Population Research

How Does the Spatial Distribution of High-Quality Compulsory Education Resources Affect Fertility Intentions? Evidence from 35 Major Chinese Cities

Zhang Anquan, Zou Lailiang, Ni Pengfei

2026, 50(1): 3-19.

Asbtract ( )

References | Related Articles | Metrics

China's urban fertility rate continues to decline. Reducing the costs associated with childbearing, childrearing, and educating children has become a core issue in population policy. The nine-year compulsory education stage, comprising primary and junior high school, constitutes a fundamental component of basic education. During this stage, families generally seek to ensure that their children can access relatively high-quality education to avoid falling behind at the starting point of schooling. However, the policy of “enrollment by school district and proximity-based admission” limits children's school options based on their residential location. Consequently, the spatial distribution of high-quality compulsory education schools within cities may directly influence households' education-related costs in areas such as housing, commuting, and extracurricular tutoring, thereby affecting fertility intentions. This study aims to empirically test these mechanisms and propose feasible approaches to enhance urban residents' fertility intentions.

Based on school rankings from multiple educational consulting platforms, this study constructs an index capturing the within-city spatial equity of high-quality compulsory education resources for a sample of 35 major Chinese cities. This index is subsequently merged with data from the 2017 China Household Finance Survey (CHFS) to empirically assess its impact on urban residents' fertility intentions. The results show that a one-standard-deviation increase in the spatial equity index is associated with an approximately 5.4% higher level of fertility intentions. This finding remains robust across multiple sensitivity checks, including replacing the measurements of key variables and conducting random sampling regressions. Further mechanism analysis indicates that, for households with children aged 4 to 15, a one-standard-deviation increase in the equity index is associated with a decrease of about 20.7% and 12.8% in the unit price and market value of their houses, respectively. Simultaneously, the probability of respondents facing a heavier commuting burden decreases by approximately 2.4%, and the proportion of those whose heavier commuting burden is attributable to their children's education drops by about 6.5%. In addition, for households with children aged 7 to 15, expenditures on extracurricular tutoring decrease by around 4.8%.

Previous studies examine the relationship between residents' fertility intentions and their children's education primarily from the perspective of education costs or parents' educational preferences. This study investigates this relationship from the supply side. By focusing on the influence of spatial distribution of high-quality compulsory education resources on fertility intentions, this study provides empirical evidence for understanding the underlying link between fertility-supportive policies and educational development strategies.

The findings have three key policy implications. First, the spatial equity of high-quality compulsory education resources within cities should be enhanced. Second, measures such as improving school district management systems, optimizing transportation infrastructure and school bus services around campuses, and expanding affordable after-school programs should be implemented to reduce the additional costs families bear in housing, commuting, and extracurricular tutoring in pursuit of high-quality education. Third, education resources planning and coordination mechanisms aligned with demographic changes should be established to stabilize families' expectations regarding access to quality education.

The Transformation of Older Adults' Perceptions of Government's Responsibility for Elderly Care under the Vision of “Ageing in Place”: Evidence from the Pilot Reform of Home and Community-Based Elderly Care Services

Li Long, Ma Qifeng, Sun Kexin

2026, 50(1): 20-35.

Asbtract ( )

References | Related Articles | Metrics

With the rapid ageing of the population and the trend toward smaller family sizes in China, the demand for community-based home elderly care services increasingly outstrips supply. Developing community-supported home care has become a critical measure to fulfill the vision of “ageing in place” for the vast elderly population. To this end, China launched a pilot reform of Home and Community-Based Elderly Care Services (HCECS) in 2016. This government-led reform aims to enhance the accessibility and perceived availability of services, directly addressing the urgent need for ageing in place while continuously signaling a shared responsibility for elderly care. This may subtly influence older adults' perceptions of government responsibility for elderly care.

Using five waves of unbalanced panel data from the China Longitudinal Aging Social Survey (CLASS) spanning 2014 to 2023, this study focuses on individuals aged 60 and above and employs a staggered difference-in-differences approach. It investigates the baseline impact of the HCECS pilot on older adults' perceptions of government responsibility for elderly care, explores underlying mechanisms, and examines heterogeneous effects. The research not only helps reveal how changes in the elderly care service delivery model reshape individual perceptions of responsibility attribution and the boundaries of public welfare provision, thereby deepening our understanding of how perceptions of government responsibility are formed, but also directly relates to whether policies can effectively shape social expectations, clarify government functions, and enhance the overall effectiveness of the elderly care service system. Thus, it holds significant theoretical and practical implications.

