The Effects of Housing Expenditure Pressure and Gender Preference on the Reproduction Willingness for Young Female Migrants: Evidence from China

The Effects of Housing Expenditure Pressure and Gender Preference on the Reproduction Willingness for Young Female Migrants: Evidence from China | 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 Article The Effects of Housing Expenditure Pressure and Gender Preference on the Reproduction Willingness for Young Female Migrants: Evidence from China Yidong Wu, Yalin Zhang, Zhilin Zhu, Weiqian Jiang, Yuanyuan Zha This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7441721/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Increasing the reproduction willingness of young female migrants is crucial for alleviating China low fertility rate and promoting long-term balanced population development. Using the 2017–2018 China Migrants Dynamic Survey (CMDS), this paper examines the effects of housing expenditure pressure and gender preference on the reproduction willingness of young female migrants. Results indicate that higher household housing expenditure pressure and the number of sons significantly reduce reproduction willingness. Furthermore, housing expenditure pressure has a weaker adverse effect on reproduction willingness than gender preference. Heterogeneity analysis reveals that women below 36 years old and those with agricultural household registration are more sensitive to both factors. Housing expenditure pressure significantly inhibits reproduction willingness among those employed outside the system, in first marriages, and migrating within provinces. Regardless of subgroup, gender preference exerts a significantly stronger negative effect on reproduction willingness than housing pressure. Analysis of one-child families shows housing expenditure pressure ceases to be a key factor influencing reproduction willingness; however, for those with a son, gender preference crowds out further reproduction willingness. This study provides a theoretical basis for enhancing female migrants’ reproduction willingness through addressing housing costs and gender norms. Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental social sciences Biological sciences/Psychology Social science/Psychology housing expenditure pressure gender preference young female migrants reproduction willingness Figures Figure 1 1. Introduction China is actively developing a system of fertility support policies and implementing a national strategy to address population ageing. In recent years, persistently low fertility rates have emerged as a major challenge to the country’s socio-economic development. To counter the sustained decline in fertility, a series of pronatalist policies have been introduced. These include the selective two-child policy in December 2013, the universal two-child policy in January 2016, and most recently, the three-child policy in July 2021, all aimed at reversing the downward fertility trend. Complementary measures focusing on high-quality births, improved childcare, and reduced child-rearing costs have also been proposed. Despite these efforts, national fertility rates and the natural population growth rate continue to decline, with the latter turning negative after 2022 (see Fig. 1 ). To improve and optimize the demographic structure, China began to implement the three-child policy in July 2021. Then support measures are proposed such as high-quality procreation, more favorable childcare and lower child-rearing cost. Enhancing the reproduction willingness of young migrants, predominantly of marriageable and childbearing age, is crucial for China demographic trajectory. Concurrently, traditional gender norms are evolving, leading to a gradual shift in family power dynamics from husband-dominance towards more egalitarian or even wife-influenced models (Zhou, 2019 ). As women status within the household improves, they increasingly assume a pivotal role in reproductive decision-making. Furthermore, rising female educational attainment and the persistent “motherhood penalty” associated with childbirth exacerbate work-family conflicts, significantly altering women’s childbearing attitudes (Mills et al., 2008 ; Miettinen et al., 2011 ). Consequently, investigating the determinants and mechanisms shaping the reproduction willingness of young female migrants is of critical importance. Large-scale population mobility is an inherent feature of socio-economic transformation and upgrading (Kuznets, 1963 ). Securing stable and safe housing, with prospects for improving living conditions, represents a fundamental aspiration for migrants. Resolving housing challenges is not only essential for migrants' stability and well-being but also a critical precondition for mitigating reproductive constraints. Housing, as a fundamental human need, underpins personal survival, development, and the very foundation of establishing a home (Lin et al., 2014 ). Recognizing this, the central government has prioritized cultivating the urban rental housing market as a key solution for migrant housing needs. Furthermore, alleviating housing difficulties for youth and new urban residents is a major objective outlined in China 14th Five-Year Plan. Migrants typically face disadvantages relative to local residents in economic resources, social networks, and job access. They also encounter significant barriers to homeownership, and a majority remain excluded from urban housing security systems (Li & Zhang, 2011 ). Within market dynamics, housing functions not only as a major consumption item but also as implicit social capital, significantly shaping residents’ subjective social status and identity (Hu & Ye, 2020 ). Consequently, housing disparities can exacerbate the “Matthew Effect” and amplify migrants’ resource vulnerabilities, directly impacting their economic choices (Wang et al., 2010 ). For young migrants, housing carries particular weight. Cultural imperatives like “settling down before marrying” and “building a nest to attract a partner” position housing as a fundamental prerequisite for marriage and childbearing decisions. Furthermore, the strong link between access to quality basic education and homeownership continuously fuels migrants’ aspirations for property purchase. Gender preference, a persistent cultural phenomenon particularly prevalent in parts of Asia, remains a significant concern. China, with its long feudal history rooted in patriarchal traditions and the reliance on children for old-age support, exemplifies this issue. Influenced by these factors, a male-biased gender preference persists among some couples, contributing critically to the country imbalanced sex ratio at birth (SRB). The SRB rose sharply from 108.47 in 1982 to 121.18 in 2004. Despite recent declines, it remains elevated (111.3 according to the Seventh National Population Census) well above the biologically normal range of 104–107 (Visaria & Visaria, 1994 ). This persistent gender imbalance has generated numerous adverse social consequences, including intensified competition for brides in the marriage market (Wilson & Daly, 1985 ; Griskevicius et al., 2009 ), reduced female labor force participation (Angrist, 2002 ; Chiappori et al., 2002 ), and elevated crime rates (Dreze & Khera, 2000 ; Hudson & Den, 2002; Barber, 2003 ; Edlund et al., 2013 ). Furthermore, gender disparities contribute to macroeconomic imbalances, potentially dampening domestic investment and consumption markets (Wei & Zhang, 2011 ). Rising educational attainment and proactive gender equality policies have fostered the rapid diffusion of egalitarian gender norms across Chinese society. For women specifically, these norms have been a key driver behind their increased participation in the labor market, pursuit of economic independence, and enhanced social status. However, the embrace of gender equality by women has simultaneously intensified the work-family conflict. Consequently, a growing number of women of reproductive age are opting against having a second or third child, exhibiting significantly lower reproduction willingness. This paper focuses on a central question: Between housing expenditure (an economic burden) and gender preferences (an ideological factor), which exerts a greater influence on the reproduction willingness of young female migrants? To address this, we utilize data from the China Migrants Dynamic Survey (CMDS) to analyze the determinants of reproduction willingness among young female migrants. Our study specifically investigates the impact of housing costs and evolving gender attitudes, broadening the scope of existing research on these factors. Furthermore, we examine heterogeneity in these effects across multiple dimensions. Based on our findings, we propose policy recommendations aimed at fostering a sustainable housing market and providing theoretical insights to mitigate declining fertility rates and alleviate socio-demographic structural tensions. The remainder of this paper is structured as follows. Section 2 reviews the relevant literature and summarizes current research progress. Section 3 details the data sources, empirical models, and variable definitions. Section 4 presents the regression results and discusses the findings. Section 5 extends the analysis by focusing specifically on one-child families. Section 6 concludes and outlines the policy implications derived from the study. 2. Literature Review 2.1 Determinants of Reproduction Willingness Building upon these theoretical frameworks, empirical research identifies numerous micro-level factors directly influencing reproduction willingness, including individual gender, family income, and the sex of the first child (Schoen, 1999; Ajzen & Klobas, 2013 ; Luo & Mao, 2014 ; Khorram, 2017). Men’s attitudes towards childbearing often align more closely with traditional views of childbearing as a necessity, whereas women frequently hold opposing perspectives. Consequently, egalitarian gender norms within the family can significantly suppress women’s reproduction willingness (Yoon, 2016 ). For women, childbirth diverts time and energy, directly impacting career advancement while increasing time and economic costs (Pailhé & Solaz, 2012 ; Hanappi et al., 2017 ). Workplace interruptions and reduced employment opportunities due to childbearing exacerbate women’s disadvantage in the labor market, indirectly diminishing the marginal utility of children and reducing their willingness to bear more children (Kravdal & Rindfuss, 2008 ). However, a stronger family economic position can mitigate the negative impact of childbearing costs on women’s fertility. Furthermore, childcare subsidies, increased paternal involvement in childcare, or intergenerational care from elders can positively influence women's fertility decisions by alleviating childrearing burdens (Tanskanen & Rotkirch, 2014 ; Ho & Pavoni, 2020 ). Drawing on reproductive choice theory, Nachoum et al. ( 2021 ) found that prenatal fertility motivations, whether autonomous or controlled, shape subsequent parenting styles, ultimately impacting the couple’s future reproductive decisions. Macro-level influences, encompassing external fertility policies and institutional factors alongside internal cultural norms and gender attitudes, also critically shape population-level fertility decision-making (Fanti & Spataro, 2013 ; Bellido et al., 2016 ). The interplay of these internal and external factors reinforces rational childbearing choices (Guo, 2008 ). Key constraints include workplace gender discrimination, the responsibility of childcare postpartum, and the detrimental impact of childbirth on career trajectories. Fertility support policies can effectively alleviate women’s work-family conflict and reduce their childbearing burden (Engelhardt & Prskawetz, 2004 ). Therefore, comprehensive governmental maternity protection measures and greater cost-sharing for childrearing are more likely to increase women’s reproduction willingness (Friedman et al., 1994 ; Feuvre et al., 2015 ). Additionally, influenced by traditional Confucian culture and familial ethical responsibilities, strong intergenerational ties characterize Chinese families (Sheng & Settles, 2006 ; Ames & Roger, 2016). However, societal changes, including generational shifts and improvements in the social security system, weaken the intrinsic motivation for bearing children as old-age support, thereby lowering family reproduction willingness (Liu & Gong, 2020 ). Simultaneously, gender perspectives significantly affect reproduction willingness (McDonald, 2000 ); stronger adherence to traditional societal attitudes correlates with higher reproduction willingness, while a preference for contemporary social norms correlates with lower intentions. When external institutional pressures conflict with internal cultural norms, individuals tend to prioritize satisfying cultural demands. Revolutionary events can also trigger ideological shifts in perceptions of social relations, subsequently impacting population fertility rates (Bailey, 2009 ). 2.2 Housing and Reproduction Willingness From a housing perspective, scholars have examined the impact of macro-level housing prices on micro-level reproduction willingness. While a correlation is widely acknowledged, the direction and nature of this relationship remain debated. Some research suggests rising housing prices exert a crowding-out effect on reproduction willingness, primarily interpreted as an increase in childrearing costs due to high housing expenses (Liu et al., 2020 ; Clark et al., 2020 ). Sensitivity to housing price changes varies significantly based on homeownership status and purchasing power. Generally, high prices burden families without homes or with poor housing conditions. For households intending to buy or already repaying a mortgage, home purchase absorbs substantial funds, creating anxiety about childbearing under housing cost pressures, leading to reduced reproduction willingness and delayed first or second births, particularly pronounced in families planning multiple children (Liu & Clark, 2017 ). Furthermore, larger families require more living space, increasing housing demand and exacerbating home purchase pressures, thereby inhibiting the desire for more children (Lino et al., 2017 ). Housing cost pressures primarily affect reproductive anxiety through their impact on residents’ subjective well-being and perceptions of social fairness (Kingston et al., 1994; Liao et al., 2022 ). Conversely, other scholars posit that rising housing prices generate a wealth effect for homeowners, potentially enhancing their reproduction willingness (Dettling & Kearney, 2014 ). However, this positive effect may be suppressed in contexts with underdeveloped credit markets or imperfect credit systems (Liu et al., 2023 ). As a fundamental necessity and prerequisite for marriage and childbearing decisions, housing, specifically homeownership, has garnered scholarly attention. Research generally concurs that homeowners exhibit relatively higher fertility propensities than renters (Hu et al., 2022 ). In China, housing property rights are often intrinsically linked to access to local infrastructure and public services, particularly education. Children in rental households may face barriers to enrolling in local schools, dampening reproduction willingness among non-homeowners. Childbearing-age households lacking secure housing may forgo children due to this insecurity. Even homeowners may reassess childbearing costs under mortgage repayment pressure, and economically vulnerable families may experience heightened fertility anxiety. Vignoli et al.(2013) argue that better housing security correlates with a higher short-term probability of increased reproduction willingness. Rising commercial housing prices and rents significantly reduce housing affordability, leading to decreased reproduction willingness (Simon & Tamura, 2009 ). Non-homeowners’ reproduction willingness are more adversely affected by rising housing prices, while households owning multiple properties show a stronger preference for sons. 2.3 Gender preference and reproduction willingness The impact of gender preference on the reproduction willingness of reproductive-age populations is a prominent research focus (Edlund, 1999 ; Zhuang et al., 2020 ). Some scholars identify China as having one of the world’s strongest gender preferences (Birdsall & Boulier, 1985 ). This enduring bias is institutionally rooted in China long history of patriarchal systems, patrilineal inheritance traditions, and patrilocal residence norms (Wang, 2005 ). While industrialization, evolving childbearing concepts, and improved social security policies have moderated this preference, it persists (Xiaolei et al., 2013 ). Furthermore, the relaxation of fertility restrictions provided opportunities to fulfill unmet gender preferences, evidenced by the resurgence of high sex ratio at birth (SRB) imbalances post-policy change, confirming the persistent gender preference among Chinese residents (Jiang et al., 2016 ). The relationship between gender preference and reproduction willingness remains contested. Some studies suggest gender preference significantly increases reproduction willingness (Park & Cho, 1995 ; Morgan, 2003 ); families may choose to have more children to achieve their desired offspring sex composition if policy allows (Morgan et al., 2009 ). Research indicates families whose first child is a girl are substantially more likely to have a second child than those whose first is a boy (Chen & Jin, 2011 ), a phenomenon more prevalent in rural areas (Qian, 1997 ). A gender preference also shortens birth intervals in families with only girls or multiple girls (Guilmoto, 2012 ). Conversely, other scholars argue gender preference suppresses overall fertility desires and negatively impacts completed family size (Cohen et al., 1967 ; Ehrlich, 1968 ). Under strict family planning policies, couples unable to achieve their ideal sex ratio through multiple births might resort to sex-selective abortion (Keyfitz & Caswell, 2005 ), directly contributing to SRB imbalance (Yang & Wang, 2006 ). Traditional cultural values like “more children, more blessings”, “continuing the family line”, and “raising sons for old-age support” historically fostered both a preference for larger families and a strong gender preference (Guo, 2008 ). However, the implementation of gender equality policies, rising female educational attainment, and improved professional status are changing gender preferences in China (Zheng et al., 2009 ). The ideal is increasingly shifting towards “one son and one daughter,” which is positively correlated with the desired number of children (Jiang et al., 2013 ). The limited effectiveness of the two-child and three-child policies in boosting birth rates further reflects these evolving attitudes in practice (Wenzhan et al., 2022 ; Yang et al., 2023 ). 