Effects of the three-child policy on the employment bias against professional women: evidence from 260 enterprises in Jiangxi province

The study addresses whether China's universal three-child policy, introduced in 2021 to counteract declining fertility and population aging, intensifies employment and career development difficulties for professional women of childbearing age. In 2020 China’s total fertility rate dropped to 1.3 and marriage rates and ages at first marriage shifted, increasing concerns about fertility and labor market dynamics. Prior work highlights women’s dual roles and time poverty, with heightened challenges under the three-child policy. This paper focuses on employers—often overlooked in prior analyses—to understand HR managers’ attitudes toward hiring and promoting women amid the policy environment. Using data from 260 enterprises in Jiangxi Province, the study tests hypotheses that: (H1) female employees face greater employment bias in male-dominated enterprises; (H2) job competency is negatively related to gender employment bias (lower skill requirements correlate with more bias); and (H3) the three-child policy increases employment bias against women, with HR understanding of the policy associated with higher bias in hiring decisions.

The literature is summarized in three strands. (1) Improvements in female employment: technology and socialization of housework can reduce women’s family burden and support higher employment, especially among low-skilled women; public preschool services also help; sectoral employment patterns show concentration of women in public management, education, and health; higher female employment shares can improve firm profitability and productivity. (2) Differential treatment and discrimination: women face disadvantages in job opportunities, participation, wages, and promotion; gender bias is prevalent across organizations; women are overrepresented in low-skilled jobs and encounter marital/fertility inquiries during hiring; higher thresholds and ceilings exist for women’s advancement, with fewer female managers and differential support for supervisors by gender. (3) Causes of bias: lack of gender perspective in public policies, discrimination across career stages, family expectations increasing women’s household time demands, managerial benevolent sexism affecting women’s advancement, parental leave deepening occupational segregation, and traditional gender norms and industry gender distributions fueling workplace bias. Gaps identified include limited attention to employers’ perspectives under the three-child policy.

Design and sample: A cross-sectional survey in 2022 targeted one HR manager from each of 260 enterprises in Jiangxi Province (selected via the Jiangxi HR Club). HR managers represent enterprise management and make staffing decisions, offering insight into employer attitudes. A pretest of 50 questionnaires underwent item and factor analyses. Measurement development: Employment bias was decomposed into measurable indicators through expert interviews, group discussions, and case interviews. The final instrument included HR and enterprise demographics and 18 items across multiple dimensions, scored on a 5-point Likert scale (1=strongly agree to 5=strongly disagree). Factor analysis: KMO=0.859 (>0.8) and Bartlett’s test was significant (p<0.01), supporting factorability. Exploratory principal component analysis (rotated component matrix) yielded four factors with loadings ≥0.5 after removing double-loading items: (1) Female employment bias (5 items: fewer women in key positions among new hires; more men than women among new hires; reduced number of new female employees; preference for men among equal candidates; reduced proportion of women in management among new hires). (2) Employee change (3 items: post-policy increase in male–female tension; reduced employee-generated profit; decreased work enthusiasm). (3) Career development bias (5 items: considering childbearing willingness in promotions; greater difficulty for women’s promotion; reduced salary/benefits for women after the policy; fewer training opportunities; higher turnover of women post-policy). (4) Policy impact (3 items: three-child policy affects HR strategy, personnel adjustment, and employment). A fifth dimension, job competency, was separately constructed; each dimension was scored by summing items. Data collection: 284 questionnaires distributed; 260 valid responses (effective rate 91.54%). Sample profile: 79.62% female HR; ages 30–39 (65%) dominated; 42.31% had >10 years’ experience; enterprises spanned 14 industries; 70.38% had ≥101 employees; 51.92% operated with male-majority workforces; 85.77% were private/foreign/HK-Macao-Taiwan invested. Analytic strategy: One-way ANOVA tested differences in gender employment bias across HR/enterprise characteristics (gender, age, tenure, workforce gender composition, ownership, size, policy understanding). Pearson correlations assessed relationships among job competency, employee change, career development bias, policy impact, and gender employment bias. Multiple linear regression modeled gender employment bias (Y) on job competency (X1), employee change (X2), career development bias (X3), and policy impact (X4): Y=β0+β1X1+β2X2+β3X3+β4X4+e. Diagnostics included VIF and tolerance for multicollinearity.

- One-way ANOVA: Workforce gender composition significantly affected gender employment bias scores (P=0.000), indicating greater bias in male-dominated enterprises, supporting H1. HR gender, age, working years, enterprise size, and ownership were not significant predictors. HR understanding of the three-child policy showed a marginal difference (P=0.075), with very limited cases of non-understanding (n=2). - Correlations (Table 4): Job competency negatively correlated with gender employment bias (r=-0.190, p=0.002). Employee change (r=0.421, p<0.001), career development bias (r=0.602, p<0.001), and policy impact (r=0.336, p<0.001) positively correlated with gender employment bias. - Multiple regression (Adj. R^2=0.402; F=44.49, p<0.05; no multicollinearity: VIFs 1.025–1.405): Y = 4.511 − 0.108X1 + 0.251X2 + 0.523X3 + 0.265X4. Coefficients: job competency B=−0.108 (p=0.026), employee change B=0.251 (p=0.012), career development bias B=0.523 (p<0.001), policy impact B=0.265 (p=0.013). Career development bias had the largest standardized effect (β=0.476). - Interpretation: Lower job competency requirements are associated with higher gender employment bias (H2 supported). Greater perceived impact of the three-child policy is associated with higher gender employment bias (H3 supported). Overall, employers confirm bias against professional women; the three-child policy exacerbates this bias, especially in male-dominated enterprises and low-skilled roles.

Findings address the hypotheses by demonstrating that employer-side perceptions and organizational conditions shape gendered employment outcomes under the three-child policy. Bias is strongest where workforces are male-dominated and in contexts of low job competency requirements, aligning with time poverty and economic rationality frameworks: anticipated costs from maternity leave, disruptions, and welfare burdens heighten employer reluctance to hire/promote women of childbearing age. The policy environment appears to produce negative spillovers on women’s employment prospects, particularly in low-skilled jobs where substitutability is high and dismissal costs are low. This extends prior literature on occupational gender bias by centering employer perspectives and linking fertility policies to labor market discrimination. Policy relevance is high: mitigating employer cost concerns and improving supportive measures (e.g., subsidies, coverage for maternity leave costs) could reduce bias and better align fertility policy goals with gender equality in employment.

The study contributes employer-based evidence from 260 enterprises in Jiangxi Province that the three-child policy has increased employment bias against professional women. Key conclusions: (1) Bias is greater in male-majority enterprises; HR/enterprise demographics (HR gender, age, tenure), enterprise size, and ownership are not primary drivers. Incentives (e.g., subsidies/tax relief) targeted to enterprises employing women and government coverage of maternity-related costs are recommended to reduce bias. (2) Bias is especially pronounced in low-skilled, highly substitutable roles; continuing education and skills training can enhance women’s job competency and employment prospects. Social measures to redistribute household labor and expand training support can further improve outcomes. (3) As understanding and implementation of the three-child policy deepen, employment and promotion challenges for professional women are likely to intensify. Employers’ uncertainty about maternity leave may produce stronger bias against unmarried or one-child professional women. Future research should expand to national samples and conduct heterogeneity analyses across regions and industries.

The sample is limited to enterprises in Jiangxi Province and to HR managers’ perspectives in 2022, which may constrain generalizability beyond the local context. The cross-sectional survey and self-reported measures from employers may not capture causal mechanisms. The authors note plans to expand to a national sample and conduct heterogeneity analyses to improve external validity and deepen industry/regional insights.