1. Introduction
With the rapid transformation of global economies and societies, population issues have become central to national governance and sustainable development. Since the beginning of the 21st century, most countries have faced the dual pressures of sustained fertility decline and accelerating population ageing, profoundly affecting labour supply, social security sustainability, and economic prospects (Sahoo et al., Reference Sahoo, Rout and Jakovljevic2023; Siddig and Charalampous, Reference Siddig and Charalampous2024). As one of the largest developing countries, China has experienced an especially rapid and profound demographic transition. By 2024, the total fertility rate fell to 1.01, entering a typical ‘ultra-low fertility trap’, while the proportion of the population aged 60 and above reached 22.0%, significantly increasing familial and social pressures arising from structural population changes.
China’s unique history of population policies has further shaped current fertility and eldercare patterns. The one-child policy, implemented from the 1980s, compressed family size over decades, substantially reducing potential intergenerational support and concentrating eldercare responsibilities within households. Although the policy formally ended in 2016, transitioning to a universal two-child policy and later a three-child policy in 2021, fertility briefly rebounded but then continued to decline, reflecting ongoing demographic challenges. Policy implementation also varied across urban and rural areas, with exceptions such as rural ‘single-daughter households’ permitted to have two children, producing complex regional differences in population structure and family caregiving patterns. Against the backdrop of policy legacy effects and rapid ageing, childbearing women face increasingly pronounced conflicts between caregiving obligations and fertility decisions.
To address eldercare pressures and weakened family caregiving functions, China has promoted the development of home- and community-based elderly care services (HCBS) since the 13th Five-Year Plan. HCBS aims to establish a multi-layered care system centred on home-based care, supported by community services and supplemented by institutional care, providing diversified services such as in-home assistance, community day care, meal and cleaning services, and rehabilitation support. Since 2016, the Ministry of Civil Affairs and Ministry of Finance have gradually implemented nationwide HCBS pilot reforms, leveraging central fiscal subsidies and local matching funds to support facility construction, operational innovation, and multi-actor participation, thereby offering households institutionalised alternatives and support for caregiving.
Scholars widely acknowledge that, in the context of low fertility and population ageing, relaxing birth restrictions alone is insufficient to increase fertility levels; the crucial factor is enhancing individual fertility intentions, particularly among women of childbearing age (Zhang et al., Reference Zhang, Wang and Zhao2023). The theory of planned behaviour posits that fertility intentions are jointly determined by attitudes, subjective norms, and perceived behavioural control (Fantaye et al., Reference Fantaye, Damtew and Sene2025). At present, women face dual pressures from occupational and family roles (Barbiellini et al., Reference Barbiellini Amidei, Di Addario, Gomellini and Piselli2023), insufficient caregiving resources, and employment penalties, collectively suppressing fertility intentions. While substantial research has examined the role of social security systems, especially pension schemes, in influencing fertility behaviour, relatively little attention has been paid to how social service systems – particularly elderly care services – shape fertility expectations by releasing family resources and improving perceived behavioural control.
Based on this, the study treats the pilot reforms of HCBS, implemented nationwide since 2016, as a quasi-natural experiment. Using data from the 2014–2018 China Labor-force Dynamics Survey (CLDS) and applying a difference-in-differences (DID) approach, it examines the effect of HCBS expansion on the fertility expectations of women of childbearing age. The study makes three primary contributions: (1) it highlights the spillover effects of elderly care policies on non-target populations, enriching the literature on the social consequences of such policies; (2) drawing on policy feedback theory, it develops a framework in which HCBS influences fertility intentions through both resource provision and expectation adjustment; and (3) by leveraging high-quality microdata and a quasi-natural experimental design, it provides robust empirical evidence to inform the development of fertility-friendly social policies.
2. Policy background and theoretical analysis
2.1. Policy background
China’s reform of the elderly care service system is closely linked to the long-term effects of the family planning policy. Since the nationwide implementation of the one-child policy in the late 1970s, family size has shrunk markedly, and the ‘4–2–1’ family structure has become widespread, substantially weakening the traditional family-based eldercare function that relied on multiple children. Although the policy was enforced more flexibly in rural areas – for example, through locally adaptive arrangements such as allowing a second birth when the first child was a girl – the overall decline in fertility remained evident. Even after the policy was fully abolished in 2016, its cumulative impact continues to shape family structures and cannot be reversed in the short term to offset the structural tensions arising from a growing dependent elderly population and expanding care needs. The combination of diminishing family caregiving capacity and accelerating population ageing has made the construction of a public, multi-tiered elderly care service system an urgent task of national governance, providing essential institutional context for the launch of the Home- and Community-Based Services (HCBS) reform pilot.