2.4 Research Gaps and Contributions Overall, existing research on housing costs, gender preference, and reproduction willingness provides a valuable foundation, yet significant gaps remain, offering avenues for this study’s contribution. First, regarding the research focus, most studies concentrate on resident families or youth groups generally, with less attention paid specifically to young female migrants. Enhancing the reproduction willingness of this group is crucial for addressing China fertility challenges. As women’s labor force participation rises and their role in family economic decision-making strengthens, their perspectives on housing and childbearing have diversified. Focusing on young female migrants’ reproduction willingness is therefore highly relevant. Second, this study innovatively compares the effects of housing expenditure pressure and gender preference on young female migrants’ reproduction willingness. While housing costs represent an economic burden and gender preference an ideological factor, both significantly influence their childbearing decisions. Existing research often examines these factors in isolation. Third, this study specifically investigates young female migrants’ willingness to have additional children. The sequential model within reproductive choice theory suggests individuals reassess reproduction willingness after their first child. Furthermore, reproduction willingness partially translate into actual behavior, which ultimately impacts national population dynamics. Analyzing the intention for further childbearing among this group is thus critical. Based on this, the paper takes young female migrants as the research object and utilizes the data of China Migrants Dynamic Survey for empirical analysis. This study empirically analyzes the effects of household housing expenditure pressure and gender preference on young female migrant reproduction willingness, and focuses on the heterogeneous influence characteristics at multiple levels. The findings of this paper, as well as the proposed countermeasures, can not only supplement empirical evidence for related studies, but also provide ideas for improving China fertility rate and promoting balanced population development. 3. Data and Methods 3.1 Data Source and Sample Selection We utilize data from the China Migrants Dynamic Survey (CMDS), conducted annually by the National Health Commission since 2009. The survey covers the floating population across 31 provinces, collecting information on migrants’ demographics, settlement intentions, household income/expenditure, health access, and social integration. Given its nationally representative sample, CMDS is widely used in migration research. The sample selection criteria are as follows: (1) Restrict to married women aged 20–45 with children (aligning with China’s legal marriage age and prime reproductive years); (2) Exclude outliers in income/housing expenditure (top/bottom 1%); (3) Remove missing/invalid responses. The final sample comprises 85,517 observations from the 2017–2018 waves, postdating the 2016 universal two-child policy to capture recent fertility dynamics. 3.2 Model structure We estimate a Probit model to examine how housing expenditure pressure and gender preference affect young female migrant reproduction willingness: \(\:Pr{(Reproduction\_Willingness=1)}_{ijt}=\alpha\:+{\beta\:}_{1}{ℎousing\_pressure}_{ijt}+{\beta\:}_{2}{gender\_preference}_{ijt}+{\lambda\:}{X}_{ijt}+{\delta\:}_{t}+{\theta\:}_{j}+{\epsilon\:}_{ijt}\) (1) In the model, \(\:Reproduction\_Willingness\) refers to the reproduction willingness of young female migrants, which is the dependent variable of this study. Meanwhile, \(\:ℎousing\_pressure\) and \(\:gender\_preference\) refer to housing expenditure pressure and gender preference, which are the core explanatory variables of this study. The coefficients of the explanatory variables of interest are \(\:{\beta\:}_{1}\) and \(\:{\beta\:}_{2}\) , which are the focus of attention in this paper. \(\:X\) denotes the set of control variables. In addition, subscripts \(\:i\) , \(\:j\) and \(\:\:t\) denote the individual respondent, the respondent’s city, and the year of the interview, respectively. Finally, \(\:\delta\:\) and \(\:\theta\:\) denote time fixed effects and city fixed effects, respectively. Dependent variable: \(\:\:Reproduction\_Willingness\) The reproduction willingness is assigned by the response to the question “Do you intend to have children in the next one or two years?” in the CMDS questionnaire. There are three options in the questionnaire that can be divided into three categories, including “yes”, “no” and “not yet decided”. Considering that the focus group of this study is the migrant population with a clear intention to have children, this paper redefines the above three categorical variables into dummy variables, assigning a value of 1 to the samples that answered “yes” and 0 to the others. (2) Explanatory variables: \(\:ℎousing\_pressure\) and \(\:gender\_preference\) First, the housing expenditure pressure is expressed as the proportion of the annual housing expenditure of the interviewed household in the local area to the total expenditure. It should be noted that housing expenditure in the questionnaire includes both rent and mortgage. The variable of rent only refers to the rent that the surveyed households need to pay for their residence in the destination, and does not include rent that needs to be paid for production and operation. The variable of mortgage only refers to the installment payment amount that the surveyed households need to pay for purchasing a house, and does not include the down payment and full payment for purchasing a house. Second, since the traditional concept of “gender preference” has persisted in China for a long time, this paper defines gender preference as the number of boys in a family. (3) Control variables: \(\:X\) The control variables contain both individual characteristics and household characteristics. Individual characteristics include the respondent’s age, squared term of age/1000, education, urban and rural household type, nationality, marital status, health status, range of mobility, duration of mobility, nature of the work units, participation in health insurance, and the spouse’s education and household type. Household characteristics variables include household size, total household income, and household expenditures other than housing. The names and definitions of the variables involved in this paper are shown in Table 1 . Table 1 Definition and Measurement Variable Variable definition Explained Variable Reproduction Willingness An indicator variable that equals to one if the respondent has reproduction willingness within one or two years, and equals to zero otherwise Explanatory variable Housing pressure The proportion of the household’s local housing expenditure in their total expenditure Gender preference The number of boys owned by the respondent Individual characteristic variables Age Age of individuals Square of age The square of age divided by 1000 Education The highest educational attainment of individual, unschooled = 1, elementary school = 2, middle school = 3, high school = 4, junior college = 5, undergraduate = 6, master or PhD = 7 Non-agricultural Hukou An indicator variable that equals to one if the respondent with non-agricultural hukou , and equals to zero otherwise Han An indicator variable that equals to one if the respondent with Han nationality, and equals to zero otherwise Marriage An indicator variable that equals to one if the respondent with first marriage, and equals to zero otherwise Health An ordered variable of self-assessed health status, which is measured on a 4-point scale, ranging from 1 (very unhealthy) to 4 (very healthy) Inter-province mobilization An indicator variable that equals to one if respondent is inter-province mobilization, and equal to zero otherwise Mobilization time The time of the respondent’s current mobilization Employment type An indicator variable that equals to one if respondent is employed by state organs, party and mass organizations, enterprises and institutions, and equal to zero otherwise Insurance An indicator variable that equals to one if the respondent has the urban essential medical insurance or enjoys public health services, and equals to zero otherwise Education level of spouse The highest educational attainment of individual’s spouse, unschooled = 1, elementary school = 2, middle school = 3, high school = 4, junior college = 5, undergraduate = 6, master or PhD = 7 Urban hukou of spouse An indicator variable that equals to one if the respondent’s spouse with non-agricultural hukou , and equals to zero otherwise Family characteristic variables Family size The total number of family members Household income Total household income in the last year (RMB) Household expenditure Total household expenditure other than housing in the last year (RMB) Notes: Hukou refers to China household registration system. Housing expenditure excludes production-related rent/purchase costs. Continuous variables (income/expenditure) are log-transformed in regressions. 3.3 Variable descriptive statistics Descriptive statistics of the main variables involved in the empirical analysis of this paper are shown in Table 2 . In the full sample selected for this paper, about 9.1% of the respondents have the intention to have another child within one or two years. In addition, the minimum value of the housing expenditure pressure indicator is 0, the maximum value is 1, and the mean and standard deviation are 0.122 and 0.132. This data indicates that there is a certain disparity in housing expenditure pressure among the groups of young female migrants. At the same time, to a certain extent, this can also reflect the more obvious polarization of regional housing prices or rent levels in China’s housing market. In this paper, gender preference is defined as the number of boys in the household. On average, there are 0.817 boys in the sample households. Table 2 Summary statistics Variable Observations Mean Std. dev. Min Max Reproduction Willingness 85,517 0.091 0.288 0 1 Housing pressure 85,517 0.122 0.132 0 1 Gender preference 85,517 0.817 0.624 0 3 Age 85,517 33.050 5.816 20 45 Square of age 85,517 1.126 0.391 0.4 1.936 Education 85,517 3.499 1.122 1 7 Urban hukou 85,517 0.133 0.340 0 1 Han 85,517 0.907 0.290 0 1 Marriage 85,517 0.978 0.146 0 1 Health 85,517 3.868 0.370 1 4 Inter-province mobilization 85,517 0.481 0.500 0 1 Mobilization time 85,517 5.844 4.906 0 30 Employment type 85,517 0.078 0.267 0 1 Insurance 85,517 0.933 0.250 0 1 Education level of spouse 85,517 3.635 1.103 1 7 Urban hukou of spouse 85,517 0.180 0.384 0 1 Familysize 85,517 3.639 0.759 3 8 Household income 85,517 96614.05 69511.25 2400 960000 Household expenditure 85,517 39686.58 28368.92 1080 480000 Notes: In order to better present the original characteristics of the sample, the data on the two continuous variables of total household income and other household expenditures in this table are characterized by their values before taking logarithms. However, in the process of empirical analysis, this paper will take the logarithm of these two variables in order to alleviate the problem of heteroskedasticity. 4. Results 4.1 Baseline Results This study empirically examines the effects of housing expenditure pressure and gender preference on the reproduction willingness of young female migrants using Model (1). Table 3 presents the baseline regression results, where Columns (1) and (2) report estimates incorporating housing expenditure pressure and gender preference separately, while Column (3) includes both variables simultaneously.The regression results indicate that the coefficient for housing expenditure pressure is significantly negative at the 5% level, suggesting that increased household housing expenditure burden leads to a significant decline in young female migrants’ willingness to have additional children. Concurrently, the gender preference coefficient exhibits a statistically significant negative effect at the 1% level, indicating that a higher number of existing sons in the household correlates with reduced reproduction willingness among young female migrants. Table 3 Baseline Results Explained Variable: Reproduction Willingness (1) (2) (3) Coefficient Marginal effect Coefficient Marginal effect Coefficient Marginal effect Housing pressure -0.089 ** -0.013 ** -0.086 ** -0.012 ** (0.043) (0.043) Gender preference -0.423 *** -0.060 *** -0.423 *** -0.060 *** (0.012) (0.012) Age 0.149 *** 0.022 *** 0.172 *** 0.024 *** 0.173 *** 0.025 *** (0.014) (0.014) (0.014) Square of age -3.191 *** -0.465 *** -3.490 *** -0.494 *** -3.501 *** -0.495 *** (0.216) (0.220) (0.220) Education -0.005 -0.001 -0.021 ** -0.003 ** -0.020 ** -0.003 ** (0.009) (0.009) (0.010) Urban hukou 0.006 0.001 -0.002 0.000 -0.001 0.000 (0.023) (0.023) (0.023) Han -0.105 *** -0.015 *** -0.105 *** -0.015 *** -0.104 *** -0.015 *** (0.023) (0.024) (0.024) Marriage -0.446 *** -0.065 *** -0.441 *** -0.062 *** -0.441 *** -0.062 *** (0.042) (0.043) (0.043) Health 0.034 * 0.005 * 0.035 * 0.005 * 0.034 * 0.005 * (0.019) (0.020) (0.020) Inter-province mobilization -0.051 *** -0.007 *** -0.042 ** -0.006 ** -0.043 *** -0.006 *** (0.016) (0.017) (0.017) Mobilization time 0.010 *** 0.001 *** 0.010 *** 0.001 *** 0.010 *** 0.001 *** (0.002) (0.002) (0.002) Employment type 0.018 0.003 0.022 0.003 0.021 0.003 (0.025) (0.025) (0.025) Insurance 0.061 ** 0.009 ** 0.058 ** 0.008 ** 0.057 ** 0.008 ** (0.027) (0.028) (0.028) Education level of spouse 0.026 *** 0.004 *** 0.018 * 0.003 * 0.019 ** 0.003 ** (0.009) (0.009) (0.009) Urban hukou of spouse 0.013 0.002 0.001 0.000 -0.001 0.000 (0.021) (0.021) (0.021) Familysize -0.485 *** -0.071 *** -0.422 *** -0.060 *** -0.422 *** -0.060 *** (0.015) (0.015) (0.015) Ln(Household income) 0.134 *** 0.020 *** 0.122 *** 0.017 *** 0.135 *** 0.019 *** (0.017) (0.016) (0.018) Ln(Household expenditure) -0.033 ** -0.005 ** -0.007 -0.001 -0.027 * -0.004 * (0.016) (0.013) (0.016) _cons -1.489 *** -1.939 *** -1.888 *** (0.319) (0.325) (0.326) Year dummies Yes Yes Yes City dummies Yes Yes Yes Pseudo R 2 0.1238 0.1479 0.1480 Observations 85517 85517 85517 Notes: (1) Standard errors in parentheses; (2) *** \(\:\text{p}\) < 0.01, ** \(\:\text{p}\) < 0.05, * \(\:\text{p}\) < 0.1. Fundamentally, reproductive decisions represent utility-maximizing consumption behaviors within household economic frameworks. Housing expenditure pressure influences both reproduction willingness and actual reproductive choices by crowding out other household consumption categories through excessive housing-related expenditures. When homeownership becomes intricately linked to accessing quality basic education resources, households face intensified incentives to purchase property, thereby elevating childbearing costs while diminishing the perceived benefits of additional children.The modern paradigm of female independence has further encouraged women’s labor force participation, diverting time and energy from childcare responsibilities. To advance career development and improve living conditions for local integration, women increasingly prioritize work over childrearing activities. Considering the income and substitution effects associated with childbearing, coupled with gender preference, families with existing male children typically exhibit lower propensity to continue childbearing. While the ordered Probit model yields specific coefficient estimates, these values provide limited interpretive insight beyond sign and significance. This section therefore presents marginal effect analyses, revealing that the negative impact of housing expenditure pressure on reproduction willingness is significantly weaker in magnitude than that of gender preference. These findings suggest that altering gender attitudes may be more critical than alleviating housing expenses for enhancing household fertility willingness. 4.2 Robustness checks To ensure the robustness and credibility of the baseline regression results, this study employs two robustness testing strategies: alternative measurement approaches and core explanatory variable substitutions. First, using the full sample, this section estimates the model using ordinary least squares (OLS) and Logit specifications instead of the ordered Probit model. Second, housing expenditure pressure is redefined as a binary variable (coded 1 if housing expenditure exceeds the sample mean, 0 otherwise), while gender preference is similarly converted into a dummy variable. The ordered Probit model is then re-estimated with these revised variables. Table 4 presents the results of these robustness checks, which consistently show that the coefficient for housing expenditure pressure remains significantly negative at the 5% level, and the coefficient for gender preference retains its 1% level significance with a negative sign. Marginal effect analyses further confirm that the magnitude of the negative impact of housing expenditure pressure on young female migrants’ reproduction willingness is significantly smaller than that of gender preference. These findings suggest that altering gender attitudes may be more critical than alleviating housing expenses for enhancing household fertility willingness. Table 4 Robustness check: by replacing estimation method and the core explanatory variables Explained Variable: Reproduction Willingness Replacing estimation method Replacing the core explanatory variable OLS Logit (3) (1) (2) Coefficient Coefficient Marginal effect Coefficient Marginal effect Housing pressure -0.013 ** -0.185 ** -0.014 ** (0.006) (0.083) High housing pressure -0.035 ** -0.005 ** (0.015) Gender preference -0.055 *** -0.784 *** -0.059 *** (0.002) (0.023) Son_dummy -0.410 *** -0.058 *** (0.014) Control variable Yes Yes Yes Year dummies Yes Yes Yes City dummies Yes Yes Yes R 2 0.0784 Pseudo R 2 0.1481 0.1413 Observations 85,827 85,517 85,517 Notes: (1) Standard errors in parentheses; (2) *** < 0.01, ** < 0.05, * < 0.1. Additional robustness tests were conducted through sample replacement procedures. First, 50% of observations were randomly sampled from the full dataset using both with-replacement and without-replacement methods, and regressions were re-estimated on these subsamples. Second, respondents who answered “I haven’t figured it out yet" to the question ” “Do you intend to have a child in the coming year or two?” were excluded from the analysis. Table 5 reports the results of these sample-based robustness checks, which show that the coefficient for housing expenditure pressure remains significantly negative at the 10% level, while the gender preference coefficient maintains 1% level significance with a negative sign. Marginal effect estimates again indicate that housing expenditure pressure exerts a significantly weaker negative impact on reproduction willingness compared to gender preference. These results not only validate the representativeness of the sample but also confirm the robustness of the baseline regression findings. Table 5 Robustness check: by replacing the sample Explained Variable: Reproduction Willingness Screening samples by random sampling method Exclude samples with the answer is “I haven't figured it out yet” 50% random sampling without replacement 50% random sampling with replacement (3) (1) (2) Coefficient Marginal effect Coefficient Marginal effect Coefficient Marginal effect Housing pressure -0.111 * -0.016 * -0.106 * -0.015 * -0.137 *** -0.020 *** (0.061) (0.062) (0.046) Gender preference -0.410 ** -0.059 *** -0.397 *** -0.055 *** -0.492 *** -0.073 *** (0.016) (0.017) (0.013) Control variable Yes Yes Yes Year dummies Yes Yes Yes City dummies Yes Yes Yes Pseudo R 2 0.1472 0.1595 0.1964 Observations 42,143 41,824 74,144 Notes: (1) Standard errors in parentheses; (2) *** < 0.01, ** < 0.05, * < 0.1. 