Abstract
Background
Health inequality is a global issue, with a particularly significant impact on older adults. In China, differences in the urban administrative hierarchy may lead to uneven allocation of public resources, resulting in the concentration of public resources in cities with higher administrative hierarchies and, consequently, health inequality among older adults. Therefore, this study aims to explore the relationship between urban administrative hierarchies and older adults’ subjective physical and mental health, while also analyzing the role of resource allocation mechanisms in shaping this dynamic.
Methods
This study utilizes data from the China Family Panel Studies, the China City Statistical Yearbook, and the China Urban Construction Statistical Yearbook, employing a multi-dimensional fixed effects model, incorporating province, individual, and time variables, to evaluate the impact of the urban administrative hierarchy on the subjective physical and mental health inequality of older adults. The study considers medical and environmental resources as potential mediating variables and explores the moderating role of marketization.
Results
The findings reveal a positive correlation between the urban administrative hierarchy and older adults’ subjective physical and mental health, with those in cities with higher administrative hierarchies enjoying higher quality of life and subjective health levels. This is primarily due to cities with higher administrative hierarchies owning medical and environmental resources. Furthermore, the level of marketization has a positive moderating effect on the positive relationship between urban administrative hierarchy and older adults’ mental health, but has no significant impact on physical health. Heterogeneity analysis by region and age indicates that the impact of the urban administrative hierarchy on the physical and mental health of older adults is more pronounced in economically less developed regions and among younger elderly individuals.
Conclusion
The study highlights the inequalities in the subjective physical and mental health of older adults across cities with different administrative hierarchies in China. By providing more resources, cities with higher administrative hierarchies can significantly improve older adults’ life quality and subjective health. Meanwhile, marketization further strengthens the positive impact of urban administrative hierarchy on mental health. By introducing the urban administrative hierarchy as a macro-level political system into the study of individual health disparities, this research not only expands the analytical perspective on health inequalities among older adults, but also provides empirical support for understanding the current trends in elderly migration for retirement. Moreover, it offers valuable insights for global aging governance and promoting health equity.
Keywords: Health inequality, Older adults, Urban administrative hierarchy, Resource concentration, Subjective physical and mental health
Introduction
The urban administrative hierarchy is an important component of China’s administrative division system. Cities can be classified according to administrative hierarchy into direct-controlled municipalities, sub-provincial cities specifically designated in the state plan, sub-provincial capital cities, provincial capital cities (non-sub-provincial), prefecture-level cities, and county-level cities. There are significant differences in resource allocation and policy benefits across cities of different administrative hierarchies [1]. Generally, cities with higher administrative hierarchies possess more decision-making power and often receive greater fiscal allocations, infrastructure development, and public service support [2]. leading to the concentration of public resources in certain cities. Meanwhile, rapid aging has become a common challenge faced by Chinese cities. Thus, does the concentration of public resources across cities of different administrative hierarchies contribute to health inequality among older adults?
Existing research rarely directly examines the relationship between urban administrative hierarchy and the health of older adults. Instead, it primarily focuses on how other urban characteristics affect residents’ health (e.g. economic development level, degree of marketization, ecological environment, city size). Firstly, the level of economic development is a critical determinant of population health. Economically advanced cities typically possess more developed healthcare systems, infrastructure, and social security networks, which provide higher-quality health services and living conditions, thereby contributing to improvements in residents’ health and well-being [3]. Meanwhile, higher levels of economic development generally correspond to higher individual income levels, and previous studies have consistently shown that individuals with higher income tend to report better mental health and self-rated health [4, 5]. Secondly, a higher degree of marketization, particularly the advancement of market integration, not only enhances the overall quality and efficiency of healthcare services but also improves the health protection capacity of various social groups, especially those previously marginalized in access to medical resources, thereby enhancing population health and well-being [6–8]. Thirdly, the quality of the ecological environment has a direct impact on individuals’ quality of life and health status. Clean air, safe water sources, and well-maintained green spaces contribute positively to both physical and mental health, while heavily polluted environments can result in a range of health issues [9–11]. For example, residents of cities with poor air quality are more likely to suffer from respiratory illnesses, cardiovascular diseases, and lung cancer [12]. In addition, airborne pollutants, once inhaled into the lungs, may enter the bloodstream and trigger inflammation and oxidative stress in the brain, thereby exacerbating anxiety and depression and negatively impacting the mental health of older adults [13]. Finally, existing studies on the impact of city size on individual health can be roughly divided into two categories: the first category suggests a linear relationship between city size and residents’ health. Most scholars argue that city size is negatively correlated with individual health, as larger cities tend to have more pollution and higher population density, which exacerbate mental health problems and reduce overall health levels [14–16]. A minority of scholars hold the opposite view, suggesting that larger city size can improve residents’ physical and mental health through income effects, social participation effects, and healthcare security effects [17] The second category of research suggests a non-linear relationship between city size and health. Studies based on classical urban economics theories indicate an “inverted U-shaped” relationship between city size and residents’ health [18], where medium-sized cities provide the greatest health benefits [19, 20]. However, analyses based on city size theories offer a contrasting perspective, suggesting that city size and self-rated health follow a “positive U-shaped” relationship [21], meaning that residents of megacities report the best self-rated health, followed by residents of small cities, with large and medium-sized cities performing the worst.
However, significant disparities exist among cities in terms of economic development, levels of marketization, population scale, and urban administrative hierarchy. Although there is partial overlap between population scale and administrative hierarchy in China,1 the mechanisms through which these urban characteristics influence residents’ health differ substantially. Economic development and marketization affect individuals’ physical and mental health through market mechanisms. Population scale, on the other hand, primarily impacts health through natural urban development processes such as agglomeration and congestion effects. As a form of administrative division, urban administrative hierarchy can directly determine resource allocation by concentrating resources in cities with higher administrative status, thereby influencing not only the development of those cities but also that of subordinate administrative units [2, 22]. Therefore, the resource concentration mechanism driven by administrative authority—unique to urban administrative hierarchy—can lead to unequal resource distribution. This phenomenon is not inherent in the characteristics of economic development or population scale and may have a more pronounced impact on older adults, who are more sensitive to changes in their physical and mental health [23, 24].
The purpose of this study is to explore the impact of the urban administrative hierarchy on the subjective physical and mental health of older adults in China and to analyze whether the urban administrative hierarchy exacerbates health inequality among older adults by influencing the allocation of public resources. In Chinese society, there is a path dependency in the allocation of public resources between cities based on administrative hierarchy, which tends to result in mismatched supply and demand, further intensifying social inequality. This study addresses the health effects of the urban administrative hierarchy at the micro-level, providing a complementary perspective on how macro-political institutions affect individual health. It contributes to the body of research on the health effects of urban hierarchies, specifically from the perspective of health welfare. Additionally, this study expands the theory of resource misallocation into the fields of public health and older adult health, enhancing its applicability and explanatory power. By focusing on the health inequality of older adults, this research assesses the health effects of urban resource allocation in China, offering macro-level insights into addressing health inequality and providing micro-level guidance for older adults’ decisions on retirement migration. Figure 1 shows the analytical framework of the research.