The findings indicate that the HCECS pilot significantly strengthens older adults' perceptions of government responsibility for elderly care, and the results remain robust after a series of sensitivity tests. Mechanism analysis reveals that the HCECS pilot reinforces the tendency to attribute responsibility to the government by reducing financial support from adult children, while simultaneously generating a masking effect through increased instrumental and emotional support from children. Heterogeneity analysis shows that the policy impact is particularly pronounced among older adults with lower education levels, poorer family economic status, and better health conditions. Based on these findings, the study proposes recommendations in three areas： first, optimizing institutional design to guide older adults toward rational expectations of shared responsibility for elderly care; second, improving family-community collaboration mechanisms to achieve complementary integration between formal and informal support; and third, identifying policy-sensitive groups to implement targeted service delivery and perception guidance strategies.

This study extends beyond prior policy evaluations that often focus on objective outcomes such as health and economic status. By adopting a cultural perspective, it explores how the HCECS pilot reshapes older adults' perceptions of government responsibility, thereby broadening the scope of policy assessment research. Additionally, the quasi-experimental design helps identify causal policy effects, addressing previous limitations in establishing robust causal inference regarding the relationship between elderly care security policies and individual perceptions of government responsibility. Furthermore, this study thoroughly analyzes and verifies the mechanisms and heterogeneity of the policy effects, providing new empirical evidence for understanding how policies reshape older adults' perceptions of government responsibility for elderly care.

The Relationship between Childcare Experience and Child Development: Evidence from CFPS

Jin Guangzhao, Zheng Boyan

2026, 50(1): 36-50.

Asbtract ( )

References | Related Articles | Metrics

Childcare in the first three years coincides with a critical window of early development, offering a growth environment that is distinctive from family care in terms of daily routines, motor activities, social networks, and interactional contexts, and thus may have long-term implications for child development. Against the backdrop of low fertility and rapid population ageing, China has placed high expectations on childcare as part of its strategy for high-quality population development, yet empirical evidence on its child developmental consequences remains limited. Therefore, this study examines how early childcare services are associated with children's multidimensional development in China.

Drawing on data from the China Family Panel Studies (CFPS) spanning 2010 to 2022, we link children's childcare experiences before age 3 to developmental outcomes in physical and health, cognitive-academic, and social-emotional domains observed between ages 3 and 15. Depending on the type of outcome measures, we use ordinary least square model, linear mixed effect model, and logistic random effects model for empirical analyses. To address non-random selection into childcare, we control for individual and family background characteristics, and further implement an advanced sensitivity analysis to confirm that our major findings are robust to unmeasured confounders that are up to three times as strong as those already adjusted for.

The findings indicate substantial selection into childcare service use： children born to better-educated parents, with non-agricultural Hukou, whose mothers had their first birth at an older age, and of lower birth order are more likely to receive early childcare services. Conditional on these factors, in the physical and health dimension, early childcare experience is significantly and negatively associated with stunting and illness frequency, and these effects are relatively stable over time. Meanwhile, early childcare has no significant relationship with overweight or obesity. In the cognitive-academic dimension, early childcare is not significantly related to verbal and numerical cognitive skills after 10, but it is associated with parents' positive and lasting ratings of academic performance in Chinese and mathematics, suggesting a modest yet sustained advantage in school achievement. In the social-emotional dimension, early childcare is not significantly associated with peer relationships, self-esteem or locus of control, but it shows a negative link with the sense of responsibility. This pattern is consistent with concerns that early and prolonged separation from parents is related to weakened parent-child attachments and limited social-emotional development. International evidence suggests, however, that high-quality childcare service and parenting interaction can mitigate or avoid potential social-emotional risks associated with early childcare experience.

This study provides new population-based evidence from China regarding the potential benefits and trade-offs of early childcare services on children's development. The results indicate that early childcare experience can support children's physical and health development and academic performance, while raising cautions about possible adverse effects on specific aspects of social-emotional development. These findings underscore the need to accelerate the development of an inclusive childcare service system in China. In doing so, it is essential to increase investment in high-quality childcare provision and strengthen coordination between childcare providers and families.

How Does Artificial Intelligence Development Affect Employment Quality?

Shen Ke, Shi Xiaofeng, Zhang Anni

2026, 50(1): 51-67.

Asbtract ( )

References | Related Articles | Metrics

As a core technology driving technological revolution and industrial transformation, artificial intelligence (AI) is profoundly reshaping the labor market. While existing research has extensively examined its impact on employment quantity, studies focusing on employment quality remain relatively limited. Given the breakthrough advances in AI technologies and their deepening penetration across industries, a systematic investigation into how AI development affects workers' employment quality carries significant theoretical and practical relevance,particularly for advancing the policy goal of “promoting high-quality and full employment.”