4.3 Endogenous testing Endogeneity concerns arising from potential reverse causality cannot be overlooked. While children gender is generally exogenous to individual preferences or choices, endogeneity primarily stems from the housing expenditure pressure variable. This study measures household housing expenditure as the previous year’s housing costs, whereas reproduction willingness refer to plans for the survey year and subsequent two years. This temporal separation between housing consumption decisions and fertility planning partially mitigates reverse causality concerns, though residual endogeneity may still exist. To address this, the instrumental variable (IV) method is employed. Drawing on group effect theory, which posits that individual characteristics are correlated with group-level characteristics within the same region but unaffected by other individual attributes, this study uses the district/county-level mean of housing expenditures (excluding the respondent’s own data) as an instrumental variable for two-stage least squares (2SLS) estimation. This instrument satisfies relevance criteria by reflecting regional housing consumption levels and exhibits conditional exogeneity by excluding individual-specific housing expenditure pressure, thereby avoiding direct influence on personal reproduction willingness. Furthermore, based on “peer effect” theory, which suggests individual behavior is shaped by group characteristics among similar socioeconomic status, samples were reclassified by household registration type, ethnicity, and public-sector employment status within districts/counties. District/county-level mean housing expenditures of peer groups were then generated as alternative instruments for 2SLS estimation. Table 6 presents the IV regression results, which confirm that the housing expenditure pressure coefficient remains significantly negative. First-stage F-statistics exceed 10, and Wald tests confirm instrument validity at the 1% significance level, indicating the selected instrumental variables possess strong explanatory power and credibility. Table 6 Endogenous testing: Instrumental variables Mean value of samples within one county Mean value of the same type within one county (1) (2) (3) (4) First stage Second stage First stage Second stage Housing pressure -1.171 *** -1.250 *** (0.210) (0.240) Mean of Housing pressure 0.0617 *** 0.0414 *** (0.010) (0.008) Gender preference -0.421 *** -0.422 *** (0.012) (0.012) Control variable Yes Yes Yes Yes Year dummies Yes Yes Yes Yes City dummies Yes Yes Yes Yes R 2 0.4578 0.4516 F statistic of first stage 190.70 184.70 Wald test of exogeneity 27.99 *** 24.51 *** Observations 85,516 85,516 84,272 84,272 Notes: (1) Standard errors in parentheses; (2) *** \(\:\text{p}\) < 0.01, ** \(\:\text{p}\) < 0.05, * \(\:\text{p}\) < 0.1. At the same time, the endogenous problem caused by omitted variables will bias the conclusions of this study. Although this study has controlled for multiple dimensions of variables, time fixed effects, and city fixed effects in the benchmark regression model, it cannot completely rule out potential endogenous issues caused by omitted variables. Therefore, this paper sets up two regression models with limited set of control variables and full set of control variables. In addition, this section will record the estimated coefficients of the core explanatory variables as \(\:{\widehat{\beta\:}}^{R}\) and \(\:{\widehat{\beta\:}}^{F}\) , respectively. The form of constructing index \(\:{Ratio}_{R,F}\) is as follows. \(\:{Ratio}_{R,F}=\left|\frac{{\widehat{\beta\:}}^{F}}{{\widehat{\beta\:}}^{R}-{\widehat{\beta\:}}^{F}}\right|\) (2) The implication of this index is that the smaller the gap between \(\:{\widehat{\beta\:}}^{R}\) and \(\:{\widehat{\beta\:}}^{F}\) , the greater the explanatory power of the observable variables in the model. That is, if the value of \(\:{Ratio}_{R,F}\) is larger, the likelihood of bias in the estimation results due to the problem of omitted variables is smaller. In this paper, three finite set control variables are selected for regression based on the Probit model. In addition, in this part, the coefficients obtained from the regressions of the finite set control variables are each subjected to the \(\:{Ratio}_{R,F}\) index construction with the coefficients obtained from the regressions of the full set control variables. The results are shown in Table 7 . The \(\:{Ratio}_{R,F}\) index for the core explanatory variables of housing pressure and gender preference are in the range of [7.039, 27.946] and [1.000, 15.844] respectively. The mean values of these two indices are calculated to be 14.265 and 8.746, respectively. This suggests that if the omitted variables were to bias the results of the benchmark regression, their explanatory power would need to exceed that of the controlled variables by more than 14.265 and 8.746 times on average. It can be concluded that the endogenous problem of omitted variables does not significantly bias the results of the benchmark regression. Table 7 Endogenous testing: Estimate the degree of bias caused by omitted variables Finite set of control variables Full set of control variables Calculation of indicator \(\:{Ratio}_{R,F}\) Housing pressure Gender preference Only controlling family characteristics and urban fixed effects All the control variables 7.039 9.395 Only controlling individual characteristics, family characteristics, and urban fixed effects All the control variables 7.809 1.000 Only controlling individual characteristics, family characteristics, and year fixed effects All the control variables 27.946 15.844 Average value of indicator \(\:{Ratio}_{R,F}\) 14.265 8.746 4.4 Heterogeneity analysis To further examine how housing expenditure pressure and gender preference affect the reproduction willingness among distinct subgroups of young female migrants, this subsection presents heterogeneity analysis results stratified by individual age and household registration status (Table 8 ). The findings reveal that the negative impact of housing expenditure pressure on reproduction willingness is statistically significant only among respondents aged 35 and younger, with gender preference also exerting a stronger negative effect within this age group. This suggests that women aged 35 or below are more vulnerable to reproduction deterrents stemming from employment demands, family care responsibilities, and economic constraints. Elevated housing costs intensify labor force participation incentives for women, while childbearing imposes opportunity costs by diverting time and energy from career development, factors that significantly reduce reproduction willingness among those already parenting. With respect to household registration type, excessive housing expenditure significantly diminishes reproduction willingness among rural-registered female migrants, whereas the negative impact of gender preference shows no significant variation by hukou status. Notably, across all subgroup analyses, gender preference consistently exerts a significantly stronger negative influence on reproduction willingness compared to housing expenditure pressure. Table 8 Heterogeneity analysis by age and hukou registration Divide into groups according to age Divide into groups according to hukou 20 ≤ age ≤ 35 36 ≤ age ≤ 45 rural household registration Urban household registration (1) (2) (3) (4) Coefficient Marginal effect Coefficient Marginal effect Coefficient Marginal effect Coefficient Marginal effect Housing pressure -0.115 ** -0.021 ** 0.100 0.007 -0.095 ** -0.013 ** -0.035 -0.006 (0.048) (0.105) (0.048) (0.104) Gender preference -0.434 *** -0.079 *** -0.368 *** -0.025 *** -0.429 *** -0.060 *** -0.394 *** -0.064 *** (0.013) (0.031) (0.013) (0.031) Control variable Yes Yes Yes Yes Year dummies Yes Yes Yes Yes City dummies Yes Yes Yes Yes Pseudo R 2 0.1013 0.2195 0.1577 0.1167 Observations 55979 26346 73,958 10,560 Notes: (1) Standard errors in parentheses; (2) *** \(\:\text{p}\) < 0.01, ** \(\:\text{p}\) < 0.05, * \(\:\text{p}\) < 0.1. To further investigate the heterogeneous effects of housing expenditure pressure and gender preference on young female migrants’ reproduction willingness, this study analyzes subgroup variations across job categories and marital statuses (Table 9 ). Results indicate that housing expenditure exerts a statistically significant negative effect on reproduction willingness exclusively among non-public sector employees. For public sector workers, whose employment, income, and life expectations tend to be more stable, housing expenditures do not significantly impact their reproduction willingness. Conversely, non-public sector employees face wage instability, wherein housing costs crowd out non-housing consumption and thereby reduce the willingness to have additional children. Regarding marital status, housing expenditure pressure significantly inhibits reproduction willingness among first-married female migrants. Notably, across all subgroup specifications, gender preference consistently demonstrates a significant negative effect on reproduction willingness, with its impact magnitude significantly exceeding that of housing expenditure pressure. This subsection further examines heterogeneous effects of housing expenditure pressure and gender preference on young female migrants’ reproduction willingness, focusing on individual mobility characteristics and housing tenure differences. Results in Columns (1)-(2) of Table 10 indicate that while housing expenditure pressure exhibits a consistent negative direction across all mobility subgroups, statistical significance emerges only among intra-provincial migrants. Narrower geographic mobility correlates with stronger social network advantages; yet, even within such contexts, excessive housing expenditure intensifies psychological distress, thereby weakening reproduction willingness. Table 9 Heterogeneity analysis by employment type and marriage stage Divide into groups according to employment type Divide into groups according to marriage stage Employment within the system Employment outside the system First marriage remarriage (1) (2) (3) (4) Coefficient Marginal effect Coefficient Marginal effect Coefficient Marginal effect Coefficient Marginal effect Housing pressure 0.004 0.001 -0.098 ** -0.014 ** -0.088 ** -0.012 ** -0.043 -0.008 (0.127) (0.046) (0.044) (0.319) Gender preference -0.374 *** -0.070 *** -0.428 *** -0.059 *** -0.427 *** -0.060 *** -0.356 *** -0.067 *** (0.040) (0.012) (0.012) (0.074) Control variable Yes Yes Yes Yes Year dummies Yes Yes Yes Yes City dummies Yes Yes Yes Yes Pseudo R 2 0.1234 0.1537 0.1476 0.2202 Observations 6,035 78,765 83,630 1,410 Notes: (1) Standard errors in parentheses; (2) *** \(\:\text{p}\) < 0.01, ** \(\:\text{p}\) < 0.05, * \(\:\text{p}\) < 0.1. Additionally, this study operationalizes housing expenditure to include both rental payments and mortgage installments, recognizing fundamental differences in property rights between renting and homeownership. To address potential heterogeneity by housing type, the 2017 CMDS questionnaire data, detailing current housing status, classifies respondents as homeowners or renters. Columns (3)-(4) of Table 10 reveal that housing expenditure exerts a statistically significant negative effect on reproduction willingness only among homeowners (p < 0.10). Across all subgroup analyses, gender preference consistently demonstrates a stronger negative impact on reproduction willingness compared to housing expenditure pressure. 5. Further Discussion To explore how housing expenditure pressure and gender preference affect young female migrants’ reproduction willingness among families with one child, this section draws on the ordinal model theory of reproductive choice, which posits that couples gain clearer insight into the costs and benefits of childbearing only after the birth of their first child, prompting reassessments of whether and when to have additional children. Since reproduction intention here refers to families’ future fertility plans after having their first child, the analysis focuses on one-child families. For this subgroup, gender preference is defined by the gender of the existing child. Table 10 Heterogeneity analysis by inter-province mobilization and housing types Divide into groups according to inter-province mobilization Divide into groups according to housing types inter-province mobilization inner-province mobilization homeowners (mortgage) Tenant (housing rent) (1) (2) (3) (4) Coefficient Marginal effect Coefficient Marginal effect Coefficient Marginal effect Coefficient Marginal effect Housing pressure -0.077 -0.010 -0.111 * -0.017 * -0.160 * -0.027 * -0.059 -0.008 (0.065) (0.059) (0.084) (0.086) Gender preference -0.433 *** -0.057 *** -0.424 *** -0.064 *** -0.423 *** -0.070 *** -0.440 *** -0.063 *** (0.017) (0.016) (0.026) (0.020) Control variable Yes Yes Yes Yes Year dummies Yes Yes Yes Yes City dummies Yes Yes Yes Yes Pseudo R 2 0.1564 0.1489 0.1304 0.1762 Observations 40,358 44,047 14,261 29,626 Notes: (1) Standard errors in parentheses; (2) *** \(\:\text{p}\) < 0.01, ** \(\:\text{p}\) < 0.05, * \(\:\text{p}\) < 0.1. Regression results (Table 11 ) align with the benchmark findings in Table 3 . Columns (1) and (2) present regressions incorporating housing expenditure pressure and gender preference separately, while Column (3) includes both indicators. The results show that the coefficient for housing expenditure pressure remains negative but loses statistical significance. In contrast, the gender preference coefficient remains significantly negative at the 1% level. This indicates that for one-child families, housing financial pressure is no longer a key determinant of reproduction willingness. Instead, among young female migrants who already have a boy, gender preference crowds out their willingness to have another child. Table 11 Further Discussion Explained Variable: Reproduction Willingness (1) (2) (3) Coefficient Marginal effect Coefficient Marginal effect Coefficient Marginal effect Housing pressure -0.057 -0.012 -0.059 -0.013 (0.047) (0.047) son -0.281 *** -0.061 *** -0.281 *** -0.061 *** (0.015) (0.015) Control variable Yes Yes Yes Year dummies Yes Yes Yes City dummies Yes Yes Yes Pseudo R 2 0.0894 0.0980 0.0980 Observations 47939 47939 47939 Notes: (1) Standard errors in parentheses; (2) *** \(\:\text{p}\) < 0.01, ** \(\:\text{p}\) < 0.05, * \(\:\text{p}\) < 0.1. 6. Conclusions and policy implications Enhancing the reproduction willingness of young female migrants represents a critical pathway to boosting China fertility rate and fostering long-term balanced population development. Addressing housing challenges and transforming traditional gender norms serve as prerequisites for mitigating fertility-related conflicts. Against this backdrop, this study focuses on young female migrants as the research subject and utilizes data from the 2017–2018 China Migrants Dynamic Survey (CMDS) to empirically examine the impacts of housing expenditure pressure and gender preference on their reproduction willingness. The findings reveal that increased household housing expenditure pressure and a higher number of boys significantly reduce young migrant women’s willingness to have additional children. Furthermore, the negative effect of housing expenditure pressure on young female migrants’ reproduction willingness is significantly weaker than that of gender preference, with these results remaining robust across a series of robustness tests. Concurrently, the impacts of housing expenditure pressure and gender preference exhibit heterogeneity based on individual characteristics. Specifically, the negative effect of housing expenditure pressure on reproduction willingness is statistically significant only among those aged 35 and below and those with agricultural household registration. Gender preference exerts a more pronounced negative impact on reproduction willingness within younger cohorts. Moreover, housing expenditure pressure significantly suppresses reproduction willingness among young female migrants employed in the non-public sector, those in first-marriage status, and intra-provincial migrants. For home-buying households, the negative effect of housing expenditures on young female migrants’ reproduction willingness is statistically significant at the 10% level. Regardless of the subgroup analysis, however, gender preference consistently demonstrates a significant negative effect on young female migrants’ reproduction willingness, with its impact magnitude significantly exceeding that of housing expenditure pressure. Finally, based on a subsample of one-child families, this study finds that housing financial pressure no longer constitutes a key factor influencing reproduction willingness; instead, among young female migrants who already have a son, gender preference crowds out their willingness to have another child. In conclusion, to further enhance young female migrants’ reproduction willingness and promote balanced population development, policymakers should not only prioritize alleviating housing expenditure pressure but also implement measures to transform gender attitudes. Specific policy recommendations include: (1) comprehensively considering differences in housing demand among migrant populations, promoting dual-track operation of the housing market and housing security systems, and providing moderate housing subsidies or in-kind assistance to low-income and housing-vulnerable families while ensuring market stability; (2) advocating for gender equality practices, promoting gender equality as a mainstream social norm, and striving to transform fertility-related perceptions; and (3) establishing a robust reproductive welfare system centered on women's needs, enhancing support for women’s childbearing, and actively fostering a fertility-friendly social environment. Declarations Competing Interests The authors declare no competing interests. Ethical Approval This article does not contain any studies with human participants performed by any of the authors. Informed Consent This article does not contain any studies with human participants performed by any of the authors. Funding Statement The work was supported by the National Natural Science Foundation of China Youth Program [72404002; 72404145]; Ministry of Education Humanities and Social Sciences Youth Foundation [23YJC790154; 23YJCZH308]. Author Contribution The conceptual framework was developed by Yidong Wu. The methodology was crafted by Yalin Zhang. Zhilin Zhu was responsible for the software aspect. Weiqian Jiang contributed to the validation process. For formal analysis, Yidong Wu was in charge. Yuanyuan Zha handled the data compilation. The initial draft was written by Yidong Wu. Yalin Zhang worked on the review and editing of the manuscript. Yidong Wu has reviewed and approved the final version of the paper. Data Availability The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. 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Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAqklEQVRIiWNgGAWjYHACwweMDWCGAdFajA2AWiRI0mImQZoWgxvJ2yp+7jhcx8DevE2CoeYOMVrSym72njkswcBzrEyC4dgzIlx1I8fsBm8bUItEDsiFh4nTUvgXpEX+DQlamCG28BCpxf7Ms2Jp2bZ0yTaetGKLhGNEaJFsT9748W2bNT8/++GNNz7UEKGFQSABQrOBiAQiNDAw8B8gStkoGAWjYBSMZAAAtkY4A83HdzUAAAAASUVORK5CYII=","orcid":"","institution":"Anhui University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Yidong","middleName":"","lastName":"Wu","suffix":""},{"id":531820372,"identity":"8f8d9383-7a67-4983-821c-902108566044","order_by":1,"name":"Yalin Zhang","email":"","orcid":"","institution":"Nanjing University of Posts and Telecommunications","correspondingAuthor":false,"prefix":"","firstName":"Yalin","middleName":"","lastName":"Zhang","suffix":""},{"id":531820373,"identity":"f9e690f8-a2e7-41ec-bdb9-a63a829934b4","order_by":2,"name":"Zhilin Zhu","email":"","orcid":"","institution":"Anhui University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Zhilin","middleName":"","lastName":"Zhu","suffix":""},{"id":531820375,"identity":"b79b6f5f-69c5-40b4-8f07-524d10bd55cb","order_by":3,"name":"Weiqian Jiang","email":"","orcid":"","institution":"Anhui University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Weiqian","middleName":"","lastName":"Jiang","suffix":""},{"id":531820376,"identity":"b6709294-643d-45aa-9d9d-3594cf09ef3a","order_by":4,"name":"Yuanyuan Zha","email":"","orcid":"","institution":"Beijing University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yuanyuan","middleName":"","lastName":"Zha","suffix":""}],"badges":[],"createdAt":"2025-08-23 13:53:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7441721/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7441721/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":94139255,"identity":"2f87668f-de52-47bc-b036-6fe602566aee","added_by":"auto","created_at":"2025-10-22 19:32:05","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":141069,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.docx","url":"https://assets-eu.researchsquare.com/files/rs-7441721/v1/17c7ea98f974abbd601d5ccf.docx"},{"id":94139256,"identity":"a6443514-d820-478a-8159-b229b1ef6887","added_by":"auto","created_at":"2025-10-22 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19:32:05","extension":"xml","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":219096,"visible":true,"origin":"","legend":"","description":"","filename":"6406e2535d3b494f8b9429fd9c1c7ef31structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7441721/v1/bea9bfcbf4125ae016b0fe6a.xml"},{"id":94139259,"identity":"3fe5e8fa-7ecb-4f4c-8d0b-49094921f013","added_by":"auto","created_at":"2025-10-22 19:32:05","extension":"html","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":225635,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7441721/v1/8b3c5912ff6e2522ea2d81c7.html"},{"id":94139254,"identity":"abb8b5cf-aa27-4b94-8058-a0304b8c675e","added_by":"auto","created_at":"2025-10-22 19:32:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":44402,"visible":true,"origin":"","legend":"\u003cp\u003eTrends of China total population, birth rate, and natural growth rate from 2000 to 2023\u003c/p\u003e\n\u003cp\u003eSource: National Statistical Office.