To actively respond to China’s rapidly accelerating population ageing and to improve the multi-tiered elderly care service system, the central government has continuously promoted institutional development in home- and community-based elderly care since the 13th Five-Year Plan period. The Fifth Plenary Session of the 18th CPC Central Committee explicitly called for ‘building a multi-level elderly care service system that is home-based, community-supported, and institutionally supplemented’, providing the strategic direction for subsequent reforms.
Against this policy backdrop, the Ministry of Civil Affairs and the Ministry of Finance jointly issued the Notice on Supporting the Pilot Reform of Home- and Community-Based Elderly Care Services with Central Government Funds (Minhan [2016] No. 200) in 2016, officially launching the reform pilots. Guided by the principles of ‘central guidance with local leadership’, ‘government-led development with social participation’, and ‘focusing on priorities and piloting first’, the pilots emphasised seven major areas: fostering social service providers, integrating medical and elderly care services, developing smart elderly care, promoting ‘Internet Plus’ service models, strengthening the care workforce, improving service standardisation and regulatory mechanisms, and advancing the construction of urban and rural community elderly care facilities.
From 2016 to 2020, five rounds of pilot reforms were implemented, covering 203 cities and districts across all 31 provinces and creating a large-scale policy experimentation platform spanning both urban and rural regions. Pilot cities were selected through a process of voluntary municipal application, provincial-level review, and central evaluation. The Ministry of Civil Affairs and the Ministry of Finance assessed applicants based on population size, degree of ageing, socio-economic development level, fiscal capacity, and the existing foundation of elderly care services. Because the selection criteria emphasised representativeness and balanced foundational conditions, the final list included provincial capitals as well as a substantial number of medium-sized cities, small cities, and county-level districts. The pilots did not introduce preferential policies unique to specific regions, and the selection process did not systematically favour any particular type of city. As a result, the pilot cities are broadly representative of Chinese cities in general.
Statistical evidence shows that, with the advancement of pilot programmes, China’s elderly care system expanded markedly. By 2020, the number of institutions and facilities nationwide rose from about 140,000 in 2016 to nearly 330,000, more than doubling and establishing a foundational home- and community-based network; by 2024, it reached around 406,000. The reforms not only enhanced service accessibility for older adults but also alleviated caregiving pressures on families, freeing time and reducing emotional burdens for women and other primary caregivers. These innovations provide an important context for China’s elderly care system and a solid basis for examining the external effects of elderly care services on fertility intentions of women of childbearing age.
2.2. Theoretical framework
As a form of public and social welfare policy, HCBS exhibits typical externality characteristics (Cowen, Reference Cowen and Cowen2024). Its impact extends beyond its target population – older adults – to a broader range of social groups, including women of childbearing age, through mechanisms of institutional diffusion and behavioural incentives. These services subtly reshape individual decisions, family structures, and fertility behaviour.
First, elderly care policies externalise the caregiving responsibilities traditionally borne by families through fiscal investment and institutional provision. At the macro level, this public provision enhances the welfare security of older adults. At the micro level, however, it reshapes intergenerational relationships and responsibility structures within the family. According to life cycle theory, individuals, constrained by limited resources, must rationally allocate between ‘child-rearing’ and ‘elder care’ (Cigno, Reference Cigno1993). When the public care system partially substitutes for the traditional family-based model, the instrumental rationale for fertility as an intergenerational investment is weakened. The functional and perceived necessity of family reproduction is thereby partially replaced by institutional mechanisms. Consequently, women of childbearing age may reduce or forgo childbirth as a means of conserving resources that might otherwise be used for future institutional elder care (Mynarska and Rytel, Reference Mynarska and Rytel2023).
Second, the ‘de-familialization’ of elderly care is not absolute. Rather than a full transfer of caregiving duties, it constitutes a functional substitution. In practice, HCBS often rely on the collaborative involvement of family members – especially women – in service delivery. While caregiving burdens appear reduced on the surface, women are frequently transformed from primary caregivers into auxiliary providers. They must still manage responsibilities for raising young children while also assisting in the care of older family members. In response, women may opt for reduced fertility – or none at all – as a strategy to optimise their lifetime resource allocation and pursue self-actualisation. Based on this, we propose the following hypothesis:
H1: HCBS reduce fertility intentions among women of childbearing age.
The development of elderly care services, through the enhancement of community-based care systems and public support networks, has increased women’s perceived community safety. This process can be understood from the perspective of policy feedback theory, encompassing both resource and cognitive effects (Pierson, Reference Pierson1993; Mettler and Soss, Reference Mettler and Soss2004). Institutional provisions not only directly improve the accessibility of care resources but also shape perceptions of future old-age risks. Within this mechanism, perceived community safety serves as a psychological mediator, reflecting subjective assessments of environmental stability and controllable risks, with changes arising from the expansion of institutional support rather than mere experiential judgement. In addition, social capital theory emphasises that stronger community support networks reduce uncertainty and enhance social trust (Putnam, Reference Putnam2000), further reinforcing women’s reliance on public elderly care services and strengthening psychological security.
