Age Discrimination, Personal wellbeing, and Fertility Intentions: Evidence from the 2021 Chinese Social Survey

Age Discrimination, Personal wellbeing, and Fertility Intentions: Evidence from the 2021 Chinese Social Survey | 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 Age Discrimination, Personal wellbeing, and Fertility Intentions: Evidence from the 2021 Chinese Social Survey Yajie Wang, Jianguo Sun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6672477/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The decline in fertility rates has emerged as a critical policy challenge for governments worldwide. This paper provides the first empirical analysis of the impact of perceived age discrimination on fertility intentions, leveraging data from the 2021 Chinese Social Survey. We document a significant negative relationship between perceived age discrimination and fertility intentions. Further analysis reveals that individuals who report higher levels of perceived age discrimination face a greater risk of short-term unemployment and experience declines in life expectancy, life satisfaction, and overall well-being. Structural equation modeling indicates that the adverse effect of age discrimination on fertility intentions is primarily mediated through reductions in personal well-being. Subsample analysis shows that, among individuals under 45, men exhibit stronger fertility intentions than women. These findings highlight the broader demographic and economic implications of labor market age discrimination. Social science/Development studies Social science/Economics Social science/Sociology Fertility intentions Age discrimination 35-year-old Personal wellbeing China Figures Figure 1 1. Introduction Declining fertility rates have become a pressing policy concern in many countries, including China. In 2024, China’s crude birth rate fell to 6.77‰, while the natural population growth rate declined to -0.99‰, signaling an accelerating demographic contraction. Despite sustained government efforts to incentivize childbirth through pronatalist policies, sub-replacement fertility and rapid population aging remain persistent challenges. These demographic shifts have far-reaching economic consequences, dampening aggregate demand, weakening long-term growth expectations, and eroding domestic investor confidence. In turn, capital outflows and the emigration of highly skilled and affluent individuals have further exacerbated structural economic pressures. If unaddressed, these trends could significantly undermine China’s productive capacity and global economic standing in the coming decades. A vast literature has explored the determinants of fertility, highlighting the roles of economic growth (Chatterjee and Vogl, 2018 ), income shocks (Alam and Pörtner, 2018 ), commodity price cycles (Gallego and Lafortune, 2023 ), and housing market dynamics (Liu et al., 2023 ; Atalay et al., 2021 ; Daysal et al., 2021 ; Liu et al., 2020 ). However, fertility decisions are influenced by a broader set of factors beyond these well-documented economic variables. In China, two key drivers of declining fertility rates have been widely recognized. First, the absolute number of young adults and women of childbearing age is shrinking. Second, societal preferences regarding marriage and childbearing are shifting, with rising rates of singlehood and a growing inclination toward childlessness. Against this backdrop, understanding fertility intentions among younger cohorts is critical for assessing the trajectory of China’s demographic transition. This paper introduces a novel perspective by examining the role of perceived age discrimination in the labor market as a determinant of fertility intentions. Labor market discrimination remains a central issue in both economics and sociology, with extensive research documenting disparities based on gender (e.g., Green, 2023 ; Goldin, 2021 ; Goldin and Lawrence, 2018 ; Horrell and Oxley, 2016 ), race (e.g., Agan and Starr, 2018 ; Glover et al., 2017 ; Charles and Guryan, 2008 ; Bertrand and Mullainathan, 2004 ), and body weight (e.g., Kesaite and Greve, 2024 ; Mukhopadhyay, 2021 ; Campos-Vazquez and Gonzalez, 2020 ). Age discrimination, while widely acknowledged, has been studied primarily in the context of older workers (e.g., Carlsson and Eriksson, 2019 ; Neumark et al., 2019 ; Neumark and Song, 2013 ; Johnson and Neumark, 1997 ), with findings indicating that older individuals face lower employment prospects and a higher risk of involuntary retirement. In contrast, relatively little attention has been paid to age discrimination among mid-career workers and its broader socioeconomic consequences. China’s labor market presents an unusual case, where age discrimination manifests earlier than in many other economies. In the United States, for instance, workers in their 50s are often considered to be in their professional prime. By contrast, in China, the peak of the earnings curve shifted as early as the 2010s to age 35 (Fang and Qiu, 2023 ). The so-called “35-year-old threshold” has become a defining feature of the Chinese labor market, with large firms frequently using this age as a cutoff for layoffs and many civil service and public-sector recruitment exams imposing strict age limits. While national labor laws formally prohibit age-based hiring restrictions, job listings frequently impose implicit barriers at this threshold. A 2023 survey by Zhaopin, one of China’s largest recruitment platforms, found that 60.2% of respondents identified age discrimination as the most pressing challenge in employment, while 85% of white-collar workers reported encountering the 35-year-old hiring barrier. Given that individuals in their mid-30s are typically considered to be in the prime of their careers, the economic and social implications of this form of discrimination warrant closer scrutiny. From a theoretical perspective, standard economic and behavioral models suggest that perceived age discrimination could influence fertility decisions through multiple channels (e.g., Becker, 1971 ; Becker, 1993 ; Ajzen and Klobas, 2013 ). Employment uncertainty, income instability, and career concerns may directly lower fertility intentions, while declines in perceived job security and life satisfaction could indirectly affect reproductive choices. While anecdotal reports suggest that labor market barriers at age 35 discourage young adults from marriage and childbearing, empirical evidence on this issue remains scarce. This paper seeks to fill this gap by leveraging data from the 2021 Chinese Social Survey (CSS) to examine the prevalence of perceived age discrimination and its impact on fertility intentions. The study further investigates the mechanisms through which labor market age discrimination influences reproductive decisions, shedding light on the broader implications for demographic and economic policy. This study makes three substantive contributions to the literature. First, we provide the first systematic evidence on age-based discrimination in developing economies’ labor markets, extending the established literature on wage and employment discrimination to a critical but understudied demographic dimension. Our findings reveal distinct patterns of age discrimination that diverge from those observed in advanced economies. Second, we establish a robust, causal relationship between age-35 labor market discrimination and fertility intentions—a novel mechanism that helps explain the persistent fertility decline in China beyond traditional economic and cultural explanations. Third, our analysis uncovers an important life-cycle dimension to demographic research: mid-career labor market vulnerabilities emerge as significant predictors of fertility outcomes, suggesting that conventional models of fertility behavior may need to account for career-cycle effects in rapidly aging societies. The results provide a microfoundation for understanding how labor market institutions interact with demographic transitions. Forth, by exploring the connection between emerging forms of age discrimination in midlife and fertility outcomes, this study offers policy recommendations for addressing fertility challenges in developing countries. The paper is organized as follows. Section 2 provides a detailed overview of the sample, variables, and model specifications used in the analysis. Section 3 presents the empirical results on the effect of perceived age discrimination on fertility intentions, including IV estimations, subsample regressions and robustness checks. Section 4 examines the mechanisms through which perceived age discrimination influences fertility intentions. The final section concludes by summarizing the main findings and their implications. 2. Research Design 2.1 Sample The data used in this study come from the Chinese Social Survey (CSS), a large-scale, nationally representative longitudinal probability sampling project conducted by the Institute of Sociology at the Chinese Academy of Social Sciences. The survey covers all 31 provinces, autonomous regions, and municipalities in China, with each round of data collection involving 7,000 to 10,000 households. We use the most recent survey data from 2021, which includes 10,136 households. After excluding samples with unclear responses to questions regarding age discrimination, our final sample consists of 9,495 observations. In the whole sample, approximately 28% of individuals report no perceived age discrimination, 39% perceive age discrimination but consider it mild, and 33% perceive it as serious. Figure 1 . presents the distribution of perceived age discrimination across different age groups. Notably, individuals in their 20s and 30s report a significantly lower proportion of never experiencing age discrimination compared to other age groups. This pattern supports the existence of the “35-year-old crisis” in Chinese society. Figure 1 . presents the distribution of perceived age discrimination across different age groups. Notably, individuals in their 20s and 30s are significantly less likely to report never having perceived age discrimination compared to other age groups. This pattern supports the existence of the “35-year-old threshold” in Chinese society. 2.2 Variables The primary dependent variable in this study is Children, which represents an individual’s fertility intention, measured by the number of children an individual considers ideal for a family. The key independent variable is Age Discrimination, which captures individuals’ perceptions of age discrimination in society. This variable is categorical and is coded as follows: 1 indicates no perceived age discrimination, 2 indicates perceived mild age discrimination, and 3 indicates perceived severe age discrimination. We control for a variety of sociodemographic and economic factors, including age, gender, education level, marital status, ethnicity, party membership, household registration system, religious belief, employment status, household size, household properties, and family income balance. A detailed description of all variables is provided in Table 1 . Table 1 Variable definitions. Variable Type Definitions Children Count Variable Fertility intention: Desired number of children in a family Current_Children Count Variable Alternative measure of fertility intention: current number of children in the family Age_Discrimination Ordinal Variable Perceived social age discrimination: 1 = no age discrimination, 2 = mild age discrimination, 3 = severe age discrimination Age Continuous Variable Individual’s age (years) Gender Categorical Variable Gender: 1 = male, 0 = female Education_Level Ordinal Variable Educational attainment (scale: 1–4, where 1 = lowest, 4 = highest) Marital_Status Categorical Variable Marital status: 1 = married, 0 = unmarried/divorced/widowed Ethnicity Categorical Variable Ethnicity: 1 = Han ethnic group, 0 = otherwise Party_Membership Categorical Variable Communist Party membership: 1 = member, 0 = non-member Hukou_Type Categorical Variable Household registration type: 1 = rural, 0 = non-rural Religious_Belief Categorical Variable Religious belief: 1 = religious, 0 = non-religious. Employment_Status Categorical Variable Current employment status: 1 = employed, 0 = unemployed Household_Size Count Variable Number of members currently living in the household Job_Loss_Risk Ordinal Variable Likelihood of job loss within six months(scale: 1–5, where1 = very unlikely, 5 = very likely) Household_Properties Count Variable Number of properties owned by the household Family_Income_Balance Ordinal Variable Family income balance last year: 1 = deficit, 2 = balanced, 3 = surplus Life_Satisfaction Ordinal Variable Overall life satisfaction (scale: 1–10, where 1 = very dissatisfied, 10 = very satisfied) Personal_Wellbeing Ordinal Variable Personal wellbeing (scale: 1–4, where 1 = unhappy, 4 = very happy) Life_Expectation Ordinal Variable Likelihood of life deterioration within five years (scale: 1–5, where 1 = very unlikely, 5 = very likely) 2.3 Model Given that the dependent variable is count data, directly applying linear regression could yield negative predicted values and violate the distributional properties of count variables. To address this, we employ a Poisson regression model, which is well-suited for modeling the mean of count outcomes and assumes that the dependent variable follows a Poisson distribution. The model is specified as follows: where \(\:{\lambda\:}_{i}\) = E( \(\:{Child}_{i})\) denotes the expected number of children for individual i, \(\:{\theta\:}_{province}\) represents province fixed effects, controlling for regional differences such as policy and cultural factors. 3. Emperial Results 3.1 Summary Statistics Descriptive statistics for the variables are presented in Table 2 . A key assumption of the Poisson regression model is that the mean and variance of the dependent variable are equal. However, as shown in Table 2 , there is a slight discrepancy between the mean and variance of the dependent variable, Children, which may violate this assumption. This deviation could lead to inefficiency or bias in the estimation of the regression coefficients. To address this issue and ensure the robustness of our results, we apply robust standard errors in the regression analysis, which help mitigate the potential impact of heteroscedasticity and provide more reliable inference. Table 2 Summary statistics. Variable Obs Mean Std. dev. Min Max Children 9,495 2.1264 1.0370 0 10 Current_Children 9,495 1.5488 1.0581 0 9 Age_Discrimination 9,495 2.0559 0.7787 1 3 Age 9,495 45.9588 14.4860 18 70 Gender 9,495 0.4473 0.4972 0 1 Education_Level 9,493 1.8929 1.2244 0 4 Marital_Status 9,495 0.7786 0.4152 0 1 Ethnicity 9,495 0.9120 0.2834 0 1 Party_Membership 9,495 0.1036 0.3048 0 1 Hukou_Type 9,495 0.6433 0.4791 0 1 Religious_Belief 9,495 0.1368 0.3437 0 1 Employment_Status 9,494 0.5299 0.4991 0 1 Household_Size 9,495 4.5736 2.0817 1 25 Job_Loss_Risk 4,068 2.4676 1.3618 1 5 Household_Properties 9,471 1.2193 0.6233 0 11 Family_Income_Balance 9,241 1.8587 0.7769 1 3 Life_Satisfaction 4,706 7.3668 2.1670 1 10 Personal_Wellbeing 4,773 3.3738 0.6773 1 4 Life_Expectation 8,758 2.2296 1.0063 1 5 3.2 Baseline Results The regression results examining the relationship between perceived age discrimination and fertility intentions are presented in Table 3 . Columns (1) and (2) report Poisson regression results, while Columns (3) and (4) display the results from negative binomial (NB) regression, and Columns (5) and (6) show the ordinary least squares (OLS) results. For each model, Column (2), Column (4), and Column (6) include province fixed effects to account for regional variations in policies and cultural factors. In terms of the key explanatory variables, the coefficient for mild age discrimination is -0.0247 (Column 1, Poisson), indicating that individuals perceiving mild age discrimination have a predicted reduction of approximately 2.47% [1 – exp (− 0.0247) = 2.47%] in the expected number of ideal children. 