In this context, this study aims to clarify the impact of AI development on workers' overall employment quality and its sub-dimensions, explore the underlying mechanisms, and examine heterogeneity across different groups. To address these issues, this study combines city-level AI patent data with individual-level data from the China Family Panel Studies (CFPS), constructing a comprehensive employment quality index at the worker level and an AI patent density indicator at the city level. A fixed-effects model is employed for baseline regression analysis. To address potential endogeneity concerns, this study further applies an instrumental variable approach, the Heckman two-step method, and an exogenous shock identification strategy. In addition, a series of robustness checks are conducted.

The main findings are as follows. First, AI development significantly improves workers' overall employment quality： a one-standard-deviation increase in city-level AI patent density raises employment quality by 2.21%. This result holds after accounting for endogeneity and conducting multiple robustness tests. Second, analysis of the sub-dimensions of employment quality shows that AI development significantly increases labor income, improves welfare security, enhances job stability, and reduces the risk of overwork, while its effect on job satisfaction is statistically insignificant. Third, mechanism analysis indicates that AI development improves employment quality mainly by promoting occupational upgrading, strengthening human capital accumulation, and improving job-skill matching. Fourth, heterogeneity analysis indicates that the employment quality enhancement effect of AI development varies across groups. The positive effects are more prominent among workers with lower employment quality, female workers, and those with stronger non-cognitive skills.

Based on these findings, this study proposes several policy implications： actively expanding new forms of human-machine collaboration to create more high-quality jobs; building a lifelong learning system to facilitate workers' skill upgrading; and integrating non-cognitive skill development into the education system to enhance workers' comparative advantages in human-machine collaboration. Together, these measures can help achieve broad-based improvement in employment quality in the AI era. In summary, through rigorous empirical analysis, this study provides new micro-level evidence and theoretical explanations for understanding the evolution of employment quality in the AI era. It deepens theoretical insights into the role of technology in empowering workers, and offers more targeted policy implications for guiding AI development toward the promotion of high-quality employment and the creation of a more equitable labor market.

The Impact of Artificial Intelligence Development on the Long-term Settlement Intention of the Floating Population

Yu Yunjiang, Chen Yumeng, Gao Xiangdong, Liu Jianghui

2026, 50(1): 68-85.

Asbtract ( )

References | Related Articles | Metrics

While previous studies have primarily focused on the impact of artificial intelligence (AI) on labor markets, its effects on population migration and settlement behavior remain insufficiently explored. How does AI development reshape the long-term settlement intentions of migrants through labor market mechanisms? Does this effect exhibit significant heterogeneity across different skill levels which migrants have? To address these questions, this study draws on data from the China Migrants Dynamic Survey (CMDS) (2012-2018) and Baidu migration data (2019-2024) to systematically examine the impact of AI development on migrants' long-term settlement intentions.

The results indicate that AI development significantly increases migrants' long-term settlement intentions, this conclusion remains robust after various robustness checks and addressing endogeneity concerns. Mechanism analysis reveals that, at the micro level, AI development enhances migrants' long-term settlement intentions by raising their income levels and labor market participation, thereby improving economic returns and employment stability. At the city level, AI development fosters migrants' long-term settlement intentions by stimulating urban economic growth, optimizing public services provision, and enhancing urban amenities. Heterogeneity analysis further demonstrates that the positive effect of AI development is more pronounced among high-skilled and high-income migrants, as well as those engaged in non-routine cognitive tasks, whereas low-skilled, low- to middle-income migrants, and those performing routine, easily replaceable tasks benefit significantly lesser. Further analysis reveals that AI development also exerts a notable positive effect on population migration behavior.

This study contributes to the literature in three main ways. First, in terms of research content, it integrates AI development into the analytical framework of population migration by focusing settlement intentions, thereby deepening the understanding of the nexus between technological change and population dynamics. Second, regarding research design, unlike most existing studies that rely on industrial robot adoption as a proxy for AI, this paper extracts firms' business scope data from the National Enterprise Credit Information Publicity System. By leveraging Large Language Models (LLMs) for keyword filtering, it constructs a city-level indicator of AI enterprise density. This approach more accurately measures the practical application and industrialization of AI, overcoming the manufacturing bias of robot-based data. Third, from a research perspective, this paper moves beyond the conventional view of migrants as a homogeneous group. By focusing on skill structures, it reveals the heterogeneous settlement decisions under technological shocks, providing new empirical evidence for the evolution of demographic structures.