Perceived community safety, in turn, influences fertility decisions. Drawing on behavioral economics and social choice theory (Becker, Reference Becker and Roberts1960; Willis, Reference Willis1973), fertility intentions are shaped by risk evaluation, cost-benefit trade-offs, and perceptions of future uncertainty. When communities provide reliable care guarantees, women’s reliance on children for old-age support declines, weakening the risk-substitution motive for childbearing (Yang, Reference Yang2025). Moreover, heightened psychological security leads women to consider opportunity costs and personal development choices in their fertility decisions, rather than focusing solely on the functional role of children as old-age security (Deng et al., Reference Deng, Wang and Tao2025). This analysis demonstrates that institutional provisions exert an indirect effect on fertility intentions by shaping psychological expectations. Based on this reasoning, the following hypothesis is proposed:
H2: HCBS reduce fertility intentions among women of childbearing age by increasing perceived community safety.
As HCBS continues to expand, women’s reliance on the pension insurance system has grown, reflecting the resource effect within the policy feedback mechanism. Pension insurance provides older adults with a stable source of economic support and serves as a crucial institutional pillar for implementing HCBS. In practice, pension service policies are often promoted in conjunction with pension insurance, sending a clear policy signal that the state encourages enrolment and advocates institutional elder care over traditional family-based support.
Against this backdrop, the government has steadily increased fiscal investment in pension services, improving the accessibility of pension resources for older adults. This enhances not only their expected quality of life but also raises awareness and willingness among younger workers – particularly women of childbearing age – to participate in pension schemes to secure future access to care services. However, while social security systems raise the level of elder support, they may simultaneously suppress fertility behaviour (Zhang et al., Reference Zhang, Li and Tang2022). On one hand, under a pay-as-you-go pension system, younger generations bear a heavier contribution burden, which reduces their disposable income and crowds out resources for child-rearing (Cipriani and Pascucci, Reference Cipriani and Pascucci2020). On the other hand, as institutional pension mechanisms gradually substitute for the traditional family model, reliance on children for elder support declines, weakening the marginal utility of additional children for household old-age security. This institutional substitution further erodes individual fertility motivations (Ji and Dai, Reference Ji and Dai2023). Based on this, we propose the following hypothesis:
H3: HCBS reduce fertility intentions among women of childbearing age by increasing pension insurance participation.
From the perspective of the resource effect in policy feedback theory, elderly care services alleviate household economic pressure and substitute for family caregiving, thereby objectively improving economic satisfaction. As a key component of these services, HCBS reduce both financial and time burdens on middle-aged families through subsidies, service provision, and medical coordination mechanisms, releasing resources previously allocated to intergenerational care (Yang et al., Reference Yang, Han and Wang2023). This reallocation of resources enhances material security but may also produce a suppressive effect on fertility intentions. In China’s current context – where the cost of raising children is high and universal childcare remains limited – young families tend to direct surplus resources towards improving quality of life, career advancement, or intensive parenting, rather than increasing family size. This mechanism is especially pronounced among emerging middle-class households and women of childbearing age, reflecting the structural constraints of policy feedback on fertility behaviour.
From the perspective of the interpretive effect, institutionalised elderly care conveys a clear shift from ‘family-driven’ to ‘public-driven’ intergenerational support. This policy signal alters individuals’ cognitive frameworks for future risk, encouraging greater reliance on institutional arrangements rather than children for old-age security (Cheung, Reference Cheung2019). As a result, economic satisfaction is no longer merely a subjective assessment of current living conditions but also reflects trust in the welfare system and optimism about future life quality – essentially functioning as a psychological expression of institutional trust. As public care systems gradually replace family-based elder support, the traditional rationale of ‘raising children for old-age support’ loses its necessity, further diminishing the institutional and cultural motivations for fertility. Based on this, we propose the following hypothesis:
H4: HCBS reduce fertility intentions among women of childbearing age by increasing economic satisfaction.
In conclusion, the research framework for this study is presented in Figure 1.
Research framework.
3. Methods
3.1. Data sources
The data used in this study come from CLDS, which covers over 400 communities across 29 provinces (including autonomous regions and municipalities directly under the central government) and includes information from 158 cities. The survey employs a multi-stage stratified probability sampling method, ensuring good national representativeness. The dataset contains multidimensional information on individual fertility intentions, social security, family structure, and community characteristics. As the 2012 wave did not include data on fertility intentions, this study uses the 2014, 2016, and 2018 waves to construct a panel dataset, restricting the sample to women of childbearing age (20–45 years) to ensure the relevance of fertility-related measures.
