The Mechanisms of China's Fertility Intentions from an Institutional Embeddedness Perspective: Gender Norms, Hukou Segmentation Pathways, and Policy Implications

The Mechanisms of China's Fertility Intentions from an Institutional Embeddedness Perspective: Gender Norms, Hukou Segmentation Pathways, and Policy Implications | 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 Mechanisms of China's Fertility Intentions from an Institutional Embeddedness Perspective: Gender Norms, Hukou Segmentation Pathways, and Policy Implications Jieyu Li, Zhong Fei, Hongfeng Han This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8361693/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract China's fertility rate continues to decline, yet rural populations with lower average incomes exhibit stronger fertility intentions (FI) than their urban counterparts. This "low-income, high-intention" phenomenon poses a challenge to traditional economic theories. This study addresses this puzzle by investigating Chinese fertility decision-making through the integrated lens of "institutional embeddedness". Drawing on five waves of the Chinese General Social Survey (CGSS, 2012-2021) and employing hierarchical regression and mediation analysis, it examines how the hukou system, gender norms, and subjective well-being interact to shape fertility intentions. It demonstrates that institutional structures, rather than economic calculus alone, are the dominant drivers. This occurs because the hukou system creates a resource buffer in rural areas, while gender norms asymmetrically channel men’s and women’s social mobility expectations into reproductive plans. These findings challenge the generalizability of rational choice models, highlight the deterministic role of locally embedded institutions on demographic outcomes, and provide a more refined theoretical framework for policy design. Social science/Anthropology Social science/Development studies Business and commerce/Economics Social science/Economics Social science/Sociology fertility intention hukou system subjective expectations of social mobility subjective well-being institutional embedding Figures Figure 1 Figure 2 Figure 3 1. Introduction Since the 1970s, there has been a general decline in global fertility, particularly in countries that have achieved higher levels of economic and social development (Zhou, 2015). A similar pattern was exhibited by China, where the birth population declined to 9.02 million in 2023, representing the most modest level recorded since 1949. Nonetheless, it is important to acknowledge that rural populations with comparatively modest incomes exhibit higher levels of fertility intentions (FI) in comparison to their urban counterparts (Shi et al., 2022). This prompts a critical inquiry: what factors underpin these distinct disparities in FI? The extant literature has principally elucidated FI through economic frameworks, including income levels and educational expenditures. However, the mechanisms that underpin the observation that rural populations, despite their relatively weaker economic conditions, exhibit a stronger willingness to have children remain unclear. This contradiction suggests the possibility that, in addition to economic factors, the institutional environment may also function as a determinant. It is important to note that current research has paid insufficient attention to three institutional dimensions. Firstly, how the hukou system influences fertility decision-making through resource allocation mechanisms, including rural land security and urban-rural disparities in public services. Secondly, how gender norms shape divergent perceptions of fertility responsibilities, such as men's greater inclination toward childbearing and women's heightened concerns about career repercussions. And thirdly, how social competitive pressures – such as high-intensity work – deplete emotional and cognitive resources, thereby dampening FI. In order to comprehend this phenomenon, the present study examines the conjoint effects of the hukou system, gender norms, and social competition on fertility decision-making from an institutional perspective. The findings are expected to provide a more comprehensive understanding of the root causes of FI disparities in China and to inform the formulation of more targeted fertility support policies, such as the advancement of the balancing of public services across regions and the alleviation of excessive work pressure. 2. Literature Review Nee and Ingram (1998) proposed institutional embeddedness theory, which provides a core framework for understanding fertility decision-making. The theory posits that institutions are comprised of a series of interrelated norms that govern people's relationships and profoundly influence their behaviour. Individual choices are constrained by both formal institutions (regulations, hukou system) and informal institutions (norms, culture, gender roles) embedded in them. Formal institutions are established and enforced by political authorities; however, their construction, evolution, and implementation are significantly influenced by informal institutions (Baum & Oliver, 1992). This theory reveals the relationship between human activities and institutional structure, and is the key to understanding the unique fertility phenomenon in China. The modern academic tradition of FI research has its origins in a reflection on the Demographic Transition Theory (DTT). Notestein (1945) proposed a macro framework for industrialization-driven fertility decline. However, it faces challenges in explaining the paradox between sustained growth and fertility recovery in Europe. Lesthaeghe & López-Gay(2013) Second Demographic Transition (SDT) points out that the synergies between cultural and structural factors, successive secularization waves, and different constraints under the framework of the "Ready, Willing, and Able" paradigm. The joint impact of these factors affects the "postponement" and "non-traditional" patterns of fertility decision-making in the SDT, revealing the multidimensional complexity of fertility decision-making. In economics, Grebenik & Leibenstein(1959) first introduced the cost-utility theory and proposed the rational choice theory by employing the marginal analysis method. Becker (1960) then introduced the consumer choice theory into the analysis of family fertility decisions, expanding the "quantity-quality" perspective of fertility decisions. In the follow-up analysis of fertility costs, scholars direct their attention to the direct costs of fertility, such as education, housing, and medical care, while also emphasising the opportunity cost of fertility, i.e., the phenomenon commonly referred to as the "motherhood penalty". Goldin et al. (2024), a recent Nobel laureate in economics, argue that gender inequality in the division of labor is at the heart of low fertility. This perspective deviates from Becker's "rational choice" model in that the former accentuates the role of institutionalised role allocation (for instance, the "motherhood penalty") in distorting the decision-making environment, whereas the latter presupposes that individuals possess the capacity for independent decision-making within an equitable market. Generally speaking, in the field of Western economics, fertility is generally regarded as a rational behavior of families based on their own cost-benefit analysis (Yang, 2021). The field of sociology is more concerned with the influence of culture and social structure. Bourdieu (1977)'s concept of "reproductive strategy" emphasizes the role of cultural capital and institutional constraints in the reproduction of intergenerational status. From the perspective of social stratification, Billy (1985) posited that social mobility, both intergenerational and intragenerational, exerts an influence on reproductive intentions. However, socioeconomic status is not solely determined by the material wealth held by an individual; it is also influenced by the subjective perception of wealth ( Kraus & Stephens, 2012 ). Therefore, more and more people study FI from the perspective of Subjective Social Mobility (SSM). On the one hand, according to the Western Social Capillarity Theory, resource competition leads to higher subjective social mobility, and the accompanying achievement motivation inhibits FI (Dumont, 1990). On the other hand, in the context of China's unique institutional embeddedness, the relationship between SSM and FI presents distinct uniqueness. Specifically, individuals with higher SSM tend to have more children (CHEN et al., 2022; Yan et al., 2025) because their relationship is influenced by institutions in China (Ruan, 2024) and psychological factors (Li et al., 2023). The adjustment of the system embeddedness reflects the explanatory power of the system embeddedness theory. Furthermore, demographic variables such as age, gender, income, and education level have been demonstrated to exert varying degrees of influence on fertility intentions (Zhao & Chen, 2023; Wu & KC, 2022; Costa Dias et al., 2018). Subjective well-being (SWB) is an individual's cognitive and emotional evaluation of his life (Diener, 2000). There is a broad consensus that both social mobility perception and subjective social mobility expectation exert a positive influence on SWB ( Chen et al., 2022 ). The study demonstrates a positive correlation between SWB and FI, particularly in instances where couples' income surpasses expectations and their optimistic outlook towards the future prompts early marriage and increased childbearing ( L. & Easterlin, 1980 ). Furthermore, couples in positive states have more children with the aim of strengthening their relationship and their own happiness ( Isen & Patrick, 1983; Parr, 2010 ). However, in China's highly competitive environment, characterised by a "996" work culture, the SWB, acquisition and security of youth groups are significantly suppressed ( Nie & Feng, 2020; Huang et al., 2023 ), resulting in a contradictory mentality of "wanting to have children" and "not daring to have children" ( Xing, 2020 ). From the perspective of the "emotional labor" theory, fertility requires a significant amount of additional emotional energy ( Tropman et al., 1984 ), which may suggest that SWB needs to attain a certain threshold to elicit its influence on fertility intention in China's highly competitive environment. Institutional structures in China, especially hukou system and gender norms, profoundly shape the relationship between SSM and FI (Yu & Cheng, 2024). As a core resource allocation mechanism, the hukou system significantly affects FI( Cai & Zhong, 2025) . Rural hukou holders have higher FI due to land and collective welfare protection (Han et al., 2019), but such benefits may blunt the additional boosting effect of SSM on their FI (Huang, 2020). Urban hukou holders are constrained by high costs, high pressures and high competition (Dong & Goodburn, 2019), and their expectations of SSM further promote fertility (Yang & Guo, 2023). Gender norms (informal institutions) have been shown to result in an increased allocation of child-rearing responsibilities to women, whilst concomitantly engendering a phenomenon termed the "motherhood penalty", which has been demonstrated to inhibit reproductive intentions ( Wu et al., 2019; Lui & Chan, 2020) . The advent of gender equality concepts has been shown to engender a further reduction in women's intentions ( Yu et al., 2023 ). Research indicates that men who are influenced by traditional gender roles tend to have higher FI ( Shi & Zheng, 2023 ) and a higher preference for boys ( Fei & Li, 2025 ). Concurrently, men convert SSM into FI with significantly higher intensity than women (Vignoli et al., 2020), indicating the potential impact of gender norms on SSM-FI relationships. In general, extant theories have the following limitations in explaining the fertility phenomenon in China: (1) rational choice theory (RCT) ignores the differences in fertility intention brought about by the hukou system in China; (2) the correlation between expectations of SSM and FI ignores the filtering effect of gender norms in China; and (3) the association between SWB and FI does not pay attention to threshold. Therefore, based on the institutional embeddedness theory (Nee & Ingram, 1998) , this study employs a multi-level analysis framework integrating macro (formal/informal institutions such as hukou and gender), meso (SSM under institutional regulation) and micro (SWB threshold) levels, with a view to comprehensively revealing the unique institutional logic and cultural context of the formation of FI in China. 3. Research Hypothesis 3.1 The Basis of FI in Institutional Environment: SSME and FI Classical theory suggests that competition for resources causes high SSME to inhibit FI (Dumont, 1990). However, China's data indicates that FI is higher for those with high SSME ( Yan et al., 2025 ). This reflects the limited rationality of institutional embedding: under the constraints of hukou registration and gender norms, upward mobility expectations enhance FI through intergenerational investment or collective welfare. Therefore, we propose H1: Subjective Social Mobility Expectation (SSME) positively affects FI. The stronger an individual's expectation of upward mobility, the higher his fertility intention. 3.2 Institutional Constraints on Emotion: The Mediating Role of SWB In the context of China's intensely competitive environment, SWB is regarded as a significant indicator of emotional resources, which may exert an influence on FI through nonlinear pathways. Empirical studies show that SWB and FI of childbearing age population have an inverted U-shaped relationship (Xiang et al., 2019), SSME significantly increases SWB by increasing confidence in the future and indirectly promotes FI (Zhang & Wang, 2024), employment pressure has a significant inhibitory effect on FI only in low SWB groups (Vignoli et al., 2020), and institutional constraints such as hukou system and gender norms make the SWB-FI relationship present situation-dependent nonlinear characteristics (Wang, 2015). Therefore, we propose H2a: SWB has a positive main effect on FI, the higher the SWB, the stronger the FI; H2b: there is an affective threshold for this effect, and the mediating path of SSME affecting FI through SWB is only significant in the extremely high SWB population. 