1 After controlling for province fixed effects in Column (2), the coefficient decreases slightly to -0.0195, suggesting that individuals perceiving mild age discrimination experience a reduction of 1.93% in fertility intentions. Similarly, for severe age discrimination, the coefficient is -0.0326 in Column (1) and − 0.0265 in Column (2), indicating reductions of 3.26% and 2.62%, respectively, in the expected number of ideal children. These findings demonstrate that perceived age discrimination, particularly when severe, negatively affects fertility intentions, with stronger discrimination leading to larger reductions in fertility expectations. The NB regression results in Columns (3) and (4) corroborate the Poisson regression findings. The coefficients for mild and severe age discrimination are consistent across both models, with the expected reductions in fertility intentions being approximately 2.47% and 3.26% in Column (3) and 1.93% and 2.62% in Column (4). Similarly, the OLS results presented in Columns (5) and (6) further confirm the robustness of the Poisson and negative binomial findings. The coefficients for mild and severe age discrimination correspond to reductions in fertility intentions of approximately 5.30% and 6.99% in Column (5), and 4.30% and 5.81% in Column (6). In addition to the age discrimination variables, several control variables are included in the regressions. Age is positively associated with fertility intentions, with coefficients suggesting that older individuals tend to have higher fertility expectations. Ethnicity also shows a significant negative association with fertility intentions, indicating that individuals from minority ethnic groups report lower fertility intentions. Other significant variables include hukou type, marital status, and household size, with results suggesting that those with urban hukou, married individuals, and larger households generally report higher fertility intentions. Overall, the regression results consistently show that perceived age discrimination is negatively associated with fertility intentions, highlighting the importance of addressing age-related discrimination in policies aimed at improving fertility outcomes. Furthermore, the robustness of these findings is confirmed across different regression models, providing strong evidence for the impact of age discrimination on fertility intentions. Table 3 The effect of perceived age discrimination on fertility intentions. Poisson Poisson NB NB 0LS 0LS (1) (2) (3) (4) (5) (6) Mild age discrimination -0.0247** (0.0119) -0.0195* (0.0117) -0.0247** (0.0119) -0.0195* (0.0117) -0.0530** (0.0258) -0.0430* (0.0252) Severe age discrimination -0.0326** (0.0130) -0.0265** (0.0128) -0.0326** (0.0130) -0.0265** (0.0128) -0.0699*** (0.0280) -0.0581** (0.0278) Gender 0.0066 (0.0103) 0.0024 (0.0102) 0.0066 (0.0103) 0.0024 (0.0102) 0.0136 (0.0218) 0.0053 (0.0217) Age 0.0041*** (0.0004) 0.0047*** (0.0004) 0.0041*** (0.0004) 0.0047*** (0.0004) 0.0089*** (0.0009) 0.0102*** (0.0009) Marital_Status -0.0131 (0.0144) -0.0075 (0.0142) -0.0131 (0.0144) -0.0075 (0.0142) -0.0406 (0.0303) -0.0280 (0.0300) Ethnicity -0.0865*** (0.0178) -0.0591*** (0.0219) -0.0865*** (0.0178) -0.0591*** (0.0219) -0.1969*** (0.0419) -0.1314*** (0.0491) Hukou_Type 0.0743*** (0.0102) 0.0664*** (0.0102) 0.0743*** (0.0102) 0.0664*** (0.0102) 0.1514*** (0.0210) 0.1362*** (0.0210) Party_Membership 0.0208 (0.0166) 0.0111 (0.0165) 0.0208 (0.0166) 0.0111 (0.0165) 0.0426 (0.0350) 0.0216 (0.0349) Religious_Belief 0.0542*** (0.0139) 0.0112 (0.0161) 0.0542*** (0.0139) 0.0112 (0.0161) 0.1190*** (0.0313) 0.0230 (0.0354) Education_Level -0.0028 (0.0048) -0.0005 (0.0047) -0.0028 (0.0048) -0.0005 (0.0047) -0.0034 (0.0098) 0.0018 (0.0096) Household_Properties -0.0007 (0.0101) 0.0058 (0.0100) -0.0007 (0.0101) 0.0058 (0.0100) -0.0028 (0.0212) 0.0108 (0.0212) Household_Size 0.0333 (0.0030) 0.0243*** (0.0030) 0.0333*** (0.0030) 0.0243*** (0.0030) 0.0773*** (0.0073) 0.0579*** (0.0074) Employment_Status 0.0044 (0.0102) -0.0014 (0.0102) 0.0044 (0.0102) -0.0014 (0.0102) 0.0105 (0.0218) -0.0027 (0.0218) Family_Income_Balance -0.0101 (0.0061) -0.0050 (0.0059) -0.0101* (0.0061) -0.0050 (0.0059) -0.0200 (0.0128) -0.0095 (0.0125) Constant 0.4767*** (0.0367) 0.2911*** (0.0638) 0.4767*** (0.0367) 0.2911*** (0.0638) 1.5308*** (0.0800) 1.1746*** (0.1213) /Lnalpha -30.7648 -38.3569 Alpha 0.0000 0.0000 Province fixed effects NO YES NO YES NO YES R^2 0.103 0.159 0.103 0.159 0.065 0.102 N 9224 9224 9224 9224 9224 9224 Notes : Robust standard errors are reported in parentheses; in the NB regression, the values of alpha and /lnalpha provide insights into the degree of over-dispersion in the data; ∗p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01. 3.3 IV Regression To address potential endogeneity arising from unobserved individual factors influencing perceptions of age discrimination, we employ an instrumental variable (IV) approach. Specifically, we construct an instrument based on the group mean of perceived age discrimination, aggregated according to key individual characteristics, including five-year age cohorts, marital status, education level, ethnicity, hukou type, and province. Each individual is assigned to a unique group based on these attributes, and the within-group mean serves as the instrument. This approach leverages variation at the group level to isolate exogenous sources of variation in individual perceptions, thereby mitigating biases from unobserved heterogeneity. The validity of this instrument hinges on two key assumptions. First, relevance: the group mean reflects the collective perception of age discrimination within the group, ensuring a strong correlation with individual perceptions. Since individuals with similar demographic and socioeconomic characteristics are likely to experience comparable levels of discrimination, the instrument satisfies the relevance condition. Second, and most critically, exogeneity: the instrument must be uncorrelated with unobserved individual characteristics that directly affect the outcome of interest. This exogeneity assumption is justified in two ways. First, because the instrument is constructed from group-level characteristics rather than individual-specific factors, it mitigates concerns that individual perceptions of age discrimination are driven by idiosyncratic, unobserved traits. Second, the grouping characteristics—such as age cohort, education level, and province—are predetermined and largely exogenous to individual perceptions, further ensuring that the instrument captures exogenous variation rather than endogenous individual differences. Additionally, since the instrument averages out idiosyncratic reporting biases within each group, any remaining variation reflects broader social and structural factors rather than personal predispositions. By leveraging this IV approach, we mitigate potential biases arising from individual-level endogeneity and establish a more credible identification strategy for estimating the causal impact of perceived age discrimination on fertility preferences. To ensure the validity of our instrument, we first conduct a first-stage regression. The results confirm the instrument’s relevance, as it exhibits a significantly positive coefficient at the 1% level, with an F-statistic of 746—well above conventional thresholds for weak instrument concerns. Given that our dependent variable, the ideal number of children, is a count variable, we proceed with an IV Poisson regression to obtain consistent estimates. The IV results, presented in Table 4 , reinforce our initial findings: individuals who perceive greater age discrimination tend to prefer fewer children. Specifically, the estimated coefficient implies that for each unit increase in perceived age discrimination, the ideal number of children decreases by approximately 4.85%. This result underscores the negative association between age discrimination perceptions and fertility preferences, highlighting the broader social and psychological implications of age-related biases. Table 4 IV results of age discrimination’s effect on fertility intentions. Variable IV Poisson (1) IV Poisson (2) IV-age discrimination -0.0524*** (0.0129) -0.0488*** (0.0123) Gender 0.0140 (0.0103) Party_Membership 0.0174 (0.0161) Religious_Belief 0.0636*** (0.0141) Household_Properties -0.0064 (0.0100) Household_Size 0.0394*** (0.0029) Employment_Status -0.0010 (0.0101) Family_Income_Balance -0.0291*** (0.0062) Cons 0.8613*** (0.0269) 0.7127*** (0.0347) N 9224 9226 Notes : Robust standard errors are reported in parentheses; the IV Poisson regression does not directly provide goodness-of-fit measures such as R-squared; ∗p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01. 3.4 Subsample Regression Given our focus on examining the impact of age discrimination at 35 on fertility—and acknowledging the age-specific nature of fertility behavior, where individuals aged 45 and younger are the primary group for marriage and childbearing—we divide the sample into two groups: those aged 18–45 and those aged 45–70. Table 5 presents the results of these subsample regressions. Columns (1) and (2) reveal that the effect of age discrimination on fertility differs slightly between the two groups. Among individuals aged 18–45, severe age discrimination has a significant negative impact on fertility preferences, whereas mild discrimination shows no significant effect. In contrast, for individuals aged 45 and older, mild age discrimination appears to influence fertility preferences, while severe discrimination does not. This pattern suggests that, in the older age group, fertility preferences may be shaped by factors beyond age discrimination, such as broader social norms, financial considerations, or health constraints. Column (3) of Table 5 presents the IV regression results for individuals aged 18–45, further illustrating the impact of perceived age discrimination on fertility preferences. A one-unit increase in perceived age discrimination is associated with a 3.43% decline in the expected ideal number of children, translating to an approximate reduction of 0.07 children based on the sample mean of 1.994. This finding underscores the negative influence of age discrimination on younger individuals’ fertility aspirations. Moreover, the results indicate a significant gender difference in fertility preferences. The ideal number of children is notably higher for men than for women, with a coefficient of 0.0271, suggesting that men’s expected ideal number of children is 2.75% greater than that of women. This gender disparity is particularly pronounced among younger individuals, highlighting the role of traditional gender norms and expectations in shaping fertility intentions. Societal factors, such as differing familial responsibilities, career considerations, and social pressures, may contribute to this divergence in fertility preferences between men and women. However, this gender gap diminishes in the older cohort, where the difference in fertility preferences between men and women is no longer statistically significant. The differing gender gaps in fertility intentions between younger and older cohorts are likely driven by changes in fertility preferences among younger women. Goldin ( 2024 ) indicates that the fundamental divergence in fertility intentions between men and women stems from the greater childcare responsibilities typically assumed by women, which often requires them to make career sacrifices and face heightened economic vulnerability. Compared to earlier generations, modern women are increasingly required to balance their careers with family responsibilities, leading to more cautious fertility decisions. As a result, the gender gap in fertility intentions is more pronounced in the younger cohort, while it tends to narrow in the older cohort. This trend not only reflects the evolution of gender roles and social structures but also suggests that fertility decisions are becoming more profoundly influenced by economic and social factors, as female labor force participation increases and gender equality norms become more widespread. Table 5 Subsample results of age discrimination’s effect on fertility intentions. Variable Poisson Poisson IV Poisson IV Poisson (1) 18–45 (2) 45–70 (3) 18–45 (4) 45–70 Mild age discrimination -0.0104 (0.0150) -0.0317* (0.0166) Severe age discrimination -0.0446** (0.0171) -0.0126 (0.0171) IV- age discrimination -0.0349** (0.0169) -0.0598*** (0.0171) Age 0.0036** (0.0012) 0.0079*** (0.0011) Marital_Status 0.0122 (0.0192) 0.0009 (0.0234) Ethnicity -0.0595** (0.0264) -0.0511 (0.0326) Hukou_Type 0.0299** (0.0127) 0.0980*** (0.0162) Education_Level -0.0084 (0.0056) 0.0089 (0.0090) Gender 0.0325** (0.0132) -0.0191 (0.0148) 0.0271** (0.0130) -0.0038 (0.0150) Party_Membership 0.0161 (0.0221) 0.0137 (0.0229) 0.0070 (0.0218) 0.0128 (0.0221) Religious_Belief 0.0169 (0.0197) 0.0077 (0.0236) 0.0681*** (0.0164) 0.0634*** (0.0210) Household_Properties 0.0166 (0.0138) -0.0038 (0.0139) 0.0044 (0.0135) -0.0190 (0.0142) Household_Size 0.0240*** (0.0036) 0.0224*** (0.0041) 0.0377*** (0.0033) 0.0389*** (0.0038) Employment_Status -0.0109 (0.0140) 0.0144 (0.0157) 0.0158 (0.0121) 0.0013 (0.0145) Family_Income_Balance 0.0002 (0.0078) -0.0088 (0.0087) -0.0191** (0.0083) -0.0251** (0.0089) Cons 0.3652*** (0.1038) 0.0440 (0.0936) 0.5921*** (0.0465) 0.7914*** (0.0476) Province fixed effects YES YES R^2 0.105 0.183 N 4126 5270 4127 5271 Notes : Since the construction of the IV involves provincial-level information, there is no need to control for provincial fixed effects in the model; ∗p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01. 3.5 Robustness Tests To ensure the robustness of our results, we conducted additional analyses by replacing the dependent variable with the actual number of children individuals have, as an alternative measure of fertility preferences. The IV regression results remain consistent, further supporting the robustness of our findings. The detailed results are provided in Table 6 . The regression results for the 18–70 age group show that the perception of age discrimination has a negative and statistically significant impact on fertility intentions (coefficient = -0.0798, p < 0.001). The negative impact of age discrimination on fertility intentions is particularly significant in younger individuals, suggesting that age discrimination affects fertility decisions through multiple mechanisms, including perceived social status, economic security, and self-identity. The differential effects across age groups highlight the complexity of age discrimination's impact on fertility intentions, further confirming the important role of perceived age discrimination in fertility decisions, particularly among younger individuals. The consistency of the IV regression results further supports the robustness of our findings. Table 6 Results of robustness tests. Current_Children Variable (1) 18–70 (2) 18–45 (3) 45–70 IV-age discrimination -0.0798*** (0.0166) -0.1233*** (0.0289) -0.0476** (0.0182) Gender -0.0951*** (0.0138) -0.3165*** (0.0274) -0.0340** (0.0137) Party_Membership 0.0234 (0.0217) -0.0697 (0.0468) -0.0210 (0.0208) Religious_Belief 0.0673*** (0.0187) 0.1376*** (0.0335) 0.0370* (0.0201) Household_Properties -0.0507*** (0.0107) -0.0936*** (0.0211) -0.0271** (0.0105) Household_Size 0.0975*** (0.0030) 0.1306*** (0.0075) 0.0790*** (0.0028) Employment_Status 0.0918*** (0.0136) 0.3517*** (0.0279) 0.0360** (0.0134) Family_Income_Balance -0.1546*** (0.0091) -0.1850*** (0.0170) -0.0873*** (0.0090) Cons 0.4543*** (0.0445) 0.1272 (0.0824) 0.5226*** (0.0481) N 9226 4127 5271 Notes : Robust standard errors are reported in parentheses;∗p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01. 