Regarding the policy variable, the government launched HCBS pilot programmes in 36 cities in three waves between 2016 and 2018: the first wave was announced in November 2016, the second in November 2017, and the third in May 2018. The CLDS survey waves were conducted from July to September in 2014, 2016, and 2018. To accurately code the treatment status, cities were assigned a value of 1 in a given survey wave if the policy had been announced before that wave; otherwise, they were coded as 0. For example, cities in the first wave, announced in November 2016, were coded as 0 in the 2016 survey conducted in July–September. By the 2018 survey, all three waves of policy had been implemented, and all treatment cities were coded as 1.
Based on this coding scheme, a multi-period DID model is constructed, with the pilot cities serving as the treatment group and non-pilot cities as the control group. After excluding missing and outlier observations, the final analytical sample comprises 10,978 woman-year observations; the sample selection process is illustrated in Figure 2. This approach, which codes the policy based on its actual announcement date, accurately reflects the staggered implementation of the programme and ensures a correct distinction between treatment and control groups in each survey wave, thereby allowing for reliable identification of the policy’s impact on women’s fertility intentions.
Sample selection and construction flowchart.
3.2. Variable definitions
The dependent variable is fertility intentions among women of childbearing age. This is measured by the ‘ideal number of children’, which captures an individual’s subjective preference regarding the desired family size. Widely adopted in international demographic research, this variable is regarded as a stable proxy for fertility attitudes and cultural orientation. The data are drawn from responses to the CLDS question: ‘What do you think is the ideal number of children for a family?’
The key independent variable is the treatment effect of the HCBS policy, operationalised as the interaction between a binary variable indicating whether the individual resides in a pilot city and a binary variable representing the policy implementation period. This interaction term captures whether the respondent is exposed to the policy, with a value of 1 indicating exposure (ie., being in a pilot city during the implementation period) and 0 indicating no exposure.
The mediating variables include perceived community safety, pension insurance participation, and economic satisfaction. Perceived community safety is based on respondents’ answers to the question ‘How safe do you consider your community?’, and the variable has been reverse-coded so that higher values indicate a stronger perception of safety. Pension insurance participation is a binary indicator coded as 1 if the respondent is enrolled in any type of pension insurance and 0 otherwise. Economic satisfaction is measured by the respondent’s reported level of satisfaction with their household’s economic situation, with higher scores representing greater satisfaction.
To mitigate potential endogeneity concerns arising from omitted variable bias, the model includes a series of control variables that may influence fertility intentions. Following the approach of Li (Reference Li2025), Ray et al. (Reference Ray, Harcey, McQuillan and Greil2020), and Li et al. (Reference Li, Wang, Zheng and Xu2025), both individual- and family-level characteristics are included. These controls comprise age, education level, political affiliation, household registration type, health status, and employment status; at the individual level, and number of siblings, marital status, household income, and number of children at the family level.
The specific variable definitions and descriptive statistics are presented in Table 1.
Variable definitions and descriptive statistics
3.3. Model specification
To assess the impact of elderly care services on the fertility intentions of women of childbearing age, a widely adopted empirical strategy treats the pilot implementation of HCBS as an exogenous shock – ie. a quasi-natural experiment – and employs a DID approach to estimate the policy effect. The baseline regression model is specified as follows:
${Y_{ict}} = {\alpha _0} + {\beta _1}DI{D_{ct}} + {\beta _2}Control{s_{ict}} + {\gamma _c} + {\delta _t} + {\varepsilon _{ict}}$
where the subscripts i, c, and t denote the individual, city, and year, respectively.
${Y_{ict}}$
is the dependent variable, representing the fertility intentions of individual i in city c at time t.
$DI{D_{ct}}$
is defined as the interaction between a dummy variable indicating whether city c is a HCBS pilot city and a dummy variable indicating the post-policy period, capturing the differential change in fertility intentions in pilot cities relative to non-pilot cities after the policy implementation.
$Control{s_{ict}}$
represents the vector of control variables.
${\gamma _c}$
and
${\delta _t}$
denote city and year fixed effects, respectively.
${\varepsilon _{ict}}$
is the random error term. Robust standard errors are used, clustered at the city level to account for potential within-cluster correlation.
4. Results
4.1. Baseline regression
Table 2 reports the baseline regression results. Model (1), which controls only for time and regional fixed effects, shows that the coefficient of the HCBS pilot policy on fertility intentions is –0.1470 (p < 0.01). Model (2) further incorporates individual-level controls, yielding a coefficient of –0.1329 (p < 0.01). Model (3) additionally includes household-level controls, with the coefficient estimated at –0.1196 (p < 0.01). Overall, the results indicate that the implementation of the HCBS pilot policy significantly reduces fertility intentions among women of childbearing age.
Baseline regression results
Standard errors clustered at the city level are in parentheses.
*p < 0.1, **p < 0.05, ***p < 0.01.