3.3 Differential Adjustment of Institutional Factors Hukou system divides fertility opportunities: rural-hukou holder has land security and collective welfare, which reduces sensitivity to future flows and thus weakens the role of SSME in FI (Shao, 2000; Han et al., 2019; Xing et al., 2023. The link between SSME and FI is amplified by the high education and housing costs faced by urban-hukou holders (Dong & Goodburn, 2020; Yang & Guo, 2023). Gender norms differ through role division: paternal culture encourages men to convert SSME to FI more quickly (Cai & Xie, 2024). Women suffer from the "motherhood penalty", SSME transformation to FI is blocked (Yu et al., 2023), and the biological clock narrative intensifies fertility anxiety (Yopo Díaz, 2020). Therefore, we propose H3a: hukou type significantly affects FI (rural > non-rural). The FI of rural-hukou holder individuals is higher than that of urban-hukou holder individuals. H3b: Gender significantly affects FI (males > females). Men have higher FI than women. H3c: Role of the hukou system in regulating SSME (rural weakening). SSME has a weak effect on FI in rural-hukou groups. H3d: Role of gender norms in regulating SSME (male reinforcement). SSME has been demonstrated to have a more significant impact on FI in the male population.The conceptual framework is depicted in Fig. 1 . 4. Data, Variable, and Methods 4.1 Data Sources and Sample Characteristics The research data comes from the five-round survey data of The Chinese General Social Survey (CGSS) 2012–2021. CGSS is a nationwide, comprehensive, and continuous academic survey project in China. This study focused on the population aged 20–40 years of childbearing age, and 11,594 valid samples were obtained after missing values of variables were processed. The sample covers 31 provincial-level administrative regions in China, with rural-hukou holders accounting for 55.3%, males accounting for 46.5%, and an average age of 30.52 years (SD = 6.32), which conforms to the demographic characteristics of China's child-bearing population (National Bureau of Statistics of China, 2021). 4.2 Variable 4.2.1. Dependent Variable FI uses the open-ended question from CGSS: "How many children would you like to have if policy restrictions were not imposed?" Direct measurement, value range 0–5 (excluding outliers > 5, accounting for 2.5%). This index is a continuous variable, and it has been demonstrated that the higher the value, the stronger the fertility intention. This approach circumvents the possible divergence of the compound index, rendering it particularly well-suited for the examination of the impact of institutional constraints, such as hukou registration and policy, on fundamental fertility preferences. It is adept at capturing individual fertility intentions within the context of institutional constraints. 4.2.2. Independent Variable SSME is calculated by the difference between the CGSS "current family social class"(1–10 points) and "class expectations for the next 10 years"(1–10 points), with the formula: SSME = subjective expected class-subjective current class. A negative value indicates a decline in expectations, and a positive value indicates an increase in expectations. To enhance the theoretical explanatory power, SSME were transformed into 1–5 ordered variables: 1 (≤-1, expected to decline), 2 (0, stable), 3 (+ 1, gradually rising), 4 (+ 2, significantly rising), 5 ( ≥ + 3, sharply rising). In previous studies, based on the same data, in order to avoid the interference of extreme values, 5-level combined measurement is generally adopted ( Chen & Zhang, 2018 ). T his classification method has been demonstrated to simplify the data processing and analysis process. Furthermore, it has been shown to reduce the influence of multicollinearity on statistical analysis through centralization processing. In addition to these benefits, the method has been demonstrated to improve the estimation accuracy and interpretation power of the model. Frequency analysis showed that only 1% of individuals expected to decline, 34% expected to gradually increase, 24% expected to significantly increase, and 21% expected to sharply increase. This classification can clearly reflect different expectations of future social status, and each grade is distributed in the sample, indicating that individuals have diverse expectations of future social status, further verifying the rationality of this classification. 4.2.3 Metavariable SWB was measured by a 5-point Likert scale (1= "very unhappy",2= "unhappy",3= "general",4= "happy",5= "very happy"), based on a right-skewed distribution of raw data ("happy" + "very happy" 87%), reclassified into four groups: low (1–2 points, 5.60%), moderate (3 points, 14.12%), high (4 points, 61.84%), and very high (5 points, 18.44%). This classification preserves extreme heterogeneity and optimizes sample distribution to facilitate testing for "affective threshold" effects. 4.2.4 Moderator According to the urban-rural binary structure theory (Wang & Zeng, 2025), hukou was recoded as rural (+ 1) and non-rural (-1), and the effect coding was used to compare the group mean differences. Rural hukou covers the original rural category and the non-agricultural category of farmers, while non-rural hukou includes non-agricultural, former non-agricultural and military categories (military accounts for only 3%). Gender uses effect coding (male + 1, female- 1) to capture the impact of the norm of "male external female internal" on fertility decisions (Shi & Zheng, 2023). 4.2.5 Control variable Our analysis included a range of control variables. The data include individual-level attributes such as age, education level of spouse, and income. Age is a continuous variable (20–40 years), and this range is consistent with biological and social time constraints in China on fertility decisions ( Lu et al., 2005 ). Education levels are classified according to CGSS original 13 levels (0 = no education to 13 = graduate). The natural logarithm of revenue is taken after top and bottom 1% tail reduction to reduce skewness. 4.3 Method In order to explore the complex relationship among SSME, SWB and FI, hierarchical regression analysis was used to solve the different research problems step by step. To be specific, Model 1 analyzed baseline associations between control variables (age, education, income) and FI; Model 2 included centralized SSME to assess their independent effects on FI; Model 3 introduced moderating variables (household registration, gender) and SWB to analyze the interaction between structural inequality and affective factors; Model 4 included SSME × household registration, SSME × gender interactions to test institutional moderating effects (Wen et al., 2005). To examine the mediating effect of SWB, we used 1000 resampling ( Wen & Ye, 2014 ). Meanwhile, we divide SWB into four groups: "low SWB","moderate SWB","high SWB" and "very high SWB", and investigate whether there is a mediating path between different groups to determine the "threshold effect" of SWB in the impact of SSME on FI. All effect sizes are calculated as follows: (1) For adjustment effect size calculations, calculated by percentage decay or enhancement $$\:({\beta\:}_{interaction}/{\beta\:}_{mobility})\times\:100\%$$ ; (2)To test for mediating effects, we decompose the total effect ( \(\:\tau\:\) ) into two parts: direct effects ( \(\:\tau\:\) ') and indirect paths through mediating variables $$\:1-\left(\frac{\tau\:{\prime\:}}{\tau\:}\right)$$ ; (3)We use a stratified approach to calculate the contribution of institutional factors to the outcome: ( \(\:({\varDelta\:{R}^{2}}_{main\:effects}+{\varDelta\:{R}^{2}}_{interactions})/{{R}^{2}}_{final\:model}\) ) where the main effect of social mobility expectations contributes were $$\:{{\varDelta\:R}^{2}}_{mobility}/{{R}^{2}}_{final\:model}$$ . In addition, continuous variables were normalized to facilitate comparison of the influence of different variables. SPSS software was used for analysis, including stratified regression, descriptive statistics, and mediation analysis. Cross-sectional data describe the situation well, but are not strong enough to prove causation. We solve this problem in three ways: (1) Use institutional externality to represent causation. Hukou and gender are key institutional variables because people cannot change them at will, and they exist before fertility intentions are formed. (2) Expectations of social mobility are forward-looking indicators that measure the difference between current social class perceptions and future expectations of social classes, and that exist before childbearing intentions are formed. (3) Our "institutional embeddedness model" may suggest that China's hukou system and gender norms have influenced people's cognition before the formation of fertility intention. The hukou system creates "institutional instability"(Wu & KC, 2022), and gender norms require "emotional labor" (Tropman et al., 1984). These factors collectively influence perceptions even before the emergence of the intention to procreate. 5. Result 5.1 Descriptive statistical analysis and preliminary analysis Table 1 shows the key distributions of 11,594 samples at different levels of SWB. SSME differed significantly among groups ( χ² =122.16, p < 0.001), with a higher proportion of people in the "very happy" group believing that their future social class would rise significantly (25.6% vs. 18.0% in the moderately happy group). Fertility intention (FI) varied little between groups (1.78–1.86 children), and this convergence suggests that institutional constraints compress the overall space for fertility decisions by shaping habits (Bourdieu, 1977). It's worth noting that people with higher education levels have higher mean subjective well-being (7.38 vs. 5.63, F = 61.52, p < 0.001); rural hukou holders accounted for 63.5% of the low SWB group, while the proportion dropped to 53.3% in the very high SWB group; gender composition shifted from male dominance in the low SWB group (52.4%) to female dominance in the high SWB group (55.9%;χ²=33.64, p < 0.001)). The mean age of the sample (30.5 ± 6.3 years) decreased with increasing SWB (29.9 vs 31.3 years). These patterns lay the foundation for multivariate analysis. Table 1 Distribution of variables across subjective well-being groups Low SWB Group ( n = 649) Moderate SWB Group ( n = 1,637) High SWB Group ( n = 7,170) Very High SWB Group ( n = 2,138) Expected to decline SSME( n(%) ) 24 (3.70) 35 (2.14) 157 (2.19) 51 (2.39) Stable SSME( n(%) ) 164 (25.27) 413 (25.23) 1,574 (21.95) 388 (18.15) Gradually rising SSME ( n(%) ) 199 (30.66) 554 (33.84) 2,347 (32.73) 587 (27.46) Significantly rising SSME( n(%) ) 112 (17.26) 341 (20.83) 1,764 (24.60) 564 (26.38) Sharply rising SSME( n(%) ) 150 (23.11) 294 (17.96) 1,328 (18.52) 548 (25.63) Fertility intention ( M;SD ) 1.82; 0.79 1.78; 0.68 1.82; 0.66 1.86; 0.71 Age( M;SD ) 31.28; 6.14 30.62; 6.16 30.62; 6.31 29.89; 6.46 Income 8.19; 3.94 8.76; 3.76 8.46; 4.17 8.44; 4.27 Educational level ( M;SD ) 5.63; 3.19 6.54; 3.37 7.22; 3.47 7.38; 3.47 Rural-hukou( n(%) ) 412 (63.48) 972 (59.38) 3,892 (54.28) 1,139 (53.27) Male( n(%) ) 340 (52.39) 845 (51.62) 3,268 (45.58) 943 (44.11) Figure 2 shows the distribution patterns of FI between rural and urban residential settings and gender subgroups at different SSME-SWB levels. We find: (1) Rural men have the strongest intention to have children: FI is generally higher among rural men in most SSME-SWB categories. This suggests that in rural areas, men may be more prone to procreation, which may be related to local cultural and social structures. (2) FI is lowest among urban women: In contrast, FI is lower among urban women across all categories. This may reflect the impact of factors such as cost of living, educational and career opportunities in urban living environments on FI. (3) Influence of SSME change: FI of different groups fluctuated with SSME change. For instance, FI increased for all populations in the case of stable SSME or rising SSME, whereas FI decreased in the case of falling SSME. (4) Differences between urban and rural hukou: The FI of urban-hukou males and rural-hukou females is between that of rural-hukou males and urban-hukou females, thus demonstrating the impact of the differences between urban and rural hukou on FI. 5.2 Testing System Effects: Stratified Regression Results Table 2 shows the results of the hierarchical regression model. Model 1 (control variable only) showed a positive correlation between age and FI ( β = 0.008, p < 0.001), consistent with biological and social temporal perspectives (Michael et al., 2019). The negative coefficient of education level ( β =-0.021, p < 0.001) showed that FI was negatively correlated with education level. It is worth noting that personal income has no significant effect ( β = 0.001), suggesting that institutional factors may override the influence of economic rationality on FI. Model 2 showed that the model fit improved significantly when SSME were included ( ΔR² = 0.006, p < 0.001). SSME had a significant positive effect on FI ( β = 0.047, p < 0.001), supporting H1. Specifically, for every 1 unit increase in upward mobility expectations, the ideal number of children increases by 0.047 units. Although ΔR² is small, suggesting that the role of SSME may be mediated by situational factors, we will examine it further in Model 4. The results of Model 3 analysis showed that the explanatory power of Model 3 was further improved after gender, hukou, and SWB were included. Specifically, FI was significantly higher in males than in females ( β = 0.042, p < 0.001), a finding that supports the H3-b hypothesis about the influence of gender norms, and significantly stronger in rural hukou holders ( β = 0.053, p < 0.001), validating the H3-a hypothesis about the role of the hukou system. SWB showed an independent predictive effect ( β = 0.043, p < 0.001), providing supporting evidence for H2-a. Notably, the effect of SSME, while still significant ( β = 0.043, p < 0.001), was reduced, suggesting that its effect may be partially mediated by other variables. The analysis of the