, respondents express significantly more positive fertility attitudes toward the couple. Predicted probabilities indicate that the likelihood of reporting an ideal family size of two increases from 24.7% when the husband rarely performs housework to 35.3% when housework is equally divided.\u003c/p\u003e \u003cp\u003eCompared with low-income families, respondents believe that couples with medium or high incomes should have more children. In substantive terms, the predicted probability that respondents report an ideal family size of two increases from 20.5% under the low-income vignette to 39.7% when the couple is described as having a high income, holding other characteristics constant.\u003c/p\u003e \u003cp\u003eChildcare support within or outside the family also significantly enhances pronatalist attitudes. Both the availability of grandparental childcare and the provision of free public childcare services are strongly associated with more favorable fertility evaluations. When grandparents are available to help with childcare, the predicted probability that respondents report an ideal family size of two increases from 22.3% to 37.1%. Similarly, when free public childcare services are available, the probability of reporting an ideal family size of two increases by approximately 15 percentage points. These results provide support for Hypothesis \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eIn contrast, the educational attainment of the wife described in the vignette does not significantly influence respondents\u0026rsquo; fertility attitudes. The coefficient for wives having a college degree is small and statistically insignificant, suggesting that respondents do not perceive women\u0026rsquo;s higher education as incompatible with childbearing in these metropolitan contexts.\u003c/p\u003e \u003cp\u003eThe results also reveal significant associations between respondents\u0026rsquo; individual characteristics and the conditional ideal number of children. Men and older respondents are more likely than women and younger individuals to believe that the fictitious couples described in the vignettes should have more children. Respondents with higher socioeconomic status indicated by college education and employment tend to report lower conditional ideal numbers of children than their counterparts. This pattern is consistent with prior research suggesting that individuals with higher education and more prestigious occupations are more likely to perceive the opportunity costs associated with childbearing (Budig and England \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Moreover, respondents with a local \u003cem\u003ehukou\u003c/em\u003e tend to assign lower ideal numbers of children to the couples described in the vignettes than migrants, despite their better access to the social welfare system. One possible explanation is that many migrants originate from less urbanized areas where traditional family norms remain stronger and the perceived opportunity costs of childbearing are lower.\u003c/p\u003e \u003cp\u003eModels 2 through 6 examine whether the effects of vignette-level characteristics differ between men and women. Model 2 indicates that the positive association between equal housework division and fertility attitudes is significantly stronger among women than among men. Although women generally express less pronatalist attitudes than men, the gender gap narrows substantially when the husband in the vignette is described as sharing housework equally. Predicted probabilities suggest that equal housework division increases the probability of reporting an ideal family size of two by 13.4 percentage points for women, compared with 8.6 percentage points for men, suggesting that gender equality in household labor is particularly salient for women\u0026rsquo;s fertility evaluations.\u003c/p\u003e \u003cp\u003eModel 5 shows that the positive association between grandparental childcare support and fertility attitudes is weaker among women than among men. In other words, although both men and women report more favorable fertility attitudes when grandparents can provide childcare, the increase is significantly smaller among women. Taken together, these findings support Hypothesis \u003cspan refid=\"FPar2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eNo statistically significant gender differences are found in the effects of family income, wife\u0026rsquo;s education, or the availability of public childcare services. The results are robust to alternative model specifications using multinomial logit models (see Appendix Table A3).\u003c/p\u003e \u003cp\u003e[Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e About Here]\u003c/p\u003e \u003cp\u003eTo further explore whether gender differences in normative fertility attitudes vary by education level, I divide respondents into two groups: those with a college degree and those without. Ordered logit models are estimated separately for each group, including interaction terms between gender and the vignette characteristics. The results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\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\u003e Ordered logit coefficients of normative fertility attitudes on vignettes and socioeconomic characteristics: By gender and education level\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\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\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eModel 4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eModel 5\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCollege\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eCollege\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eCollege\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eCollege\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eCollege\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\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\u003eNo\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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTreatment variables * Female\u003c/b\u003e\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 \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEqual division of housework * Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.578***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\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(0.146)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.162)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium-income family * Female\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 \u003cp\u003e-0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.400*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.173)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.195)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-income family * Female\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 \u003cp\u003e-0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.183)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.198)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWife has a college degree * Female\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 \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.146)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.161)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrandparenting * Female\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 \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.357*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.147)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.161)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFree public childcare * Female\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 \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.112\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.145)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(0.160)\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\u003e3,037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3,037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2,672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3,037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2,672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3,037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2,672\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdj. R-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eNote: Robust standard errors in parentheses. *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. All treatment variable and covariates are included but not shown in the table.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe findings indicate that gender differences in the effects of housework division and grandparental support are concentrated among college-educated respondents. Among respondents with a college degree, women report significantly more pronatalist attitudes than men when the husband in the vignette is described as sharing housework equally (Model 1). By contrast, when grandparents are described as available to provide childcare, college-educated women report less pronatalist attitudes than their male counterparts (Model 4). This suggests that although extended-family support can alleviate childcare burdens, it does not increase normative fertility attitudes among highly educated women to the same extent as it does among men. These results provide support for Hypothesis \u003cspan refid=\"FPar3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eAmong respondents without a college education, however, the interaction effects between gender and the vignette characteristics are not statistically significant, indicating that men and women respond similarly to these contextual conditions. One exception is family income: women respond less positively than men when the vignette couple is described as having a medium income rather than a low income. This pattern may reflect gender differences in perceived economic constraints associated with childbearing among less-educated respondents.\u003c/p\u003e \u003cp\u003e[Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e About Here]\u003c/p\u003e \u003c/div\u003e "},{"header":"Discussion and Conclusion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003cp\u003eThis study examines how family conditions shape normative fertility attitudes in urban China using a factorial survey experiment, paying particular attention to gender inequality in the division of labor within families. The results show that respondents evaluate couples as better positioned to have children when they possess more egalitarian household arrangements, greater economic resources, and more childcare support. In particular, the division of housework emerges as a key factor shaping pronatalist fertility norms. Couples described as sharing housework equally are perceived as more suitable for childbearing, and this effect is significantly stronger among women than among men. Moreover, gender differences in these evaluations are especially pronounced among college-educated respondents. Together, these findings highlight the importance of gender inequality within families in shaping fertility-related norms in contemporary urban China.\u003c/p\u003e \u003cp\u003eThese results contribute to the broader literature on the relationship between gender equality and fertility. Consistent with the two-stage gender revolution framework, this study shows that in the most socioeconomically developed Chinese cities, women attach greater importance to equality in the private sphere as they gain more equal opportunities in the public sphere. Compared with their male counterparts, women are more likely to value an equal division of housework when evaluating the ideal family size for the vignette couples, suggesting that gender inequality in household labor remains a salient concern. This pattern is consistent with the persistence of the \u0026ldquo;second shift\u0026rdquo; among mothers. Even when childcare responsibilities can be partially outsourced to extended family members or formal institutions, mothers often remain the primary caregivers upon returning home from work (Short et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). These findings suggest that increasing the availability of childcare services alone may not be sufficient to enhance fertility intentions in China. Persistent gender inequality within households may continue to shape how individuals evaluate the feasibility of childbearing.\u003c/p\u003e \u003cp\u003eThe findings regarding grandparental childcare support further highlight the gendered nature of family and fertility norms. While extended-family support is often considered an important resource for young parents in China, its perceived benefits vary between men and women. Chinese grandparents frequently play an active role in the daily upbringing of their grandchildren and often co-reside with young couples. This arrangement allows parents to participate in the workforce without the financial burden of high-cost childcare services and also shapes family dynamics (Miao and Wu \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, because childcare responsibilities remain unequally distributed within families, mothers are typically more directly involved in coordinating childcare and interacting with grandparents. As a result, mothers are also more likely to experience disagreements or conflicts with grandparents, particularly in mother-daughter-in-law relationships (Song and Zhang \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Consistent with this pattern, the results of this study suggest that men and women may perceive the benefits of grandparental childcare differently. Because husbands often bear fewer childcare responsibilities, they may be less directly exposed to intergenerational caregiving conflicts, which may in turn lead them to evaluate grandparental childcare support more positively than women.