4. Mechanism Analysis The impact of age discrimination on fertility intentions can be analyzed through multiple theoretical lenses. Drawing on insights from economic and sociological research (e.g., Becker, 1971 ; Becker, 1993 ; Ajzen & Klobas, 2013 ), we hypothesize that perceived age discrimination influences fertility intentions through several interrelated mechanisms. First, personal well-being plays a critical role in decision-making, particularly in contexts of social discrimination or exclusion. Both Self-Identity Theory and Social Identity Theory suggest that individuals’ well-being is closely tied to their sense of belonging and social acceptance. When individuals experience age discrimination, their well-being may deteriorate, leading to shifts in attitudes and behaviors, including those related to fertility. A decline in well-being may reduce individuals’ confidence in their future prospects and, consequently, their willingness to have children. Second, expectations about life circumstances are a key determinant of fertility decisions. Expectation Theory posits that individuals’ perceptions of their future shape their current economic behaviors and long-term planning. When individuals anticipate a stable and promising future, they are more likely to invest in family expansion. Conversely, if age discrimination fosters pessimistic expectations—such as concerns about career advancement, financial security, or social standing—individuals may delay or forgo childbearing. Third, life satisfaction is a well-established predictor of fertility intentions. Individuals with higher life satisfaction tend to express greater willingness to have children, as they perceive their current circumstances as conducive to family formation. However, experiencing age discrimination may erode life satisfaction by diminishing individuals’ sense of control and fulfillment, thereby indirectly discouraging childbearing. Fourth, perceived unemployment risk and economic uncertainty represent crucial constraints on fertility decisions. Economic models of fertility emphasize the role of financial stability in shaping reproductive choices. Heightened unemployment risk and income uncertainty typically increase economic pressures, prompting individuals to adopt more cautious financial behaviors, such as postponing or reducing fertility plans. In the context of age discrimination, concerns about job security and career prospects may become more pronounced, leading individuals to prioritize economic survival over family expansion. Taken together, these mechanisms suggest that perceived age discrimination can exert a substantial influence on fertility intentions by shaping individuals’ well-being, future expectations, life satisfaction, and economic security. Understanding these pathways is crucial for policymakers seeking to address the broader social and economic implications of age discrimination. We first examined the impact of perceived age discrimination on individuals’ short-term unemployment expectations, life situation expectations, life satisfaction, and personal wellbeing. Given that the dependent variables are ordinal categorical variables, we employed ordered logit models for regression analysis. The results, presented in Table 7 , align with those of Wei et al. ( 2024 ), indicating that perceived age discrimination significantly increases individuals’ unemployment risk and decreases their expectations for future life, life satisfaction, and personal wellbeing. Table 7 Results of age discrimination on on job loss risk, life expectation, life satisfaction and personal wellbeing. Variable Job_Loss_Risk Life_Expectation Life_Satisfaction Personal_Wellbeing (1) (2) (3) (4) IV- age discrimination 0.2590*** (0.0705) 0.1441*** (0.0479) -0.3738*** (0.0653) -0.4375*** (0.0711) Gender 0.1735*** (0.0577) 0.1177*** (0.0420) -0.1629*** (0.0539) -0.2151*** (0.0599) Party_Membership -0.7878*** (0.0861) -0.0623 (0.0690) 0.6231*** (0.0802) 0.4898*** (0.0919) Religious_Belief -0.0532 (0.0831) -0.2070*** (0.0580) 0.1941** (0.0832) -0.0560 (0.0870) Household_Properties -0.1945*** (0.0441) 0.0100 (0.0327) 0.1434*** (0.0383) 0.0460 (0.0474) Household_Size 0.0174 (0.0158) -0.0350*** (0.0104) -0.0102 (0.0134) 0.0423*** (0.0152) Employment_Status -0.8421*** (0.1097) -0.0528 (0.0413) -0.0120 (0.0539) -0.0087 (0.0587) Family_Income_Balance -0.3947*** (0.0387) -0.2731*** (0.0272) 0.4018*** (0.0347) 0.1602*** (0.0375) R^2 0.031 0.064 0.153 0.121 N 3997 8521 4567 4643 Notes : Robust standard errors are reported in parentheses;∗p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01. To examine the mediating mechanisms underlying the relationship between perceived age discrimination and fertility intentions, we employed Structural Equation Modeling (SEM) for mediation analysis. Given that instrumental variables can effectively mitigate potential endogeneity concerns, we utilized the instrumental variable for age discrimination to measure individuals’ perceptions of age discrimination within the mediation framework. Furthermore, recognizing that the peak fertility age in China is around 28, we restricted our analysis to a subsample of individuals aged 45 and below to ensure the relevance of our findings. After conducting a series of robustness checks, we found that perceived unemployment risk, life satisfaction, and expectations about life circumstances did not significantly mediate the effect of perceived age discrimination on fertility intentions. However, personal self-satisfaction emerged as a key mediating factor. The regression results for personal self-satisfaction as a mediator are presented in Table 8 . Table 8 Mediation Effect of Personal Wellbeing on Fertility Intentions. Personal_wellbeing Children Variable (1) (2) IV- age discrimination -0.1354*** (0.0334) -0.0589 (0.0472) Personal_wellbeing 0.0831** (0.0383) Gender -0.0664** (0.0288) 0.0628 (0.0383) Party_Membership 0.0986** (0.0457) -0.0855* (0.0434) Religious_Belief -0.0559 (0.0423) 0.1158** (0.0546) Household_Properties 0.0088 (0.0221) -0.00298 (0.0443) Household_Size 0.0129 (0.0081) 0.0888*** (0.0113) Employment_Status -0.00095 (0.0286) 0.0515 (0.037) Family_Income_Balance 0.0766*** (0.0176) -0.0173 (0.0231) Cons 3.4094*** (0.0885) 1.4148*** (0.1913) N 2029 2029 Notes : Robust standard errors are reported in parentheses;∗p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01. The results indicate that age discrimination significantly reduces personal wellbeing, with a coefficient of -0.1354 (p < 0.001), suggesting that an increase in age discrimination leads to a marked decline in individuals’ personal wellbeing. In contrast, age discrimination does not have a statistically significant direct effect on fertility intentions, as evidenced by a coefficient of -0.0589 (p = 0.211), implying a minimal and insignificant direct impact on fertility intentions. However, personal wellbeing is found to have a significant positive effect on fertility intentions, with a coefficient of 0.0831 (p = 0.030), indicating that higher personal satisfaction is associated with stronger fertility intentions. Importantly, the impact of age discrimination on fertility intentions is indirect, primarily operating through its effect on personal satisfaction. While the direct effect of age discrimination on fertility intentions is not significant, it reduces personal wellbeing, which, in turn, positively influences fertility intentions. Thus, the primary mechanism through which age discrimination influences fertility intentions is indirect, mediated by its reduction of personal wellbeing. A decline in satisfaction may diminish an individual’s sense of control and psychological resources, thereby influencing decisions related to family planning. These findings highlight the importance of psychological well-being in shaping reproductive choices, particularly in the context of perceived age discrimination. 5. Conclusion Declining fertility rates pose a significant challenge for many countries undergoing economic and social transformations. While traditional factors such as cultural norms, economic conditions, and religious beliefs have been widely studied in the context of fertility decisions, emerging evidence suggests that fertility intentions are also shaped by less conventional influences. This study introduces a novel perspective by examining the role of perceived age discrimination in shaping fertility intentions. Drawing on data from the 2021 CSS, this study investigates the impact of perceived age discrimination—particularly among younger individuals—on fertility decisions. In China’s labor market, concerns over age discrimination have intensified, especially for workers approaching the age of 35. The empirical findings suggest that higher levels of perceived age discrimination are associated with a decline in fertility intentions, indicating that age-related biases may discourage individuals from planning for children. Furthermore, perceived age discrimination is strongly correlated with increased short-term unemployment risk, lower life satisfaction, and diminished personal well-being. These findings highlight the broader consequences of age discrimination, demonstrating its effects beyond immediate employment outcomes and into key aspects of individuals’ overall quality of life. Using structural equation modeling, we further explore the mechanisms through which age discrimination influences fertility intentions. The results reveal that the primary pathway is through reductions in personal well-being. While age discrimination does not exert a statistically significant direct effect on fertility intentions, it significantly undermines individuals’ well-being, which in turn lowers their willingness to have children. This underscores the psychological and emotional toll of age-related discrimination, revealing its indirect but substantial impact on fertility decisions. A subsample analysis further highlights the role of gender in shaping fertility intentions. Among individuals under the age of 45, men exhibit stronger fertility intentions than women, suggesting the presence of gender-specific dynamics in the relationship between age discrimination and reproductive choices. This divergence may stem from differences in how men and women experience and respond to age discrimination in the workplace, influencing their long-term family planning decisions. Given that women often face compounded discrimination related to both age and gender, they may be more susceptible to the negative effects of age-related biases on fertility intentions. The implications of these findings extend beyond individual fertility decisions. Age discrimination not only erodes personal wellbeing but also contributes to inefficient human resource allocation and unfavorable long-term demographic trends. As China and other aging societies grapple with declining birth rates, addressing age-related biases in the labor market becomes a critical policy issue. We recommend that the government establish clear legal protections against age discrimination, recognizing “age” as an independent category of discrimination under labor laws. Additionally, policies should be developed to mitigate age-related biases in hiring and employment practices while simultaneously providing stronger social and economic support for individuals making fertility decisions. By tackling age discrimination at both the legal and institutional levels, policymakers can improve individual wellbeing and promote more balanced and sustainable demographic outcomes. Declarations Author Contribution Yajie Wang wrote the main manuscript text . All authors reviewed the manuscript. Data Availability Sequence data that support the findings of this study can be find in the website of Chinese Social Survey (http://css.cssn.cn/css_sy/zlysj/lnsj/) .The authors have no rights to share the data. References Agan, A., Starr, S (2018). Ban the box, criminal records, and racial discrimination: A field experiment. Quar J Econ 133(1): 191–235. https://doi.org/10.1093/qje/qjx028 Ajzen, I., Klobas, J (2013). Fertility intentions: An approach based on the theory of planned behavior. Demographic Research 8(29):203–232. https://doi.org/10.4054/DemRes.2013.29.8. Alam, S.A., Pörtner, C.C (2018) Income shocks, contraceptive use, and timing of fertility. J Dev Econ 131: 96–103. https://doi.org/10.1016/j.jdeveco.2017.10.007. Atalay, K., Li, A., Whelan, S (2021) Housing wealth, fertility intentions and fertility. J Hous Econ 54:101787. https://doi.org/10.1016/j.jhe.2021.101787. Becker, G.S (1993) Human capital: a theoretical and empirical analysis, with special reference to education. Chicago. https://fudan-primo.hosted.exlibrisgroup.com.cn/permalink/f/11t5l4n/TN_cdi_econis_primary_166157643 Becker, G.S (1971) The Economics of Discrimination. Second Edition. Chicago. Bertrand, M., Mullainathan, S (2004) Are Emily and Greg more employable than Lakisha and Jamal? A field experiment on labor market discrimination. Amer Econ Rev 94(4):991–1013. https://www.aeaweb.org/articles?id=10.1257/0002828042002561 Campos-Vazquez M.R., Gonzalez E (2020) Obesity and hiring discrimination. Econ Hum Biol 5(37):100850. https://doi.org/10.1016/j.ehb.2021.101017 Carlsson, M., Eriksson, S (2019) Age discrimination in hiring decisions: Evidence from a field experiment in the labor market. Lab Econ 59: 173–183. https://doi.org/10.1016/j.labeco.2019.03.002 Charles, K.K., Guryan, J (2008) Prejudice and wages: an empirical assessment of Becker’s The Economics of Discrimination, J Polit Econ 116(5):773–809. Available at: https://doi.org/10.1086/593073. Chatterjee, S., Vogl, T (2018) Escaping Malthus: economic growth and fertility change in the developing world. Ame.Econ.Rev.108(6),1440–1467. https://doi.org/10.1257/aer.20170748. Daysal, N. M., Lovenheim, M., Siersbæk, N., Wasser, D. N (2021) Home prices, fertility, and early-life health outcomes. J Pub Econ 198:104366. https://doi.org/10.1016/j.jpubeco.2021.104366 Fang H, Qiu X-C (2023) Golden ages: a tale of the labor markets in China and the United States. J Polit Econ Macro 1(4):665-706. https://doi.org/10.1086/726843 Gallego, F., Lafortune, J (2023) Baby commodity booms? The impact of commodity shocks on fertility decisions and outcomes. J Popul Econ 36:295–320. https://doi.org/10.1007/s00148-021-00855-0 Glover, D., Pallais, A., Pariente, W (2017) Discrimination as a Self-Fulfilling Prophecy: Evidence from French Grocery Stores. Quar J Econ132(3): 1219–1260. https://doi.org/10.1093/qje/qjx006 Goldin, C (2024) Babies and the Macroeconomy. No. w33311. National Bureau of Economic Research.2024. https://www.nber.org/papers/w33311 Goldin, C (2021) Career and Family: Women’s Century-Long Journey toward Equity. Princeton. Goldin, C., Lawrence F. K (2018) Women Working Longer: Increased Employment at Older Ages first ed. Chicago. Green, T.K (2023) Racial Emotion at Work : Dismantling Discrimination and Building Racial Justice in the Workplace. California. Horrell S., Oxley D (2016) Gender bias in nineteenth-century England: Evidence from factory children. Econ Hum Biol 9(22):47-64. https://doi.org/10.1016/j.ehb.2016.03.006 Johnson, R. W., Neumark, D (1997) Age discrimination, job separations, and employment status of older workers: Evidence from self-reports. J Human Reso 32(4): 779–811. https://doi.org/10.2307/146428 Kesaite V., Greve J (2024) The impact of excess body weight on employment outcomes: A systematic review of the evidence. Econ Hum Biol 8(54):101398. https://doi.org/10.1016/j.ehb.2024.101398 Liu H., Liu L., Wang F (2023) Housing wealth and fertility: evidence from China. J Popul Econ:36 (1),359–395. https://link.springer.com/10.1007/s00148-021-00879-6. Liu, J., Xing C-B, Zhang Q (2020) House price, fertility rates and reproductive intentions. Chi Econ Rev 62:101496. https://www.sciencedirect.com/science/article/pii/S1043951X20300936. Mukhopadhyay S (2021) Do employers discriminate against obese employees? Evidence from individuals who are simultaneously self-employed and working for an employer. Econ Hum. Biol 8(42):101017. https://doi.org/10.1016/j.ehb.2021.101017 Neumark, D., Burn, I., Button, P (2019) Is it harder for older workers to find jobs? New and improved evidence from a field experiment. J Pub Econ 127(2):922–970. https://doi.org/10.1086/701029 Neumark, D., Song, J (2013) Do stronger age discrimination laws make Social Security reforms more effective? J Pub Econ 108:1–16. https://doi.org/10.1016/j.jpubeco.2013.09.006 Wei, J., Gao, W., Ni, C (2024) Age discrimination in the Chinese labor market and its impact: An analysis based on the China Social Survey (CSS) Data. J Popu Econ 3:97-110. https://kns.cnki.net/kcms2/article/abstract?v=7fc2yiS_nyBi6ByYLwttO0PDycHFj5lFsErsqVsH-eTNgdx6Ut-0hm1p_zLnLs7Fk7BDkxc88PE-zg-qlQjV4jPkN6ORts1RL38eHh6leuuVCak2EfUAOFvTFfbSMJRixjblyz3_iTH-e_dbps1MV1CO9csn-QvHzt6tHzuOVDcG_Vj_fdOADucWCNC2Vy98&uniplatform=NZKPT&language=CHS Footnotes In Poisson regression, the coefficient represents how changes in an independent variable affect the log-mean of the dependent variable. Therefore, when interpreting the results, the coefficient is exponentiated (exp) to express its impact. The same applies below, and the calculation method will not be repeated. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6672477","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":488479084,"identity":"0c5a8441-3898-418f-ba15-9d516dc77a50","order_by":0,"name":"Yajie Wang","email":"data:image/png;base64,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","orcid":"","institution":"Fudan University","correspondingAuthor":true,"prefix":"","firstName":"Yajie","middleName":"","lastName":"Wang","suffix":""},{"id":488479085,"identity":"d2ecad3d-0564-407b-ae43-7e0040b30e04","order_by":1,"name":"Jianguo Sun","email":"","orcid":"","institution":"Henan University","correspondingAuthor":false,"prefix":"","firstName":"Jianguo","middleName":"","lastName":"Sun","suffix":""}],"badges":[],"createdAt":"2025-05-15 12:08:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6672477/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6672477/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87260248,"identity":"51146081-ff4e-4aad-8449-6341c856d466","added_by":"auto","created_at":"2025-07-22 07:01:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":24288,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of perceived age discrimination types by age group.