Fig. 1.
Analytical framework
Method
Data
The data used in this study are sourced from the China Family Panel Studies (CFPS), the China City Statistical Yearbook (CCSY), and the China Urban Construction Statistical Yearbook (CUCSY). Firstly, the CFPS is conducted by the Institute of Social Science Survey (ISSS) at Peking University, aimed at collecting data at individual, family, and community levels to reflect the changes in Chinese society, economy, population, education, and health. This longitudinal survey adopts a multi-stage, implicit stratification, and probability proportional to size sampling design. It focuses on the economic and non-economic well-being of Chinese residents, with a sample that spans 25 provinces, municipalities, and autonomous regions. The target sample size is 16,000 households, and all family members within each sample household are included as respondents. This approach ensures the representativeness, reliability, validity, and scientific rigor of the data. The survey began in 2010 and has been conducted biennially, with the most recent wave completed in 2022.2 Secondly, the CCSY is organized by the Department of Urban Socio-Economic Surveys at the National Bureau of Statistics, which includes major statistical data on the socio-economic development of cities of all levels throughout the years. Thirdly, the CUCSY is compiled by the Ministry of Housing and Urban-Rural Development based on the annual urban construction statistics reported by the construction administrative departments of provinces, autonomous regions, and municipalities, comprehensively reflecting the construction and development status of urban municipal utilities in China.
Statistical analyses in this study were conducted using Stata 18.This study integrates macro-level urban data with micro-level individual data. To ensure consistency in survey questions as well as data validity and completeness, we selected data from the most recent four waves (2016–2022) of the CFPS.3 Based on standardized prefecture-level city codes, we horizontally matched and merged the CFPS data with the CCSY and CUCSY databases. As the study focuses on older adults aged 60 and above, we first excluded samples outside the target age group, followed by the removal of samples with missing values on key variables and those observed in only a single wave. The detailed data screening process is illustrated in Fig. 2, which clearly outlines the steps from raw data to the final dataset. The final valid sample size is 11,951.
Fig. 2.
Data filtering
Variables
Dependent variables
Subjective Physical Health. This refers to an individual’s self-assessment of their physical health status. It can positively predict the incidence of various diseases and mortality risk among older adults, making it crucial for observing their overall health condition [25]. Consistent with Zhu et al. (2023) [26], this variable is derived from the CFPS question: “How would you rate your health condition?” Based on respondents’ answers to their perceived health status, it is categorized into five levels: “Unhealthy,” “Fair,” “Relatively Healthy,” “Very Healthy,” and “Extremely Healthy.” The study assigns scores from 1 to 5 to these categories, with higher scores indicating better subjective physical health.
Subjective Mental Health. Existing research commonly uses symptoms of depression to measure mental health status, which has been proven to be an effective indicator for assessing an individual’s mental health [27]. CFPS employs the Epidemiologic Studies Depression Scale (CES-D) to measure symptoms of depression [28], with each item scored from 1 to 4 based on frequency (1 = Never; 2 = Sometimes, 1–2 days; 3 = Often, 3–4 days; 4 = Most of the time, 5–7 days). It is noteworthy that in 2012, CFPS used the 20-item CESD20. To compare depression scores across different survey waves, CFPS in 2016, 2018, and 2020 used CESD20 for a random 1/5 of the respondents, while 4/5 were assessed with CESD8; the scores were then equated using percentile equating methods to generate comparable scores, CESD20sc. CESD20sc, selected as the dependent variable in this study, has a score range of 20–80, with higher scores indicating higher levels of depression.
Independent variables
Urban Administrative Hierarchy. The differences in administrative hierarchy among Chinese cities significantly affect resource allocation and residents’ living environments. Following the research by Yu & Zhou (2023) [29], this study assigns categorical values to cities based on their administrative status, classifying Chinese cities into three administrative hierarchies. From lowest to highest, general prefecture-level cities are coded as 1, provincial capitals and sub-provincial cities specifically designated in the state plan as 2, and direct-controlled municipalities as 3.
Control variables
This study aims to examine the impact of urban administrative hierarchy on the subjective physical and mental health of older adults. To enhance the explanatory power and robustness of the empirical analysis, a comprehensive set of control variables is introduced at the individual, household, and societal levels.
At the individual level, the study controls for demographic variables such as age, gender, educational level, and marital status, which have been widely shown to be associated with subjective health [30, 31]. Additionally, household registration type (hukou) and place of residence capture institutional and spatial disparities in resource access. The presence of chronic diseases is included as a direct indicator of health burden, while receipt of pension benefits reflects the level of economic security in later life-both of which may significantly affect an individual’s physical and mental health.
At the household level, household size and financial support from children are included to account for the role of intergenerational support in shaping older adults’ health status [32].
At the societal level, three domains are considered. First, social security, measured by participation in pension and medical insurance, captures the extent of formal economic and healthcare support, both of which have been shown to positively influence older adults’ health [33–35]. Second, social capital is represented by the level of interpersonal trust, which has been consistently linked to better health outcomes [36–38]. Third, social services are proxied by the share of the tertiary sector in GDP, as the development of service industries is typically associated with the expansion of older adults’ care, health management, and welfare services, which may indirectly enhance subjective health status [39].
Mediating variables
Cities in China at different administrative hierarchies display significant differences in the allocation of public resources, which have a considerable impact on the quality of life for residents, especially the vulnerable elderly population. Among the various public resources influencing the subjective physical and mental health of older adults, medical, and environmental resources are particularly crucial [40]. Therefore, this study selects these three types of resources as mediating variables to explore their mechanisms of impact on older adults’ subjective health. Specific indicators are as follows:
Specifically, medical resources are measured using three indicators: number of hospital and health clinic facilities per thousand people, number of hospital beds per thousand people, and number of doctors per thousand people. Medical resources directly affect the accessibility and quality of medical services for older adults. The number of hospital and health clinic facilities per thousand people reflects the coverage of urban medical services, while the number of beds and doctors per thousand people indicates the supply capacity of medical services.
Environmental resources are assessed through two key indicators: the area of urban park green space and the green coverage rate in built-up areas. The former serves as an important measure of a city’s ecological environment and residents’ access to recreational space, while the latter reflects the overall coverage of green infrastructure across urban areas. A higher level of greenness is generally associated with improved air quality, a more comfortable living environment, and enhanced opportunities for outdoor activities and mental health among older adults.
After selecting the relevant indicators, this study employs the entropy method to construct composite indices for both medical and environmental resources. These indices provide a systematic measure of the relative level of resource allocation across cities.