Theoretically, this study elucidates how AI influences migrants' settlement intentions through labor market channels and urban amenities, enriching the discourse on migration. Practically, it advocates for inclusive AI development policies and the establishment of universal, forward-looking lifelong learning and reskilling systems. Particular emphasis should be placed on supporting low- and middle-skilled and low-income groups, ensuring that the dividends of AI development are shared more broadly to promote the synergy between technological progress and high-quality population development.

Artificial Intelligence Focus, Labor Skill Structure, and Employment Demand: Evidence from Recruitment Data of Listed Companies

Sun Meng, Lin Jianxin

2026, 50(1): 86-103.

Asbtract ( )

References | Related Articles | Metrics

In the context of artificial intelligence (AI) transforming labor markets, this study shifts focus from the downstream effects of AI adoption, such as job displacement, to the earlier stage of corporate strategic thinking. We analyze Chinese A-share listed companies from 2007 to 2023. A company-level measure of AI focus is constructed using text analysis in annual reports, capturing the company's AI focus. We precisely measure employment demand by leveraging large-scale online recruitment data from major Chinese platforms. This allows us to track real-time dynamics in AI-related job postings, including variety, scale, required education, experience, and salary offerings. The research examines how a company's AI focus influences AI-related employment demand.

Empirical findings show that greater AI focus significantly increases both the variety and number of AI job postings, confirming that it creates new demand. This effect depends on a company's internal labor conditions. Companies with a higher proportion of high-skilled labor see stronger job demand creation due to better technological fit. In contrast, companies with inefficient labor allocation use AI focus to correct this imbalance, leading to more employment demand. A key mechanism is that companies rely more on external hiring than on internal training to adapt to technological change. This suggests AI focus may crowd out investment in retraining current staff, thereby generating external demand. The job demand creation effect is stronger in smaller companies, in non-manufacturing industries, and in regions with high-level AI development. Furthermore, AI focus changes the structure of labor demand： it concentrates educational requirements on bachelor's degrees, raises experience requirements, and leads to a balanced increase in salary levels across average, minimum, and maximum wages. This indicates trends of both polarization and inclusive upgrading of job quality.

This study contributes to the literature in three ways. First, it introduces a front-end cognitive perspective by analyzing strategic attention before actual technology adoption, offering early signals of AI's labor market impact. Second, it develops and tests a three-part framework explaining how AI focus affects employment demand by correcting labor misallocation, by leveraging high-skilled labors for better adaptation, and by crowding out internal training investments. This advances beyond simple views of technology replacing jobs. Third, it combines text-based measures of company strategy with detailed, real-time recruitment data, providing strong micro-level evidence on AI-driven labor demand.

The results have important policy implications. First, governments can promote a technology-friendly environment and provide technical interpretation to raise corporate AI awareness, using its job creation potential to ease structural employment pressures. Second, because companies strongly prefer external hiring, governments should guide them to balance recruitment with internal training to avoid over-relying on external human capital. Companies with labor imbalances should optimize their labor structure, while high-skilled companies should use their adaptive advantage. Third, the shift toward bachelor's degrees, greater experience, and balanced wage growth calls for corresponding educational reforms. Governments should guide universities to update AI-related curricula and strengthen continuing education to help labors adapt to new job demands.

China's Proactive Response System to Population Ageing: Theoretical Foundations, Evolutionary Trajectory, and Strategic Implications

Zhou Xuexin, Wu Bo, Zhu Wenyan

2026, 50(1): 104-120.

Asbtract ( )

References | Related Articles | Metrics

Population ageing is an objective trend in human development, a global issue, and a fundamental national condition for China in the coming long term. Proactively addressing population ageing constitutes China's action plan to meet the challenges of an ageing society, with vital implications for sustainable national economic and social development as well as the improvement of people's wellbeing. Strengthening institutional development is a key initiative in China's active response to population ageing and an essential pillar for implementing the corresponding national strategy. Hence, during this critical period of executing the national strategy on active ageing and advancing Chinese modernization supported by high-quality population development, this paper—based on institutional adaptation theory and the concept of active ageing, and considering the systematic, long-term, adaptive and dynamic dimensions of institution building—innovatively constructs an institutional framework for proactively addressing population ageing. Centered on three core elements, namely “health support,” “social participation,” and “social security,” the framework lays a theoretical foundation for the strategic goals set forth in the National Medium- and Long-Term Plan for Proactively Addressing Population Ageing (2019), which aims to initially establish an institutional framework by 2022, develop more scientific and effective institutional arrangements by 2035, and achieve mature and complete institutional arrangements compatible with a modern socialist power by the mid-21st century.