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7441721/v1/72119094cc81fa44f48de7cc.png"},{"id":94140816,"identity":"c6ad8132-213a-4aee-90b2-7692c608ac36","added_by":"auto","created_at":"2025-10-22 19:48:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2005138,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7441721/v1/c6d5b099-1926-4052-a567-de662c820db9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Effects of Housing Expenditure Pressure and Gender Preference on the Reproduction Willingness for Young Female Migrants: Evidence from China","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eChina is actively developing a system of fertility support policies and implementing a national strategy to address population ageing. In recent years, persistently low fertility rates have emerged as a major challenge to the country\u0026rsquo;s socio-economic development. To counter the sustained decline in fertility, a series of pronatalist policies have been introduced. These include the selective two-child policy in December 2013, the universal two-child policy in January 2016, and most recently, the three-child policy in July 2021, all aimed at reversing the downward fertility trend. Complementary measures focusing on high-quality births, improved childcare, and reduced child-rearing costs have also been proposed. Despite these efforts, national fertility rates and the natural population growth rate continue to decline, with the latter turning negative after 2022 (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). To improve and optimize the demographic structure, China began to implement the three-child policy in July 2021. Then support measures are proposed such as high-quality procreation, more favorable childcare and lower child-rearing cost. Enhancing the reproduction willingness of young migrants, predominantly of marriageable and childbearing age, is crucial for China demographic trajectory. Concurrently, traditional gender norms are evolving, leading to a gradual shift in family power dynamics from husband-dominance towards more egalitarian or even wife-influenced models (Zhou, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). As women status within the household improves, they increasingly assume a pivotal role in reproductive decision-making. Furthermore, rising female educational attainment and the persistent \u0026ldquo;motherhood penalty\u0026rdquo; associated with childbirth exacerbate work-family conflicts, significantly altering women\u0026rsquo;s childbearing attitudes (Mills et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Miettinen et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Consequently, investigating the determinants and mechanisms shaping the reproduction willingness of young female migrants is of critical importance.\u003c/p\u003e\u003cp\u003eLarge-scale population mobility is an inherent feature of socio-economic transformation and upgrading (Kuznets, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1963\u003c/span\u003e). Securing stable and safe housing, with prospects for improving living conditions, represents a fundamental aspiration for migrants. Resolving housing challenges is not only essential for migrants' stability and well-being but also a critical precondition for mitigating reproductive constraints. Housing, as a fundamental human need, underpins personal survival, development, and the very foundation of establishing a home (Lin et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Recognizing this, the central government has prioritized cultivating the urban rental housing market as a key solution for migrant housing needs. Furthermore, alleviating housing difficulties for youth and new urban residents is a major objective outlined in China 14th Five-Year Plan. Migrants typically face disadvantages relative to local residents in economic resources, social networks, and job access. They also encounter significant barriers to homeownership, and a majority remain excluded from urban housing security systems (Li \u0026amp; Zhang, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Within market dynamics, housing functions not only as a major consumption item but also as implicit social capital, significantly shaping residents\u0026rsquo; subjective social status and identity (Hu \u0026amp; Ye, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Consequently, housing disparities can exacerbate the \u0026ldquo;Matthew Effect\u0026rdquo; and amplify migrants\u0026rsquo; resource vulnerabilities, directly impacting their economic choices (Wang et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). For young migrants, housing carries particular weight. Cultural imperatives like \u0026ldquo;settling down before marrying\u0026rdquo; and \u0026ldquo;building a nest to attract a partner\u0026rdquo; position housing as a fundamental prerequisite for marriage and childbearing decisions. Furthermore, the strong link between access to quality basic education and homeownership continuously fuels migrants\u0026rsquo; aspirations for property purchase.\u003c/p\u003e\u003cp\u003eGender preference, a persistent cultural phenomenon particularly prevalent in parts of Asia, remains a significant concern. China, with its long feudal history rooted in patriarchal traditions and the reliance on children for old-age support, exemplifies this issue. Influenced by these factors, a male-biased gender preference persists among some couples, contributing critically to the country imbalanced sex ratio at birth (SRB). The SRB rose sharply from 108.47 in 1982 to 121.18 in 2004. Despite recent declines, it remains elevated (111.3 according to the Seventh National Population Census) well above the biologically normal range of 104\u0026ndash;107 (Visaria \u0026amp; Visaria, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). This persistent gender imbalance has generated numerous adverse social consequences, including intensified competition for brides in the marriage market (Wilson \u0026amp; Daly, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; Griskevicius et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), reduced female labor force participation (Angrist, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Chiappori et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), and elevated crime rates (Dreze \u0026amp; Khera, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Hudson \u0026amp; Den, 2002; Barber, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Edlund et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Furthermore, gender disparities contribute to macroeconomic imbalances, potentially dampening domestic investment and consumption markets (Wei \u0026amp; Zhang, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Rising educational attainment and proactive gender equality policies have fostered the rapid diffusion of egalitarian gender norms across Chinese society. For women specifically, these norms have been a key driver behind their increased participation in the labor market, pursuit of economic independence, and enhanced social status. However, the embrace of gender equality by women has simultaneously intensified the work-family conflict. Consequently, a growing number of women of reproductive age are opting against having a second or third child, exhibiting significantly lower reproduction willingness.\u003c/p\u003e\u003cp\u003eThis paper focuses on a central question: Between housing expenditure (an economic burden) and gender preferences (an ideological factor), which exerts a greater influence on the reproduction willingness of young female migrants? To address this, we utilize data from the China Migrants Dynamic Survey (CMDS) to analyze the determinants of reproduction willingness among young female migrants. Our study specifically investigates the impact of housing costs and evolving gender attitudes, broadening the scope of existing research on these factors. Furthermore, we examine heterogeneity in these effects across multiple dimensions. Based on our findings, we propose policy recommendations aimed at fostering a sustainable housing market and providing theoretical insights to mitigate declining fertility rates and alleviate socio-demographic structural tensions.\u003c/p\u003e\u003cp\u003eThe remainder of this paper is structured as follows. Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reviews the relevant literature and summarizes current research progress. Section \u003cspan refid=\"Sec7\" class=\"InternalRef\"\u003e3\u003c/span\u003e details the data sources, empirical models, and variable definitions. Section \u003cspan refid=\"Sec11\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the regression results and discusses the findings. Section \u003cspan refid=\"Sec16\" class=\"InternalRef\"\u003e5\u003c/span\u003e extends the analysis by focusing specifically on one-child families. Section \u003cspan refid=\"Sec17\" class=\"InternalRef\"\u003e6\u003c/span\u003e concludes and outlines the policy implications derived from the study.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Determinants of Reproduction Willingness\u003c/h2\u003e\u003cp\u003eBuilding upon these theoretical frameworks, empirical research identifies numerous micro-level factors directly influencing reproduction willingness, including individual gender, family income, and the sex of the first child (Schoen, 1999; Ajzen \u0026amp; Klobas, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Luo \u0026amp; Mao, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Khorram, 2017). Men\u0026rsquo;s attitudes towards childbearing often align more closely with traditional views of childbearing as a necessity, whereas women frequently hold opposing perspectives. Consequently, egalitarian gender norms within the family can significantly suppress women\u0026rsquo;s reproduction willingness (Yoon, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). For women, childbirth diverts time and energy, directly impacting career advancement while increasing time and economic costs (Pailh\u0026eacute; \u0026amp; Solaz, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Hanappi et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Workplace interruptions and reduced employment opportunities due to childbearing exacerbate women\u0026rsquo;s disadvantage in the labor market, indirectly diminishing the marginal utility of children and reducing their willingness to bear more children (Kravdal \u0026amp; Rindfuss, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). However, a stronger family economic position can mitigate the negative impact of childbearing costs on women\u0026rsquo;s fertility. Furthermore, childcare subsidies, increased paternal involvement in childcare, or intergenerational care from elders can positively influence women's fertility decisions by alleviating childrearing burdens (Tanskanen \u0026amp; Rotkirch, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Ho \u0026amp; Pavoni, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Drawing on reproductive choice theory, Nachoum et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) found that prenatal fertility motivations, whether autonomous or controlled, shape subsequent parenting styles, ultimately impacting the couple\u0026rsquo;s future reproductive decisions.\u003c/p\u003e\u003cp\u003eMacro-level influences, encompassing external fertility policies and institutional factors alongside internal cultural norms and gender attitudes, also critically shape population-level fertility decision-making (Fanti \u0026amp; Spataro, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Bellido et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The interplay of these internal and external factors reinforces rational childbearing choices (Guo, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Key constraints include workplace gender discrimination, the responsibility of childcare postpartum, and the detrimental impact of childbirth on career trajectories. Fertility support policies can effectively alleviate women\u0026rsquo;s work-family conflict and reduce their childbearing burden (Engelhardt \u0026amp; Prskawetz, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Therefore, comprehensive governmental maternity protection measures and greater cost-sharing for childrearing are more likely to increase women\u0026rsquo;s reproduction willingness (Friedman et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Feuvre et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Additionally, influenced by traditional Confucian culture and familial ethical responsibilities, strong intergenerational ties characterize Chinese families (Sheng \u0026amp; Settles, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Ames \u0026amp; Roger, 2016). However, societal changes, including generational shifts and improvements in the social security system, weaken the intrinsic motivation for bearing children as old-age support, thereby lowering family reproduction willingness (Liu \u0026amp; Gong, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Simultaneously, gender perspectives significantly affect reproduction willingness (McDonald, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2000\u003c/span\u003e); stronger adherence to traditional societal attitudes correlates with higher reproduction willingness, while a preference for contemporary social norms correlates with lower intentions. When external institutional pressures conflict with internal cultural norms, individuals tend to prioritize satisfying cultural demands. Revolutionary events can also trigger ideological shifts in perceptions of social relations, subsequently impacting population fertility rates (Bailey, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Housing and Reproduction Willingness\u003c/h2\u003e\u003cp\u003eFrom a housing perspective, scholars have examined the impact of macro-level housing prices on micro-level reproduction willingness. While a correlation is widely acknowledged, the direction and nature of this relationship remain debated. Some research suggests rising housing prices exert a crowding-out effect on reproduction willingness, primarily interpreted as an increase in childrearing costs due to high housing expenses (Liu et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Clark et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Sensitivity to housing price changes varies significantly based on homeownership status and purchasing power. Generally, high prices burden families without homes or with poor housing conditions. For households intending to buy or already repaying a mortgage, home purchase absorbs substantial funds, creating anxiety about childbearing under housing cost pressures, leading to reduced reproduction willingness and delayed first or second births, particularly pronounced in families planning multiple children (Liu \u0026amp; Clark, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Furthermore, larger families require more living space, increasing housing demand and exacerbating home purchase pressures, thereby inhibiting the desire for more children (Lino et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Housing cost pressures primarily affect reproductive anxiety through their impact on residents\u0026rsquo; subjective well-being and perceptions of social fairness (Kingston et al., 1994; Liao et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Conversely, other scholars posit that rising housing prices generate a wealth effect for homeowners, potentially enhancing their reproduction willingness (Dettling \u0026amp; Kearney, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). However, this positive effect may be suppressed in contexts with underdeveloped credit markets or imperfect credit systems (Liu et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAs a fundamental necessity and prerequisite for marriage and childbearing decisions, housing, specifically homeownership, has garnered scholarly attention. Research generally concurs that homeowners exhibit relatively higher fertility propensities than renters (Hu et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In China, housing property rights are often intrinsically linked to access to local infrastructure and public services, particularly education. Children in rental households may face barriers to enrolling in local schools, dampening reproduction willingness among non-homeowners. Childbearing-age households lacking secure housing may forgo children due to this insecurity. Even homeowners may reassess childbearing costs under mortgage repayment pressure, and economically vulnerable families may experience heightened fertility anxiety. Vignoli et al.(2013) argue that better housing security correlates with a higher short-term probability of increased reproduction willingness. Rising commercial housing prices and rents significantly reduce housing affordability, leading to decreased reproduction willingness (Simon \u0026amp; Tamura, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Non-homeowners\u0026rsquo; reproduction willingness are more adversely affected by rising housing prices, while households owning multiple properties show a stronger preference for sons.