Moderating variable
Marketization Level. This study uses the distance from a city to the nearest port as a proxy for its level of marketization. Ports serve as critical nodes in external economic connectivity, and the distance to a port can partly reflect a city’s degree of openness and market development. In general, coastal regions in China were earlier participants in market-oriented reforms and tend to have more mature market mechanisms, whereas inland areas often experience stronger government intervention and lower levels of marketization. Therefore, this variable captures the spatial variation in market development and is employed to examine its moderating role in the relationship between urban administrative hierarchy and older adults’ subjective physical and mental health.
Model
This study aims to thoroughly explore the potential impact of urban administrative hierarchy on the subjective physical and mental health of older adults, using a multi-dimensional fixed effects model for analysis. The model is set as follows:
 |
1 |
Where 
represents the subjective physical and mental health status of older adults individual i at time t. 
captures the administrative hierarchy of the respective city at time t, which is expected to reveal how the city’s administrative status shapes the health perceptions of older adults. 
includes control variables across the dimensions of individual, family, social, aiming to isolate and control other potential influencing factors. 
denotes the province fixed effects4, controlling for inherent differences between provinces. 
represents the year fixed effects, used to correct for potential impacts of time trends on the study outcomes.
represents individual fixed effects, which are used to control for time-invariant individual characteristics during the observation period. 
is the random error term. 
is the focal point of this study, quantifying the specific impact of urban administrative hierarchy on the subjective physical and mental health of older adults.
Results
Table 3 reports the baseline regression results on the relationship between the urban administrative hierarchy and the subjective physical and mental health of older adults. Models 1 and 4 estimate the effects of the urban administrative hierarchy on the subjective health of older adults. Models 2 and 5 incorporate individual- and family-level control variables, while Models 3 and 6 further include social control variables. The interpretation of the results primarily focuses on Models 3 and 6.
For subjective physical health, Model (1) shows a coefficient of 0.414 for urban administrative hierarchy, which is not statistically significant. However, after controlling for relevant individual, household, and social factors in Models (2) and (3), the coefficients remain at 0.414 and rise to 0.506, respectively, with Model (3) achieving statistical significance at the 5% level. This suggests that older adults living in cities with higher administrative status tend to report better physical health. The increasing magnitude of the coefficient across models indicates that the positive effect of administrative hierarchy becomes more pronounced once confounding factors are accounted for.
Regarding subjective mental health, results from Models (4) to (6) consistently reveal a significant negative association between urban administrative hierarchy and depressive symptoms among older adults. Specifically, each one-unit increase in administrative hierarchy is associated with a reduction of approximately 3.91 to 4.88 points in depressive scores, with significance levels ranging from 10% to 5%. This indicates that older adults in cities with higher administrative status experience better mental health, potentially due to more comprehensive social services, superior healthcare infrastructure, and enhanced living environments.
In summary, the regression results provide clear evidence of a positive association between urban administrative hierarchy and older adults’ perceived physical and mental health. These findings highlight the health inequalities resulting from disparities in public health and welfare resource allocation across cities of different administrative levels in China, underscoring the need for targeted health support policies for older populations in lower-tier cities to promote health equity.
Descriptive statistics
Table 1 provides detailed definitions and descriptive statistics for the variables involved in this study. Regarding the dependent variables, the average score for older adults’ self-rated physical health is 2.54, while the average score for subjective mental health is 32.91, indicating a moderate overall health status. For the key independent variable, the mean value of urban administrative hierarchy is 1.44, suggesting that most of the sample comes from ordinary prefecture-level cities, and that the overall administrative level is relatively low.
Table 1.
Descriptive statistics
| Variable | Definition | Mean | Std. | Min | Max | Observations |
|---|---|---|---|---|---|---|
| Dependent variables | ||||||
| Subjective physical health | Proportional to subjective physical health level, scored from 1 to 5 | 2.544 | 1.216 | 1 | 5 | 11,951 |
| Subjective mental health | Inversely proportional to subjective mental health level, scored from 20 to 80 | 32.908 | 8.770 | 20 | 72 | 11,951 |
| Independent variables | 11,951 | |||||
| Urban Administrative hierarchy | Direct-controlled municipalities = 3; Provincial capitals and sub-provincial cities specifically designated in the state plan = 2; Ordinary prefecture-level cities = 1 | 1.437 | 0.730 | 1 | 3 | 11,951 |
| Control variables | 11,951 | |||||
| Age | Age of the individual | 68.272 | 5.255 | 61 | 79 | 11,951 |
| Gender | Male = 1; Female = 0 | 0.532 | 0.499 | 0 | 1 | 11,951 |
| Education level | Illiterate = 1; Primary School = 6; Junior High = 9; High School = 12; College = 15; Bachelor’s = 16; Master’s/Doctorate = 19 | 9.258 | 7.680 | 0 | 19 | 11,951 |
| Marital status | Married = 1; Unmarried/Cohabiting/Divorced/Widowed = 0 | 11,951 | ||||
| Household registration | Non-agricultural = 1; Agricultural = 0 | 0.346 | 0.475 | 0 | 1 | 11,951 |
| Place of residence | Urban = 1; Rural = 0 | 0.509 | 0.450 | 0 | 1 | 11,951 |
| Chronic diseases | Yes = 1; No = 0 | 0.311 | 0.463 | 0 | 1 | 11,951 |
| Retirement pension | Yes = 1; No = 0 | 0.228 | 0.420 | 0 | 1 | 11,951 |
| Family size | Number of family members | 3.611 | 1.829 | 1 | 7 | 11,951 |
| Financial support from children | Yes = 1; No = 0 | 0.438 | 0.496 | 0 | 1 | 11,951 |
| Pension insurance | Yes = 1; No = 0 | 0.681 | 0.466 | 0 | 1 | 11,951 |
| Medical insurance | Yes = 1; No = 0 | 0.934 | 0.249 | 0 | 1 | 11,951 |
| Social trust | Trust = 1; Suspicion = 0 | 0.569 | 0.495 | 0 | 1 | 11,951 |
| Degree of service industry development | Logarithm of the proportion of added value of the tertiary industry in GDP | 3.908 | 0.199 | 3.571 | 4.273 | 11,951 |
As for control variables, at the individual level, the average age of respondents is 68.3 years, with males accounting for 53.2%, indicating a relatively balanced gender distribution. The average years of schooling is 9.26, mostly at the junior secondary level, reflecting generally low educational attainment; 85.3% of older adults are married. In terms of household registration, 34.6% hold a non-agricultural hukou, and 50.9% reside in urban areas. Additionally, 31.1% suffer from chronic diseases, and 22.8% receive pensions.