moderating effect of Model 4 further reveals the boundary conditions of SSME action. Gender significantly moderated the relationship between SSME and FI, and this positive association was more pronounced in males ( β = 0.020, p < 0.001), which supported the hypothesis of a moderating effect of H3-d. In contrast, rural hukou attenuated the SSME effect ( β =-0.013, p < 0.05), confirming the H3-c hypothesis. SWB maintained a stable main effect ( β = 0.228, p < 0.01), but did not show a significant moderating effect ( β =-0.004). The explanatory power of the final model reaches R² =0.039, indicating that FI is shaped by social system factors and psychological factors. Table 2 Hierarchical regression analysis of FI (N = 11,594) model 1 model 2 model 3 model 4 B SE B SE B SE B SE Constant 1.716** 0.037 1.657** 0.038 1.446** 0.046 1.601** 0.078 Age 0.008** 0.001 0.010** 0.001 0.012** 0.001 0.012** 0.001 Educational level -0.021** 0.002 -0.021** 0.002 -0.014** 0.002 -0.014** 0.002 Income 0.001 0.002 0.001 0.002 -0.001 0.002 -0.001 0.002 SSME 0.047** 0.006 0.043** 0.006 0.058** 0.022 Gender 0.042** 0.006 0.042** 0.006 Hukou 0.053** 0.007 0.054** 0.007 SWB 0.043** 0.008 0.228** 0.072 SSME × gender 0.020** 0.006 SSME × hukou -0.013* 0.006 SSME × SWB -0.004 0.007 R 2 0.022 0.028 0.037 0.039 Aadjusted R 2 0.022 0.027 0.037 0.039 F 87.649*** 82.499*** 64.253*** 43.211*** ∆R 2 0.022 0.006 0.01 0.002 ∆F 87.649*** 65.586*** 38.846*** 6.186*** Note: ***p < 0.001, **p < 0.01, *p < 0.05 5.3 Happiness Threshold: An Analysis of Mediating Effects Table 3 summarizes the mediating role of SWB between SSME and FI. The overall effect of SSME on FI was significant ( β = 0.045, SE = 0.006, p < 0.001, 95% CI [0.033, 0.056]) by 1,000 Bootstrap samples. After controlling for SWB, the direct effect remained significant ( β = 0.043, SE = 0.006, p < 0.001, 95%CI [0.031, 0.054]), suggesting that SWB only partially mediated. Further analysis revealed that indirect effects, although statistically significant, were small in size ( β = 0.002, SE = 0.001, 95%CI [0.001, 0.007]) and accounted for only 4.4% of the total effect. Group test showed that this mediating effect only existed in the very high SWB group (top 18.44%), while the other groups did not show a significant mediating path ( β ≈ 0, p > 0.05), which supported the existence of an affective threshold. In contrast, the direct effect ( β = 0.043) accounted for 95.6% of the total effect, indicating that the impact of SSME on FI was mainly realized through non-emotional pathways, further confirming the dominant role of institutional factors in fertility decision-making. Table 3 SWB mediation analysis of the SSME-FI relationship(N = 11,594) Path β SE p 95% BootCI conclusion Total effect: SSME→ FI 0.045 0.006 < 0.001 [0.033, 0.056] — Direct effects(control SWB): SSME→ FI 0.043 0.006 < 0.001 [0.031, 0.054] — Indirect effects : SSME→SWB→FI Very high SWB 0.002 0.001 0.109 [0.001, 0.007] partial mediating High SWB 0 0.001 0.443 [-0.002, 0.000] unmediated Low and moderate SWB 0 0.001 0.821 [-0.001, 0.002] unmediated Total indirect effect 0.002 0.001 0.017 [0.002, 0.005] significant 6. Discussion This study calls into question the Western RCT by demonstrating how institutional structures (hukou system and gender norms) and subjective social mobility expectations systematically shape individual fertility intentions in China. The primary conclusions that were derived from this study were as follows. The institution assumes a significant role: hukou ( β = 0.053***) and gender ( β = 0.042***) have far more influence on fertility intention than income, education level, and age, which indicates that fertility decision in China is mainly driven by institutional environment rather than economic rationality. It is evident that rural families are less constrained by economic rationality in their reproductive decisions, due to collective welfare guarantees such as land use rights and rural healthcare. In contrast, urban families experience instability in their access to education and housing as a result of the hukou system ( Liu et al., 2023 ). Institutionally filtered subjective social mobility expectation (SSME): SSME has a positive effect on FI ( β = 0.047***), but the transformation of FI by SSME is moderated by hukou and gender. Hukou weakens social mobility responses in rural areas ( β = 0.013*), which is consistent with the buffer effect of rural hukou welfare on FI studied by Liu(2023) , while gender amplifies the male response. ( β = 0.020*** ), which is consistent with fertility norms in China patrilineal society (Yang, 2004; Cai & Xie, 2024) and with patrilineal norms in Asia that emphasize male heirs to continue the family lineage (Den & H, 2017; Srinivas et al., 2024). At the same time, gender norms also inhibit women's FI, because in the fiercely competitive urban labor market," motherhood penalty", these invisible gender norms, let women bear a heavy reproductive cost (Ji Yingchun et al., 2020). Institutional factors such as hukou and gender norms shape the role of SSMEs on FI, rather than FI in turn shaping SSMEs (Liu Jindong et al., 2023). The reason is that hukou institutions and gender norms exist before individuals form expectations of social mobility (Xie Yu, 2023), and these institutional factors predetermine the impact path of SSMEs on FI by allocating resources and shaping social roles. Institutional constraints on emotional resources: Subjective well-being (SWB) mediated the relationship between SSME and FI only in the happiest of individuals ( β = 0.002*, p < 0.05), accounting for 4.4% of the total effect. This finding correlates significantly with mainstream positive patterns (Elena et al., 2025; Vignoli et al., 2020), reflecting China's characteristic "involution" competition (Chen Rong, 2020; Cai Weicheng & Xie Yu, 2024). Although there is no significant difference (1.78–1.86) in FI among different SWB groups, this phenomenon reflects the institutional constraints of China's low-fertility society. The Hukou system, gender inequality, and competitive culture restrain FI as a whole and make it rigid. Specifically, rural hukou weakens the impact of social mobility expectations on fertility through welfare buffers (such as land security)(β=-0.013*), decoupling fertility decisions from economic rationality. Gender norms amplify men's ability to translate expectations of social mobility into reproductive intentions, reflecting the logic of intergenerational investment in paternal culture. SWB mediated FI weakly ( 4.4% ) only when it exceeded the threshold (top 18.44%), highlighting the polarization of emotional resources allocation in high-pressure environments. These findings suggest that institutional factors continue to influence individual fertility decisions by reshaping the psychological mechanism formed by willingness, resulting in the difficulty of improving overall FI. Based on these findings, the "institutional embeddedness model" proposed in this study reveals that hukou and gender norms influence reproductive decision-making through dual paths: a macro-level-resource allocation path, institutions (hukou and gender norms) stratify reproductive resources by allocating resources and enforcing social roles. Hukou, for example, determines access to education through school zoning (Wenjing et al., 2019), while gendered workplace policies limit the use of parental leave (Yu & Cheng, 2024). At the meso level, the cognitive construction path: social norms asymmetrically transform social mobility into fertility intention. Men showed a stronger response (β = 0.020***), viewing children as investments in social advancement (Vignoli et al., 2020), while women faced lower FI due to "motherhood penalty"(Oriel, 2019; Yu & Cheng, 2024). Micro level-Affective threshold adjustment: Subjective well-being (SWB) mediates FI by 4.4% (β = 0.002*) only among the happiest of individuals. Consistent with Goldin's "motherhood penalty" and "paternal bonus," this study found that Chinese men showed stronger fertility intentions (β = 0.042 ), but the CFPS2018 data showed that paternal participation accounted for only of maternal participation in parenting (Zhang Lu et al., 2023). This "will-act split" is common in low-fertility societies around the world. However, China is unique in that gender role differences are further institutionalized and reinforced through the hukou system: in rural areas, land rights are directly bound to paternal inheritance systems (Qu Xiangfei, 2019), systematically reinforcing son preferences and traditional gender division patterns (Fei & Li, 2025). In urban areas, highly educated women face a "hukou + sex" double discrimination: research has found that for every additional child, women's wage income drops by an average of about 7.4%, while men's wage income is almost unaffected (Li & Feng, 2023). The findings indicate that gender inequality in China is not solely attributable to cultural lag or market failure, but is also the consequence of formal and informal institutions. The hukou system has been identified as a factor that binds welfare rights to gender roles, thereby reinforcing the prevailing gender division of labor. This finding not only lends further support to Golding's core view that gender division of labor affects reproductive decision-making but also advances the theoretical understanding of how institutional factors shape gender division of labor. Furthermore, it provides a more complete "institution-gender" analytical framework for understanding low fertility.This institution-gender interaction is visualized in Fig. 3 . China's fertility intention under institutional constraints has three unique characteristics that distinguish it from other countries in the West and East Asia: (1) Hukou creates institutional welfare cliffs, not economic gradients. Rural hukou holders have higher FIs due to land security and collective health care benefits (β = 0.053**, p < 0.001), in sharp contrast to Western income-driven models (Schellekens & Poppel, 2012;Becker, 1960). (2) Gender differences in FIs between urban and rural areas persist, with CGSS2012-2021 data showing that rural hukou men have a higher ideal number of children (2.15) than women (1.80, gap = 0.35), which may be influenced by paternal inheritance norms (Lui & Chan, 2020). In urban settings, the gender gap narrowed (men = 1.86, women = 1.58) and education levels were negatively correlated with FIs (β= -0.014**, p < 0.001), consistent with Western and East Asian studies reflecting the "substitution effect of education and fertility"(Schellekens & Poppel, 2012; Hellstrand et al., 2021; Bloom et al., 2024). (3) Happiness must exceed the "highest threshold"(SWB of the highest 18.44 percent of the sample, β = 0.002*, p < 0.05 ) to barely offset institutional stress. Traditionally, FIs are linearly related to subjective well-being (Arnstein et al. 2015; Kohler & Mencarini 2016). However, China's "996" labor culture polarizes this relationship-only the happiest 18.44 percent overcome competitive pressures to have children, consistent with South Korea and Japan's competition-driven pattern of only extremely happy people having children (Shin & Bang, 2009; Naohiro Ogawa, 2010). 7. Conclusions and recommendations Based on the data of CGSS from 2012 to 2021, this study deeply analyzes the influence of institutional constraints (hukou system, gender norms) and subjective well-being on FI of the child-bearing population, constructs an "institutional embeddedness model," and reveals the unique social logic of fertility decision-making in China. The results show that the institutional environment dominates FI: hukou institution and gender norms significantly affect FI through resource allocation and social role shaping; institutions regulate SSME: SSME's positive influence on FI is moderated by hukou and gender; emotional resources are constrained by institutions: subjective well-being only plays a weak mediating role in FI in extremely happy groups. These findings challenge the universality of Western rational choice theory and provide a localized theoretical framework for understanding the phenomenon of low fertility in China. The results of this study have important policy implications for China facing the challenge of fertility decline, as follows: First, promote hukou system reform. The hukou system, as a core resource allocation mechanism, significantly affects FI. The FI of rural hukou is higher because of land and collective welfare, but the SSME has a weak promotion effect on FI, while the FI of urban hukou is suppressed because of high living costs and a fierce competition environment. Therefore, hukou system reform should be gradually promoted, hukou barriers should be broken down, and equalization of public services should be realized. For example, we can learn from Shanghai's residence permit system to provide basic public services such as education and medical care for the floating population, improve their quality of life and sense of belonging, and thus promote FI promotion. The research of Li Yonghui et al.