4.2. Robustness checks
Parallel trend test. The DID approach relies on the parallel trend assumption, which posits that before policy implementation, trends in fertility intentions should be similar between the treatment and control groups. This study conducts an event study analysis using the period prior to policy implementation as the baseline. Figure 3 plots the estimated coefficients with 95% confidence intervals. The horizontal axis marks the timing of policy implementation, while the vertical axis represents policy effects across time periods. The results show that the pre-treatment coefficients fluctuate around zero and lie within the 95% confidence intervals, indicating no significant difference in trends of fertility intentions among women of childbearing age between pilot and non-pilot regions before the policy took effect. After policy implementation, the coefficients become significantly negative, suggesting that HCBS suppresses fertility intentions, and this effect does not stem from pre-existing trend differences. These findings support the robustness of the baseline results.
Parallel trend test.
PSM-DID. Given that the selection of HCBS pilot cities was not fully random, potential sample self-selection bias may exist. Although the DID approach can partially mitigate endogeneity arising from unobservable factors, systematic differences in observable characteristics between the treatment and control groups may still remain. To address this concern, we conduct a robustness check using the Propensity Score Matching–Difference-in-Differences (PSM-DID) approach. Specifically, propensity scores are estimated based on all control variables, including age, educational attainment, political affiliation, household registration, health status, employment status, number of siblings, marital status, household income, and number of children. Matched samples are then obtained using radius matching (caliper = 0.01), four-nearest-neighbour matching, and local linear regression matching, after which the treatment effect is re-estimated controlling for time and regional fixed effects.
As shown in Table 3, under the three matching methods, the estimated policy effects are –0.1189 (p < 0.01), –0.1017 (p < 0.01), and –0.0866 (p < 0.05), respectively. The direction and statistical significance of these estimates are largely consistent with the baseline regressions, indicating strong robustness of the findings. This suggests that the HCBS pilot policy significantly reduces fertility intentions among women of childbearing age.
Robustness checks: PSM-DID
Standard errors clustered at the city level are in parentheses.
*p < 0.1, **p < 0.05, ***p < 0.01.
Placebo test. To account for potential biases arising from both observable and unobservable factors influencing policy implementation – and given that the true policy effect is unobservable – this study conducts a placebo test by constructing a ‘pseudo treatment group’. Specifically, while keeping the sample structure unchanged, policy pilot cities are randomly assigned based on the actual timing of HCBS implementation across cities. This simulation is repeated 500 times to generate a distribution of placebo treatment effects. As shown in Figure 4, the distribution of the pseudo coefficients is centred around zero and approximately follows a normal distribution, with most p-values exceeding 0.1. The actual estimated coefficient from the baseline regression (−0.1196) significantly deviates from this distribution, suggesting that the observed policy effect is unlikely to be driven by random chance, thus affirming the robustness of the baseline results.
Placebo test.
4.3. Mechanism analysis
The previous section demonstrates that HCBS significantly reduces fertility intentions among women of childbearing age. This section further explores the underlying mechanisms driving this effect. Specifically, Models 1–2, 3–4, and 5–6 in Table 4 examine the mediating roles of perceived community safety, participation in pension insurance, and economic satisfaction, respectively. The regression results suggest that the expansion of HCBS lowers fertility intentions by enhancing perceived community safety, increasing pension insurance participation, and improving economic satisfaction.
Mechanism test
Standard errors clustered at the city level are in parentheses.
*p < 0.1, **p < 0.05, ***p < 0.01.
To test the statistical significance of the mediating effects, the study applies the Bootstrap method. A mediating effect is deemed significant if the corresponding Bootstrap confidence interval (CI) does not include zero. As presented in Table 5, the indirect effects of perceived community safety (95% CI: −0.0048, −0.0003), pension insurance participation (95% CI: −0.0058, −0.0006), and economic satisfaction (95% CI: −0.0049, −0.0004) are all statistically significant. These results are consistent with the preceding regression analyses, confirming the robustness of the identified mediating pathways.
Bootstrap method for mediating effect testing
4.4. Heterogeneity analysis
Having established the overall negative effect of HCBS on fertility intentions among women of childbearing age, we further conduct subgroup regressions to examine heterogeneity in policy effects. Table 6 reports the results by only-child status, fertility status, urban/rural residence, and age.
Heterogeneity analysis of fertility intentions
Standard errors clustered at the city level are in parentheses.
*p < 0.1, **p < 0.05, ***p < 0.01.