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData source\u003c/em\u003e: Calculated based on data from CSS 2021.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6672477/v1/e237866d7c03b1411317c739.png"},{"id":103504489,"identity":"5d43ab74-acf4-4b84-bb20-70f1502fc92b","added_by":"auto","created_at":"2026-02-26 13:20:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1071396,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6672477/v1/513c773c-65aa-4ebf-9140-9c22de62bfc0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Age Discrimination, Personal wellbeing, and Fertility Intentions: Evidence from the 2021 Chinese Social Survey","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eDeclining fertility rates have become a pressing policy concern in many countries, including China. In 2024, China\u0026rsquo;s crude birth rate fell to 6.77\u0026permil;, while the natural population growth rate declined to -0.99\u0026permil;, signaling an accelerating demographic contraction. Despite sustained government efforts to incentivize childbirth through pronatalist policies, sub-replacement fertility and rapid population aging remain persistent challenges. These demographic shifts have far-reaching economic consequences, dampening aggregate demand, weakening long-term growth expectations, and eroding domestic investor confidence. In turn, capital outflows and the emigration of highly skilled and affluent individuals have further exacerbated structural economic pressures. If unaddressed, these trends could significantly undermine China\u0026rsquo;s productive capacity and global economic standing in the coming decades.\u003c/p\u003e\u003cp\u003eA vast literature has explored the determinants of fertility, highlighting the roles of economic growth (Chatterjee and Vogl, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), income shocks (Alam and P\u0026ouml;rtner, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), commodity price cycles (Gallego and Lafortune, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and housing market dynamics (Liu et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Atalay et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Daysal et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, fertility decisions are influenced by a broader set of factors beyond these well-documented economic variables. In China, two key drivers of declining fertility rates have been widely recognized. First, the absolute number of young adults and women of childbearing age is shrinking. Second, societal preferences regarding marriage and childbearing are shifting, with rising rates of singlehood and a growing inclination toward childlessness. Against this backdrop, understanding fertility intentions among younger cohorts is critical for assessing the trajectory of China\u0026rsquo;s demographic transition.\u003c/p\u003e\u003cp\u003eThis paper introduces a novel perspective by examining the role of perceived age discrimination in the labor market as a determinant of fertility intentions. Labor market discrimination remains a central issue in both economics and sociology, with extensive research documenting disparities based on gender (e.g., Green, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Goldin, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Goldin and Lawrence, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Horrell and Oxley, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), race (e.g., Agan and Starr, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Glover et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Charles and Guryan, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Bertrand and Mullainathan, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), and body weight (e.g., Kesaite and Greve, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Mukhopadhyay, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Campos-Vazquez and Gonzalez, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Age discrimination, while widely acknowledged, has been studied primarily in the context of older workers (e.g., Carlsson and Eriksson, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Neumark et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Neumark and Song, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Johnson and Neumark, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), with findings indicating that older individuals face lower employment prospects and a higher risk of involuntary retirement. In contrast, relatively little attention has been paid to age discrimination among mid-career workers and its broader socioeconomic consequences.\u003c/p\u003e\u003cp\u003eChina\u0026rsquo;s labor market presents an unusual case, where age discrimination manifests earlier than in many other economies. In the United States, for instance, workers in their 50s are often considered to be in their professional prime. By contrast, in China, the peak of the earnings curve shifted as early as the 2010s to age 35 (Fang and Qiu, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The so-called \u0026ldquo;35-year-old threshold\u0026rdquo; has become a defining feature of the Chinese labor market, with large firms frequently using this age as a cutoff for layoffs and many civil service and public-sector recruitment exams imposing strict age limits. While national labor laws formally prohibit age-based hiring restrictions, job listings frequently impose implicit barriers at this threshold. A 2023 survey by Zhaopin, one of China\u0026rsquo;s largest recruitment platforms, found that 60.2% of respondents identified age discrimination as the most pressing challenge in employment, while 85% of white-collar workers reported encountering the 35-year-old hiring barrier. Given that individuals in their mid-30s are typically considered to be in the prime of their careers, the economic and social implications of this form of discrimination warrant closer scrutiny.\u003c/p\u003e\u003cp\u003eFrom a theoretical perspective, standard economic and behavioral models suggest that perceived age discrimination could influence fertility decisions through multiple channels (e.g., Becker, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1971\u003c/span\u003e; Becker, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Ajzen and Klobas, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Employment uncertainty, income instability, and career concerns may directly lower fertility intentions, while declines in perceived job security and life satisfaction could indirectly affect reproductive choices. While anecdotal reports suggest that labor market barriers at age 35 discourage young adults from marriage and childbearing, empirical evidence on this issue remains scarce. This paper seeks to fill this gap by leveraging data from the 2021 Chinese Social Survey (CSS) to examine the prevalence of perceived age discrimination and its impact on fertility intentions. The study further investigates the mechanisms through which labor market age discrimination influences reproductive decisions, shedding light on the broader implications for demographic and economic policy.\u003c/p\u003e\u003cp\u003eThis study makes three substantive contributions to the literature. First, we provide the first systematic evidence on age-based discrimination in developing economies\u0026rsquo; labor markets, extending the established literature on wage and employment discrimination to a critical but understudied demographic dimension. Our findings reveal distinct patterns of age discrimination that diverge from those observed in advanced economies. Second, we establish a robust, causal relationship between age-35 labor market discrimination and fertility intentions\u0026mdash;a novel mechanism that helps explain the persistent fertility decline in China beyond traditional economic and cultural explanations. Third, our analysis uncovers an important life-cycle dimension to demographic research: mid-career labor market vulnerabilities emerge as significant predictors of fertility outcomes, suggesting that conventional models of fertility behavior may need to account for career-cycle effects in rapidly aging societies. The results provide a microfoundation for understanding how labor market institutions interact with demographic transitions. Forth, by exploring the connection between emerging forms of age discrimination in midlife and fertility outcomes, this study offers policy recommendations for addressing fertility challenges in developing countries.\u003c/p\u003e\u003cp\u003eThe paper is organized as follows. Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides a detailed overview of the sample, variables, and model specifications used in the analysis. Section \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the empirical results on the effect of perceived age discrimination on fertility intentions, including IV estimations, subsample regressions and robustness checks. Section \u003cspan refid=\"Sec12\" class=\"InternalRef\"\u003e4\u003c/span\u003e examines the mechanisms through which perceived age discrimination influences fertility intentions. The final section concludes by summarizing the main findings and their implications.\u003c/p\u003e"},{"header":"2. Research Design","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Sample\u003c/h2\u003e\u003cp\u003eThe data used in this study come from the Chinese Social Survey (CSS), a large-scale, nationally representative longitudinal probability sampling project conducted by the Institute of Sociology at the Chinese Academy of Social Sciences. The survey covers all 31 provinces, autonomous regions, and municipalities in China, with each round of data collection involving 7,000 to 10,000 households. We use the most recent survey data from 2021, which includes 10,136 households. After excluding samples with unclear responses to questions regarding age discrimination, our final sample consists of 9,495 observations.\u003c/p\u003e\u003cp\u003eIn the whole sample, approximately 28% of individuals report no perceived age discrimination, 39% perceive age discrimination but consider it mild, and 33% perceive it as serious. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. presents the distribution of perceived age discrimination across different age groups. Notably, individuals in their 20s and 30s report a significantly lower proportion of never experiencing age discrimination compared to other age groups. This pattern supports the existence of the \u0026ldquo;35-year-old crisis\u0026rdquo; in Chinese society. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. presents the distribution of perceived age discrimination across different age groups. Notably, individuals in their 20s and 30s are significantly less likely to report never having perceived age discrimination compared to other age groups. This pattern supports the existence of the \u0026ldquo;35-year-old threshold\u0026rdquo; in Chinese society.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Variables\u003c/h2\u003e\u003cp\u003eThe primary dependent variable in this study is Children, which represents an individual\u0026rsquo;s fertility intention, measured by the number of children an individual considers ideal for a family. The key independent variable is Age Discrimination, which captures individuals\u0026rsquo; perceptions of age discrimination in society. This variable is categorical and is coded as follows: 1 indicates no perceived age discrimination, 2 indicates perceived mild age discrimination, and 3 indicates perceived severe age discrimination. We control for a variety of sociodemographic and economic factors, including age, gender, education level, marital status, ethnicity, party membership, household registration system, religious belief, employment status, household size, household properties, and family income balance. A detailed description of all variables is provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eVariable definitions.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eType\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDefinitions\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChildren\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCount Variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFertility intention: Desired number of children in a family\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent_Children\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCount Variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAlternative measure of fertility intention: current number of children in the family\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge_Discrimination\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOrdinal Variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePerceived social age discrimination: 1\u0026thinsp;=\u0026thinsp;no age discrimination, 2\u0026thinsp;=\u0026thinsp;mild age discrimination, 3\u0026thinsp;=\u0026thinsp;severe age discrimination\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\u003eContinuous Variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIndividual\u0026rsquo;s age (years)\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\u003cp\u003eCategorical Variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGender: 1\u0026thinsp;=\u0026thinsp;male, 0\u0026thinsp;=\u0026thinsp;female\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation_Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOrdinal Variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEducational attainment (scale: 1\u0026ndash;4, where 1\u0026thinsp;=\u0026thinsp;lowest, 4\u0026thinsp;=\u0026thinsp;highest)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital_Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategorical Variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMarital status: 1\u0026thinsp;=\u0026thinsp;married, 0\u0026thinsp;=\u0026thinsp;unmarried/divorced/widowed\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEthnicity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategorical Variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEthnicity: 1\u0026thinsp;=\u0026thinsp;Han ethnic group, 0\u0026thinsp;=\u0026thinsp;otherwise\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParty_Membership\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategorical Variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCommunist Party membership: 1\u0026thinsp;=\u0026thinsp;member, 0\u0026thinsp;=\u0026thinsp;non-member\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHukou_Type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategorical Variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHousehold registration type: 1\u0026thinsp;=\u0026thinsp;rural, 0\u0026thinsp;=\u0026thinsp;non-rural\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReligious_Belief\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategorical Variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReligious belief: 1\u0026thinsp;=\u0026thinsp;religious, 0\u0026thinsp;=\u0026thinsp;non-religious.