(2021) shows that the FI of the floating population with affordable housing has increased significantly, which indicates that social welfare measures can effectively improve the quality of life and sense of belonging of the floating population. Similarly, the equalization of education and health care through the residence permit system will also help migrant populations better integrate into urban life and improve their overall quality of life. Second, optimize gender equality policies. Gender norms play an important role in reproductive decision-making in China. Men are influenced by paternal culture, FI is stronger, and SSME promotes FI more significantly; women face a FI gap due to "maternal punishment". Therefore, we should promote gender equality in the workplace, eliminate gender discrimination, and reduce women's child-rearing burden. For example, we can learn from the Nordic "father quota" model and promote non-transferable paternity leave, requiring men to take the prescribed paternity leave to encourage men to participate more in the childcare process and reduce the burden of childcare for women, thus promoting gender equality and FI promotion. Studies have shown that Norway's "father quota" policy significantly increases male participation in childcare, reduces gender inequality in childcare responsibilities, and provides more balanced support for family fertility (Naz, 2010). Finally, promote subjective well-being. Subjective well-being plays an important role in FI formation, but its influence is only significant in the extremely happy group. This shows that in order to improve FI, it is necessary to enhance the subjective well-being of society as a whole, relieve the pressure of social competition, and create a friendly reproductive environment. Specific measures include: providing child-care subsidies for couples, combining with community child-care centers and peer support centers, learning from Japan's child-care support policy system, and improving family FI through three core strategies: balancing work and life, supporting family child-care, and changing marriage and child-bearing concepts. Specifically, the conflict between women's career development and child-rearing responsibilities has been effectively mitigated through measures such as providing child-rearing subsidies, establishing community-based child-rearing centers, and encouraging enterprises to implement flexible working systems. At the same time, the enhancement of community support and the provision of childcare guidance serve to alleviate the burden of childcare on families, thereby enhancing their subjective well-being. Furthermore, through publicity and educational activities, traditional social attitudes towards marriage and childbirth are gradually being transformed to create a more favourable environment for childbirth. These comprehensive measures have been shown to alleviate the family's child-rearing difficulties and reverse the negative fertility concept from the root, thus significantly improving FI. Although this study advances our understanding of institutional effects, there are still some limitations. First, the fuzziness of causality: cross-sectional data cannot determine chronological order. Although we mitigate this problem by exploiting the exogeneity of hukou and gender (innate attributes), by exploiting the foresight of SSMEs (measured as future expectations), and by emphasizing "structure over selection" based on institutional theory. However, future longitudinal studies (e.g., CLDS panel data) are needed to determine causality. Second, numbers and generational gaps: The analysis did not consider the role of social media in shaping fertility norms (e.g.,"infertility anxiety" narratives on TikTok) or shifts in intergenerational values (e.g., post-90s attitudes toward marriage). Finally, timeliness of policy: Data on the enhancement of the three-child policy predate 2023, and future research is needed to assess the effectiveness of the policy. Future research should employ experimental designs (statement preference surveys) in order to quantify the marginal effects of institutional change. In addition, the integration of hybrid approaches is advised in order to capture cultural nuances and to explore digital age phenomena such as algorithmic information cocoons. Further elucidation can be gained from comparative studies of East Asian welfare states (e.g., China and Japan) on the matter of how institutional configurations drive fertility outcomes. Declarations Author Contribution J.Y.L. designed and performed all experiments, analyzed the data, prepared the figures and wrote the manuscript.Z.F. and H.H. supervised the project, provided critical guidance and revised the manuscript.All authors reviewed and approved the final manuscript. Data Availability This study uses publicly available micro-data from the Chinese General Social Survey (CGSS) 2012, 2015, 2017, 2018 and 2021 waves. 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Additional Declarations No competing interests reported. Supplementary Files README.docx Modelspecification.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 17 Apr, 2026 Reviews received at journal 29 Mar, 2026 Reviewers agreed at journal 18 Mar, 2026 Reviewers agreed at journal 17 Mar, 2026 Reviewers invited by journal 16 Mar, 2026 Editor assigned by journal 16 Mar, 2026 Editor invited by journal 06 Jan, 2026 Submission checks completed at journal 29 Dec, 2025 First submitted to journal 29 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8361693","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":607830064,"identity":"5f1987dd-b872-4c24-95ee-62ef33f4b7fc","order_by":0,"name":"Jieyu Li","email":"","orcid":"","institution":"Zhejiang Gongshang University Hangzhou College of Commerce","correspondingAuthor":false,"prefix":"","firstName":"Jieyu","middleName":"","lastName":"Li","suffix":""},{"id":607830065,"identity":"9053a900-30c5-45ed-a503-79fdfedc430d","order_by":1,"name":"Zhong Fei","email":"","orcid":"","institution":"Zhejiang Gongshang University Hangzhou College of Commerce","correspondingAuthor":false,"prefix":"","firstName":"Zhong","middleName":"","lastName":"Fei","suffix":""},{"id":607830066,"identity":"d22d75c7-d8d0-4638-bb9f-98bad2381ca0","order_by":2,"name":"Hongfeng Han","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYBACAwYGNhDND8SMD4CEDESQCC2SDQwMzCClPCRpYZMgSos5+9ljDz7uqJXgl26/VvGz7Q4PA3vzNgmGmjs4tVj25KUbzjxzXEJyzpmym71tz3gYeI6VSTAce4bbYQdyzKR5247VGdzISbvN2HaYh0Eix0yCseEwbi3n34C1SNgDtRSDtci/IaDlBtiWGgkDifRjzBBbeAhpeWMmObPtgITEjRxmyZ5zz3jYeNKKLRKO4XMY0OUf2+ok+GekP/zwo+yOHD/74Y03PtTg1gIFIAU8oOg4AImmBEIaGBjqgJj9AVjLKBgFo2AUjAJ0AAD9jlPS+WgOYQAAAABJRU5ErkJggg==","orcid":"","institution":"China University of Political Science and Law","correspondingAuthor":true,"prefix":"","firstName":"Hongfeng","middleName":"","lastName":"Han","suffix":""}],"badges":[],"createdAt":"2025-12-15 04:23:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8361693/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8361693/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104888782,"identity":"fe6cfcb5-4707-419d-9b98-2ba58fcfa95c","added_by":"auto","created_at":"2026-03-18 10:19:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":35459,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eConceptual Model and Hypotheses\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8361693/v1/bafd08167da461b06fb694b2.png"},{"id":104888784,"identity":"80b9d591-6e2e-437b-bdd3-eab4b67382cf","added_by":"auto","created_at":"2026-03-18 10:19:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":26653,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eCharacteristic Distribution of Fertility Intention (FI) Among Urban - Rural - Gender Groups Based on SME and SWB in CGSS(2012,2015,2017,2018, and 2021)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8361693/v1/262b6499cebc7fac3149f7a3.png"},{"id":104888783,"identity":"83b94284-31aa-4a46-bfd5-b8401dcba196","added_by":"auto","created_at":"2026-03-18 10:19:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":52457,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSSME's influence on FI under the \"institution-gender\" framework.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8361693/v1/46559cb7d9bd01b723719bb7.png"},{"id":105034559,"identity":"df54c297-53b2-4f86-b2fe-d91459708c64","added_by":"auto","created_at":"2026-03-20 07:23:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1102857,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8361693/v1/a369d216-3297-4cfe-a2b7-dcc49782d868.pdf"},{"id":104888785,"identity":"05a121b5-1a8a-4586-82e0-12e9bc6a81fb","added_by":"auto","created_at":"2026-03-18 10:19:50","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14191,"visible":true,"origin":"","legend":"","description":"","filename":"README.docx","url":"https://assets-eu.researchsquare.com/files/rs-8361693/v1/e12fe2a0512f02567f3d1f70.docx"},{"id":104888786,"identity":"d0ad7857-21e4-4f2f-b129-4fd42716230c","added_by":"auto","created_at":"2026-03-18 10:19:51","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":14558,"visible":true,"origin":"","legend":"","description":"","filename":"Modelspecification.docx","url":"https://assets-eu.researchsquare.com/files/rs-8361693/v1/27bbd47862cf48a6d322dfe9.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Mechanisms of China's Fertility Intentions from an Institutional Embeddedness Perspective: Gender Norms, Hukou Segmentation Pathways, and Policy Implications","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSince the 1970s, there has been a general decline in global fertility, particularly in countries that have achieved higher levels of economic and social development (Zhou, 2015). A similar pattern was exhibited by China, where the birth population declined to 9.02\u0026nbsp;million in 2023, representing the most modest level recorded since 1949. Nonetheless, it is important to acknowledge that rural populations with comparatively modest incomes exhibit higher levels of fertility intentions (FI) in comparison to their urban counterparts (Shi et al., 2022). This prompts a critical inquiry: what factors underpin these distinct disparities in FI? The extant literature has principally elucidated FI through economic frameworks, including income levels and educational expenditures. However, the mechanisms that underpin the observation that rural populations, despite their relatively weaker economic conditions, exhibit a stronger willingness to have children remain unclear. This contradiction suggests the possibility that, in addition to economic factors, the institutional environment may also function as a determinant. It is important to note that current research has paid insufficient attention to three institutional dimensions. Firstly, how the hukou system influences fertility decision-making through resource allocation mechanisms, including rural land security and urban-rural disparities in public services. Secondly, how gender norms shape divergent perceptions of fertility responsibilities, such as men's greater inclination toward childbearing and women's heightened concerns about career repercussions. And thirdly, how social competitive pressures \u0026ndash; such as high-intensity work \u0026ndash; deplete emotional and cognitive resources, thereby dampening FI. In order to comprehend this phenomenon, the present study examines the conjoint effects of the hukou system, gender norms, and social competition on fertility decision-making from an institutional perspective. The findings are expected to provide a more comprehensive understanding of the root causes of FI disparities in China and to inform the formulation of more targeted fertility support policies, such as the advancement of the balancing of public services across regions and the alleviation of excessive work pressure.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eNee and Ingram (1998) proposed institutional embeddedness theory, which provides a core framework for understanding fertility decision-making. The theory posits that institutions are comprised of a series of interrelated norms that govern people's relationships and profoundly influence their behaviour. Individual choices are constrained by both formal institutions (regulations, hukou system) and informal institutions (norms, culture, gender roles) embedded in them. Formal institutions are established and enforced by political authorities; however, their construction, evolution, and implementation are significantly influenced by informal institutions (Baum \u0026amp; Oliver, 1992). This theory reveals the relationship between human activities and institutional structure, and is the key to understanding the unique fertility phenomenon in China.\u003c/p\u003e \u003cp\u003eThe modern academic tradition of FI research has its origins in a reflection on the Demographic Transition Theory (DTT). Notestein (1945) proposed a macro framework for industrialization-driven fertility decline. However, it faces challenges in explaining the paradox between sustained growth and fertility recovery in Europe. Lesthaeghe \u0026amp; L\u0026oacute;pez-Gay(2013) Second Demographic Transition (SDT) points out that the synergies between cultural and structural factors, successive secularization waves, and different constraints under the framework of the \"Ready, Willing, and Able\" paradigm. The joint impact of these factors affects the \"postponement\" and \"non-traditional\" patterns of fertility decision-making in the SDT, revealing the multidimensional complexity of fertility decision-making.\u003c/p\u003e \u003cp\u003eIn economics, Grebenik \u0026amp; Leibenstein(1959) first introduced the cost-utility theory and proposed the rational choice theory by employing the marginal analysis method. Becker (1960) then introduced the consumer choice theory into the analysis of family fertility decisions, expanding the \"quantity-quality\" perspective of fertility decisions. In the follow-up analysis of fertility costs, scholars direct their attention to the direct costs of fertility, such as education, housing, and medical care, while also emphasising the opportunity cost of fertility, i.e., the phenomenon commonly referred to as the \"motherhood penalty\". Goldin et al. (2024), a recent Nobel laureate in economics, argue that gender inequality in the division of labor is at the heart of low fertility. This perspective deviates from Becker's \"rational choice\" model in that the former accentuates the role of institutionalised role allocation (for instance, the \"motherhood penalty\") in distorting the decision-making environment, whereas the latter presupposes that individuals possess the capacity for independent decision-making within an equitable market.