Models 1 and 2 assess heterogeneity by only-child status. The results show that the policy effect is only marginally significant for only-child women (β = −0.1777, p < 0.1), whereas it significantly suppresses fertility intentions among non-only-child women (β = −0.1030, p < 0.01). Models 3 and 4 group respondents by fertility status. The policy effect is not significant for women without children (p > 0.1) but is significantly negative for women who already have children (β = −0.1087, p < 0.01). Models 5 and 6 examine urban–rural differences. HCBS significantly reduces fertility intentions among rural women (β = −0.0976, p < 0.05), whereas the effect is not significant for urban women. Models 7–9 further stratify the sample by women’s childbearing age groups (<25, 25–34, and ≥35 years). The policy effect is marginally significant among women younger than 25 (β = −0.1794, p < 0.1), becomes stronger and highly significant for those aged 25–34 (β = −0.1475, p < 0.01), and remains significantly negative but smaller in magnitude among women aged 35 and above (β = −0.0813, p < 0.05).
Overall, these findings suggest that the fertility-reducing effect of the HCBS pilot policy is more pronounced among non-only-child women, women with children, rural women, and those in their prime childbearing years (aged 25–34).
5. Discussion
This study employs the nationwide rollout of the HCBS reform pilot since 2016 as a quasi-natural experiment. Using data from the 2014–2018 CLDS, a multi-period DID approach is adopted to identify the effects of HCBS expansion on the fertility intentions of women of childbearing age. The findings are as follows:
First, HCBS policy is significantly negatively associated with fertility intentions among women of childbearing age. On average, the policy reduces the expected number of children by approximately 0.1196. This result provides new empirical evidence that elderly care institutions shape family fertility decisions and aligns with the ‘fertility–elder care substitution mechanism’ proposed in existing literature. For example, Danzer and Zyska (Reference Danzer and Zyska2023) found that a more than threefold increase in pension wealth led to an 8% decline in short-term fertility probability and a reduction of about 1.3 children in the long run. While previous studies have mainly focused on macro-level pension systems or micro-level determinants such as female education (Kim, Reference Kim2023) and family stress (Zhao et al., Reference Zhao, Qi, Cheng, Hao, Yuan, Jin, Wang, Lv, Wu and Hu2024), few have examined the micro-level behavioural impact of specific elderly care policies. In contrast, this study treats the HCBS reform as a quasi-natural experiment and extends the analytical lens to the behavioural outcomes of public service systems. It identifies how institutional care provision can affect women’s fertility expectations through institutional pathways, offering a robust causal supplement to the literature on fertility–elder care substitution. Beyond empirically confirming the suppressive effect of elderly care services on fertility intentions, this study deepens the discourse on how social policies are embedded in household decision-making and underscores the crucial role of institutional design in shaping individual fertility behaviour.
Second, the effect of the HCBS pilot policy exhibits heterogeneity across population subgroups. The fertility-suppressing effect is particularly pronounced among women who are non–only children, have existing children, reside in rural areas, and those in their prime childbearing ages (25–34). At the micro level, these findings reveal differentiated mechanisms through which elderly care services affect fertility intentions, contingent on family structure and individual resource endowments. Women with siblings are more responsive to the policy. Compared with only children, women from larger families typically grow up in households where siblings share responsibilities for supporting parents (Chanfreau and Goisis, Reference Chanfreau and Goisis2024). This experience reduces their concentrated eldercare obligations in adulthood. Consequently, when institutionalised elderly care services improve, they are more likely to perceive these services as a substitute for future old-age support, which in turn manifests as a stronger fertility-suppressing response. Women with existing children also exhibit a stronger policy response. Having children indicates that part of their fertility plan has already been fulfilled, leaving relatively little flexibility for additional childbearing. In this context, HCBS policies reduce the reciprocal demand between fertility and eldercare, making women with children more likely to refrain from further fertility, thereby exhibiting stronger fertility-suppressing effects.
The policy effect is particularly salient among rural women. Rural areas have long suffered from weak formal elderly care provision, making families highly dependent on intergenerational support (Liu and Liu, Reference Liu and Liu2024). When pilot HCBS policies provide institutionalised care substitutes, rural women experience a stronger substitution effect, thereby reducing their reliance on childbearing as a form of old-age security. This aligns with prior literature showing that formal elderly care can weaken the fertility function of families (Wang and Liu, Reference Wang and Liu2024). The suppressive effect is more pronounced among women in their prime childbearing years, particularly those aged 25–34. Women in this age group are at a critical window for fertility decision-making (Willan et al., Reference Willan, Gibbs, Petersen and Jewkes2020), and their fertility plans remain relatively adjustable and responsive to changes in public service provision. By contrast, women under age 25 may not yet have fully entered the stage of concrete fertility planning, while for those aged 35 and above, fertility arrangements are more likely to have stabilised or to be constrained by biological factors, thereby attenuating the magnitude of the policy effect. Moreover, compared with older cohorts, women in the core childbearing stage of the younger generation tend to rely less on traditional familism and norms of intergenerational reciprocity (Padilla et al., Reference Padilla, McHale, Rovine, Updegraff and Umaña-Taylor2016). In a context of expanded elderly care provision, this group is therefore more inclined to reassess fertility decisions in light of risk substitution and opportunity cost considerations, resulting in a more pronounced suppressive policy effect. Overall, these results indicate that HCBS policies not only reduce aggregate fertility intentions but also have asymmetrical effects across different social groups. This finding enriches our understanding of the relationship between elderly care policies and fertility behaviour, highlighting that public service systems do not exert uniform effects but instead interact with family structure, resource constraints, and life-cycle stages in complex ways. Future policy design should aim to expand access to elderly care while simultaneously assessing potential fertility responses among different female subgroups, thereby promoting coordination between fertility support policies and elderly care service provision to prevent misalignment of policy objectives.