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Gender preference and reproduction willingness\u003c/h2\u003e\u003cp\u003eThe impact of gender preference on the reproduction willingness of reproductive-age populations is a prominent research focus (Edlund, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Zhuang et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Some scholars identify China as having one of the world\u0026rsquo;s strongest gender preferences (Birdsall \u0026amp; Boulier, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1985\u003c/span\u003e). This enduring bias is institutionally rooted in China long history of patriarchal systems, patrilineal inheritance traditions, and patrilocal residence norms (Wang, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). While industrialization, evolving childbearing concepts, and improved social security policies have moderated this preference, it persists (Xiaolei et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Furthermore, the relaxation of fertility restrictions provided opportunities to fulfill unmet gender preferences, evidenced by the resurgence of high sex ratio at birth (SRB) imbalances post-policy change, confirming the persistent gender preference among Chinese residents (Jiang et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe relationship between gender preference and reproduction willingness remains contested. Some studies suggest gender preference significantly increases reproduction willingness (Park \u0026amp; Cho, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Morgan, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2003\u003c/span\u003e); families may choose to have more children to achieve their desired offspring sex composition if policy allows (Morgan et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Research indicates families whose first child is a girl are substantially more likely to have a second child than those whose first is a boy (Chen \u0026amp; Jin, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), a phenomenon more prevalent in rural areas (Qian, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). A gender preference also shortens birth intervals in families with only girls or multiple girls (Guilmoto, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Conversely, other scholars argue gender preference suppresses overall fertility desires and negatively impacts completed family size (Cohen et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e1967\u003c/span\u003e; Ehrlich, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e1968\u003c/span\u003e). Under strict family planning policies, couples unable to achieve their ideal sex ratio through multiple births might resort to sex-selective abortion (Keyfitz \u0026amp; Caswell, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), directly contributing to SRB imbalance (Yang \u0026amp; Wang, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Traditional cultural values like \u0026ldquo;more children, more blessings\u0026rdquo;, \u0026ldquo;continuing the family line\u0026rdquo;, and \u0026ldquo;raising sons for old-age support\u0026rdquo; historically fostered both a preference for larger families and a strong gender preference (Guo, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). However, the implementation of gender equality policies, rising female educational attainment, and improved professional status are changing gender preferences in China (Zheng et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The ideal is increasingly shifting towards \u0026ldquo;one son and one daughter,\u0026rdquo; which is positively correlated with the desired number of children (Jiang et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The limited effectiveness of the two-child and three-child policies in boosting birth rates further reflects these evolving attitudes in practice (Wenzhan et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Research Gaps and Contributions\u003c/h2\u003e\u003cp\u003eOverall, existing research on housing costs, gender preference, and reproduction willingness provides a valuable foundation, yet significant gaps remain, offering avenues for this study\u0026rsquo;s contribution. First, regarding the research focus, most studies concentrate on resident families or youth groups generally, with less attention paid specifically to young female migrants. Enhancing the reproduction willingness of this group is crucial for addressing China fertility challenges. As women\u0026rsquo;s labor force participation rises and their role in family economic decision-making strengthens, their perspectives on housing and childbearing have diversified. Focusing on young female migrants\u0026rsquo; reproduction willingness is therefore highly relevant.\u003c/p\u003e\u003cp\u003eSecond, this study innovatively compares the effects of housing expenditure pressure and gender preference on young female migrants\u0026rsquo; reproduction willingness. While housing costs represent an economic burden and gender preference an ideological factor, both significantly influence their childbearing decisions. Existing research often examines these factors in isolation.\u003c/p\u003e\u003cp\u003eThird, this study specifically investigates young female migrants\u0026rsquo; willingness to have additional children. The sequential model within reproductive choice theory suggests individuals reassess reproduction willingness after their first child. Furthermore, reproduction willingness partially translate into actual behavior, which ultimately impacts national population dynamics. Analyzing the intention for further childbearing among this group is thus critical.\u003c/p\u003e\u003cp\u003eBased on this, the paper takes young female migrants as the research object and utilizes the data of China Migrants Dynamic Survey for empirical analysis. This study empirically analyzes the effects of household housing expenditure pressure and gender preference on young female migrant reproduction willingness, and focuses on the heterogeneous influence characteristics at multiple levels. The findings of this paper, as well as the proposed countermeasures, can not only supplement empirical evidence for related studies, but also provide ideas for improving China fertility rate and promoting balanced population development.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Data and Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Data Source and Sample Selection\u003c/h2\u003e\u003cp\u003eWe utilize data from the China Migrants Dynamic Survey (CMDS), conducted annually by the National Health Commission since 2009. The survey covers the floating population across 31 provinces, collecting information on migrants\u0026rsquo; demographics, settlement intentions, household income/expenditure, health access, and social integration. Given its nationally representative sample, CMDS is widely used in migration research. The sample selection criteria are as follows: (1) Restrict to married women aged 20\u0026ndash;45 with children (aligning with China\u0026rsquo;s legal marriage age and prime reproductive years); (2) Exclude outliers in income/housing expenditure (top/bottom 1%); (3) Remove missing/invalid responses. The final sample comprises 85,517 observations from the 2017\u0026ndash;2018 waves, postdating the 2016 universal two-child policy to capture recent fertility dynamics.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Model structure\u003c/h2\u003e\u003cp\u003eWe estimate a Probit model to examine how housing expenditure pressure and gender preference affect young female migrant reproduction willingness:\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"2\"\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\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Pr{(Reproduction\\_Willingness=1)}_{ijt}=\\alpha\\:+{\\beta\\:}_{1}{ℎousing\\_pressure}_{ijt}+{\\beta\\:}_{2}{gender\\_preference}_{ijt}+{\\lambda\\:}{X}_{ijt}+{\\delta\\:}_{t}+{\\theta\\:}_{j}+{\\epsilon\\:}_{ijt}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\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\u003eIn the model, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Reproduction\\_Willingness\\)\u003c/span\u003e\u003c/span\u003e refers to the reproduction willingness of young female migrants, which is the dependent variable of this study. Meanwhile, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:ℎousing\\_pressure\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:gender\\_preference\\)\u003c/span\u003e\u003c/span\u003e refer to housing expenditure pressure and gender preference, which are the core explanatory variables of this study. The coefficients of the explanatory variables of interest are \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{1}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{2}\\)\u003c/span\u003e\u003c/span\u003e, which are the focus of attention in this paper. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:X\\)\u003c/span\u003e\u003c/span\u003e denotes the set of control variables. In addition, subscripts \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:j\\)\u003c/span\u003e\u003c/span\u003e and\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:t\\)\u003c/span\u003e\u003c/span\u003e denote the individual respondent, the respondent\u0026rsquo;s city, and the year of the interview, respectively. Finally, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\delta\\:\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\theta\\:\\)\u003c/span\u003e\u003c/span\u003e denote time fixed effects and city fixed effects, respectively.\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eDependent variable:\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:Reproduction\\_Willingness\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eThe reproduction willingness is assigned by the response to the question \u0026ldquo;Do you intend to have children in the next one or two years?\u0026rdquo; in the CMDS questionnaire. There are three options in the questionnaire that can be divided into three categories, including \u0026ldquo;yes\u0026rdquo;, \u0026ldquo;no\u0026rdquo; and \u0026ldquo;not yet decided\u0026rdquo;. Considering that the focus group of this study is the migrant population with a clear intention to have children, this paper redefines the above three categorical variables into dummy variables, assigning a value of 1 to the samples that answered \u0026ldquo;yes\u0026rdquo; and 0 to the others.\u003c/p\u003e\u003cp\u003e(2) Explanatory variables: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:ℎousing\\_pressure\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:gender\\_preference\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003eFirst, the housing expenditure pressure is expressed as the proportion of the annual housing expenditure of the interviewed household in the local area to the total expenditure. It should be noted that housing expenditure in the questionnaire includes both rent and mortgage. The variable of rent only refers to the rent that the surveyed households need to pay for their residence in the destination, and does not include rent that needs to be paid for production and operation. The variable of mortgage only refers to the installment payment amount that the surveyed households need to pay for purchasing a house, and does not include the down payment and full payment for purchasing a house.\u003c/p\u003e\u003cp\u003eSecond, since the traditional concept of \u0026ldquo;gender preference\u0026rdquo; has persisted in China for a long time, this paper defines gender preference as the number of boys in a family.\u003c/p\u003e\u003cp\u003e(3) Control variables: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:X\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003eThe control variables contain both individual characteristics and household characteristics. Individual characteristics include the respondent\u0026rsquo;s age, squared term of age/1000, education, urban and rural household type, nationality, marital status, health status, range of mobility, duration of mobility, nature of the work units, participation in health insurance, and the spouse\u0026rsquo;s education and household type. Household characteristics variables include household size, total household income, and household expenditures other than housing.\u003c/p\u003e\u003cp\u003eThe names and definitions of the variables involved in this paper are shown 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\u003eDefinition and Measurement\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVariable definition\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExplained Variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReproduction Willingness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAn indicator variable that equals to one if the respondent has reproduction willingness within one or two years, and equals to zero otherwise\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eExplanatory variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHousing pressure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThe proportion of the household\u0026rsquo;s local housing expenditure in their total expenditure\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGender preference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThe number of boys owned by the respondent\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"12\" rowspan=\"13\"\u003e\u003cp\u003eIndividual characteristic variables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAge of individuals\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSquare of age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThe square of age divided by 1000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThe highest educational attainment of individual, unschooled\u0026thinsp;=\u0026thinsp;1, elementary school\u0026thinsp;=\u0026thinsp;2, middle school\u0026thinsp;=\u0026thinsp;3, high school\u0026thinsp;=\u0026thinsp;4, junior college\u0026thinsp;=\u0026thinsp;5, undergraduate\u0026thinsp;=\u0026thinsp;6, master or PhD\u0026thinsp;=\u0026thinsp;7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-agricultural Hukou\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAn indicator variable that equals to one if the respondent with non-agricultural \u003cem\u003ehukou\u003c/em\u003e, and equals to zero otherwise\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAn indicator variable that equals to one if the respondent with Han nationality, and equals to zero otherwise\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMarriage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAn indicator variable that equals to one if the respondent with first marriage, and equals to zero otherwise\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHealth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAn ordered variable of self-assessed health status, which is measured on a 4-point scale, ranging from 1 (very unhealthy) to 4 (very healthy)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInter-province mobilization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAn indicator variable that equals to one if respondent is inter-province mobilization, and equal to zero otherwise\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMobilization time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThe time of the respondent\u0026rsquo;s current mobilization\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEmployment type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAn indicator variable that equals to one if respondent is employed by state organs, party and mass organizations, enterprises and institutions, and equal to zero otherwise\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInsurance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAn indicator variable that equals to one if the respondent has the urban essential medical insurance or enjoys public health services, and equals to zero otherwise\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEducation level of spouse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThe highest educational attainment of individual\u0026rsquo;s spouse, unschooled\u0026thinsp;=\u0026thinsp;1, elementary school\u0026thinsp;=\u0026thinsp;2, middle school\u0026thinsp;=\u0026thinsp;3, high school\u0026thinsp;=\u0026thinsp;4, junior college\u0026thinsp;=\u0026thinsp;5, undergraduate\u0026thinsp;=\u0026thinsp;6, master or PhD\u0026thinsp;=\u0026thinsp;7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUrban \u003cem\u003ehukou\u003c/em\u003e of spouse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAn indicator variable that equals to one if the respondent\u0026rsquo;s spouse with non-agricultural \u003cem\u003ehukou\u003c/em\u003e, and equals to zero otherwise\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eFamily characteristic variables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFamily size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThe total number of family members\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHousehold income\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal household income in the last year (RMB)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHousehold expenditure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal household expenditure other than housing in the last year (RMB)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eNotes: Hukou refers to China household registration system. Housing expenditure excludes production-related rent/purchase costs. Continuous variables (income/expenditure) are log-transformed in regressions.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Variable descriptive statistics\u003c/h2\u003e\u003cp\u003eDescriptive statistics of the main variables involved in the empirical analysis of this paper are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In the full sample selected for this paper, about 9.1% of the respondents have the intention to have another child within one or two years. In addition, the minimum value of the housing expenditure pressure indicator is 0, the maximum value is 1, and the mean and standard deviation are 0.122 and 0.132. This data indicates that there is a certain disparity in housing expenditure pressure among the groups of young female migrants. At the same time, to a certain extent, this can also reflect the more obvious polarization of regional housing prices or rent levels in China\u0026rsquo;s housing market. In this paper, gender preference is defined as the number of boys in the household. On average, there are 0.817 boys in the sample households.\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\u003eSummary statistics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStd. dev.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReproduction Willingness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e85,517\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousing pressure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e85,517\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender preference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e85,517\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.817\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.624\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3\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\u003e85,517\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e33.050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.816\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSquare of age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e85,517\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.391\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.936\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e85,517\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.499\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban \u003cem\u003ehukou\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e85,517\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.340\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e85,517\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.907\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.290\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarriage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e85,517\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.978\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.146\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e85,517\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.868\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.370\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInter-province mobilization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e85,517\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.481\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMobilization time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e85,517\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.844\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.906\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployment type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e85,517\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInsurance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e85,517\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.933\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation level of spouse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e85,517\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.635\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban \u003cem\u003ehukou\u003c/em\u003e of spouse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e85,517\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.180\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.384\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFamilysize\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e85,517\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.639\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.759\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold income\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e85,517\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e96614.