\u003c/p\u003e \u003cp\u003eGenerally speaking, in the field of Western economics, fertility is generally regarded as a rational behavior of families based on their own cost-benefit analysis (Yang, 2021). The field of sociology is more concerned with the influence of culture and social structure. Bourdieu (1977)'s concept of \"reproductive strategy\" emphasizes the role of cultural capital and institutional constraints in the reproduction of intergenerational status. From the perspective of social stratification, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eBilly (1985)\u003c/span\u003e posited that social mobility, both intergenerational and intragenerational, exerts an influence on reproductive intentions. However, socioeconomic status is not solely determined by the material wealth held by an individual; it is also influenced by the subjective perception of wealth (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eKraus \u0026amp; Stephens, 2012\u003c/span\u003e). Therefore, more and more people study FI from the perspective of Subjective Social Mobility (SSM). On the one hand, according to the Western Social Capillarity Theory, resource competition leads to higher subjective social mobility, and the accompanying achievement motivation inhibits FI (Dumont, 1990). On the other hand, in the context of China's unique institutional embeddedness, the relationship between SSM and FI presents distinct uniqueness. Specifically, individuals with higher SSM tend to have more children (CHEN et al., 2022; Yan et al., 2025) because their relationship is influenced by institutions in China (Ruan, 2024) and psychological factors (Li et al., 2023). The adjustment of the system embeddedness reflects the explanatory power of the system embeddedness theory. Furthermore, demographic variables such as age, gender, income, and education level have been demonstrated to exert varying degrees of influence on fertility intentions (Zhao \u0026amp; Chen, 2023; Wu \u0026amp; KC, 2022; Costa Dias et al., 2018).\u003c/p\u003e \u003cp\u003eSubjective well-being (SWB) is an individual's cognitive and emotional evaluation of his life (Diener, 2000). There is a broad consensus that both social mobility perception and subjective social mobility expectation exert a positive influence on SWB (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eChen et al., 2022\u003c/span\u003e). The study demonstrates a positive correlation between SWB and FI, particularly in instances where couples' income surpasses expectations and their optimistic outlook towards the future prompts early marriage and increased childbearing (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eL. \u0026amp; Easterlin, 1980\u003c/span\u003e). Furthermore, couples in positive states have more children with the aim of strengthening their relationship and their own happiness (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eIsen \u0026amp; Patrick, 1983; Parr, 2010\u003c/span\u003e). However, in China's highly competitive environment, characterised by a \"996\" work culture, the SWB, acquisition and security of youth groups are significantly suppressed (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNie \u0026amp; Feng, 2020; Huang et al., 2023\u003c/span\u003e), resulting in a contradictory mentality of \"wanting to have children\" and \"not daring to have children\" (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eXing, 2020\u003c/span\u003e). From the perspective of the \"emotional labor\" theory, fertility requires a significant amount of additional emotional energy (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eTropman et al., 1984\u003c/span\u003e), which may suggest that SWB needs to attain a certain threshold to elicit its influence on fertility intention in China's highly competitive environment.\u003c/p\u003e \u003cp\u003eInstitutional structures in China, especially hukou system and gender norms, profoundly shape the relationship between SSM and FI (Yu \u0026amp; Cheng, 2024). As a core resource allocation mechanism, the hukou system significantly affects FI(\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eCai \u0026amp; Zhong, 2025)\u003c/span\u003e. Rural hukou holders have higher FI due to land and collective welfare protection (Han et al., 2019), but such benefits may blunt the additional boosting effect of SSM on their FI (Huang, 2020). Urban hukou holders are constrained by high costs, high pressures and high competition (Dong \u0026amp; Goodburn, 2019), and their expectations of SSM further promote fertility (Yang \u0026amp; Guo, 2023). Gender norms (informal institutions) have been shown to result in an increased allocation of child-rearing responsibilities to women, whilst concomitantly engendering a phenomenon termed the \"motherhood penalty\", which has been demonstrated to inhibit reproductive intentions (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eWu et al., 2019; Lui \u0026amp; Chan, 2020)\u003c/span\u003e. The advent of gender equality concepts has been shown to engender a further reduction in women's intentions (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eYu et al., 2023\u003c/span\u003e). Research indicates that men who are influenced by traditional gender roles tend to have higher FI (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eShi \u0026amp; Zheng, 2023\u003c/span\u003e) and a higher preference for boys (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eFei \u0026amp; Li, 2025\u003c/span\u003e). Concurrently, men convert SSM into FI with significantly higher intensity than women (Vignoli et al., 2020), indicating the potential impact of gender norms on SSM-FI relationships.\u003c/p\u003e \u003cp\u003eIn general, extant theories have the following limitations in explaining the fertility phenomenon in China: (1) rational choice theory (RCT) ignores the differences in fertility intention brought about by the hukou system in China; (2) the correlation between expectations of SSM and FI ignores the filtering effect of gender norms in China; and (3) the association between SWB and FI does not pay attention to threshold. Therefore, based on the institutional embeddedness theory \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e(Nee \u0026amp; Ingram, 1998)\u003c/span\u003e, this study employs a multi-level analysis framework integrating macro (formal/informal institutions such as hukou and gender), meso (SSM under institutional regulation) and micro (SWB threshold) levels, with a view to comprehensively revealing the unique institutional logic and cultural context of the formation of FI in China.\u003c/p\u003e"},{"header":"3. Research Hypothesis","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 The Basis of FI in Institutional Environment: SSME and FI\u003c/h2\u003e \u003cp\u003eClassical theory suggests that competition for resources causes high SSME to inhibit FI (Dumont, 1990). However, China's data indicates that FI is higher for those with high SSME (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eYan et al., 2025\u003c/span\u003e). This reflects the limited rationality of institutional embedding: under the constraints of hukou registration and gender norms, upward mobility expectations enhance FI through intergenerational investment or collective welfare. Therefore, we propose H1: Subjective Social Mobility Expectation (SSME) positively affects FI. The stronger an individual's expectation of upward mobility, the higher his fertility intention.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Institutional Constraints on Emotion: The Mediating Role of SWB\u003c/h2\u003e \u003cp\u003eIn the context of China's intensely competitive environment, SWB is regarded as a significant indicator of emotional resources, which may exert an influence on FI through nonlinear pathways. Empirical studies show that SWB and FI of childbearing age population have an inverted U-shaped relationship (Xiang et al., 2019), SSME significantly increases SWB by increasing confidence in the future and indirectly promotes FI (Zhang \u0026amp; Wang, 2024), employment pressure has a significant inhibitory effect on FI only in low SWB groups (Vignoli et al., 2020), and institutional constraints such as hukou system and gender norms make the SWB-FI relationship present situation-dependent nonlinear characteristics (Wang, 2015). Therefore, we propose H2a: SWB has a positive main effect on FI, the higher the SWB, the stronger the FI; H2b: there is an affective threshold for this effect, and the mediating path of SSME affecting FI through SWB is only significant in the extremely high SWB population.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Differential Adjustment of Institutional Factors\u003c/h2\u003e \u003cp\u003eHukou system divides fertility opportunities: rural-hukou holder has land security and collective welfare, which reduces sensitivity to future flows and thus weakens the role of SSME in FI (Shao, 2000; Han et al., 2019; Xing et al., 2023. The link between SSME and FI is amplified by the high education and housing costs faced by urban-hukou holders (Dong \u0026amp; Goodburn, 2020; Yang \u0026amp; Guo, 2023). Gender norms differ through role division: paternal culture encourages men to convert SSME to FI more quickly (Cai \u0026amp; Xie, 2024). Women suffer from the \"motherhood penalty\", SSME transformation to FI is blocked (Yu et al., 2023), and the biological clock narrative intensifies fertility anxiety (Yopo D\u0026iacute;az, 2020). Therefore, we propose H3a: hukou type significantly affects FI (rural\u0026thinsp;\u0026gt;\u0026thinsp;non-rural). The FI of rural-hukou holder individuals is higher than that of urban-hukou holder individuals. H3b: Gender significantly affects FI (males\u0026thinsp;\u0026gt;\u0026thinsp;females). Men have higher FI than women. H3c: Role of the hukou system in regulating SSME (rural weakening). SSME has a weak effect on FI in rural-hukou groups. H3d: Role of gender norms in regulating SSME (male reinforcement). SSME has been demonstrated to have a more significant impact on FI in the male population.The conceptual framework is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Data, Variable, and Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Data Sources and Sample Characteristics\u003c/h2\u003e \u003cp\u003eThe research data comes from the five-round survey data of The Chinese General Social Survey (CGSS) 2012\u0026ndash;2021. CGSS is a nationwide, comprehensive, and continuous academic survey project in China. This study focused on the population aged 20\u0026ndash;40 years of childbearing age, and 11,594 valid samples were obtained after missing values of variables were processed. The sample covers 31 provincial-level administrative regions in China, with rural-hukou holders accounting for 55.3%, males accounting for 46.5%, and an average age of 30.52 years (SD\u0026thinsp;=\u0026thinsp;6.32), which conforms to the demographic characteristics of China's child-bearing population (National Bureau of Statistics of China, 2021).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Variable\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e4.2.1. Dependent Variable\u003c/h2\u003e \u003cp\u003eFI uses the open-ended question from CGSS: \"How many children would you like to have if policy restrictions were not imposed?\" Direct measurement, value range 0\u0026ndash;5 (excluding outliers\u0026thinsp;\u0026gt;\u0026thinsp;5, accounting for 2.5%). This index is a continuous variable, and it has been demonstrated that the higher the value, the stronger the fertility intention. This approach circumvents the possible divergence of the compound index, rendering it particularly well-suited for the examination of the impact of institutional constraints, such as hukou registration and policy, on fundamental fertility preferences. It is adept at capturing individual fertility intentions within the context of institutional constraints.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e4.2.2. Independent Variable\u003c/h2\u003e \u003cp\u003eSSME is calculated by the difference between the CGSS \"current family social class\"(1\u0026ndash;10 points) and \"class expectations for the next 10 years\"(1\u0026ndash;10 points), with the formula: SSME\u0026thinsp;=\u0026thinsp;subjective expected class-subjective current class. A negative value indicates a decline in expectations, and a positive value indicates an increase in expectations. To enhance the theoretical explanatory power, SSME were transformed into 1\u0026ndash;5 ordered variables: 1 (\u0026le;-1, expected to decline), 2 (0, stable), 3 (+\u0026thinsp;1, gradually rising), 4 (+\u0026thinsp;2, significantly rising), 5 (\u0026thinsp;\u0026ge;\u0026thinsp;+\u0026thinsp;3, sharply rising). In previous studies, based on the same data, in order to avoid the interference of extreme values, 5-level combined measurement is generally adopted (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eChen \u0026amp; Zhang, 2018\u003c/span\u003e). \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eT\u003c/span\u003ehis classification method has been demonstrated to simplify the data processing and analysis process. Furthermore, it has been shown to reduce the influence of multicollinearity on statistical analysis through centralization processing. In addition to these benefits, the method has been demonstrated to improve the estimation accuracy and interpretation power of the model. Frequency analysis showed that only 1% of individuals expected to decline, 34% expected to gradually increase, 24% expected to significantly increase, and 21% expected to sharply increase. This classification can clearly reflect different expectations of future social status, and each grade is distributed in the sample, indicating that individuals have diverse expectations of future social status, further verifying the rationality of this classification.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e4.2.3 Metavariable\u003c/h2\u003e \u003cp\u003eSWB was measured by a 5-point Likert scale (1= \"very unhappy\",2= \"unhappy\",3= \"general\",4= \"happy\",5= \"very happy\"), based on a right-skewed distribution of raw data (\"happy\" + \"very happy\" 87%), reclassified into four groups: low (1\u0026ndash;2 points, 5.60%), moderate (3 points, 14.12%), high (4 points, 61.84%), and very high (5 points, 18.44%). This classification preserves extreme heterogeneity and optimizes sample distribution to facilitate testing for \"affective threshold\" effects.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e4.2.4 Moderator\u003c/h2\u003e \u003cp\u003eAccording to the urban-rural binary structure theory (Wang \u0026amp; Zeng, 2025), hukou was recoded as rural (+\u0026thinsp;1) and non-rural (-1), and the effect coding was used to compare the group mean differences. Rural hukou covers the original rural category and the non-agricultural category of farmers, while non-rural hukou includes non-agricultural, former non-agricultural and military categories (military accounts for only 3%). Gender uses effect coding (male\u0026thinsp;+\u0026thinsp;1, female- 1) to capture the impact of the norm of \"male external female internal\" on fertility decisions (Shi \u0026amp; Zheng, 2023).