\u003c/p\u003e \u003cp\u003eThe results also highlight the role of educational stratification in shaping fertility-related evaluations. Gender differences in the perceived desirability of childbearing conditions are more pronounced among college-educated respondents. Highly educated women often face greater opportunity costs associated with childbearing, including potential career interruptions and income penalties related to motherhood (Budig and England \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). At the same time, highly educated urban women in China frequently encounter demanding work environments while still bearing primary responsibility for domestic tasks. Under these circumstances, the perceived feasibility of childbearing may depend strongly on whether family arrangements allow women to reconcile work and family roles. The stronger gender differences observed among college-educated respondents suggest that tensions between career aspirations and unequal household labor may play a particularly important role in shaping fertility norms among highly educated urban populations.\u003c/p\u003e \u003cp\u003eThe findings of this study offer valuable insights into understanding the persistently low fertility rates observed in many East Asian societies, such as Japan, South Korea, Hong Kong, and Taiwan. Previous research has primarily concentrated on the impact of economic and institutional factors, including industrialization, educational expansion, family planning programs, and social welfare policies aimed at supporting families (Cheng \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In response to these factors, governments in the region have introduced a variety of pro-natalist policies, such as extended paid parental leave, tax incentives for families with children, and expanded access to free childcare. While it is premature to fully evaluate the long-term effectiveness of these recent policies, ongoing declines in both marriage and fertility rates suggest that additional measures are necessary to stimulate fertility intentions (Cheng \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Research has highlighted the significant empowerment of women in East Asian societies through education and employment over the past several decades. However, the persistence of traditional social norms and the relatively low level of men\u0026rsquo;s contributions to household labor have created a mismatch that reduces women\u0026rsquo;s incentives to marry and have children (Kan et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Thus, while policies aimed at alleviating the economic burdens of families are important, shifts in gender norms and ideologies may be central to addressing the region\u0026rsquo;s ultra-low fertility levels.\u003c/p\u003e \u003cp\u003eThe results of this study offer important policy implications for the design and implementation of demographic and family policies aimed at addressing the challenges posed by low fertility rates in China and similar contexts. First, the study finds that the average conditional ideal number of children among adults in the four surveyed cities is 1.24, which is higher than the actual fertility rates observed in three of the four cities studied. This gap suggests that extremely low fertility in these metropolitan areas may not solely reflect a lack of desire for children, but may also be shaped by constraints that limit individuals\u0026rsquo; ability to realize their fertility preferences. In other words, many adults may still view having children as desirable under more favorable family and social conditions. This finding indicates that supportive policy interventions still have the potential to influence fertility outcomes in highly developed urban contexts.\u003c/p\u003e \u003cp\u003eSecond, the findings suggest that efforts to encourage childbearing may need to move beyond financial incentives and childcare expansion to address gender inequality within families. Policies that promote greater involvement of fathers in childcare and domestic work may play an important role in shaping fertility decisions. Prior research shows that men\u0026rsquo;s participation in housework and childcare is more likely when social policies explicitly encourage or require such engagement (Goldin \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). For example, parental leave policies that reserve nontransferable leave specifically for fathers, such as the \u0026ldquo;father quotas\u0026rdquo; implemented in several Nordic countries have been shown to increase fathers\u0026rsquo; involvement in childcare (Tamm \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Similarly, workplace practices that support work-family balance for both men and women, including flexible working arrangements may encourage fathers to participate more actively in caregiving (Kuang, Perelli-Harris, and Berrington \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Together, these policy approaches may help reduce the unequal caregiving burdens faced by mothers and promote broader cultural shifts that normalize men\u0026rsquo;s participation in caregiving.\u003c/p\u003e \u003cp\u003eThird, while support from grandparents is a crucial source of childcare for dual-income families in large Chinese cities, the impacts of grandparenting on family dynamics warrant further attention. The findings of this study suggest that men and women may perceive the benefits of grandparental childcare differently, reflecting the gendered nature of caregiving responsibilities within families. Because mothers are more directly involved in childcare and daily interactions with grandparents, they may be more likely to experience tensions or conflicts, particularly in mother-daughter-in-law relationships. In this context, husbands may play an important role in mediating intergenerational disagreements and facilitating communication between their spouses and parents. Community-based family education programs and parenting workshops, such as those organized through community parenting schools in some large Chinese cities, may help improve grandparenting practices and promote constructive conflict resolution within families. Such initiatives may help maintain harmonious family relationships and enhance the effectiveness of intergenerational childcare support.