At the household level, the average household size is 3.61 persons, and 43.8% of older adults report receiving financial support from their children. At the social level, 68.1% are enrolled in pension insurance and 93.4% in medical insurance, indicating generally high but uneven coverage of social security. Regarding social capital, 56.9% of older adults express trust in others, reflecting a moderate level of interpersonal trust. Finally, in terms of social environment, the average value of service sector development is 3.91, suggesting that the regions covered in the study generally have well-developed service industries.
Baseline regression
Before examining the impact of urban administrative hierarchy on older adults’ perceived physical and mental health, we first conducted a Variance Inflation Factor (VIF) test to assess multicollinearity among the explanatory variables. The results in Table 2 show that all VIF values are below 5, with a mean VIF of 1.37, indicating no significant multicollinearity issues and confirming the robustness of the regression estimates.
Table 2.
Results of the VIF test
| Variable | VIF | 1/VIF |
|---|---|---|
| Urban Administrative hierarchy | 2.57 | 0.390 |
| Degree of service industry development | 2.40 | 0.416 |
| Household registration | 1.93 | 0.517 |
| Retirement pension | 1.59 | 0.630 |
| Place of residence | 1.40 | 0.713 |
| Education level | 1.20 | 0.830 |
| Gender | 1.12 | 0.890 |
| Age | 1.08 | 0.923 |
| Pension insurance | 1.08 | 0.926 |
| Marital status | 1.07 | 0.934 |
| Family size | 1.07 | 0.938 |
| Medical insurance | 1.03 | 0.971 |
| Financial support from children | 1.02 | 0.979 |
| Chronic diseases | 1.02 | 0.983 |
| Social trust | 1.02 | 0.988 |
| Mean VIF | 1.37 | |
Table 3 reports the baseline regression results on the relationship between the urban administrative hierarchy and the subjective physical and mental health of older adults. Models 1 and 4 estimate the effects of the urban administrative hierarchy on the subjective health of older adults. Models 2 and 5 incorporate individual- and family-level control variables, while Models 3 and 6 further include social control variables. The interpretation of the results primarily focuses on Models 3 and 6.
Table 3.
The impact of urban administrative hierarchy on subjective physical and mental health of older adults
| Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | |
|---|---|---|---|---|---|---|
| Subjective physical health | Subjective physical health | Subjective physical health | Subjective mental health | Subjective mental health | Subjective mental health | |
| Urban administrative hierarchy | 0.414 | 0.414* | 0.506** | −3.913* | −4.062* | −4.881** |
| (0.261) | (0.216) | (0.203) | (2.126) | (2.246) | (2.421) | |
| Age | −0.014 | −0.013 | −0.221 | −0.243 | ||
| (0.021) | (0.021) | (0.175) | (0.176) | |||
| Gender | −0.540 | −0.529 | 3.542 | 3.563 | ||
| (0.407) | (0.392) | (6.772) | (6.718) | |||
| Education level | −0.000 | −0.000 | 0.033 | 0.035 | ||
| (0.004) | (0.004) | (0.027) | (0.027) | |||
| Marital status | 0.039 | 0.047 | 0.782 | 0.727 | ||
| (0.114) | (0.114) | (0.790) | (0.790) | |||
| Household registration | −0.021 | −0.017 | −0.066 | −0.071 | ||
| (0.062) | (0.062) | (0.489) | (0.487) | |||
| Place of residence | −0.009 | −0.007 | 0.199 | 0.224 | ||
| (0.077) | (0.078) | (0.474) | (0.475) | |||
| Chronic diseases | −0.270*** | −0.271*** | 1.075*** | 1.087*** | ||
| (0.025) | (0.025) | (0.190) | (0.190) | |||
| Retirement pension | 0.009 | −0.001 | 0.081 | 0.097 | ||
| (0.033) | (0.033) | (0.235) | (0.236) | |||
| Family size | −0.009 | −0.009 | −0.296*** | −0.286*** | ||
| (0.013) | (0.013) | (0.088) | (0.088) | |||
| Financial support from children | 0.030 | 0.029 | −0.145 | −0.135 | ||
| (0.024) | (0.024) | (0.170) | (0.170) | |||
| Pension insurance | −0.032 | −0.014 | ||||
| (0.024) | (0.173) | |||||
| Medical insurance | −0.078* | 0.306 | ||||
| (0.045) | (0.319) | |||||
| Social trust | 0.049** | −0.507*** | ||||
| (0.023) | (0.172) | |||||
| Degree of service industry development | −0.443** | 3.105** | ||||
| (0.173) | (1.230) | |||||
| Year fixed effect | yes | yes | yes | yes | yes | yes |
| Individual fixed effect | yes | yes | yes | yes | yes | yes |
| Provincial fixed effect | yes | yes | yes | yes | yes | yes |
| _cons | 1.948*** | 3.286** | 4.827*** | 38.532*** | 51.638*** | 42.193*** |
| (0.376) | (1.478) | (1.566) | (3.055) | (12.900) | (13.463) | |
| N | 11,947 | 11,947 | 11,947 | 11,947 | 11,947 | 11,947 |
| R² | 0.680 | 0.685 | 0.686 | 0.688 | 0.690 | 0.691 |
(1) Clustered standard errors are in parentheses. (2) *, **, *** indicate significance at the 10%, 5%, and 1% levels, respectively
For subjective physical health, Model (1) shows a coefficient of 0.414 for urban administrative hierarchy, which is not statistically significant. However, after controlling for relevant individual, household, and social factors in Models (2) and (3), the coefficients remain at 0.414 and rise to 0.506, respectively, with Model (3) achieving statistical significance at the 5% level. This suggests that older adults living in cities with higher administrative status tend to report better physical health. The increasing magnitude of the coefficient across models indicates that the positive effect of administrative hierarchy becomes more pronounced once confounding factors are accounted for.
Regarding subjective mental health, results from Models (4) to (6) consistently reveal a significant negative association between urban administrative hierarchy and depressive symptoms among older adults. Specifically, each one-unit increase in administrative hierarchy is associated with a reduction of approximately 3.91 to 4.88 points in depressive scores, with significance levels ranging from 10% to 5%. This indicates that older adults in cities with higher administrative status experience better mental health, potentially due to more comprehensive social services, superior healthcare infrastructure, and enhanced living environments.
In summary, the regression results provide clear evidence of a positive association between urban administrative hierarchy and older adults’ perceived physical and mental health. These findings highlight the health inequalities resulting from disparities in public health and welfare resource allocation across cities of different administrative levels in China, underscoring the need for targeted health support policies for older populations in lower-tier cities to promote health equity.
Robustness checks
To verify the scientific validity and credibility of the research results, robustness checks were conducted using the following three methods.