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e4.2.5 Control variable\u003c/h2\u003e \u003cp\u003eOur analysis included a range of control variables. The data include individual-level attributes such as age, education level of spouse, and income. Age is a continuous variable (20\u0026ndash;40 years), and this range is consistent with biological and social time constraints in China on fertility decisions (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eLu et al., 2005\u003c/span\u003e). Education levels are classified according to CGSS original 13 levels (0\u0026thinsp;=\u0026thinsp;no education to 13\u0026thinsp;=\u0026thinsp;graduate). The natural logarithm of revenue is taken after top and bottom 1% tail reduction to reduce skewness.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Method\u003c/h2\u003e \u003cp\u003eIn order to explore the complex relationship among SSME, SWB and FI, hierarchical regression analysis was used to solve the different research problems step by step. To be specific, Model 1 analyzed baseline associations between control variables (age, education, income) and FI; Model 2 included centralized SSME to assess their independent effects on FI; Model 3 introduced moderating variables (household registration, gender) and SWB to analyze the interaction between structural inequality and affective factors; Model 4 included SSME \u0026times; household registration, SSME \u0026times; gender interactions to test institutional moderating effects (Wen et al., 2005). To examine the mediating effect of SWB, we used 1000 resampling (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eWen \u0026amp; Ye, 2014\u003c/span\u003e). Meanwhile, we divide SWB into four groups: \"low SWB\",\"moderate SWB\",\"high SWB\" and \"very high SWB\", and investigate whether there is a mediating path between different groups to determine the \"threshold effect\" of SWB in the impact of SSME on FI.\u003c/p\u003e \u003cp\u003eAll effect sizes are calculated as follows: (1) For adjustment effect size calculations, calculated by percentage decay or enhancement\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:({\\beta\\:}_{interaction}/{\\beta\\:}_{mobility})\\times\\:100\\%$$\u003c/div\u003e\u003c/div\u003e;\u003c/p\u003e \u003cp\u003e(2)To test for mediating effects, we decompose the total effect \u003cem\u003e(\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\tau\\:\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003e)\u003c/em\u003e into two parts: direct effects\u003cem\u003e(\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\tau\\:\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003e')\u003c/em\u003eand indirect paths through mediating variables\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:1-\\left(\\frac{\\tau\\:{\\prime\\:}}{\\tau\\:}\\right)$$\u003c/div\u003e\u003c/div\u003e;\u003c/p\u003e \u003cp\u003e(3)We use a stratified approach to calculate the contribution of institutional factors to the outcome:\u003c/p\u003e \u003cp\u003e \u003cem\u003e(\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:({\\varDelta\\:{R}^{2}}_{main\\:effects}+{\\varDelta\\:{R}^{2}}_{interactions})/{{R}^{2}}_{final\\:model}\\)\u003c/span\u003e \u003c/span\u003e \u003cem\u003e)\u003c/em\u003e \u003c/p\u003e \u003cp\u003ewhere the main effect of social mobility expectations contributes were\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:{{\\varDelta\\:R}^{2}}_{mobility}/{{R}^{2}}_{final\\:model}$$\u003c/div\u003e\u003c/div\u003e.\u003c/p\u003e \u003cp\u003eIn addition, continuous variables were normalized to facilitate comparison of the influence of different variables.\u003c/p\u003e \u003cp\u003eSPSS software was used for analysis, including stratified regression, descriptive statistics, and mediation analysis. Cross-sectional data describe the situation well, but are not strong enough to prove causation.\u003c/p\u003e \u003cp\u003eWe solve this problem in three ways: (1) Use institutional externality to represent causation. Hukou and gender are key institutional variables because people cannot change them at will, and they exist before fertility intentions are formed. (2) Expectations of social mobility are forward-looking indicators that measure the difference between current social class perceptions and future expectations of social classes, and that exist before childbearing intentions are formed. (3) Our \"institutional embeddedness model\" may suggest that China's hukou system and gender norms have influenced people's cognition before the formation of fertility intention. The hukou system creates \"institutional instability\"(Wu \u0026amp; KC, 2022), and gender norms require \"emotional labor\" (Tropman et al., 1984). These factors collectively influence perceptions even before the emergence of the intention to procreate.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Result","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Descriptive statistical analysis and preliminary analysis\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the key distributions of 11,594 samples at different levels of SWB. SSME differed significantly among groups (\u003cem\u003eχ\u0026sup2;\u003c/em\u003e=122.16, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with a higher proportion of people in the \"very happy\" group believing that their future social class would rise significantly (25.6% vs. 18.0% in the moderately happy group). Fertility intention (FI) varied little between groups (1.78\u0026ndash;1.86 children), and this convergence suggests that institutional constraints compress the overall space for fertility decisions by shaping habits (Bourdieu, 1977). It's worth noting that people with higher education levels have higher mean subjective well-being (7.38 vs. 5.63, \u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;61.52, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001); rural hukou holders accounted for 63.5% of the low SWB group, while the proportion dropped to 53.3% in the very high SWB group; gender composition shifted from male dominance in the low SWB group (52.4%) to female dominance in the high SWB group (55.9%;χ\u0026sup2;=33.64, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001)). The mean age of the sample (30.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.3 years) decreased with increasing SWB (29.9 vs 31.3 years). These patterns lay the foundation for multivariate analysis.\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\u003eDistribution of variables across subjective well-being groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow SWB Group\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;649)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate SWB Group\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1,637)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh SWB Group (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7,170)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVery High SWB Group (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2,138)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExpected to decline SSME(\u003cem\u003en(%)\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24 (3.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35 (2.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e157 (2.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e51 (2.39)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStable SSME(\u003cem\u003en(%)\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e164 (25.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e413 (25.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,574 (21.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e388 (18.15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGradually rising SSME\u003c/p\u003e \u003cp\u003e(\u003cem\u003en(%)\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e199 (30.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e554 (33.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,347 (32.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e587 (27.46)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSignificantly rising SSME(\u003cem\u003en(%)\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e112 (17.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e341 (20.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,764 (24.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e564 (26.38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSharply rising SSME(\u003cem\u003en(%)\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e150 (23.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e294 (17.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,328 (18.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e548 (25.63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFertility intention\u003c/p\u003e \u003cp\u003e(\u003cem\u003eM;SD\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.82; 0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.78; 0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.82; 0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.86; 0.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(\u003cem\u003eM;SD\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31.28; 6.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.62; 6.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.62; 6.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29.89; 6.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.19; 3.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.76; 3.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.46; 4.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.44; 4.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational level (\u003cem\u003eM;SD\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.63; 3.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.54; 3.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.22; 3.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.38; 3.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural-hukou(\u003cem\u003en(%)\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e412 (63.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e972 (59.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3,892 (54.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,139 (53.27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale(\u003cem\u003en(%)\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e340 (52.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e845 (51.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3,268 (45.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e943 (44.11)\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\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the distribution patterns of FI between rural and urban residential settings and gender subgroups at different SSME-SWB levels. We find: (1) Rural men have the strongest intention to have children: FI is generally higher among rural men in most SSME-SWB categories. This suggests that in rural areas, men may be more prone to procreation, which may be related to local cultural and social structures. (2) FI is lowest among urban women: In contrast, FI is lower among urban women across all categories. This may reflect the impact of factors such as cost of living, educational and career opportunities in urban living environments on FI. (3) Influence of SSME change: FI of different groups fluctuated with SSME change. For instance, FI increased for all populations in the case of stable SSME or rising SSME, whereas FI decreased in the case of falling SSME. (4) Differences between urban and rural hukou: The FI of urban-hukou males and rural-hukou females is between that of rural-hukou males and urban-hukou females, thus demonstrating the impact of the differences between urban and rural hukou on FI.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Testing System Effects: Stratified Regression Results\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the results of the hierarchical regression model. Model 1 (control variable only) showed a positive correlation between age and FI (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), consistent with biological and social temporal perspectives (Michael et al., 2019). The negative coefficient of education level (\u003cem\u003eβ\u003c/em\u003e=-0.021, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) showed that FI was negatively correlated with education level. It is worth noting that personal income has no significant effect (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), suggesting that institutional factors may override the influence of economic rationality on FI.\u003c/p\u003e \u003cp\u003eModel 2 showed that the model fit improved significantly when SSME were included (\u003cem\u003eΔR\u0026sup2;\u003c/em\u003e = 0.006, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). SSME had a significant positive effect on FI (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.047, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), supporting H1. Specifically, for every 1 unit increase in upward mobility expectations, the ideal number of children increases by 0.047 units. Although \u003cem\u003eΔR\u0026sup2;\u003c/em\u003e is small, suggesting that the role of SSME may be mediated by situational factors, we will examine it further in Model 4.\u003c/p\u003e \u003cp\u003eThe results of Model 3 analysis showed that the explanatory power of Model 3 was further improved after gender, hukou, and SWB were included. Specifically, FI was significantly higher in males than in females (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.042, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), a finding that supports the H3-b hypothesis about the influence of gender norms, and significantly stronger in rural hukou holders (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.053, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), validating the H3-a hypothesis about the role of the hukou system. SWB showed an independent predictive effect (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.043, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), providing supporting evidence for H2-a. Notably, the effect of SSME, while still significant (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.043, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), was reduced, suggesting that its effect may be partially mediated by other variables.