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e69511.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e960000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold expenditure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e85,517\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39686.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e28368.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e480000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNotes: In order to better present the original characteristics of the sample, the data on the two continuous variables of total household income and other household expenditures in this table are characterized by their values before taking logarithms. However, in the process of empirical analysis, this paper will take the logarithm of these two variables in order to alleviate the problem of heteroskedasticity.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1 Baseline Results\u003c/h2\u003e\n \u003cp\u003eThis study empirically examines the effects of housing expenditure pressure and gender preference on the reproduction willingness of young female migrants using Model (1). Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e presents the baseline regression results, where Columns (1) and (2) report estimates incorporating housing expenditure pressure and gender preference separately, while Column (3) includes both variables simultaneously.The regression results indicate that the coefficient for housing expenditure pressure is significantly negative at the 5% level, suggesting that increased household housing expenditure burden leads to a significant decline in young female migrants\u0026rsquo; willingness to have additional children. Concurrently, the gender preference coefficient exhibits a statistically significant negative effect at the 1% level, indicating that a higher number of existing sons in the household correlates with reduced reproduction willingness among young female migrants.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cem\u003eBaseline Results\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eExplained Variable: Reproduction Willingness\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMarginal effect\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMarginal effect\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMarginal effect\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHousing pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.089\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.013\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.086\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.012\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.043)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.043)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender preference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.423\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.060\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.423\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.060\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.149\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.022\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.172\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.024\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.173\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.025\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSquare of age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.191\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.465\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.490\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.494\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.501\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.495\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.216)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.220)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.220)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.021\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.003\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.020\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.003\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban \u003cem\u003ehukou\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.105\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.015\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.105\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.015\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.104\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.015\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarriage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.446\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.065\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.441\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.062\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.441\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.062\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.042)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.043)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.043)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHealth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.034\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.035\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.034\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eInter-province mobilization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.051\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.007\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.042\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.006\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.043\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.006\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMobilization time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmployment type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.025)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.025)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.025)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInsurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.061\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.058\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.057\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.027)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.028)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.028)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eEducation level of spouse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.026\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.018\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.019\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eUrban \u003cem\u003ehukou\u003c/em\u003e of spouse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFamilysize\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.485\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.071\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.422\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.060\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.422\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.060\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eLn(Household income)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.134\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.020\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.122\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.017\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.135\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.019\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eLn(Household expenditure)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.033\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.005\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.027\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.004\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e_cons\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.489\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.939\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.888\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.319)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.325)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.326)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYear dummies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCity dummies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePseudo R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.1238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.1479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.1480\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e85517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e85517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e85517\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eNotes: (1) Standard errors in parentheses; (2) ***\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{p}\\)\u003c/span\u003e\u003c/span\u003e\u0026lt; 0.01, **\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{p}\\)\u003c/span\u003e\u003c/span\u003e\u0026lt; 0.05, *\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{p}\\)\u003c/span\u003e\u003c/span\u003e\u0026lt; 0.1.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eFundamentally, reproductive decisions represent utility-maximizing consumption behaviors within household economic frameworks. Housing expenditure pressure influences both reproduction willingness and actual reproductive choices by crowding out other household consumption categories through excessive housing-related expenditures. When homeownership becomes intricately linked to accessing quality basic education resources, households face intensified incentives to purchase property, thereby elevating childbearing costs while diminishing the perceived benefits of additional children.The modern paradigm of female independence has further encouraged women\u0026rsquo;s labor force participation, diverting time and energy from childcare responsibilities. To advance career development and improve living conditions for local integration, women increasingly prioritize work over childrearing activities. Considering the income and substitution effects associated with childbearing, coupled with gender preference, families with existing male children typically exhibit lower propensity to continue childbearing. While the ordered Probit model yields specific coefficient estimates, these values provide limited interpretive insight beyond sign and significance. This section therefore presents marginal effect analyses, revealing that the negative impact of housing expenditure pressure on reproduction willingness is significantly weaker in magnitude than that of gender preference. These findings suggest that altering gender attitudes may be more critical than alleviating housing expenses for enhancing household fertility willingness.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2 Robustness checks\u003c/h2\u003e\n \u003cp\u003eTo ensure the robustness and credibility of the baseline regression results, this study employs two robustness testing strategies: alternative measurement approaches and core explanatory variable substitutions. First, using the full sample, this section estimates the model using ordinary least squares (OLS) and Logit specifications instead of the ordered Probit model. Second, housing expenditure pressure is redefined as a binary variable (coded 1 if housing expenditure exceeds the sample mean, 0 otherwise), while gender preference is similarly converted into a dummy variable. The ordered Probit model is then re-estimated with these revised variables. Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e presents the results of these robustness checks, which consistently show that the coefficient for housing expenditure pressure remains significantly negative at the 5% level, and the coefficient for gender preference retains its 1% level significance with a negative sign. Marginal effect analyses further confirm that the magnitude of the negative impact of housing expenditure pressure on young female migrants\u0026rsquo; reproduction willingness is significantly smaller than that of gender preference. These findings suggest that altering gender attitudes may be more critical than alleviating housing expenses for enhancing household fertility willingness.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e Robustness check: by replacing estimation method and the core explanatory variables\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 70px;\"\u003e\n \u003cp\u003eExplained Variable: Reproduction Willingness\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 42px;\"\u003e\n \u003cp\u003eReplacing estimation method\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 28px;\"\u003e\n \u003cp\u003eReplacing the core explanatory variable\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eOLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 28px;\"\u003e\n \u003cp\u003eLogit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" style=\"width: 28px;\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eMarginal effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eMarginal effect\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003eHousing pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.013\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.185\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.014\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e(0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e(0.083)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003eHigh housing pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.035\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.005\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e(0.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003eGender preference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.055\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.784\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.059\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e(0.023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003eSon_dummy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.410\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.058\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e(0.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eControl variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eYear dummies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eCity dummies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.0784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003ePseudo R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 28px;\"\u003e\n \u003cp\u003e0.1481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 28px;\"\u003e\n \u003cp\u003e0.1413\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e85,827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 28px;\"\u003e\n \u003cp\u003e85,517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 28px;\"\u003e\n \u003cp\u003e85,517\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003eNotes: (1) Standard errors in parentheses; (2) ***\u003cimg width=\"7\" height=\"15\" src=\"https://myfiles.space/user_files/127393_c7e80a1c9bb65875/127393_custom_files/img1761140227.gif\" alt=\"image\"\u003e\u0026lt; 0.01, **\u003cimg width=\"7\" height=\"15\" src=\"https://myfiles.space/user_files/127393_c7e80a1c9bb65875/127393_custom_files/img1761140227.gif\" alt=\"image\"\u003e\u0026lt; 0.05, *\u003cimg width=\"7\" height=\"15\" src=\"https://myfiles.space/user_files/127393_c7e80a1c9bb65875/127393_custom_files/img1761140227.gif\" alt=\"image\"\u003e\u0026lt; 0.1.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eAdditional robustness tests were conducted through sample replacement procedures. First, 50% of observations were randomly sampled from the full dataset using both with-replacement and without-replacement methods, and regressions were re-estimated on these subsamples. Second, respondents who answered \u0026ldquo;I haven\u0026rsquo;t figured it out yet\u0026quot; to the question \u0026rdquo; \u0026ldquo;Do you intend to have a child in the coming year or two?\u0026rdquo; were excluded from the analysis. Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e reports the results of these sample-based robustness checks, which show that the coefficient for housing expenditure pressure remains significantly negative at the 10% level, while the gender preference coefficient maintains 1% level significance with a negative sign. Marginal effect estimates again indicate that housing expenditure pressure exerts a significantly weaker negative impact on reproduction willingness compared to gender preference. These results not only validate the representativeness of the sample but also confirm the robustness of the baseline regression findings.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 5\u003c/strong\u003e Robustness check: by replacing the sample\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eExplained Variable: Reproduction Willingness\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 45px;\"\u003e\n \u003cp\u003eScreening samples by random sampling method\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 33px;\"\u003e\n \u003cp\u003eExclude samples with the answer is \u0026ldquo;I haven\u0026apos;t figured it out yet\u0026rdquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 22px;\"\u003e\n \u003cp\u003e50% random sampling without replacement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 22px;\"\u003e\n \u003cp\u003e50% random sampling with replacement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" style=\"width: 33px;\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eMarginal effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eMarginal effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eMarginal effect\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eHousing pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.111\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e-0.016\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.106\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e-0.015\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e-0.137\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e-0.020\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.061)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.062)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e(0.046)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eGender preference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.410\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e-0.059\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.397\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e-0.055\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e-0.492\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e-0.073\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e(0.