First, the study replaced the measurement of the dependent variables. Specifically, for subjective physical health, the study used functional independence in daily activities as an alternative indicator. This measure captures an older adult’s ability to independently perform essential daily tasks such as outdoor activities, eating, cooking, using public transportation, shopping, cleaning, and doing laundry. It has been widely used to reflect physical autonomy and overall health status [41]. For subjective mental health, the study employed the shortened version of the CES-D scale (CESD-8) to replace the original CESD-20 depression score. CESD-8 is a concise version derived from the full CESD-20, covering key dimensions of depressive symptoms. It has demonstrated good psychometric properties and is widely adopted in studies on the mental health of older adults. The use of this simplified measure not only enhances efficiency but also provides a valid robustness check for the mental health outcomes.
Second, estimation was conducted using an alternative database. The China Health and Retirement Longitudinal Study (CHARLS), led by the National School of Development at Peking University, is a large-scale, nationally representative longitudinal survey focusing on key issues related to the health and well-being of middle-aged and older adults aged 45 and above in China. Due to the absence of the subjective mental health variable required for this study in CHARLS data prior to 2015, we utilized the most recent three waves of CHARLS data from 2015 to 2020. Based on standardized prefecture-level city codes, these data were horizontally matched and merged with the corresponding years from the CCSY and the CUCSY. Following Wang et al. (2024) [42], the subjective physical health variable was derived from respondents’ answers to the question, “How would you rate your health status?” Responses were categorized into five levels: “very poor” “poor” “fair” “good” and “very good” and coded from 1 to 5 accordingly. The subjective mental health variable was constructed using the 10-item Center for Epidemiological Studies Depression Scale (CESD-10) included in the survey. Each item was scored based on frequency of occurrence as follows: 1 = rarely or none of the time (< 1 day), 2 = some or a little of the time (1–2 days), 3 = occasionally or a moderate amount of the time (3–4 days), and 4 = most or all of the time (5–7 days).
Third, we conducted estimation using an alternative model. Given the hierarchical structure of the data, with individuals nested within cities, the study employed a Hierarchical Linear Model (HLM) to test the robustness of the empirical results [43]. HLM effectively identifies and models multilevel data structures, distinguishing explanatory variables at both the city and individual levels. This approach not only controls for cross-level influences of city-level variables on individual health outcomes but also avoids conflating variance across different levels into a single layer, thereby improving the model’s fit and the validity of inference.
Table 4 presents the results of the robustness checks on the impact of the urban administrative hierarchy on the subjective physical and mental health of older adults. As shown, the results of all robustness checks are consistent with the baseline regression results. That is, the higher the urban administrative hierarchy, the better the subjective physical and mental health of older adults. This finding not only confirms the reliability of the baseline regression results but also further emphasizes the health inequality among older adults across cities in China.
Table 4.
Robustness checks
| Reassignment of independent variable | CHARLS | HLM | ||||
|---|---|---|---|---|---|---|
| Functional Independence | Subjective Mental Health (CESD-8) | Subjective physical health | Subjective Mental Health (CESD-10) | Subjective physical health | Subjective Mental Health |
|
| Urban administrative hierarchy | 0.071** | −2.441** | 0.051** | −0.831*** | 0.178** | −1.662*** |
| (0.033) | (1.223) | (0.026) | (0.160) | (0.071) | (0.534) | |
| Control variables | yes | yes | yes | yes | yes | yes |
| Year fixed effect | yes | yes | yes | yes | yes | yes |
| Individual fixed effect | yes | yes | no | no | no | no |
| Provincial fixed effect | yes | yes | yes | yes | yes | yes |
| _cons | 0.796 | 17.097** | −0.102 | 37.222* | 4.096*** | 33.295*** |
| (0.594) | (6.763) | (2.301) | (20.804) | (0.753) | (5.496) | |
| N | 11,947 | 11,947 | 8555 | 8555 | 11,951 | 11,951 |
| R² | 0.598 | 0.692 | 0.041 | 0.135 | —— | —— |
(1) Clustered standard errors are in parentheses. (2) *, **, *** indicate significance at the 10%, 5%, and 1% levels, respectively. Due to multicollinearity issues that arose when individual fixed effects were included during the alternative database estimation and the HLM analysis, key variables were automatically omitted from the model. Therefore, in these analyses, the study controlled only for year and province fixed effects
Mediation mechanism
To delve into the underlying mechanism of how the urban administrative hierarchy affects the subjective physical and mental health of older adults, this study employs Structural Equation Modeling (SEM) to analyze the mediating effects. Existing literature has shown that the urban administrative hierarchy is closely linked to the concentration of public resources [2]. Taking the allocation of medical resources as an example, cities with higher administrative hierarchy, such as Beijing and Shanghai, have more tertiary hospitals, resulting in the overconcentration of medical resources in these cities and leading to an uneven allocation of high-quality medical services, thus causing resource misallocation [44]. The unequal allocation of public resources affects economic development at the macro level and concerns individuals’ quality of life and health at the micro level. Based on this, the study explores how the allocation of public resources mediates the relationship between the urban administrative hierarchy and the subjective physical and mental health of older adults through two dimensions: medical and environmental resources. Figure 3 presents the mediation results for both medical and environmental resources.
Fig. 3.
The mediating effect of environmental and medical resources (SEM diagram).Note: (1) Clustered standard errors are in parentheses. (2) *, **, *** indicate significance at the 10%, 5%, and 1% levels, respectively
The mediation analysis of medical resources shows that urban administrative hierarchy has a significantly positive effect on medical resources, and in turn, medical resources positively influence older adults’ subjective physical health. The direct effect of administrative hierarchy on physical health also remains significantly positive. This indicates a partial mediation effect, suggesting that higher-level cities contribute to better physical health among older adults by providing more abundant medical resources. However, along the mental health pathway, the effect of medical resources on mental health is not statistically significant, and the direct effect of administrative hierarchy on mental health is negative. Therefore, medical resources do not mediate the relationship between administrative hierarchy and subjective mental health.
In terms of environmental resources, urban administrative hierarchy positively affects the availability of environmental resources. Although the path from environmental resources to subjective physical health is positive, it does not constitute an effective mediating mechanism. This is because the direct effect of administrative hierarchy on physical health remains negative, and the model does not form a complete mediation chain. In contrast, a more substantial mediating effect is observed in the mental health pathway. Administrative hierarchy significantly affects environmental resources, which, in turn, have a significant negative effect on depressive symptoms, and the direct effect of administrative hierarchy on mental health is also significant. This indicates that factors such as urban greenery and the quality of the spatial environment have a tangible impact on older adults’ mental health.
In summary, the SEM results reveal that medical resources serve as a positive mediator between urban administrative hierarchy and subjective physical health, but do not mediate mental health outcomes. Conversely, environmental resources significantly mediate the relationship between administrative hierarchy and subjective mental health, but not physical health. These findings suggest that urban administrative hierarchy affects older adults’ health through differentiated resource allocation mechanisms. Improving access to medical services is essential for enhancing physical health, while optimizing urban ecological environments may be an effective pathway for improving mental health. The result further highlights that resource centralization under administrative hierarchy, without proper balance and redistribution, may exacerbate inequalities across regions and population groups in different dimensions of health.