\u003c/p\u003e \u003cp\u003eThis study has two main limitations. First, the data were collected through a mobile phone survey. Although mobile phone penetration is high in the studied cities, individuals with lower levels of education and income may have been less likely to complete the survey because of time constraints. As a result, these groups are underrepresented in the sample. While this potential sampling bias does not undermine the causal inference, it does limit the generalizability of the findings to the broader population. Second, the vignettes were presented to respondents via phone calls, which required a certain level of cognitive effort to process the information and provide answers. While the analytical sample comprises relatively young individuals, which helps mitigate this concern, future research could employ self-administered or mixed-mode survey designs to reduce cognitive burden and enhance response accuracy.\u003c/p\u003e \u003cp\u003eDespite these limitations, this study is the first study to investigate the normative fertility attitudes among Chinese individuals in first-tier cities. By experimentally varying key attributes of hypothetical couples, the study isolates how individuals evaluate the desirability of childbearing under different circumstances, offering new insight into the social foundations of fertility norms.\u003c/p\u003e \u003cp\u003eIn conclusion, this study shows that normative evaluations of childbearing are strongly shaped by family conditions, particularly the division of domestic labor. The results indicate that gender inequality within households remains an important factor influencing fertility norms in urban China and may help explain persistently low fertility in East Asian societies. More broadly, the study provides insights into how fertility norms evolve in societies where rapid economic transformation challenges traditional family values and gender norms, leaving individuals limited time to navigate the tensions between tradition, modernity, and changing gender roles. These insights have implications for the design of targeted, forward-looking family policies aimed at supporting individuals and couples in achieving their fertility goals.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJia Miao is the sole author of this article and was responsible for the study conception and design, data analysis, interpretation of the results, and preparation of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe data collection for the Four Metropolises Urban Neighborhood Survey was conducted by the School of Sociology and Political Science and the Center for Data and Urban Science (CENDUS) at Shanghai University. I express my gratitude to Wei Chen, Zhiming Sheng, Xiulin Sun, and Jianpin Wang for for their efforts in organizing the data collection, and to Junjie Du and Xinyang Tang for their research assistance.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data used in this study are from the Four Metropolises Urban Neighborhood Survey conducted by the School of Sociology and Political Science and the Center for Data and Urban Science (CENDUS) at Shanghai University. Due to data licensing restrictions, the dataset is not publicly available. Researchers may request access to the data from the data custodians at Shanghai University, subject to approval.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAugustine, J. M. (2017). 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Changing dynamics between the gender division of labor and fertility in Great Britain, 1991\u0026ndash;2017. \u003cem\u003eDemographic Research\u003c/em\u003e, 40(50).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e The penetration of mobile phones is expressed as the ratio of number of SIM cards to the total population, therefore, it can exceed 100% if the number of SIM cards in one place is higher than the actual population size.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"normative fertility attitudes, ideal number of children, factorial experimental design, Chinese metropolises","lastPublishedDoi":"10.21203/rs.3.rs-9097806/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9097806/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePersistently declining fertility has become a major demographic concern in China. While existing research has examined the role of economic resources and family policies in shaping fertility intentions, less attention has been paid to how gender inequality within families influences normative evaluations of childbearing. This study examines how family conditions, particularly the division of domestic labor, shape fertility norms in Beijing, Shanghai, Guangzhou, and Shenzhen. The analysis draws on data from the Four Metropolises Urban Neighborhood Survey, which incorporates a factorial survey experiment that randomly presents respondents with hypothetical couples whose family conditions vary across several dimensions. Ordered logistic regression results show that respondents evaluate couples with more egalitarian divisions of household labor, greater financial resources, and greater childcare support as better positioned for childbearing. Women respond more strongly than men to equal housework arrangements, and these gender differences are especially pronounced among college-educated respondents. These findings highlight the importance of gender inequality within families in shaping fertility-related norms and suggest that policies aimed at raising fertility in highly developed urban areas may need to address not only economic constraints but also persistent gender inequalities within households.\u003c/p\u003e","manuscriptTitle":"Normative Fertility Attitudes in Urban China: Evidence from a Factorial Survey Experiment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-23 07:06:48","doi":"10.21203/rs.3.rs-9097806/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8fe114a7-ca25-4a98-88d2-b02e164139a6","owner":[],"postedDate":"April 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-23T07:06:48+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-23 07:06:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9097806","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9097806","identity":"rs-9097806","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}