\u003c/p\u003e \u003cp\u003eThe analysis of the moderating effect of Model 4 further reveals the boundary conditions of SSME action. Gender significantly moderated the relationship between SSME and FI, and this positive association was more pronounced in males (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), which supported the hypothesis of a moderating effect of H3-d. In contrast, rural hukou attenuated the SSME effect (\u003cem\u003eβ\u003c/em\u003e=-0.013, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), confirming the H3-c hypothesis. SWB maintained a stable main effect (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.228, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), but did not show a significant moderating effect (\u003cem\u003eβ\u003c/em\u003e=-0.004). The explanatory power of the final model reaches \u003cem\u003eR\u0026sup2;\u003c/em\u003e=0.039, indicating that FI is shaped by social system factors and psychological factors.\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\u003eHierarchical regression analysis of FI (N\u0026thinsp;=\u0026thinsp;11,594)\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=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003emodel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003emodel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003emodel 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003emodel 4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.716**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.657**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.446**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.601**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.078\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.008**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.010**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.012**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.012**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.021**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.021**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.014**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.014**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSME\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\u003e0.047**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.043**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.058**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.042**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.042**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHukou\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.053**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.054**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSWB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.043**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.228**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSME \u0026times; gender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.020**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSME \u0026times; hukou\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.013*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSME \u0026times; SWB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAadjusted R\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e87.649***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e82.499***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e64.253***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e43.211***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e∆R\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e∆F\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e87.649***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e65.586***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e38.846***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e6.186***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cem\u003eNote: ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Happiness Threshold: An Analysis of Mediating Effects\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarizes the mediating role of SWB between SSME and FI. The overall effect of SSME on FI was significant (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.045, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001,\u003cem\u003e95% CI\u003c/em\u003e [0.033, 0.056]) by 1,000 Bootstrap samples. After controlling for SWB, the direct effect remained significant (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.043, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003e95%CI\u003c/em\u003e[0.031, 0.054]), suggesting that SWB only partially mediated.\u003c/p\u003e \u003cp\u003eFurther analysis revealed that indirect effects, although statistically significant, were small in size (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002, SE\u0026thinsp;=\u0026thinsp;0.001, 95%CI [0.001, 0.007]) and accounted for only 4.4% of the total effect. Group test showed that this mediating effect only existed in the very high SWB group (top 18.44%), while the other groups did not show a significant mediating path (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;\u0026asymp;\u0026thinsp;0, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), which supported the existence of an affective threshold. In contrast, the direct effect (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.043) accounted for 95.6% of the total effect, indicating that the impact of SSME on FI was mainly realized through non-emotional pathways, further confirming the dominant role of institutional factors in fertility decision-making.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSWB mediation analysis of the SSME-FI relationship(N\u0026thinsp;=\u0026thinsp;11,594)\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePath\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e95% BootCI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003econclusion\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal effect:\u003c/p\u003e \u003cp\u003eSSME\u0026rarr; FI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.033, 0.056]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect effects(control SWB):\u003c/p\u003e \u003cp\u003eSSME\u0026rarr; FI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.031, 0.054]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndirect effects : SSME\u0026rarr;SWB\u0026rarr;FI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery high SWB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.001, 0.007]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003epartial mediating\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh SWB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[-0.002, 0.000]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eunmediated\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow and moderate SWB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[-0.001, 0.002]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eunmediated\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal indirect effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.002, 0.005]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003esignificant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"6. Discussion","content":"\u003cp\u003eThis study calls into question the Western RCT by demonstrating how institutional structures (hukou system and gender norms) and subjective social mobility expectations systematically shape individual fertility intentions in China. The primary conclusions that were derived from this study were as follows.\u003c/p\u003e \u003cp\u003eThe institution assumes a significant role: hukou (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.053***) and gender (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.042***) have far more influence on fertility intention than income, education level, and age, which indicates that fertility decision in China is mainly driven by institutional environment rather than economic rationality. It is evident that rural families are less constrained by economic rationality in their reproductive decisions, due to collective welfare guarantees such as land use rights and rural healthcare. In contrast, urban families experience instability in their access to education and housing as a result of the hukou system (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eLiu et al., 2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInstitutionally filtered subjective social mobility expectation (SSME): SSME has a positive effect on FI (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.047***), but the transformation of FI by SSME is moderated by hukou and gender. Hukou weakens social mobility responses in rural areas (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013*), which is consistent with the buffer effect of rural hukou welfare on FI studied by \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eLiu(2023)\u003c/span\u003e, while gender amplifies the male response. (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020*** ), which is consistent with fertility norms in China patrilineal society (Yang, 2004; Cai \u0026amp; Xie, 2024) and with patrilineal norms in Asia that emphasize male heirs to continue the family lineage (Den \u0026amp; H, 2017; Srinivas et al., 2024). At the same time, gender norms also inhibit women's FI, because in the fiercely competitive urban labor market,\" motherhood penalty\", these invisible gender norms, let women bear a heavy reproductive cost (Ji Yingchun et al., 2020). Institutional factors such as hukou and gender norms shape the role of SSMEs on FI, rather than FI in turn shaping SSMEs (Liu Jindong et al., 2023). The reason is that hukou institutions and gender norms exist before individuals form expectations of social mobility (Xie Yu, 2023), and these institutional factors predetermine the impact path of SSMEs on FI by allocating resources and shaping social roles.\u003c/p\u003e \u003cp\u003eInstitutional constraints on emotional resources: Subjective well-being (SWB) mediated the relationship between SSME and FI only in the happiest of individuals (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002*, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), accounting for 4.4% of the total effect. This finding correlates significantly with mainstream positive patterns (Elena et al., 2025; Vignoli et al., 2020), reflecting China's characteristic \"involution\" competition (Chen Rong, 2020; Cai Weicheng \u0026amp; Xie Yu, 2024).\u003c/p\u003e \u003cp\u003eAlthough there is no significant difference (1.78\u0026ndash;1.86) in FI among different SWB groups, this phenomenon reflects the institutional constraints of China's low-fertility society. The Hukou system, gender inequality, and competitive culture restrain FI as a whole and make it rigid. Specifically, rural hukou weakens the impact of social mobility expectations on fertility through welfare buffers (such as land security)(β=-0.013*), decoupling fertility decisions from economic rationality. Gender norms amplify men's ability to translate expectations of social mobility into reproductive intentions, reflecting the logic of intergenerational investment in paternal culture. SWB mediated FI weakly ( 4.4% ) only when it exceeded the threshold (top 18.44%), highlighting the polarization of emotional resources allocation in high-pressure environments.\u003c/p\u003e \u003cp\u003eThese findings suggest that institutional factors continue to influence individual fertility decisions by reshaping the psychological mechanism formed by willingness, resulting in the difficulty of improving overall FI. Based on these findings, the \"institutional embeddedness model\" proposed in this study reveals that hukou and gender norms influence reproductive decision-making through dual paths: a macro-level-resource allocation path, institutions (hukou and gender norms) stratify reproductive resources by allocating resources and enforcing social roles. Hukou, for example, determines access to education through school zoning (Wenjing et al., 2019), while gendered workplace policies limit the use of parental leave (Yu \u0026amp; Cheng, 2024). At the meso level, the cognitive construction path: social norms asymmetrically transform social mobility into fertility intention. Men showed a stronger response (β\u0026thinsp;=\u0026thinsp;0.020***), viewing children as investments in social advancement (Vignoli et al., 2020), while women faced lower FI due to \"motherhood penalty\"(Oriel, 2019; Yu \u0026amp; Cheng, 2024). Micro level-Affective threshold adjustment: Subjective well-being (SWB) mediates FI by 4.4% (β\u0026thinsp;=\u0026thinsp;0.002*) only among the happiest of individuals.