Moderating effects
To further examine whether the impact of urban administrative hierarchy on older adults’ subjective physical and mental health varies with city-level development characteristics, this study introduces the level of marketization as a moderating variable. Marketization is proxied by the distance from a city to the nearest port, based on the context of China’s regional economic development. Proximity to a port reflects a city’s degree of openness and the maturity of its market institutions to a certain extent.
Table 5 presents the regression results of the moderation analysis. In terms of subjective physical health (Column 1), the coefficients of urban administrative hierarchy, marketization level, and their interaction term are all statistically insignificant, indicating that marketization does not significantly moderate the relationship between administrative hierarchy and physical health. In other words, the strength of the effect of administrative hierarchy on older adults’ physical health does not differ substantially across cities with varying levels of marketization.
Table 5.
Moderating effects
| (1) | (2) | |
|---|---|---|
| Subjective physical health | Subjective mental health | |
| Urban administrative hierarchy | −0.465 | −59.563*** |
| (1.584) | (4.847) | |
| Marketization | 0.762 | 47.670*** |
| (1.893) | (8.487) | |
| Urban administrative hierarchy * Marketization | 0.500 | 27.615*** |
| (0.810) | (2.574) | |
| Control variables | yes | yes |
| Year fixed effect | yes | yes |
| Individual fixed effect | yes | yes |
| Provincial fixed effect | yes | yes |
| _cons | −3.057 | −460.825*** |
| (21.614) | (101.979) | |
| N | 11,947 | 11,947 |
| R² | 0.686 | 0.691 |
(1) Clustered standard errors are in parentheses. (2) *, **, *** indicate significance at the 10%, 5%, and 1% levels, respectively
In contrast, for subjective mental health (Column 2), the interaction term between urban administrative hierarchy and marketization level is significantly positive at the 1% level, and both the main effects are also significant. This suggests that as the level of marketization increases, the positive effect of administrative hierarchy on older adults’ mental health becomes more pronounced. In other words, in cities with more developed market mechanisms, the advantages associated with higher administrative status-such as better public services, improved social environments, and stronger institutional support-are more effectively translated into enhanced mental health for older residents. This may be related to more efficient resource allocation, quicker governmental responses, and a more open social structure in market-oriented cities.
In summary, marketization significantly moderates the relationship between administrative hierarchy and older adults’ subjective mental health, but not their physical health. This finding further supports the idea that mental health is more sensitive to external institutions and environmental factors.
Heterogeneity analysis
Existing studies have shown that economically developed regions typically possess better public resource allocation, higher levels of healthcare services, and stronger social security systems. These factors may exert a significant influence on the physical and mental health of older adults [45, 46]. Moreover, as individuals age, the risks of experiencing depression and physical health problems increase markedly [47]. Therefore, considering the significant heterogeneity among Chinese cities and elderly populations, this study further explores the impact of regional and age heterogeneity on the subjective physical and mental health of older adults. In the regional heterogeneity analysis, existing studies often use geographical location as the standard for dividing cities into eastern, central, and western regions. However, considering the reality in China, even within the eastern region, there are significant differences in economic development levels across provinces. This simple geographical division may not reflect the actual situation. Therefore, this study adopts a method based on regional development levels. Referring to the World Bank’s classification of economies based on Gross National Income (GNI) per capita,5 and combining the per capita GDP of each province in 2022, regions were divided into two groups: those with high and low levels of economic development.
In the age heterogeneity analysis, this study follows the World Health Organization’s standard, dividing older adults into two groups: younger elderly (60–74 years old) and older elderly (75–89 years old). Since there are only 38 samples of elderly individuals over 90 years old in the database, they were not analyzed separately.
Table 6 presents the results of the heterogeneity analysis. In terms of regional heterogeneity, the results show a significant correlation between the urban administrative hierarchy and the subjective physical and mental health of older adults in regions with lower levels of economic development. However, in regions with higher levels of economic development, this relationship is not significant. This finding suggests that health inequality between cities of different administrative hierarchies may be more pronounced in regions with lower economic development, whereas in regions with higher economic development, even cities with lower administrative hierarchies may have sufficient resources, making health inequality less evident.
Table 6.
Heterogeneity analysis
| Heterogeneity analysis 1: Regional | ||||
|---|---|---|---|---|
| Subjective physical health | Subjective mental health | |||
| High economic development | Low economic development | High economic development | Low economic development | |
| Urban administrative hierarchy | −0.077 | 0.183*** | 0.094 | −1.401*** |
| (0.145) | (0.060) | (0.881) | (0.416) | |
| Control variables | yes | yes | yes | yes |
| Year fixed effect | yes | yes | yes | yes |
| Provincial fixed effect | yes | yes | yes | yes |
| _cons | −0.245 | −0.111 | 43.572*** | 43.695*** |
| (0.303) | (0.150) | (7.564) | (4.522) | |
| N | 4.428*** | 3.112*** | 3378 | 8571 |
| R² | (1.125) | (0.601) | 0.180 | 0.135 |
| Heterogeneity analysis 2: Age | ||||
| Subjective physical health | Subjective mental health | |||
| Younger elderly | Older elderly | Younger elderly | Older elderly | |
| Urban administrative hierarchy | 0.164*** | 0.064 | −1.070*** | −1.894** |
| (0.062) | (0.119) | (0.415) | (0.896) | |
| Control variables | yes | yes | yes | yes |
| Year fixed effect | yes | yes | yes | yes |
| Provincial fixed effect | yes | yes | yes | yes |
| _cons | yes | yes | yes | yes |
| 3.334*** | 4.161** | 46.599*** | 31.509** | |
| N | (0.583) | (1.827) | (4.279) | (13.722) |
| R² | 10,128 | 1821 | 10,128 | 1821 |
(1) Clustered standard errors are in parentheses. (2) *, **, *** indicate significance at the 10%, 5%, and 1% levels, respectively. (3) Individual fixed effects are not included in the subgroup analysis due to collinearity issues, which result in key variables being automatically omitted. Therefore, only year and province fixed effects are controlled in this part
In terms of age heterogeneity, the results show that for younger elderly individuals, a higher urban administrative hierarchy positively influences their subjective physical and mental health. However, for older elderly individuals, a higher urban administrative hierarchy only has a positive effect on their subjective mental health. This may be related to the natural decline in physical health due to aging, a physiological process that may be less influenced by external environmental factors.
Discussion
This study uses two indicators—self-rated health and depressive symptoms—to measure the subjective physical and mental health of older adults in cities with different administrative hierarchies, verifying the widespread existence of health inequality among older adults in China. The findings respond to previous research that identified older adult health inequalities closely related to regional differences [48–51]. The results confirm a significant positive correlation between urban administrative hierarchy and the subjective physical and mental health of older adults, with those living in cities with higher administrative hierarchies generally reporting better physical and mental health.