Third, the mechanism analysis indicates that the development of elderly care services suppresses fertility intentions among women of childbearing age through three channels: perceived community safety, pension insurance participation, and economic satisfaction. Improvements in women’s perceived community safety reduce their reliance on children for old-age support. When communities provide more comprehensive care and support, women can depend on institutional protection to manage future ageing risks, thereby diminishing the motivation to rely on childbirth for old-age security. This extends the findings of Lei et al. (Reference Lei, Bai, Hong and Liu2022) regarding the role of elderly care services in alleviating household caregiving burdens and further highlights how the institutional environment shapes expectations and fertility planning. Higher pension insurance participation provides tangible institutional resources, diversifying avenues for securing old-age protection and reducing dependence on children for this purpose. This mechanism reflects the resource effect of policy (Pierson, Reference Pierson1993) and differs from studies that primarily emphasise the time-substitution function of care (Zhong et al., Reference Zhong, Lu, Chen and Zeng2025), demonstrating that institutional coverage alone can indirectly influence fertility intentions by reshaping expectations. Trust in the system and experiences of participation illustrate that policy not only modifies external constraints but also alters perceptions of family obligations and ageing risk.
Enhanced economic satisfaction affects women’s cost–benefit considerations in fertility decisions. When household life is more secure, women weigh opportunity costs and personal arrangements more heavily, rather than making reproductive choices to fulfil old-age security functions. This finding aligns with Kato (Reference Kato and Hara2021) regarding the influence of economic conditions on fertility behaviour and complements analyses of fertility changes under improved social welfare provision (Zhang et al., Reference Zhang, Wang and Zhao2023). By improving both economic and institutional security, elderly care services enhance living stability while reducing the functional necessity of children, resulting in lower fertility intentions. Taken together, elderly care services exert complex effects on fertility by strengthening both institutional protection and economic conditions. Policies alter external caregiving conditions and indirectly influence fertility through adjustments in risk perception and decision-making expectations. These findings extend the application of policy feedback theory to the study of elderly care policies and fertility behaviour in China, highlighting the importance of considering potential unintended fertility responses in the design and evaluation of elderly care policies and providing empirical guidance for the alignment of population and social protection policies.
Another important finding is that HCBS significantly enhances daughter preference, which, in turn, positively influences fertility intentions – a mechanism rarely emphasised in existing research. While most studies highlight the fertility-promoting effect of son preference (Ibupoto et al., Reference Ibupoto, Shah and Loong2025), this paper shows that, in a context of expanding public services and rising gender equality, women’s preference for daughters may increase due to daughters’ unique roles in emotional bonding and responsive caregiving. This shift may generate new motivation for childbearing. However, this mechanism is largely overshadowed by the negative effects of institutional confidence and thus difficult to observe in the aggregate estimates. This study is the first to propose that daughter preference functions as a masked positive mechanism, offering a novel theoretical lens for evaluating the multifaceted effects of social policy. Accordingly, relying solely on the expansion of elderly care services is insufficient to reverse the downward trend in fertility. A coordinated approach integrating care and fertility policies is essential to ensure that women, while gaining security, maintain their fertility intentions. Special attention should be paid to women’s increasing identification with daughters as a source of emotional fulfilment, which may become a foundational value in constructing the next generation of families.
This paper contributes in three major ways. First, on the theoretical front, it incorporates the fertility–elder care substitution mechanism into the analytical framework of social service policy, moving beyond the conventional paradigm that treats elderly care systems as background variables. It introduces the novel proposition of intergenerational spillover effects of elderly care policies on non-target groups – namely, women of childbearing age – thus enriching the theoretical understanding of how social policy shapes household decision-making. Second, in terms of empirical strategy, the study constructs a quasi-natural experiment based on the HCBS reform pilot, employs a DID approach, and conducts multiple robustness checks, strictly following causal identification principles to ensure credible conclusions and relevant policy implications. Third, in mechanism identification, the study reveals in detail how HCBS suppresses fertility intentions by increasing pension insurance participation and economic satisfaction, while simultaneously stimulating fertility through enhanced daughter preference – though the latter effect is masked. The proposed perspective of masked mechanisms offers a new analytical tool for understanding the complex behavioural consequences of public service policies.