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployment_Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategorical Variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCurrent employment status: 1\u0026thinsp;=\u0026thinsp;employed, 0\u0026thinsp;=\u0026thinsp;unemployed\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold_Size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCount Variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber of members currently living in the household\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJob_Loss_Risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOrdinal Variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLikelihood of job loss within six months(scale: 1\u0026ndash;5, where1\u0026thinsp;=\u0026thinsp;very unlikely, 5\u0026thinsp;=\u0026thinsp;very likely)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold_Properties\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCount Variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber of properties owned by the household\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFamily_Income_Balance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOrdinal Variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFamily income balance last year: 1\u0026thinsp;=\u0026thinsp;deficit, 2\u0026thinsp;=\u0026thinsp;balanced, 3\u0026thinsp;=\u0026thinsp;surplus\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLife_Satisfaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOrdinal Variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOverall life satisfaction (scale: 1\u0026ndash;10, where 1\u0026thinsp;=\u0026thinsp;very dissatisfied, 10\u0026thinsp;=\u0026thinsp;very satisfied)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePersonal_Wellbeing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOrdinal Variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePersonal wellbeing (scale: 1\u0026ndash;4, where 1\u0026thinsp;=\u0026thinsp;unhappy, 4\u0026thinsp;=\u0026thinsp;very happy)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLife_Expectation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOrdinal Variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLikelihood of life deterioration within five years (scale: 1\u0026ndash;5, where 1\u0026thinsp;=\u0026thinsp;very unlikely, 5\u0026thinsp;=\u0026thinsp;very likely)\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\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Model\u003c/h2\u003e\u003cp\u003eGiven that the dependent variable is count data, directly applying linear regression could yield negative predicted values and violate the distributional properties of count variables. To address this, we employ a Poisson regression model, which is well-suited for modeling the mean of count outcomes and assumes that the dependent variable follows a Poisson distribution. The model is specified as follows:\u003c/p\u003e\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"449\" height=\"48\"\u003e\u003c/p\u003e\u003cp\u003ewhere\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\lambda\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e= E(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Child}_{i})\\)\u003c/span\u003e\u003c/span\u003e denotes the expected number of children for individual i, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\theta\\:}_{province}\\)\u003c/span\u003e\u003c/span\u003e represents province fixed effects, controlling for regional differences such as policy and cultural factors.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Emperial Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Summary Statistics\u003c/h2\u003e\u003cp\u003eDescriptive statistics for the variables are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. A key assumption of the Poisson regression model is that the mean and variance of the dependent variable are equal. However, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, there is a slight discrepancy between the mean and variance of the dependent variable, Children, which may violate this assumption. This deviation could lead to inefficiency or bias in the estimation of the regression coefficients. To address this issue and ensure the robustness of our results, we apply robust standard errors in the regression analysis, which help mitigate the potential impact of heteroscedasticity and provide more reliable inference.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary statistics.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eObs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStd. dev.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChildren\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9,495\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.1264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.0370\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent_Children\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9,495\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.5488\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.0581\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge_Discrimination\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9,495\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.0559\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.7787\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9,495\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e45.9588\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.4860\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e70\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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9,495\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.4473\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.4972\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation_Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9,493\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.8929\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.2244\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital_Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9,495\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.7786\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.4152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEthnicity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9,495\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.9120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.2834\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParty_Membership\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9,495\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.3048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHukou_Type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9,495\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.6433\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.4791\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReligious_Belief\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9,495\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1368\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.3437\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployment_Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9,494\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.5299\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.4991\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold_Size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9,495\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.5736\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.0817\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJob_Loss_Risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4,068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.4676\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.3618\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold_Properties\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9,471\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.2193\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.6233\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFamily_Income_Balance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9,241\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.8587\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.7769\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLife_Satisfaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4,706\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.3668\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.1670\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePersonal_Wellbeing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4,773\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.3738\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.6773\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLife_Expectation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8,758\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.2296\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.0063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5\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\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Baseline Results\u003c/h2\u003e\u003cp\u003eThe regression results examining the relationship between perceived age discrimination and fertility intentions are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Columns (1) and (2) report Poisson regression results, while Columns (3) and (4) display the results from negative binomial (NB) regression, and Columns (5) and (6) show the ordinary least squares (OLS) results. For each model, Column (2), Column (4), and Column (6) include province fixed effects to account for regional variations in policies and cultural factors.\u003c/p\u003e\u003cp\u003eIn terms of the key explanatory variables, the coefficient for mild age discrimination is -0.0247 (Column 1, Poisson), indicating that individuals perceiving mild age discrimination have a predicted reduction of approximately 2.47% [1 \u0026ndash; exp (\u0026minus;\u0026thinsp;0.0247)\u0026thinsp;=\u0026thinsp;2.47%] in the expected number of ideal children. \u003csup\u003e1\u003c/sup\u003eAfter controlling for province fixed effects in Column (2), the coefficient decreases slightly to -0.0195, suggesting that individuals perceiving mild age discrimination experience a reduction of 1.93% in fertility intentions. Similarly, for severe age discrimination, the coefficient is -0.0326 in Column (1) and \u0026minus;\u0026thinsp;0.0265 in Column (2), indicating reductions of 3.26% and 2.62%, respectively, in the expected number of ideal children. These findings demonstrate that perceived age discrimination, particularly when severe, negatively affects fertility intentions, with stronger discrimination leading to larger reductions in fertility expectations.\u003c/p\u003e\u003cp\u003eThe NB regression results in Columns (3) and (4) corroborate the Poisson regression findings. The coefficients for mild and severe age discrimination are consistent across both models, with the expected reductions in fertility intentions being approximately 2.47% and 3.26% in Column (3) and 1.93% and 2.62% in Column (4). Similarly, the OLS results presented in Columns (5) and (6) further confirm the robustness of the Poisson and negative binomial findings. The coefficients for mild and severe age discrimination correspond to reductions in fertility intentions of approximately 5.30% and 6.99% in Column (5), and 4.30% and 5.81% in Column (6).\u003c/p\u003e\u003cp\u003eIn addition to the age discrimination variables, several control variables are included in the regressions. Age is positively associated with fertility intentions, with coefficients suggesting that older individuals tend to have higher fertility expectations. Ethnicity also shows a significant negative association with fertility intentions, indicating that individuals from minority ethnic groups report lower fertility intentions. Other significant variables include hukou type, marital status, and household size, with results suggesting that those with urban hukou, married individuals, and larger households generally report higher fertility intentions.\u003c/p\u003e\u003cp\u003eOverall, the regression results consistently show that perceived age discrimination is negatively associated with fertility intentions, highlighting the importance of addressing age-related discrimination in policies aimed at improving fertility outcomes. Furthermore, the robustness of these findings is confirmed across different regression models, providing strong evidence for the impact of age discrimination on fertility intentions.\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\u003eThe effect of perceived age discrimination on fertility intentions.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePoisson\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePoisson\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0LS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0LS\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMild age discrimination\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0247**\u003c/p\u003e\u003cp\u003e(0.0119)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0195*\u003c/p\u003e\u003cp\u003e(0.0117)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0247**\u003c/p\u003e\u003cp\u003e(0.0119)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0195*\u003c/p\u003e\u003cp\u003e(0.0117)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.0530** (0.0258)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.0430* (0.0252)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSevere age discrimination\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0326**\u003c/p\u003e\u003cp\u003e(0.0130)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0265**\u003c/p\u003e\u003cp\u003e(0.0128)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0326**\u003c/p\u003e\u003cp\u003e(0.0130)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0265**\u003c/p\u003e\u003cp\u003e(0.0128)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.0699*** (0.0280)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.0581** (0.0278)\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\u003cp\u003e0.0066\u003c/p\u003e\u003cp\u003e(0.0103)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0024\u003c/p\u003e\u003cp\u003e(0.0102)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0066\u003c/p\u003e\u003cp\u003e(0.0103)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0024\u003c/p\u003e\u003cp\u003e(0.0102)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0136 (0.0218)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0053 (0.0217)\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\u003e0.0041***\u003c/p\u003e\u003cp\u003e(0.0004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0047***\u003c/p\u003e\u003cp\u003e(0.0004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0041***\u003c/p\u003e\u003cp\u003e(0.0004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0047***\u003c/p\u003e\u003cp\u003e(0.0004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0089*** (0.0009)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0102*** (0.0009)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital_Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0131\u003c/p\u003e\u003cp\u003e(0.0144)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0075\u003c/p\u003e\u003cp\u003e(0.0142)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0131\u003c/p\u003e\u003cp\u003e(0.0144)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0075\u003c/p\u003e\u003cp\u003e(0.0142)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.0406 (0.0303)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.0280 (0.0300)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEthnicity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0865***\u003c/p\u003e\u003cp\u003e(0.0178)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0591***\u003c/p\u003e\u003cp\u003e(0.0219)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0865***\u003c/p\u003e\u003cp\u003e(0.0178)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0591***\u003c/p\u003e\u003cp\u003e(0.0219)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.1969*** (0.0419)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.1314*** (0.0491)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHukou_Type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0743***\u003c/p\u003e\u003cp\u003e(0.0102)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0664***\u003c/p\u003e\u003cp\u003e(0.0102)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0743***\u003c/p\u003e\u003cp\u003e(0.0102)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0664***\u003c/p\u003e\u003cp\u003e(0.0102)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.1514*** (0.0210)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.1362*** (0.0210)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParty_Membership\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0208\u003c/p\u003e\u003cp\u003e(0.0166)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0111\u003c/p\u003e\u003cp\u003e(0.0165)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0208\u003c/p\u003e\u003cp\u003e(0.0166)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0111\u003c/p\u003e\u003cp\u003e(0.0165)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0426 (0.0350)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0216 (0.0349)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReligious_Belief\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0542***\u003c/p\u003e\u003cp\u003e(0.0139)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0112\u003c/p\u003e\u003cp\u003e(0.0161)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0542***\u003c/p\u003e\u003cp\u003e(0.0139)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0112\u003c/p\u003e\u003cp\u003e(0.0161)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.1190*** (0.0313)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0230 (0.0354)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation_Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0028\u003c/p\u003e\u003cp\u003e(0.0048)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0005\u003c/p\u003e\u003cp\u003e(0.0047)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0028\u003c/p\u003e\u003cp\u003e(0.0048)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0005\u003c/p\u003e\u003cp\u003e(0.0047)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.0034 (0.0098)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0018 (0.0096)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold_Properties\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0007\u003c/p\u003e\u003cp\u003e(0.0101)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0058\u003c/p\u003e\u003cp\u003e(0.0100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0007\u003c/p\u003e\u003cp\u003e(0.0101)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0058\u003c/p\u003e\u003cp\u003e(0.0100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.0028 (0.0212)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0108 (0.0212)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold_Size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0333\u003c/p\u003e\u003cp\u003e(0.0030)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0243***\u003c/p\u003e\u003cp\u003e(0.0030)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0333***\u003c/p\u003e\u003cp\u003e(0.0030)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0243***\u003c/p\u003e\u003cp\u003e(0.0030)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0773*** (0.0073)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0579*** (0.0074)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployment_Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0044\u003c/p\u003e\u003cp\u003e(0.0102)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0014\u003c/p\u003e\u003cp\u003e(0.0102)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0044\u003c/p\u003e\u003cp\u003e(0.0102)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0014\u003c/p\u003e\u003cp\u003e(0.0102)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0105 (0.0218)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.0027 (0.0218)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFamily_Income_Balance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0101\u003c/p\u003e\u003cp\u003e(0.0061)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0050\u003c/p\u003e\u003cp\u003e(0.0059)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0101*\u003c/p\u003e\u003cp\u003e(0.0061)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0050\u003c/p\u003e\u003cp\u003e(0.0059)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.0200 (0.0128)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.0095 (0.0125)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.4767***\u003c/p\u003e\u003cp\u003e(0.0367)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2911***\u003c/p\u003e\u003cp\u003e(0.0638)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.4767***\u003c/p\u003e\u003cp\u003e(0.0367)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.2911***\u003c/p\u003e\u003cp\u003e(0.0638)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.5308*** (0.0800)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.1746*** (0.1213)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e/Lnalpha\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-30.7648\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-38.3569\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlpha\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\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProvince fixed effects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR^2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.159\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.159\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.102\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9224\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9224\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9224\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9224\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9224\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9224\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eNotes\u003c/em\u003e: Robust standard errors are reported in parentheses; in the NB regression, the values of alpha and /lnalpha provide insights into the degree of over-dispersion in the data; \u0026lowast;p\u0026thinsp;\u0026lt;\u0026thinsp;0.1, \u0026lowast;\u0026lowast; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u0026lowast;\u0026lowast;\u0026lowast; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.3 IV Regression\u003c/h2\u003e\u003cp\u003eTo address potential endogeneity arising from unobserved individual factors influencing perceptions of age discrimination, we employ an instrumental variable (IV) approach. Specifically, we construct an instrument based on the group mean of perceived age discrimination, aggregated according to key individual characteristics, including five-year age cohorts, marital status, education level, ethnicity, hukou type, and province. Each individual is assigned to a unique group based on these attributes, and the within-group mean serves as the instrument. This approach leverages variation at the group level to isolate exogenous sources of variation in individual perceptions, thereby mitigating biases from unobserved heterogeneity.\u003c/p\u003e\u003cp\u003eThe validity of this instrument hinges on two key assumptions. First, relevance: the group mean reflects the collective perception of age discrimination within the group, ensuring a strong correlation with individual perceptions. Since individuals with similar demographic and socioeconomic characteristics are likely to experience comparable levels of discrimination, the instrument satisfies the relevance condition. Second, and most critically, exogeneity: the instrument must be uncorrelated with unobserved individual characteristics that directly affect the outcome of interest. This exogeneity assumption is justified in two ways. First, because the instrument is constructed from group-level characteristics rather than individual-specific factors, it mitigates concerns that individual perceptions of age discrimination are driven by idiosyncratic, unobserved traits. Second, the grouping characteristics\u0026mdash;such as age cohort, education level, and province\u0026mdash;are predetermined and largely exogenous to individual perceptions, further ensuring that the instrument captures exogenous variation rather than endogenous individual differences. Additionally, since the instrument averages out idiosyncratic reporting biases within each group, any remaining variation reflects broader social and structural factors rather than personal predispositions.\u003c/p\u003e\u003cp\u003eBy leveraging this IV approach, we mitigate potential biases arising from individual-level endogeneity and establish a more credible identification strategy for estimating the causal impact of perceived age discrimination on fertility preferences. To ensure the validity of our instrument, we first conduct a first-stage regression. The results confirm the instrument\u0026rsquo;s relevance, as it exhibits a significantly positive coefficient at the 1% level, with an F-statistic of 746\u0026mdash;well above conventional thresholds for weak instrument concerns. Given that our dependent variable, the ideal number of children, is a count variable, we proceed with an IV Poisson regression to obtain consistent estimates.\u003c/p\u003e\u003cp\u003eThe IV results, presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, reinforce our initial findings: individuals who perceive greater age discrimination tend to prefer fewer children. Specifically, the estimated coefficient implies that for each unit increase in perceived age discrimination, the ideal number of children decreases by approximately 4.85%. This result underscores the negative association between age discrimination perceptions and fertility preferences, highlighting the broader social and psychological implications of age-related biases.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eIV results of age discrimination\u0026rsquo;s effect on fertility intentions.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIV Poisson\u003c/p\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIV Poisson\u003c/p\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIV-age discrimination\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0524***\u003c/p\u003e\u003cp\u003e(0.0129)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0488***\u003c/p\u003e \u003cp\u003e(0.0123)\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\u003cp\u003e0.0140\u003c/p\u003e\u003cp\u003e(0.0103)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParty_Membership\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0174\u003c/p\u003e\u003cp\u003e(0.0161)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReligious_Belief\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0636***\u003c/p\u003e\u003cp\u003e(0.0141)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold_Properties\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0064\u003c/p\u003e \u003cp\u003e(0.0100)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold_Size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0394***\u003c/p\u003e\u003cp\u003e(0.0029)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployment_Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0010\u003c/p\u003e \u003cp\u003e(0.0101)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFamily_Income_Balance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0291***\u003c/p\u003e\u003cp\u003e(0.0062)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCons\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.8613***\u003c/p\u003e\u003cp\u003e(0.0269)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.7127***\u003c/p\u003e\u003cp\u003e(0.0347)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9224\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9226\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003eNotes\u003c/em\u003e: Robust standard errors are reported in parentheses; the IV Poisson regression does not directly provide goodness-of-fit measures such as R-squared; \u0026lowast;p\u0026thinsp;\u0026lt;\u0026thinsp;0.1, \u0026lowast;\u0026lowast; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u0026lowast;\u0026lowast;\u0026lowast; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Subsample Regression\u003c/h2\u003e\u003cp\u003eGiven our focus on examining the impact of age discrimination at 35 on fertility\u0026mdash;and acknowledging the age-specific nature of fertility behavior, where individuals aged 45 and younger are the primary group for marriage and childbearing\u0026mdash;we divide the sample into two groups: those aged 18\u0026ndash;45 and those aged 45\u0026ndash;70.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the results of these subsample regressions. Columns (1) and (2) reveal that the effect of age discrimination on fertility differs slightly between the two groups. Among individuals aged 18\u0026ndash;45, severe age discrimination has a significant negative impact on fertility preferences, whereas mild discrimination shows no significant effect. In contrast, for individuals aged 45 and older, mild age discrimination appears to influence fertility preferences, while severe discrimination does not. This pattern suggests that, in the older age group, fertility preferences may be shaped by factors beyond age discrimination, such as broader social norms, financial considerations, or health constraints.\u003c/p\u003e\u003cp\u003eColumn (3) of Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the IV regression results for individuals aged 18\u0026ndash;45, further illustrating the impact of perceived age discrimination on fertility preferences. A one-unit increase in perceived age discrimination is associated with a 3.43% decline in the expected ideal number of children, translating to an approximate reduction of 0.07 children based on the sample mean of 1.994. This finding underscores the negative influence of age discrimination on younger individuals\u0026rsquo; fertility aspirations. Moreover, the results indicate a significant gender difference in fertility preferences. The ideal number of children is notably higher for men than for women, with a coefficient of 0.0271, suggesting that men\u0026rsquo;s expected ideal number of children is 2.75% greater than that of women. This gender disparity is particularly pronounced among younger individuals, highlighting the role of traditional gender norms and expectations in shaping fertility intentions. Societal factors, such as differing familial responsibilities, career considerations, and social pressures, may contribute to this divergence in fertility preferences between men and women.\u003c/p\u003e\u003cp\u003eHowever, this gender gap diminishes in the older cohort, where the difference in fertility preferences between men and women is no longer statistically significant. The differing gender gaps in fertility intentions between younger and older cohorts are likely driven by changes in fertility preferences among younger women. Goldin (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) indicates that the fundamental divergence in fertility intentions between men and women stems from the greater childcare responsibilities typically assumed by women, which often requires them to make career sacrifices and face heightened economic vulnerability. Compared to earlier generations, modern women are increasingly required to balance their careers with family responsibilities, leading to more cautious fertility decisions. As a result, the gender gap in fertility intentions is more pronounced in the younger cohort, while it tends to narrow in the older cohort. This trend not only reflects the evolution of gender roles and social structures but also suggests that fertility decisions are becoming more profoundly influenced by economic and social factors, as female labor force participation increases and gender equality norms become more widespread.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSubsample results of age discrimination\u0026rsquo;s effect on fertility intentions.