\u003c/p\u003e \u003cp\u003eConsistent with Goldin's \"motherhood penalty\" and \"paternal bonus,\" this study found that Chinese men showed stronger fertility intentions (β\u0026thinsp;=\u0026thinsp;0.042 ), but the CFPS2018 data showed that paternal participation accounted for only of maternal participation in parenting (Zhang Lu et al., 2023). This \"will-act split\" is common in low-fertility societies around the world. However, China is unique in that gender role differences are further institutionalized and reinforced through the hukou system: in rural areas, land rights are directly bound to paternal inheritance systems (Qu Xiangfei, 2019), systematically reinforcing son preferences and traditional gender division patterns (Fei \u0026amp; Li, 2025). In urban areas, highly educated women face a \"hukou\u0026thinsp;+\u0026thinsp;sex\" double discrimination: research has found that for every additional child, women's wage income drops by an average of about 7.4%, while men's wage income is almost unaffected (Li \u0026amp; Feng, 2023). The findings indicate that gender inequality in China is not solely attributable to cultural lag or market failure, but is also the consequence of formal and informal institutions. The hukou system has been identified as a factor that binds welfare rights to gender roles, thereby reinforcing the prevailing gender division of labor. This finding not only lends further support to Golding's core view that gender division of labor affects reproductive decision-making but also advances the theoretical understanding of how institutional factors shape gender division of labor. Furthermore, it provides a more complete \"institution-gender\" analytical framework for understanding low fertility.This institution-gender interaction is visualized in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eChina's fertility intention under institutional constraints has three unique characteristics that distinguish it from other countries in the West and East Asia:\u003c/p\u003e \u003cp\u003e(1) Hukou creates institutional welfare cliffs, not economic gradients. Rural hukou holders have higher FIs due to land security and collective health care benefits (β\u0026thinsp;=\u0026thinsp;0.053**, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), in sharp contrast to Western income-driven models (Schellekens \u0026amp; Poppel, 2012;Becker, 1960).\u003c/p\u003e \u003cp\u003e(2) Gender differences in FIs between urban and rural areas persist, with CGSS2012-2021 data showing that rural hukou men have a higher ideal number of children (2.15) than women (1.80, gap\u0026thinsp;=\u0026thinsp;0.35), which may be influenced by paternal inheritance norms (Lui \u0026amp; Chan, 2020). In urban settings, the gender gap narrowed (men\u0026thinsp;=\u0026thinsp;1.86, women\u0026thinsp;=\u0026thinsp;1.58) and education levels were negatively correlated with FIs (β= -0.014**, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), consistent with Western and East Asian studies reflecting the \"substitution effect of education and fertility\"(Schellekens \u0026amp; Poppel, 2012; Hellstrand et al., 2021; Bloom et al., 2024).\u003c/p\u003e \u003cp\u003e(3) Happiness must exceed the \"highest threshold\"(SWB of the highest 18.44 percent of the sample, β\u0026thinsp;=\u0026thinsp;0.002*, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 ) to barely offset institutional stress. Traditionally, FIs are linearly related to subjective well-being (Arnstein et al. 2015; Kohler \u0026amp; Mencarini 2016). However, China's \"996\" labor culture polarizes this relationship-only the happiest 18.44 percent overcome competitive pressures to have children, consistent with South Korea and Japan's competition-driven pattern of only extremely happy people having children (Shin \u0026amp; Bang, 2009; Naohiro Ogawa, 2010).\u003c/p\u003e"},{"header":"7. Conclusions and recommendations","content":"\u003cp\u003eBased on the data of CGSS from 2012 to 2021, this study deeply analyzes the influence of institutional constraints (hukou system, gender norms) and subjective well-being on FI of the child-bearing population, constructs an \"institutional embeddedness model,\" and reveals the unique social logic of fertility decision-making in China. The results show that the institutional environment dominates FI: hukou institution and gender norms significantly affect FI through resource allocation and social role shaping; institutions regulate SSME: SSME's positive influence on FI is moderated by hukou and gender; emotional resources are constrained by institutions: subjective well-being only plays a weak mediating role in FI in extremely happy groups. These findings challenge the universality of Western rational choice theory and provide a localized theoretical framework for understanding the phenomenon of low fertility in China. The results of this study have important policy implications for China facing the challenge of fertility decline, as follows:\u003c/p\u003e \u003cp\u003eFirst, promote hukou system reform. The hukou system, as a core resource allocation mechanism, significantly affects FI. The FI of rural hukou is higher because of land and collective welfare, but the SSME has a weak promotion effect on FI, while the FI of urban hukou is suppressed because of high living costs and a fierce competition environment. Therefore, hukou system reform should be gradually promoted, hukou barriers should be broken down, and equalization of public services should be realized. For example, we can learn from Shanghai's residence permit system to provide basic public services such as education and medical care for the floating population, improve their quality of life and sense of belonging, and thus promote FI promotion. The research of Li Yonghui et al.(2021) shows that the FI of the floating population with affordable housing has increased significantly, which indicates that social welfare measures can effectively improve the quality of life and sense of belonging of the floating population. Similarly, the equalization of education and health care through the residence permit system will also help migrant populations better integrate into urban life and improve their overall quality of life.\u003c/p\u003e \u003cp\u003eSecond, optimize gender equality policies. Gender norms play an important role in reproductive decision-making in China. Men are influenced by paternal culture, FI is stronger, and SSME promotes FI more significantly; women face a FI gap due to \"maternal punishment\". Therefore, we should promote gender equality in the workplace, eliminate gender discrimination, and reduce women's child-rearing burden. For example, we can learn from the Nordic \"father quota\" model and promote non-transferable paternity leave, requiring men to take the prescribed paternity leave to encourage men to participate more in the childcare process and reduce the burden of childcare for women, thus promoting gender equality and FI promotion. Studies have shown that Norway's \"father quota\" policy significantly increases male participation in childcare, reduces gender inequality in childcare responsibilities, and provides more balanced support for family fertility (Naz, 2010).\u003c/p\u003e \u003cp\u003eFinally, promote subjective well-being. Subjective well-being plays an important role in FI formation, but its influence is only significant in the extremely happy group. This shows that in order to improve FI, it is necessary to enhance the subjective well-being of society as a whole, relieve the pressure of social competition, and create a friendly reproductive environment. Specific measures include: providing child-care subsidies for couples, combining with community child-care centers and peer support centers, learning from Japan's child-care support policy system, and improving family FI through three core strategies: balancing work and life, supporting family child-care, and changing marriage and child-bearing concepts. Specifically, the conflict between women's career development and child-rearing responsibilities has been effectively mitigated through measures such as providing child-rearing subsidies, establishing community-based child-rearing centers, and encouraging enterprises to implement flexible working systems. At the same time, the enhancement of community support and the provision of childcare guidance serve to alleviate the burden of childcare on families, thereby enhancing their subjective well-being. Furthermore, through publicity and educational activities, traditional social attitudes towards marriage and childbirth are gradually being transformed to create a more favourable environment for childbirth. These comprehensive measures have been shown to alleviate the family's child-rearing difficulties and reverse the negative fertility concept from the root, thus significantly improving FI.\u003c/p\u003e \u003cp\u003eAlthough this study advances our understanding of institutional effects, there are still some limitations. First, the fuzziness of causality: cross-sectional data cannot determine chronological order. Although we mitigate this problem by exploiting the exogeneity of hukou and gender (innate attributes), by exploiting the foresight of SSMEs (measured as future expectations), and by emphasizing \"structure over selection\" based on institutional theory. However, future longitudinal studies (e.g., CLDS panel data) are needed to determine causality. Second, numbers and generational gaps: The analysis did not consider the role of social media in shaping fertility norms (e.g.,\"infertility anxiety\" narratives on TikTok) or shifts in intergenerational values (e.g., post-90s attitudes toward marriage). Finally, timeliness of policy: Data on the enhancement of the three-child policy predate 2023, and future research is needed to assess the effectiveness of the policy. Future research should employ experimental designs (statement preference surveys) in order to quantify the marginal effects of institutional change. In addition, the integration of hybrid approaches is advised in order to capture cultural nuances and to explore digital age phenomena such as algorithmic information cocoons. Further elucidation can be gained from comparative studies of East Asian welfare states (e.g., China and Japan) on the matter of how institutional configurations drive fertility outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJ.Y.L. designed and performed all experiments, analyzed the data, prepared the figures and wrote the manuscript.Z.F. and H.H. supervised the project, provided critical guidance and revised the manuscript.All authors reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThis study uses publicly available micro-data from the Chinese General Social Survey (CGSS) 2012, 2015, 2017, 2018 and 2021 waves. The datasets can be downloaded free of charge after registration at http://cgss.ruc.edu.cn/. All data cleaning and analyses were conducted in SPSS 26; the exact point-and-click sequence is described in the supplementary README to enable replication. No executable scripts are provided.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eZhou, C. (2015). Quantitative analysis of the relationship between economic and social development and fertility change. \u003cem\u003ePopulation Research\u003c/em\u003e, \u003cem\u003e39\u003c/em\u003e(2), 40\u0026ndash;47.\u003c/li\u003e\n \u003cli\u003eShi, Z., Shao, X., Wang, Z., \u0026amp; Zheng, L. (2022). Fertility intentions of urban and rural residents under the three-child policy. \u003cem\u003ePopulation Journal, 44(3)\u003c/em\u003e, 1\u0026ndash;18. https://doi.org/10.16405/j.cnki.1004-129X.2022.03.001.\u003c/li\u003e\n \u003cli\u003eNee, V., \u0026amp; Ingram, P. 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(2025) Periods of High Uncertainty: How Fertility Intentions in Russia Changed During 2022\u0026ndash;2023, Demographic research, 52.29: 939-970.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"fertility intention, hukou system, subjective expectations of social mobility, subjective well-being, institutional embedding","lastPublishedDoi":"10.21203/rs.3.rs-8361693/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8361693/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eChina's fertility rate continues to decline, yet rural populations with lower average incomes exhibit stronger fertility intentions (FI) than their urban counterparts. This \"low-income, high-intention\" phenomenon poses a challenge to traditional economic theories. This study addresses this puzzle by investigating Chinese fertility decision-making through the integrated lens of \"institutional embeddedness\". Drawing on five waves of the Chinese General Social Survey (CGSS, 2012-2021) and employing hierarchical regression and mediation analysis, it examines how the hukou system, gender norms, and subjective well-being interact to shape fertility intentions. It demonstrates that institutional structures, rather than economic calculus alone, are the dominant drivers. This occurs because the hukou system creates a resource buffer in rural areas, while gender norms asymmetrically channel men’s and women’s social mobility expectations into reproductive plans. These findings challenge the generalizability of rational choice models, highlight the deterministic role of locally embedded institutions on demographic outcomes, and provide a more refined theoretical framework for policy design.\u003c/p\u003e","manuscriptTitle":"The Mechanisms of China's Fertility Intentions from an Institutional Embeddedness Perspective: Gender Norms, Hukou Segmentation Pathways, and Policy Implications","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-18 10:19:42","doi":"10.21203/rs.3.rs-8361693/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-17T04:24:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-29T18:31:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"333990751362663161619043899269884087545","date":"2026-03-18T04:15:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"207135959311103715826285826061224883199","date":"2026-03-17T19:38:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-16T13:29:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-16T13:28:44+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-06T16:50:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-29T06:13:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2025-12-29T06:05:28+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":"f99abda5-3282-4a82-b80c-e0fc96fa6aff","owner":[],"postedDate":"March 18th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":64678975,"name":"Social science/Anthropology"},{"id":64678976,"name":"Social science/Development studies"},{"id":64678977,"name":"Business and commerce/Economics"},{"id":64678978,"name":"Social science/Economics"},{"id":64678979,"name":"Social science/Sociology"}],"tags":[],"updatedAt":"2026-03-18T10:19:43+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-18 10:19:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8361693","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8361693","identity":"rs-8361693","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}