Despite the empirical evidence presented in this study regarding the effects of HCBC on the fertility intentions of women of reproductive age, several limitations remain. First, the available data cover only the early stage of policy implementation, making it difficult to assess long-term policy effects. Future research could draw on datasets with longer time spans to examine the persistence and potential stage-specific evolution of policy impacts. Second, the measurement of fertility behaviour and related mechanisms is constrained by the survey design. The data do not include information on the number of children at specific ages, the sex of the firstborn, or other detailed behavioural indicators. As a result, this study cannot further test how these factors moderate policy effects, nor can it directly evaluate the impact of the policy on actual fertility behaviour. Subsequent research could utilise surveys that capture more comprehensive fertility histories or link survey data with administrative birth records to better distinguish policy responses across fertility stages and family structures. Finally, the study does not fully capture regional variation in institutional supply. The pilot policy is treated as a uniform intervention, without differentiating across regions in service content, fiscal support, or implementation intensity. Future work may incorporate policy documents and implementation details to construct more finely grained institutional measures, thereby improving the precision and explanatory power of policy effect identification.
6. Conclusion
This study takes HCBS pilot policy as a quasi-natural experiment and utilises data from the 2014–2018 CLDS to systematically evaluate the impact of the expansion of HCBS on the fertility intentions of women of childbearing age. The analysis further examines the underlying mechanisms and heterogeneity across different population groups. The results indicate that the HCBS pilot policy significantly suppresses fertility intentions, reducing the expected number of children by approximately 0.1196 on average. The negative effect is particularly pronounced among women with siblings, those who already have children, rural residents, and those in their prime childbearing ages (25–34), suggesting that public eldercare services exert differentiated impacts depending on family structure and life-cycle stage. Mechanism analysis identifies three primary channels through which HCBS influences fertility intentions: enhancing perceived community safety, which improves women’s psychological expectations regarding future eldercare risks and thereby reduces the need to rely on childbearing for old-age security; increasing participation in pension programmes, providing tangible institutional resources that reduce dependence on children; and improving economic satisfaction, prompting women to more carefully weigh opportunity costs and life arrangements in their fertility decisions.
Based on these findings, the study proposes the following policy recommendations. First, integrate eldercare and fertility-support policies. While ensuring high-quality eldercare services, policies should be combined with childcare subsidies, early childhood care support, and work-family balance initiatives to both secure older adults’ well-being and alleviate reproductive pressures on women of childbearing age, thereby promoting higher fertility intentions. Second, adopt targeted interventions that account for group heterogeneity. Policies should be tailored to women’s family structures and resource endowments. For instance, rural women and those with existing children could receive additional childcare support and flexible work arrangements, whereas women in their prime childbearing ages could be offered incentives that facilitate both career development and childbearing. Third, strengthen institutional trust and service accessibility. By providing diversified and high-quality eldercare services, particularly in underdeveloped regions, women’s confidence in the pension system can be enhanced, reducing the functional necessity of childbearing for old-age security and promoting the sustainability of social protection systems. Fourth, establish dynamic evaluation and policy iteration mechanisms. Continuous assessment and monitoring of HCBS policy effects, informed by changes in social structure, population trends, and women’s fertility behaviours, can ensure timely optimisation of policy design and foster coordinated development between eldercare and fertility policies.
Data availability statement
The data used in this study are publicly available from the China Labor-force Dynamics Survey (CLDS) at http://css.cssn.cn/css_sy/fzshxsjzy/201712/t20171212_3778450.html. Researchers can access the data by registering on the CLDS website. The use of these de-identified, secondary data does not require additional ethical approval. Detailed documentation and any supplementary materials can be obtained from the corresponding author upon reasonable request.
Author’s contribution
Zhiying Li, as the first author, was primarily responsible for the design and implementation of the study, data collection and analysis, and manuscript writing. Longhua Zheng provided significant contributions to the optimisation of research methods and technical support, as well as participating in discussions and editing of the manuscript. Yunhui Wang assisted with the experimental work, contributed to data analysis, and participated in the revision and refinement of the manuscript. All authors reviewed the manuscript.
Financial support
This research received no specific grant from any funding agency, commercial, or not-for-profit sectors.
Competing interests
The authors declare no conflicts of interest.
Ethics statement
This study is based on secondary analysis of publicly available data from the China Labor-force Dynamics Survey (CLDS) conducted by the Center for Social Science Survey at Sun Yat-sen University. The data used in this research span from 2014 to 2018 and are fully anonymised, containing no identifiable personal information. Since the data are publicly accessible and have undergone ethical review by the data-collecting institution, no additional ethical approval was required for this study. The research complies with all relevant ethical standards for the use of secondary data.
Clinical trial number
Not applicable.
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