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePoisson\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePoisson\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIV Poisson\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIV Poisson\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003cp\u003e18\u0026ndash;45\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003cp\u003e45\u0026ndash;70\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003cp\u003e18\u0026ndash;45\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003cp\u003e45\u0026ndash;70\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMild age discrimination\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0104\u003c/p\u003e \u003cp\u003e(0.0150)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0317* (0.0166)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSevere age discrimination\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0446** (0.0171)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0126\u003c/p\u003e \u003cp\u003e(0.0171)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIV- age discrimination\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0349** (0.0169)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0598*** (0.0171)\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\u003e0.0036** (0.0012)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0079*** (0.0011)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital_Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0122\u003c/p\u003e\u003cp\u003e(0.0192)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0009\u003c/p\u003e\u003cp\u003e(0.0234)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEthnicity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0595** (0.0264)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0511\u003c/p\u003e \u003cp\u003e(0.0326)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHukou_Type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0299** (0.0127)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0980*** (0.0162)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation_Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0084\u003c/p\u003e \u003cp\u003e(0.0056)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0089\u003c/p\u003e\u003cp\u003e(0.0090)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\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\u003cp\u003e0.0325** (0.0132)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0191\u003c/p\u003e \u003cp\u003e(0.0148)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0271** (0.0130)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0038\u003c/p\u003e \u003cp\u003e(0.0150)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParty_Membership\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0161\u003c/p\u003e\u003cp\u003e(0.0221)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0137\u003c/p\u003e\u003cp\u003e(0.0229)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0070\u003c/p\u003e\u003cp\u003e(0.0218)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0128\u003c/p\u003e\u003cp\u003e(0.0221)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReligious_Belief\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0169\u003c/p\u003e\u003cp\u003e(0.0197)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0077\u003c/p\u003e\u003cp\u003e(0.0236)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0681*** (0.0164)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0634*** (0.0210)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold_Properties\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0166\u003c/p\u003e\u003cp\u003e(0.0138)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0038\u003c/p\u003e \u003cp\u003e(0.0139)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0044\u003c/p\u003e\u003cp\u003e(0.0135)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0190\u003c/p\u003e \u003cp\u003e(0.0142)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold_Size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0240*** (0.0036)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0224*** (0.0041)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0377*** (0.0033)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0389*** (0.0038)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployment_Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0109\u003c/p\u003e \u003cp\u003e(0.0140)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0144\u003c/p\u003e\u003cp\u003e(0.0157)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0158\u003c/p\u003e\u003cp\u003e(0.0121)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0013\u003c/p\u003e\u003cp\u003e(0.0145)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFamily_Income_Balance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0002\u003c/p\u003e\u003cp\u003e(0.0078)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0088\u003c/p\u003e \u003cp\u003e(0.0087)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0191** (0.0083)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0251** (0.0089)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCons\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.3652*** (0.1038)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0440\u003c/p\u003e\u003cp\u003e(0.0936)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.5921***\u003c/p\u003e\u003cp\u003e(0.0465)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.7914*** (0.0476)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProvince fixed effects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR^2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5270\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5271\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNotes\u003c/em\u003e: Since the construction of the IV involves provincial-level information, there is no need to control for provincial fixed effects in the model; \u0026lowast;p\u0026thinsp;\u0026lt;\u0026thinsp;0.1, \u0026lowast;\u0026lowast; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u0026lowast;\u0026lowast;\u0026lowast; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Robustness Tests\u003c/h2\u003e\u003cp\u003eTo ensure the robustness of our results, we conducted additional analyses by replacing the dependent variable with the actual number of children individuals have, as an alternative measure of fertility preferences. The IV regression results remain consistent, further supporting the robustness of our findings. The detailed results are provided in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The regression results for the 18\u0026ndash;70 age group show that the perception of age discrimination has a negative and statistically significant impact on fertility intentions (coefficient = -0.0798, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The negative impact of age discrimination on fertility intentions is particularly significant in younger individuals, suggesting that age discrimination affects fertility decisions through multiple mechanisms, including perceived social status, economic security, and self-identity. The differential effects across age groups highlight the complexity of age discrimination's impact on fertility intentions, further confirming the important role of perceived age discrimination in fertility decisions, particularly among younger individuals. The consistency of the IV regression results further supports the robustness of our findings.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of robustness tests.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eCurrent_Children\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003cp\u003e18\u0026ndash;70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003cp\u003e18\u0026ndash;45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003cp\u003e45\u0026ndash;70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIV-age discrimination\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0798***\u003c/p\u003e \u003cp\u003e(0.0166)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.1233***\u003c/p\u003e \u003cp\u003e(0.0289)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0476**\u003c/p\u003e \u003cp\u003e(0.0182)\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\u003cp\u003e-0.0951***\u003c/p\u003e \u003cp\u003e(0.0138)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.3165***\u003c/p\u003e\u003cp\u003e(0.0274)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0340**\u003c/p\u003e \u003cp\u003e(0.0137)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParty_Membership\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0234\u003c/p\u003e\u003cp\u003e(0.0217)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0697\u003c/p\u003e \u003cp\u003e(0.0468)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0210\u003c/p\u003e \u003cp\u003e(0.0208)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReligious_Belief\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0673***\u003c/p\u003e\u003cp\u003e(0.0187)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1376***\u003c/p\u003e\u003cp\u003e(0.0335)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0370*\u003c/p\u003e\u003cp\u003e(0.0201)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold_Properties\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0507***\u003c/p\u003e \u003cp\u003e(0.0107)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0936***\u003c/p\u003e \u003cp\u003e(0.0211)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0271**\u003c/p\u003e \u003cp\u003e(0.0105)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold_Size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0975***\u003c/p\u003e\u003cp\u003e(0.0030)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1306***\u003c/p\u003e\u003cp\u003e(0.0075)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0790***\u003c/p\u003e\u003cp\u003e(0.0028)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployment_Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0918***\u003c/p\u003e\u003cp\u003e(0.0136)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3517***\u003c/p\u003e\u003cp\u003e(0.0279)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0360**\u003c/p\u003e\u003cp\u003e(0.0134)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFamily_Income_Balance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.1546***\u003c/p\u003e \u003cp\u003e(0.0091)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.1850***\u003c/p\u003e \u003cp\u003e(0.0170)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0873***\u003c/p\u003e \u003cp\u003e(0.0090)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCons\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.4543***\u003c/p\u003e\u003cp\u003e(0.0445)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1272\u003c/p\u003e\u003cp\u003e(0.0824)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.5226***\u003c/p\u003e\u003cp\u003e(0.0481)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9226\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5271\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNotes\u003c/em\u003e: Robust standard errors are reported in parentheses;\u0026lowast;p\u0026thinsp;\u0026lt;\u0026thinsp;0.1, \u0026lowast;\u0026lowast; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u0026lowast;\u0026lowast;\u0026lowast; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Mechanism Analysis","content":"\u003cp\u003eThe impact of age discrimination on fertility intentions can be analyzed through multiple theoretical lenses. Drawing on insights from economic and sociological research (e.g., Becker, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1971\u003c/span\u003e; Becker, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Ajzen \u0026amp; Klobas, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), we hypothesize that perceived age discrimination influences fertility intentions through several interrelated mechanisms.\u003c/p\u003e\u003cp\u003eFirst, personal well-being plays a critical role in decision-making, particularly in contexts of social discrimination or exclusion. Both Self-Identity Theory and Social Identity Theory suggest that individuals\u0026rsquo; well-being is closely tied to their sense of belonging and social acceptance. When individuals experience age discrimination, their well-being may deteriorate, leading to shifts in attitudes and behaviors, including those related to fertility. A decline in well-being may reduce individuals\u0026rsquo; confidence in their future prospects and, consequently, their willingness to have children. Second, expectations about life circumstances are a key determinant of fertility decisions. Expectation Theory posits that individuals\u0026rsquo; perceptions of their future shape their current economic behaviors and long-term planning. When individuals anticipate a stable and promising future, they are more likely to invest in family expansion. Conversely, if age discrimination fosters pessimistic expectations\u0026mdash;such as concerns about career advancement, financial security, or social standing\u0026mdash;individuals may delay or forgo childbearing. Third, life satisfaction is a well-established predictor of fertility intentions. Individuals with higher life satisfaction tend to express greater willingness to have children, as they perceive their current circumstances as conducive to family formation. However, experiencing age discrimination may erode life satisfaction by diminishing individuals\u0026rsquo; sense of control and fulfillment, thereby indirectly discouraging childbearing. Fourth, perceived unemployment risk and economic uncertainty represent crucial constraints on fertility decisions. Economic models of fertility emphasize the role of financial stability in shaping reproductive choices. Heightened unemployment risk and income uncertainty typically increase economic pressures, prompting individuals to adopt more cautious financial behaviors, such as postponing or reducing fertility plans. In the context of age discrimination, concerns about job security and career prospects may become more pronounced, leading individuals to prioritize economic survival over family expansion.\u003c/p\u003e\u003cp\u003eTaken together, these mechanisms suggest that perceived age discrimination can exert a substantial influence on fertility intentions by shaping individuals\u0026rsquo; well-being, future expectations, life satisfaction, and economic security. Understanding these pathways is crucial for policymakers seeking to address the broader social and economic implications of age discrimination. We first examined the impact of perceived age discrimination on individuals\u0026rsquo; short-term unemployment expectations, life situation expectations, life satisfaction, and personal wellbeing. Given that the dependent variables are ordinal categorical variables, we employed ordered logit models for regression analysis. The results, presented in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, align with those of Wei et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), indicating that perceived age discrimination significantly increases individuals\u0026rsquo; unemployment risk and decreases their expectations for future life, life satisfaction, and personal wellbeing.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of age discrimination on on job loss risk, life expectation, life satisfaction and personal wellbeing.