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eControl variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eYear dummies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eCity dummies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003ePseudo R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.1472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.1595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0.1964\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 22px;\"\u003e\n \u003cp\u003e42,143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 22px;\"\u003e\n \u003cp\u003e41,824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 33px;\"\u003e\n \u003cp\u003e74,144\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003eNotes: (1) Standard errors in parentheses; (2) ***\u003cimg width=\"7\" height=\"15\" src=\"https://myfiles.space/user_files/127393_c7e80a1c9bb65875/127393_custom_files/img1761140274.gif\" alt=\"image\"\u003e\u0026lt; 0.01, **\u003cimg width=\"7\" height=\"15\" src=\"https://myfiles.space/user_files/127393_c7e80a1c9bb65875/127393_custom_files/img1761140274.gif\" alt=\"image\"\u003e\u0026lt; 0.05, *\u003cimg width=\"7\" height=\"15\" src=\"https://myfiles.space/user_files/127393_c7e80a1c9bb65875/127393_custom_files/img1761140274.gif\" alt=\"image\"\u003e\u0026lt; 0.1.\u003c/p\u003e\n \u003ch2 align=\"left\" class=\"colspec\"\u003e4.3 Endogenous testing\u003c/h2\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003cp\u003eEndogeneity concerns arising from potential reverse causality cannot be overlooked. While children gender is generally exogenous to individual preferences or choices, endogeneity primarily stems from the housing expenditure pressure variable. This study measures household housing expenditure as the previous year\u0026rsquo;s housing costs, whereas reproduction willingness refer to plans for the survey year and subsequent two years. This temporal separation between housing consumption decisions and fertility planning partially mitigates reverse causality concerns, though residual endogeneity may still exist. To address this, the instrumental variable (IV) method is employed.\u003c/p\u003e\n \u003cp\u003eDrawing on group effect theory, which posits that individual characteristics are correlated with group-level characteristics within the same region but unaffected by other individual attributes, this study uses the district/county-level mean of housing expenditures (excluding the respondent\u0026rsquo;s own data) as an instrumental variable for two-stage least squares (2SLS) estimation. This instrument satisfies relevance criteria by reflecting regional housing consumption levels and exhibits conditional exogeneity by excluding individual-specific housing expenditure pressure, thereby avoiding direct influence on personal reproduction willingness.\u003c/p\u003e\n \u003cp\u003eFurthermore, based on \u0026ldquo;peer effect\u0026rdquo; theory, which suggests individual behavior is shaped by group characteristics among similar socioeconomic status, samples were reclassified by household registration type, ethnicity, and public-sector employment status within districts/counties. District/county-level mean housing expenditures of peer groups were then generated as alternative instruments for 2SLS estimation. Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e presents the IV regression results, which confirm that the housing expenditure pressure coefficient remains significantly negative. First-stage F-statistics exceed 10, and Wald tests confirm instrument validity at the 1% significance level, indicating the selected instrumental variables possess strong explanatory power and credibility.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEndogenous testing: Instrumental variables\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMean value of samples within one county\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMean value of the same type within one county\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFirst stage\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSecond stage\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFirst stage\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSecond stage\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHousing pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.171\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.250\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.210)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.240)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean of Housing pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0617\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0414\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender preference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.421\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.422\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControl variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYear dummies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCity dummies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF statistic of first stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e190.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e184.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWald test of exogeneity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.99\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.51\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85,516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85,516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84,272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84,272\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eNotes: (1) Standard errors in parentheses; (2) ***\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{p}\\)\u003c/span\u003e\u003c/span\u003e\u0026lt; 0.01, **\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{p}\\)\u003c/span\u003e\u003c/span\u003e\u0026lt; 0.05, *\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{p}\\)\u003c/span\u003e\u003c/span\u003e\u0026lt; 0.1.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eAt the same time, the endogenous problem caused by omitted variables will bias the conclusions of this study. Although this study has controlled for multiple dimensions of variables, time fixed effects, and city fixed effects in the benchmark regression model, it cannot completely rule out potential endogenous issues caused by omitted variables. Therefore, this paper sets up two regression models with limited set of control variables and full set of control variables. In addition, this section will record the estimated coefficients of the core explanatory variables as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\widehat{\\beta\\:}}^{R}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\widehat{\\beta\\:}}^{F}\\)\u003c/span\u003e\u003c/span\u003e, respectively. The form of constructing index \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Ratio}_{R,F}\\)\u003c/span\u003e\u003c/span\u003e is as follows.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tabb\" border=\"1\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Ratio}_{R,F}=\\left|\\frac{{\\widehat{\\beta\\:}}^{F}}{{\\widehat{\\beta\\:}}^{R}-{\\widehat{\\beta\\:}}^{F}}\\right|\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe implication of this index is that the smaller the gap between \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\widehat{\\beta\\:}}^{R}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\widehat{\\beta\\:}}^{F}\\)\u003c/span\u003e\u003c/span\u003e, the greater the explanatory power of the observable variables in the model. That is, if the value of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Ratio}_{R,F}\\)\u003c/span\u003e\u003c/span\u003e is larger, the likelihood of bias in the estimation results due to the problem of omitted variables is smaller. In this paper, three finite set control variables are selected for regression based on the Probit model. In addition, in this part, the coefficients obtained from the regressions of the finite set control variables are each subjected to the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Ratio}_{R,F}\\)\u003c/span\u003e\u003c/span\u003e index construction with the coefficients obtained from the regressions of the full set control variables. The results are shown in Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e. The \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Ratio}_{R,F}\\)\u003c/span\u003e\u003c/span\u003e index for the core explanatory variables of housing pressure and gender preference are in the range of [7.039, 27.946] and [1.000, 15.844] respectively. The mean values of these two indices are calculated to be 14.265 and 8.746, respectively. This suggests that if the omitted variables were to bias the results of the benchmark regression, their explanatory power would need to exceed that of the controlled variables by more than 14.265 and 8.746 times on average. It can be concluded that the endogenous problem of omitted variables does not significantly bias the results of the benchmark regression.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEndogenous testing: Estimate the degree of bias caused by omitted variables\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eFinite set of control variables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eFull set of control variables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCalculation of indicator \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Ratio}_{R,F}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHousing pressure\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGender preference\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOnly controlling family characteristics and urban fixed effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAll the control variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.395\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOnly controlling individual characteristics, family characteristics, and urban fixed effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAll the control variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOnly controlling individual characteristics, family characteristics, and year fixed effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAll the control variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.946\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.844\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAverage value of indicator \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Ratio}_{R,F}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.746\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e4.4 Heterogeneity analysis\u003c/h2\u003e\n \u003cp\u003eTo further examine how housing expenditure pressure and gender preference affect the reproduction willingness among distinct subgroups of young female migrants, this subsection presents heterogeneity analysis results stratified by individual age and household registration status (Table \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e). The findings reveal that the negative impact of housing expenditure pressure on reproduction willingness is statistically significant only among respondents aged 35 and younger, with gender preference also exerting a stronger negative effect within this age group. This suggests that women aged 35 or below are more vulnerable to reproduction deterrents stemming from employment demands, family care responsibilities, and economic constraints. Elevated housing costs intensify labor force participation incentives for women, while childbearing imposes opportunity costs by diverting time and energy from career development, factors that significantly reduce reproduction willingness among those already parenting.\u003c/p\u003e\n \u003cp\u003eWith respect to household registration type, excessive housing expenditure significantly diminishes reproduction willingness among rural-registered female migrants, whereas the negative impact of gender preference shows no significant variation by hukou status. Notably, across all subgroup analyses, gender preference consistently exerts a significantly stronger negative influence on reproduction willingness compared to housing expenditure pressure.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab8\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eHeterogeneity analysis by age and hukou registration\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"4\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eDivide into groups according to age\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eDivide into groups according to \u003cem\u003ehukou\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e20\u0026thinsp;\u0026le;\u0026thinsp;age\u0026thinsp;\u0026le;\u0026thinsp;35\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e36\u0026thinsp;\u0026le;\u0026thinsp;age\u0026thinsp;\u0026le;\u0026thinsp;45\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003erural household registration\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eUrban household registration\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMarginal effect\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMarginal effect\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMarginal effect\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMarginal effect\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eHousing pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.115\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.021\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.095\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.013\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.048)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.105)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.048)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.104)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGender preference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.434\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.079\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.368\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.025\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.429\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.060\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.394\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.064\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.031)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.031)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControl variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYear dummies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCity dummies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePseudo R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.1013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.2195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.1577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.1167\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e55979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e26346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e73,958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e10,560\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003eNotes: (1) Standard errors in parentheses; (2) ***\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{p}\\)\u003c/span\u003e\u003c/span\u003e\u0026lt; 0.01, **\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{p}\\)\u003c/span\u003e\u003c/span\u003e\u0026lt; 0.05, *\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{p}\\)\u003c/span\u003e\u003c/span\u003e\u0026lt; 0.1.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eTo further investigate the heterogeneous effects of housing expenditure pressure and gender preference on young female migrants\u0026rsquo; reproduction willingness, this study analyzes subgroup variations across job categories and marital statuses (Table \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e). Results indicate that housing expenditure exerts a statistically significant negative effect on reproduction willingness exclusively among non-public sector employees. For public sector workers, whose employment, income, and life expectations tend to be more stable, housing expenditures do not significantly impact their reproduction willingness. Conversely, non-public sector employees face wage instability, wherein housing costs crowd out non-housing consumption and thereby reduce the willingness to have additional children.\u003c/p\u003e\n \u003cp\u003eRegarding marital status, housing expenditure pressure significantly inhibits reproduction willingness among first-married female migrants. Notably, across all subgroup specifications, gender preference consistently demonstrates a significant negative effect on reproduction willingness, with its impact magnitude significantly exceeding that of housing expenditure pressure.\u003c/p\u003e\n \u003cp\u003eThis subsection further examines heterogeneous effects of housing expenditure pressure and gender preference on young female migrants\u0026rsquo; reproduction willingness, focusing on individual mobility characteristics and housing tenure differences. Results in Columns (1)-(2) of Table \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e indicate that while housing expenditure pressure exhibits a consistent negative direction across all mobility subgroups, statistical significance emerges only among intra-provincial migrants. Narrower geographic mobility correlates with stronger social network advantages; yet, even within such contexts, excessive housing expenditure intensifies psychological distress, thereby weakening reproduction willingness.