How does the urban administrative hierarchy, as a macro-level political institution, affect the health of older adults? This study finds that the health benefits associated with the urban administrative hierarchy mainly operate through two mediating mechanisms: medical resources and environmental resources.
In China, on the one hand,, high-quality medical resources are more concentrated in cities with higher administrative hierarchies [52–54], including top-tier hospitals, professional medical staff, and advanced medical equipment, all of which can provide timely and high-quality healthcare services, directly improving the health of the elderly. Moreover, the abundance of medical resources also boosts the health confidence of the older adults, making those residing in cities with higher administrative hierarchies generally report better self-rated health. On the other hand, cities with higher administrative hierarchies typically invest more in environmental governance and urban green space planning, resulting in better environmental resources. Some studies suggest that the higher level of economic and social development in cities with high administrative hierarchies may lead to pollution from industrialization, which could negatively impact the health of older adults [55–57]. However, this study uses the indicator of per capita green park area to measure environmental resources and finds that cities with higher administrative hierarchies place greater emphasis on environmental quality and urban greening. Larger green park areas provide older adults with more space for morning exercise and social interaction, thereby improving their subjective physical health and alleviating depressive symptoms [58, 59].
In conclusion, the urban administrative hierarchy not only represents a city’s political capital but also significantly determines its capacity to attract public resources [60]. However, the allocation of public resources solely based on administrative hierarchy often leads to resource misallocation. The resource misallocation theory posits that government investment is often based on political planning rather than social needs or economic benefits. Public resources in many Chinese cities are concentrated due to government-guided policies, and because the government lacks complete information to make optimal decisions, varying degrees of resource misallocation occur across regions [61]. Through empirical analysis, this study confirms that public resources are indeed concentrated in cities with higher administrative hierarchies. In extreme cases, the allocation of resources for political purposes results in a higher concentration of quality resources in cities with higher administrative hierarchies, while cities with lower administrative hierarchies, despite having significant populations, struggle to access these resources. This misallocation negatively affects the subjective physical and mental health of older adults. These findings contribute to the resource misallocation theory by extending the resource effects of the urban administrative hierarchy into the health domain and offering insights into the micro-level health implications of resource misallocation. Our analysis shows that the urban administrative hierarchy significantly impacts health by influencing the concentration of public resources, which in turn leads to resource misallocation and exacerbates health inequalities among older adults.
On the one hand, the impact of urban administrative hierarchy on the subjective health of older adults shows regional heterogeneity. In regions with lower levels of economic development, the effect of urban administrative hierarchy on the health of older adults is particularly significant. This is because economically less developed areas often face shortages in fiscal and medical resources [62, 63] and have poorer ecological planning. Since the administrative hierarchy largely determines a city’s capacity to attract resources [64, 65], cities with higher administrative hierarchies in economically underdeveloped regions have a stronger ability to absorb resources, exacerbating the unequal allocation of public resources and worsening health inequality among older adults. Conversely, in regions with higher levels of economic development, where resources are generally abundant, even lower-ranked cities can secure sufficient fiscal and medical resources through their stronger economies, providing relatively complete public services and infrastructure [66, 67]. These cities can compensate for the disadvantages brought by lower administrative hierarchy with their economic strength, ensuring that older adults enjoy high-quality healthcare services and living conditions. In other words, in economically developed areas, abundant resources narrow the gap in health-related resources between cities, and the negative effects of resource misallocation are weaker. Therefore, health inequality between cities of different administrative hierarchies is less pronounced, and older adults can enjoy high levels of health protection and services regardless of the city in which they reside. As a result, the impact of urban administrative hierarchy on the subjective health of older adults is smaller in economically developed regions.
On the other hand, the impact of urban administrative hierarchy on the subjective health of older adults is closely related to age. For younger elderly individuals (aged 60–74), higher urban administrative hierarchy has a positive impact on both their subjective physical and mental health, as this group can more fully utilize the abundant medical resources and comprehensive public services in cities with higher administrative hierarchies to improve their health. However, for the general elderly population (aged 75 and above), higher administrative hierarchy only positively affects their subjective mental health. This may be due to the natural decline in physical health associated with aging. As people grow older, their physical functions gradually deteriorate, and the risk of chronic diseases increases [5], making it difficult for their physical health to improve significantly even when living in cities with higher administrative hierarchies and abundant public resources. This natural physiological decline reduces the impact of external environmental factors on their physical health, but the health protection and environmental resources provided by cities with higher administrative hierarchies can still alleviate depression and improve their mental health. Therefore, the impact of urban administrative hierarchy differs significantly across age groups, reflecting the different ways in which older adults assess their health at different stages of life.
The influence of urban administrative hierarchy on older adult health is also closely related to the decision of elderly migration. Where do older adults prefer to retire to maintain their health and experience a higher quality of life? Previous studies suggest that elderly individuals tend to leave large cities for retirement [68, 69], possibly due to severe pollution, fewer per capita resources, and less scenic environments in large cities. Another line of research suggests that elderly individuals over 75 are more inclined to move to cities with higher administrative hierarchies with more specialized elderly care facilities [70, 71]. This study demonstrates that in China, retiring in cities with higher administrative hierarchies is beneficial for improving the physical and mental health of older adults, supporting the viewpoint that elderly individuals prefer to move to cities with higher administrative hierarchies for retirement.
Elderly migration for retirement to some extent reflects the health inequalities that exist in contemporary society [72]. Although migration may serve as an individual-level adaptive strategy that can temporarily improve health conditions for some older adults, it is inherently incapable of fundamentally addressing the health disparities rooted in differences in the urban administrative hierarchy. This study finds that the level of marketization plays a significantly positive moderating role in the relationship between urban administrative hierarchy and the mental health of older adults. In other words, the more developed the market mechanisms are, the more effectively the advantages of higher administrative status can be translated into mental health benefits for older adults. Therefore, policymakers should prioritize strengthening market mechanisms in cities with lower administrative ranks, enhancing their capacity for resource allocation and public service provision. This would help narrow the mental health gap among older adults across different urban administrative levels and further advance health equity and the development of age-friendly cities.
Conclusion
Based on data from the CFPS, the CCSY, and the CUCSY, this study examines the impact and mechanisms of the urban administrative hierarchy on the subjective physical and mental health of older adults. The main findings are as follows: (1) There is a marked inequality in the subjective health of older adults across cities with different administrative levels, with those living in higher-ranking cities reporting better subjective physical and mental health; (2) The urban administrative hierarchy has a resource concentration effect, influencing the health of older adults by affecting the allocation of medical and environmental resources; (3) Heterogeneity analysis shows that the health effects of urban administrative hierarchy are more pronounced in economically underdeveloped regions and among younger segments of the older adult population; (4) Marketization significantly moderates the relationship between administrative rank and the mental health of older adults in a positive direction.