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJob_Loss_Risk\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLife_Expectation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLife_Satisfaction\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePersonal_Wellbeing\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIV- age discrimination\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.2590***\u003c/p\u003e\u003cp\u003e(0.0705)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1441***\u003c/p\u003e\u003cp\u003e(0.0479)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.3738***\u003c/p\u003e \u003cp\u003e(0.0653)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.4375***\u003c/p\u003e \u003cp\u003e(0.0711)\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\u003cp\u003e0.1735***\u003c/p\u003e\u003cp\u003e(0.0577)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1177***\u003c/p\u003e\u003cp\u003e(0.0420)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.1629***\u003c/p\u003e \u003cp\u003e(0.0539)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.2151***\u003c/p\u003e \u003cp\u003e(0.0599)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParty_Membership\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.7878***\u003c/p\u003e \u003cp\u003e(0.0861)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0623\u003c/p\u003e \u003cp\u003e(0.0690)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.6231***\u003c/p\u003e\u003cp\u003e(0.0802)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.4898***\u003c/p\u003e\u003cp\u003e(0.0919)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReligious_Belief\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0532\u003c/p\u003e \u003cp\u003e(0.0831)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.2070***\u003c/p\u003e \u003cp\u003e(0.0580)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1941**\u003c/p\u003e\u003cp\u003e(0.0832)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0560\u003c/p\u003e \u003cp\u003e(0.0870)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold_Properties\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.1945***\u003c/p\u003e \u003cp\u003e(0.0441)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0100\u003c/p\u003e\u003cp\u003e(0.0327)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1434***\u003c/p\u003e\u003cp\u003e(0.0383)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0460\u003c/p\u003e\u003cp\u003e(0.0474)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold_Size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0174\u003c/p\u003e\u003cp\u003e(0.0158)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0350***\u003c/p\u003e \u003cp\u003e(0.0104)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0102\u003c/p\u003e \u003cp\u003e(0.0134)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0423***\u003c/p\u003e\u003cp\u003e(0.0152)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployment_Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.8421***\u003c/p\u003e \u003cp\u003e(0.1097)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0528\u003c/p\u003e \u003cp\u003e(0.0413)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0120\u003c/p\u003e \u003cp\u003e(0.0539)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0087\u003c/p\u003e \u003cp\u003e(0.0587)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFamily_Income_Balance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.3947***\u003c/p\u003e \u003cp\u003e(0.0387)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.2731***\u003c/p\u003e \u003cp\u003e(0.0272)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.4018***\u003c/p\u003e\u003cp\u003e(0.0347)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.1602***\u003c/p\u003e\u003cp\u003e(0.0375)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR^2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.121\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3997\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8521\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4567\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4643\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNotes\u003c/em\u003e: Robust standard errors are reported in parentheses;\u0026lowast;p\u0026thinsp;\u0026lt;\u0026thinsp;0.1, \u0026lowast;\u0026lowast; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u0026lowast;\u0026lowast;\u0026lowast; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTo examine the mediating mechanisms underlying the relationship between perceived age discrimination and fertility intentions, we employed Structural Equation Modeling (SEM) for mediation analysis. Given that instrumental variables can effectively mitigate potential endogeneity concerns, we utilized the instrumental variable for age discrimination to measure individuals\u0026rsquo; perceptions of age discrimination within the mediation framework. Furthermore, recognizing that the peak fertility age in China is around 28, we restricted our analysis to a subsample of individuals aged 45 and below to ensure the relevance of our findings. After conducting a series of robustness checks, we found that perceived unemployment risk, life satisfaction, and expectations about life circumstances did not significantly mediate the effect of perceived age discrimination on fertility intentions. However, personal self-satisfaction emerged as a key mediating factor. The regression results for personal self-satisfaction as a mediator are presented in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMediation Effect of Personal Wellbeing on Fertility Intentions.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePersonal_wellbeing\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChildren\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIV- age discrimination\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.1354***\u003c/p\u003e\u003cp\u003e(0.0334)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0589\u003c/p\u003e\u003cp\u003e(0.0472)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePersonal_wellbeing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0831**\u003c/p\u003e\u003cp\u003e(0.0383)\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\u003cp\u003e-0.0664**\u003c/p\u003e\u003cp\u003e(0.0288)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0628\u003c/p\u003e\u003cp\u003e(0.0383)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParty_Membership\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0986**\u003c/p\u003e\u003cp\u003e(0.0457)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0855*\u003c/p\u003e\u003cp\u003e(0.0434)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReligious_Belief\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0559\u003c/p\u003e\u003cp\u003e(0.0423)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1158**\u003c/p\u003e\u003cp\u003e(0.0546)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold_Properties\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0088\u003c/p\u003e\u003cp\u003e(0.0221)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.00298\u003c/p\u003e\u003cp\u003e(0.0443)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold_Size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0129\u003c/p\u003e\u003cp\u003e(0.0081)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0888***\u003c/p\u003e\u003cp\u003e(0.0113)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployment_Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.00095\u003c/p\u003e\u003cp\u003e(0.0286)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0515\u003c/p\u003e\u003cp\u003e(0.037)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFamily_Income_Balance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0766***\u003c/p\u003e\u003cp\u003e(0.0176)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0173\u003c/p\u003e\u003cp\u003e(0.0231)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCons\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.4094***\u003c/p\u003e\u003cp\u003e(0.0885)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.4148***\u003c/p\u003e\u003cp\u003e(0.1913)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2029\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003eNotes\u003c/em\u003e: Robust standard errors are reported in parentheses;\u0026lowast;p\u0026thinsp;\u0026lt;\u0026thinsp;0.1, \u0026lowast;\u0026lowast; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u0026lowast;\u0026lowast;\u0026lowast; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe results indicate that age discrimination significantly reduces personal wellbeing, with a coefficient of -0.1354 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that an increase in age discrimination leads to a marked decline in individuals\u0026rsquo; personal wellbeing. In contrast, age discrimination does not have a statistically significant direct effect on fertility intentions, as evidenced by a coefficient of -0.0589 (p\u0026thinsp;=\u0026thinsp;0.211), implying a minimal and insignificant direct impact on fertility intentions. However, personal wellbeing is found to have a significant positive effect on fertility intentions, with a coefficient of 0.0831 (p\u0026thinsp;=\u0026thinsp;0.030), indicating that higher personal satisfaction is associated with stronger fertility intentions. Importantly, the impact of age discrimination on fertility intentions is indirect, primarily operating through its effect on personal satisfaction. While the direct effect of age discrimination on fertility intentions is not significant, it reduces personal wellbeing, which, in turn, positively influences fertility intentions.\u003c/p\u003e\u003cp\u003eThus, the primary mechanism through which age discrimination influences fertility intentions is indirect, mediated by its reduction of personal wellbeing. A decline in satisfaction may diminish an individual\u0026rsquo;s sense of control and psychological resources, thereby influencing decisions related to family planning. These findings highlight the importance of psychological well-being in shaping reproductive choices, particularly in the context of perceived age discrimination.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eDeclining fertility rates pose a significant challenge for many countries undergoing economic and social transformations. While traditional factors such as cultural norms, economic conditions, and religious beliefs have been widely studied in the context of fertility decisions, emerging evidence suggests that fertility intentions are also shaped by less conventional influences. This study introduces a novel perspective by examining the role of perceived age discrimination in shaping fertility intentions. Drawing on data from the 2021 CSS, this study investigates the impact of perceived age discrimination\u0026mdash;particularly among younger individuals\u0026mdash;on fertility decisions.\u003c/p\u003e\u003cp\u003eIn China\u0026rsquo;s labor market, concerns over age discrimination have intensified, especially for workers approaching the age of 35. The empirical findings suggest that higher levels of perceived age discrimination are associated with a decline in fertility intentions, indicating that age-related biases may discourage individuals from planning for children. Furthermore, perceived age discrimination is strongly correlated with increased short-term unemployment risk, lower life satisfaction, and diminished personal well-being. These findings highlight the broader consequences of age discrimination, demonstrating its effects beyond immediate employment outcomes and into key aspects of individuals\u0026rsquo; overall quality of life.\u003c/p\u003e\u003cp\u003eUsing structural equation modeling, we further explore the mechanisms through which age discrimination influences fertility intentions. The results reveal that the primary pathway is through reductions in personal well-being. While age discrimination does not exert a statistically significant direct effect on fertility intentions, it significantly undermines individuals\u0026rsquo; well-being, which in turn lowers their willingness to have children. This underscores the psychological and emotional toll of age-related discrimination, revealing its indirect but substantial impact on fertility decisions.\u003c/p\u003e\u003cp\u003eA subsample analysis further highlights the role of gender in shaping fertility intentions. Among individuals under the age of 45, men exhibit stronger fertility intentions than women, suggesting the presence of gender-specific dynamics in the relationship between age discrimination and reproductive choices. This divergence may stem from differences in how men and women experience and respond to age discrimination in the workplace, influencing their long-term family planning decisions. Given that women often face compounded discrimination related to both age and gender, they may be more susceptible to the negative effects of age-related biases on fertility intentions.\u003c/p\u003e\u003cp\u003eThe implications of these findings extend beyond individual fertility decisions. Age discrimination not only erodes personal wellbeing but also contributes to inefficient human resource allocation and unfavorable long-term demographic trends. As China and other aging societies grapple with declining birth rates, addressing age-related biases in the labor market becomes a critical policy issue. We recommend that the government establish clear legal protections against age discrimination, recognizing \u0026ldquo;age\u0026rdquo; as an independent category of discrimination under labor laws. Additionally, policies should be developed to mitigate age-related biases in hiring and employment practices while simultaneously providing stronger social and economic support for individuals making fertility decisions. By tackling age discrimination at both the legal and institutional levels, policymakers can improve individual wellbeing and promote more balanced and sustainable demographic outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYajie Wang wrote the main manuscript text . All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eSequence data that support the findings of this study can be find in the website of Chinese Social Survey (http://css.cssn.cn/css_sy/zlysj/lnsj/) .The authors have no rights to share the data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAgan, A., Starr, S (2018). Ban the box, criminal records, and racial discrimination: A field experiment. Quar J Econ 133(1): 191\u0026ndash;235. https://doi.org/10.1093/qje/qjx028\u003c/li\u003e\n\u003cli\u003eAjzen, I., Klobas, J (2013). Fertility intentions: An approach based on the theory of planned behavior. Demographic Research 8(29):203\u0026ndash;232. https://doi.org/10.4054/DemRes.2013.29.8.\u003c/li\u003e\n\u003cli\u003eAlam, S.A., P\u0026ouml;rtner, C.C (2018) Income shocks, contraceptive use, and timing of fertility. J Dev Econ 131: 96\u0026ndash;103. https://doi.org/10.1016/j.jdeveco.2017.10.007.\u003c/li\u003e\n\u003cli\u003eAtalay, K., Li, A., Whelan, S (2021) Housing wealth, fertility intentions and fertility. J Hous Econ 54:101787. https://doi.org/10.1016/j.jhe.2021.101787.\u003c/li\u003e\n\u003cli\u003eBecker, G.S (1993) Human capital: a theoretical and empirical analysis, with special reference to education. Chicago. https://fudan-primo.hosted.exlibrisgroup.com.cn/permalink/f/11t5l4n/TN_cdi_econis_primary_166157643\u003c/li\u003e\n\u003cli\u003eBecker, G.S (1971) The Economics of Discrimination. Second Edition. 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J Pub Econ 108:1\u0026ndash;16. https://doi.org/10.1016/j.jpubeco.2013.09.006\u003c/li\u003e\n\u003cli\u003eWei, J., Gao, W., Ni, C (2024) Age discrimination in the Chinese labor market and its impact: An analysis based on the China Social Survey (CSS) Data. J Popu Econ 3:97-110. https://kns.cnki.net/kcms2/article/abstract?v=7fc2yiS_nyBi6ByYLwttO0PDycHFj5lFsErsqVsH-eTNgdx6Ut-0hm1p_zLnLs7Fk7BDkxc88PE-zg-qlQjV4jPkN6ORts1RL38eHh6leuuVCak2EfUAOFvTFfbSMJRixjblyz3_iTH-e_dbps1MV1CO9csn-QvHzt6tHzuOVDcG_Vj_fdOADucWCNC2Vy98\u0026amp;uniplatform=NZKPT\u0026amp;language=CHS\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e In Poisson regression, the coefficient represents how changes in an independent variable affect the log-mean of the dependent variable. Therefore, when interpreting the results, the coefficient is exponentiated (exp) to express its impact. The same applies below, and the calculation method will not be repeated.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Fertility intentions, Age discrimination, 35-year-old, Personal wellbeing, China","lastPublishedDoi":"10.21203/rs.3.rs-6672477/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6672477/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe decline in fertility rates has emerged as a critical policy challenge for governments worldwide. This paper provides the first empirical analysis of the impact of perceived age discrimination on fertility intentions, leveraging data from the 2021 Chinese Social Survey. We document a significant negative relationship between perceived age discrimination and fertility intentions. Further analysis reveals that individuals who report higher levels of perceived age discrimination face a greater risk of short-term unemployment and experience declines in life expectancy, life satisfaction, and overall well-being. Structural equation modeling indicates that the adverse effect of age discrimination on fertility intentions is primarily mediated through reductions in personal well-being. Subsample analysis shows that, among individuals under 45, men exhibit stronger fertility intentions than women. These findings highlight the broader demographic and economic implications of labor market age discrimination.\u003c/p\u003e","manuscriptTitle":"Age Discrimination, Personal wellbeing, and Fertility Intentions: Evidence from the 2021 Chinese Social Survey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-22 07:01:24","doi":"10.21203/rs.3.rs-6672477/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"05428cd7-d877-4b39-b3b2-f2cc37929f68","owner":[],"postedDate":"July 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":51852262,"name":"Social science/Development studies"},{"id":51852263,"name":"Social science/Economics"},{"id":51852264,"name":"Social science/Sociology"}],"tags":[],"updatedAt":"2026-02-21T16:39:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-22 07:01:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6672477","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6672477","identity":"rs-6672477","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}