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab9\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eHeterogeneity analysis by employment type and marriage stage\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"4\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eDivide into groups according to employment type\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eDivide into groups according to marriage stage\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eEmployment within the system\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eEmployment outside the system\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFirst marriage\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eremarriage\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMarginal effect\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMarginal effect\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMarginal effect\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMarginal effect\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eHousing pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.098\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.014\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.088\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.012\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.127)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.046)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.044)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.319)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGender preference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.374\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.070\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.428\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.059\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.427\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.060\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.356\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.067\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.040)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.074)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControl variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYear dummies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCity dummies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePseudo R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.1234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.1537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.1476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.2202\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6,035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e78,765\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e83,630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1,410\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003eNotes: (1) Standard errors in parentheses; (2) ***\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{p}\\)\u003c/span\u003e\u003c/span\u003e\u0026lt; 0.01, **\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{p}\\)\u003c/span\u003e\u003c/span\u003e\u0026lt; 0.05, *\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{p}\\)\u003c/span\u003e\u003c/span\u003e\u0026lt; 0.1.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eAdditionally, this study operationalizes housing expenditure to include both rental payments and mortgage installments, recognizing fundamental differences in property rights between renting and homeownership. To address potential heterogeneity by housing type, the 2017 CMDS questionnaire data, detailing current housing status, classifies respondents as homeowners or renters. Columns (3)-(4) of Table \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e reveal that housing expenditure exerts a statistically significant negative effect on reproduction willingness only among homeowners (p\u0026thinsp;\u0026lt;\u0026thinsp;0.10). Across all subgroup analyses, gender preference consistently demonstrates a stronger negative impact on reproduction willingness compared to housing expenditure pressure.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"5. Further Discussion","content":"\u003cp\u003eTo explore how housing expenditure pressure and gender preference affect young female migrants\u0026rsquo; reproduction willingness among families with one child, this section draws on the ordinal model theory of reproductive choice, which posits that couples gain clearer insight into the costs and benefits of childbearing only after the birth of their first child, prompting reassessments of whether and when to have additional children. Since reproduction intention here refers to families\u0026rsquo; future fertility plans after having their first child, the analysis focuses on one-child families. For this subgroup, gender preference is defined by the gender of the existing child.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eHeterogeneity analysis by inter-province mobilization and housing types\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eDivide into groups according to inter-province mobilization\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\u003cp\u003eDivide into groups according to housing types\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003einter-province mobilization\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003einner-province mobilization\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003ehomeowners\u003c/p\u003e\u003cp\u003e(mortgage)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eTenant\u003c/p\u003e\u003cp\u003e(housing rent)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMarginal effect\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCoefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMarginal effect\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCoefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMarginal effect\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCoefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eMarginal effect\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHousing pressure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.077\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.111\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.017\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.160\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.027\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.065)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.059)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.084)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(0.086)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGender preference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.433\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.057\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.424\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.064\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.423\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.070\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.440\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.063\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.017)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.016)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.026)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(0.020)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear dummies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCity dummies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eYes\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\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.1564\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.1489\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.1304\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e0.1762\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\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e40,358\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e44,047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e14,261\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e29,626\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eNotes: (1) Standard errors in parentheses; (2) ***\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{p}\\)\u003c/span\u003e\u003c/span\u003e\u0026lt; 0.01, **\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{p}\\)\u003c/span\u003e\u003c/span\u003e\u0026lt; 0.05, *\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{p}\\)\u003c/span\u003e\u003c/span\u003e\u0026lt; 0.1.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eRegression results (Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e) align with the benchmark findings in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Columns (1) and (2) present regressions incorporating housing expenditure pressure and gender preference separately, while Column (3) includes both indicators. The results show that the coefficient for housing expenditure pressure remains negative but loses statistical significance. In contrast, the gender preference coefficient remains significantly negative at the 1% level. This indicates that for one-child families, housing financial pressure is no longer a key determinant of reproduction willingness. Instead, among young female migrants who already have a boy, gender preference crowds out their willingness to have another child.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFurther Discussion\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=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e\u003cp\u003eExplained Variable: Reproduction Willingness\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMarginal effect\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCoefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMarginal effect\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCoefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMarginal effect\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousing pressure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.012\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\u003cp\u003e-0.059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.013\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(0.047)\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\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.047)\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\u003eson\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-0.281\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.061\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.281\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.061\u003csup\u003e***\u003c/sup\u003e\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\u003cp\u003e(0.015)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.015)\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\u003eControl variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear dummies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCity dummies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eYes\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\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.0894\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.0980\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.0980\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\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e47939\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e47939\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e47939\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNotes: (1) Standard errors in parentheses; (2) ***\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{p}\\)\u003c/span\u003e\u003c/span\u003e\u0026lt; 0.01, **\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{p}\\)\u003c/span\u003e\u003c/span\u003e\u0026lt; 0.05, *\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{p}\\)\u003c/span\u003e\u003c/span\u003e\u0026lt; 0.1.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"6. Conclusions and policy implications","content":"\u003cp\u003eEnhancing the reproduction willingness of young female migrants represents a critical pathway to boosting China fertility rate and fostering long-term balanced population development. Addressing housing challenges and transforming traditional gender norms serve as prerequisites for mitigating fertility-related conflicts. Against this backdrop, this study focuses on young female migrants as the research subject and utilizes data from the 2017\u0026ndash;2018 China Migrants Dynamic Survey (CMDS) to empirically examine the impacts of housing expenditure pressure and gender preference on their reproduction willingness.\u003c/p\u003e\u003cp\u003eThe findings reveal that increased household housing expenditure pressure and a higher number of boys significantly reduce young migrant women\u0026rsquo;s willingness to have additional children. Furthermore, the negative effect of housing expenditure pressure on young female migrants\u0026rsquo; reproduction willingness is significantly weaker than that of gender preference, with these results remaining robust across a series of robustness tests. Concurrently, the impacts of housing expenditure pressure and gender preference exhibit heterogeneity based on individual characteristics. Specifically, the negative effect of housing expenditure pressure on reproduction willingness is statistically significant only among those aged 35 and below and those with agricultural household registration. Gender preference exerts a more pronounced negative impact on reproduction willingness within younger cohorts. Moreover, housing expenditure pressure significantly suppresses reproduction willingness among young female migrants employed in the non-public sector, those in first-marriage status, and intra-provincial migrants. For home-buying households, the negative effect of housing expenditures on young female migrants\u0026rsquo; reproduction willingness is statistically significant at the 10% level. Regardless of the subgroup analysis, however, gender preference consistently demonstrates a significant negative effect on young female migrants\u0026rsquo; reproduction willingness, with its impact magnitude significantly exceeding that of housing expenditure pressure. Finally, based on a subsample of one-child families, this study finds that housing financial pressure no longer constitutes a key factor influencing reproduction willingness; instead, among young female migrants who already have a son, gender preference crowds out their willingness to have another child.\u003c/p\u003e\u003cp\u003eIn conclusion, to further enhance young female migrants\u0026rsquo; reproduction willingness and promote balanced population development, policymakers should not only prioritize alleviating housing expenditure pressure but also implement measures to transform gender attitudes. Specific policy recommendations include: (1) comprehensively considering differences in housing demand among migrant populations, promoting dual-track operation of the housing market and housing security systems, and providing moderate housing subsidies or in-kind assistance to low-income and housing-vulnerable families while ensuring market stability; (2) advocating for gender equality practices, promoting gender equality as a mainstream social norm, and striving to transform fertility-related perceptions; and (3) establishing a robust reproductive welfare system centered on women's needs, enhancing support for women\u0026rsquo;s childbearing, and actively fostering a fertility-friendly social environment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting Interests\u003c/h2\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003cp\u003eThis article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eInformed Consent\u003c/strong\u003e\u003cp\u003eThis article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding Statement\u003c/h2\u003e\u003cp\u003eThe work was supported by the National Natural Science Foundation of China Youth Program [72404002; 72404145]; Ministry of Education Humanities and Social Sciences Youth Foundation [23YJC790154; 23YJCZH308].\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThe conceptual framework was developed by Yidong Wu. The methodology was crafted by Yalin Zhang. Zhilin Zhu was responsible for the software aspect. Weiqian Jiang contributed to the validation process. For formal analysis, Yidong Wu was in charge. Yuanyuan Zha handled the data compilation. The initial draft was written by Yidong Wu. Yalin Zhang worked on the review and editing of the manuscript. Yidong Wu has reviewed and approved the final version of the paper.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZhou Y (2019) The dual demands: gender equity and reproduction willingness after the one-child policy. 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A report to Shaanxi Provincial Population and Family Planning Commission\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWenzhan J, Jue L, Qiuyue M, Shikun Z, Yuanyuan L, Min L (2022) reproduction willingness to have a second or third child under China\u0026rsquo;s three-child policy: a national cross-sectional study. Hum Reprod 37(8):1907\u0026ndash;1918\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang Y, He R, Zhang N, Li L (2023) Second-child reproduction willingness among urban women in China: a systematic review and meta-analysis. Int J Environ Res Public Health 20(4):3744\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"housing expenditure pressure, gender preference, young female migrants, reproduction willingness","lastPublishedDoi":"10.21203/rs.3.rs-7441721/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7441721/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIncreasing the reproduction willingness of young female migrants is crucial for alleviating China low fertility rate and promoting long-term balanced population development. Using the 2017\u0026ndash;2018 China Migrants Dynamic Survey (CMDS), this paper examines the effects of housing expenditure pressure and gender preference on the reproduction willingness of young female migrants. Results indicate that higher household housing expenditure pressure and the number of sons significantly reduce reproduction willingness. Furthermore, housing expenditure pressure has a weaker adverse effect on reproduction willingness than gender preference. Heterogeneity analysis reveals that women below 36 years old and those with agricultural household registration are more sensitive to both factors. Housing expenditure pressure significantly inhibits reproduction willingness among those employed outside the system, in first marriages, and migrating within provinces. Regardless of subgroup, gender preference exerts a significantly stronger negative effect on reproduction willingness than housing pressure. Analysis of one-child families shows housing expenditure pressure ceases to be a key factor influencing reproduction willingness; however, for those with a son, gender preference crowds out further reproduction willingness. This study provides a theoretical basis for enhancing female migrants\u0026rsquo; reproduction willingness through addressing housing costs and gender norms.\u003c/p\u003e","manuscriptTitle":"The Effects of Housing Expenditure Pressure and Gender Preference on the Reproduction Willingness for Young Female Migrants: Evidence from China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-22 19:32:00","doi":"10.21203/rs.3.rs-7441721/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-10T12:32:28+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-18T10:48:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-14T16:43:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"210300158016278200750395127237447115401","date":"2025-10-11T19:55:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"103064766444732928812507596131280642484","date":"2025-10-09T09:35:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-09T09:18:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-09T09:16:12+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-27T18:33:37+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-16T03:01:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2025-09-16T02:57:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"1cff3d34-79cd-4932-ab73-dab8703adb76","owner":[],"postedDate":"October 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":56537821,"name":"Biological sciences/Ecology"},{"id":56537822,"name":"Earth and environmental sciences/Ecology"},{"id":56537823,"name":"Earth and environmental sciences/Environmental social sciences"},{"id":56537824,"name":"Biological sciences/Psychology"},{"id":56537825,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-05-12T05:25:39+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-22 19:32:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7441721","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7441721","identity":"rs-7441721","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}