The theoretical and practical contributions of this research are as follows. At the theoretical level, this study introduces the urban administrative hierarchy, a macro-level political institution, nto the analysis of individual health disparities, thereby expanding the analytical framework for understanding health inequality among older adults and responding to existing concerns about structural regional disparities. Furthermore, the study confirms the applicability of the resource misallocation theory in the field of health, revealing the health advantages generated by public resource concentration in higher-ranked cities. At the practical level, this study not only uncovers the important role of urban administrative hierarchy in shaping health outcomes among older adults and provides empirical evidence for understanding the recent trends in elderly retirement migration, but also emphasizes the moderating role of market mechanisms in alleviating mental health inequalities. These findings have significant implications for promoting health equity and building an age-friendly society and offer valuable insights for global aging governance.
Finally, the study has several limitations. First, although the research employs a multi-dimensional fixed effects model controlling for province, time, and individual-level time-invariant unobserved heterogeneity, the possibility of omitted variable bias cannot be completely ruled out. Second, as dependent variables, subjective physical and mental health are self-reported, which may be influenced by cognitive biases, cultural norms, education level, and other factors, leading to potential measurement error. Although robustness checks using alternative measurement approaches were conducted, the subjective nature of the indicators remains a limitation. Third, while the study explores the mediating effects of medical and environmental resources, the complexity of resource allocation may exceed what these indicators can capture. Factors such as efficiency of resource use, actual accessibility, and fairness of distribution across cities may also affect health outcomes among older adults but are not fully analyzed here. Fourth, although regional economic development and age-related heterogeneity were examined, other important demographic characteristics such as gender, household registration status, educational attainment, and marital status have yet to be thoroughly investigated. Future research should incorporate more dimensions of heterogeneity to better identify the boundaries of the effects of urban hierarchy, thus providing more targeted empirical support for precision policymaking in response to population aging. Despite these limitations, this study offers valuable evidence on the relationship between urban administrative hierarchy and the subjective physical and mental health inequalities among older adults.
Acknowledgements
Not applicable.
Abbreviations
- CFPS
The China Family Panel Studies
- CCSY
The China City Statistical Yearbook
- CUCSY
The China Urban Construction Statistical Yearbook
- CHARLS
The China Health and Retirement Longitudinal Study
- ISSS
The Institute of Social Science Survey
- HLM
Hierarchical Linear Model
Authors’ contributions
LH wrote the main manuscript text and led the conceptualization, formal analysis, methodology, and original draft writing. YY contributed to the conceptualization, formal analysis, and methodology, and supported the review and editing. JYW and YY contributed to the writing of the original draft. HC and ZXY participated in the review and provided corrections. Zihan Wang led the review and editing, provided financial support, and assisted with English translation and language polishing. All authors reviewed the manuscript and approved the final version for publication.
Funding
The authors received no financial support for the research, authorship, and publication of this article.
Data availability
The data used in this study are publicly available from the China Family Panel Studies (CFPS), the China City Statistical Yearbook (CCSY), and the China Urban Construction Statistical Yearbook (CUCSY). The CFPS data can be accessed through its official website (http://www.isss.pku.edu.cn/cfps/en) with approval, and its ethics approval was granted by Peking University’s Ethical Review Committee (IRB00001052-14010). No additional ethics approval is required for anonymized, publicly available data. The CCSY and CUCSY datasets are accessible through the National Bureau of Statistics of China and related institutions without requiring further ethics approval.
Declarations
Ethics approval and consent to participate
This study utilizes data from the China Family Panel Studies (CFPS), the China City Statistical Yearbook (CCSY), and the China Urban Construction Statistical Yearbook (CUCSY). Ethics approval for the CFPS project was granted by the Ethical Review Committee of Peking University, and all participants provided informed consent at the time of participation. The CFPS ethics review batch number is unified across different investigation rounds: IRB00001052-14010. Since the data used in this study are anonymized and publicly available, there is no need for additional ethics approval for approved data users. The CCSY and CUCSY data are also publicly available, and no further ethics approval was required for their use.
Consent for publication
Consent for publication was confirmed with all participants at the same time as consent to participate was obtained.
Competing interests
The authors declare no competing interests.
Footnotes
In practice, it is not uncommon to see a separation between the two. For example, while Suzhou is the most populous city in Jiangsu Province, Nanjing holds the highest administrative hierarchy. Among the 27 provinces in mainland China, the most populous city is not the provincial capital in seven of these provinces.
Since the baseline survey officially launched in 2010, the China Family Panel Studies (CFPS) has established a scientifically rigorous tracking system. All baseline household members, along with their biological or adopted children born after the baseline, are defined as “gene members” and are included in the permanent tracking scope, thereby ensuring the continuity and integrity of the sample. To maximize the sample retention rate, CFPS employs a diversified follow-up strategy: first, it builds a multi-level contact network and proactively tracks respondents through phone calls and field visits; second, when direct contact with respondents is not possible, it obtains information through proxies such as relatives or friends; third, it utilizes modern information technologies to maintain contact with respondents through multiple channels. This systematic tracking mechanism effectively reduces sample attrition and provides a strong foundation for the validity of longitudinal research.
Due to the fact that there are many missing values on the key variables required for this study in the CFPS data from 2014 and before, this will significantly affect the reliability of the analysis results. To ensure the robustness of the research conclusions, we gave priority to selecting data from the four periods of 2016 to 2022 that had good data integrity and maintained good consistency in variable Settings and questionnaire design.
Choosing to control for provincial fixed effects rather than city fixed effects is due to the study’s focus on the impact of urban administrative hierarchy on individual health. City fixed effects might capture city-specific factors highly correlated with individual health, leading to multicollinearity issues in the model, making it difficult to accurately estimate. In contrast, province fixed effects can more effectively control for macro differences between provinces, while avoiding estimation biases that arise from high correlations between city-specific factors and the explanatory variables.
World Bank. World Bank Group country classifications by income level for FY24 (July 1, 2023- June 30, 2024). https://blogs.worldbank.org/zh/opendata/new-world-bank-group-country-classifications-income-level-fy24. Accessed July 15, 2024.
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data used in this study are publicly available from the China Family Panel Studies (CFPS), the China City Statistical Yearbook (CCSY), and the China Urban Construction Statistical Yearbook (CUCSY). The CFPS data can be accessed through its official website (http://www.isss.pku.edu.cn/cfps/en) with approval, and its ethics approval was granted by Peking University’s Ethical Review Committee (IRB00001052-14010). No additional ethics approval is required for anonymized, publicly available data. The CCSY and CUCSY datasets are accessible through the National Bureau of Statistics of China and related institutions without requiring further ethics approval.