Moreover, this paper examines the evolution of China's institutional system for proactively addressing population ageing from three dimensions： types of institutional tools, composition of governance actors, and paradigms of institutional objectives. The study finds that the content of these institutions has expanded from basic livelihood security to comprehensive multi-domain governance, while Institutional arrangements have progressed from initial basic living safeguards to lifecycle-spanning strategic responses. This evolution reflects a shift from “reactive coping” to “proactive governance,” from “unilateral governance” to “collaborative governance,” and from “ensuring survival” to “promoting comprehensive development”—a process characterised by “adapting institutions to demographic changes.” Such an evolutionary pathway offers instructive insights for enriching and improving the institutional system of the national strategy during the 15th Five-Year Plan period and beyond.

Looking ahead, implementing the national strategy for proactively addressing population ageing should build on the established institutional framework. It will be essential to strengthen institutional guarantees through legislation, gradually advancing specialised laws for the elderly population. Institutional systematicity should be reinforced to enhance the system's capacity for dynamic response, systemic coordination, and long-term provision. Innovation in institutional implementation mechanisms is needed to improve resilience to demographic transition, economic development, and social transformations. Digital and intelligent reforms in institutions should be promoted to elevate the scientific accuracy of institutional supply. Through these measures, the adaptability of institutional design, the efficiency of institutional operation, and the feasibility of institutional safeguards can be steadily improved.

Sample Structure and Methodological Pitfalls: A Comparative Analysis Based on Large-Scale Social Survey Data in China

Liu Wenbo, Zhou Hao

2026, 50(1): 121-140.

Asbtract ( )

References | Related Articles | Metrics

Understanding the world requires unbiased and valid empirical knowledge. Numerous studies on the same topic, employing different survey data, often produce divergent analytical results and even contradictory conclusions, which undermines the effective testing of theoretical reliability and applicability. However, existing studies predominantly focus on refining statistical methods while overlooking foundational issues such as sample representativeness. There is also a scarcity of systematic examinations into the sample structures of widely used large-scale social surveys and their impact on statistical findings.

To address this gap, this study draws on six most extensively used national large-scale social surveys among Chinese scholars. It compares their sampling designs and empirically investigates the similarities and differences in their sample structures. Using a consistent model specification, this study investigates the impact of deviations in sample structure on statistical analysis results, and reveals the underlying logic by which sample structure influences statistical inference.

The main findings are as follows. First, although almost all surveys employ a multi-stage, stratified Probability Proportional to Size (PPS) random sampling method, they exhibit significant differences in sampling frame coverage, stratification principles, the sampling methods and quantities of sampling units at each stage, and within-household sampling procedures. Second, notable disparities exist in the distributions of key demographic variables across the surveys. Moreover, each survey's sample structure deviates to some extent from that of the 2015 National 1% Population Sample Survey. Third, differences in sample structure lead to variations in statistical results. Under identical models, analyses based on different survey data yield both a consensus component reflecting shared social realities and significant discrepancies in the significance and direction of effects for certain variables. Fourth, adjustments in population definitions, weighting schemes, variable selection, and operationalization alter the joint distribution of variables within a sample, thereby significantly affecting statistical outcomes. When sample structures differ initially, such adjustments may further amplify discrepancies in results across different survey datasets. Fifth, the foundational role of sample structure in the methodology of statistical inference must be fully acknowledged.

Based on these findings, the study recommends that researchers should meticulously review survey technical documentation,prudently select appropriate survey data based on research objectives, appropriately address data missingness and weighting, prioritize robustness checks of analytical results, and thoroughly evaluate or explain the sample representativeness of the survey data used. Survey institutions, on the other hand, should provide more detailed weighting information and comprehensive technical documentation to enable researchers to use the data more appropriately.

The primary contributions of this study are as follows. (1) It employs empirical methods to systematically examine the sample structures of six large-scale social surveys and the impact of sample structure deviations on statistical results, revealing methodological pitfalls that offer a new perspective for understanding the contradictory conclusions drawn from different datasets in existing literature. (2) Theoretically, it extends methodological reflection in quantitative research from model specification back to the data-collection stage, broadening scholarly discourse. (3) Practically, it provides empirical guidance for standardizing data usage in quantitative research, thereby enhancing the comparability and robustness of research conclusions.

Table of Content