Disposable Income per Capita: Urban: Middle Income data was reported at 48,508.000 RMB in 2024. This records an increase from the previous number of 46,276.000 RMB for 2023. Disposable Income per Capita: Urban: Middle Income data is updated yearly, averaging 8,678.295 RMB from Dec 1985 (Median) to 2024, with 40 observations. The data reached an all-time high of 48,508.000 RMB in 2024 and a record low of 737.280 RMB in 1985. Disposable Income per Capita: Urban: Middle Income data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Household Survey – Table CN.HD: Income by Income Level. Since 2013, All households in the sample are grouped, by per capita disposable income of the household, into groups of low income, lower middle income, middle income, upper middle income, and high income, each group consisting of 20%, 20%, 20%, 20%, and 20% of all households respectively.

Disposable Income per Capita: Middle Income data was reported at 33,925.000 RMB in 2024. This records an increase from the previous number of 32,195.000 RMB for 2023. Disposable Income per Capita: Middle Income data is updated yearly, averaging 24,111.810 RMB from Dec 2013 (Median) to 2024, with 12 observations. The data reached an all-time high of 33,925.000 RMB in 2024 and a record low of 15,697.999 RMB in 2013. Disposable Income per Capita: Middle Income data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Household Survey – Table CN.HD: Income by Income Level.

In 2024, the average annual per capita disposable income of households in China amounted to approximately 41,300 yuan. Annual per capita income in Chinese saw a significant rise over the last decades and is still rising at a high pace. During the last ten years, per capita disposable income roughly doubled in China. Income distribution in China As an emerging economy, China faces a large number of development challenges, one of the most pressing issues being income inequality. The income gap between rural and urban areas has been stirring social unrest in China and poses a serious threat to the dogma of a “harmonious society” proclaimed by the communist party. In contrast to the disposable income of urban households, which reached around 54,200 yuan in 2024, that of rural households only amounted to around 23,100 yuan. Coinciding with the urban-rural income gap, income disparities between coastal and western regions in China have become apparent. As of 2023, households in Shanghai and Beijing displayed the highest average annual income of around 84,800 and 81,900 yuan respectively, followed by Zhejiang province with 63,800 yuan. Gansu, a province located in the West of China, had the lowest average annual per capita household income in China with merely 25,000 yuan. Income inequality in China The Gini coefficient is the most commonly used measure of income inequality. For China, the official Gini coefficient also indicates the astonishing inequality of income distribution in the country. Although the Gini coefficient has dropped from its high in 2008 at 49.1 points, it still ranged at a score of 46.5 points in 2023. The United Nations have set an index value of 40 as a warning level for serious inequality in a society.

China Disposable Income per Capita: Urban: Upper Middle Income data was reported at 68,151.000 RMB in 2024. This records an increase from the previous number of 65,430.000 RMB for 2023. China Disposable Income per Capita: Urban: Upper Middle Income data is updated yearly, averaging 11,827.130 RMB from Dec 1985 (Median) to 2024, with 40 observations. The data reached an all-time high of 68,151.000 RMB in 2024 and a record low of 861.960 RMB in 1985. China Disposable Income per Capita: Urban: Upper Middle Income data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Household Survey – Table CN.HD: Income by Income Level. Since 2013, All households in the sample are grouped, by per capita disposable income of the household, into groups of low income, lower middle income, middle income, upper middle income, and high income, each group consisting of 20%, 20%, 20%, 20%, and 20% of all households respectively.

This dataset contains information about 371 debt contracts between Chinese state-owned creditors and borrowers in 60 low-income, middle-income, and high-income countries.

This dataset contains information about China’s portfolio of collateralized loans to borrowing institutions in low-income and middle-income countries that qualify as public and publicly-guaranteed (PPG) debt. It captures 620 collateralized PPG loan commitments worth $418 billion from 31 Chinese state-owned creditors to 158 borrowers in 57 countries between 2000 and 2021.

China Net Income per Capita: Rural Household: HH Business: Middle Income data was reported at 3,124.737 RMB in 2012. This records an increase from the previous number of 2,856.741 RMB for 2011. China Net Income per Capita: Rural Household: HH Business: Middle Income data is updated yearly, averaging 1,977.965 RMB from Dec 2002 (Median) to 2012, with 11 observations. The data reached an all-time high of 3,124.737 RMB in 2012 and a record low of 1,359.360 RMB in 2002. China Net Income per Capita: Rural Household: HH Business: Middle Income data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Household Survey – Table CN.HD: Income and Expenditure by Income Level: Rural.

The dataset captures 20,985 projects across 165 low- and middle-income countries supported by loans and grants from official sector institutions in China worth $1.34 trillion. It tracks projects over 22 commitment years (2000-2021) and provides details on the timing of project implementation over a 24-year period (2000-2023).

China Disposable Income per Capita: Upper Middle Income data was reported at 53,359.000 RMB in 2024. This records an increase from the previous number of 50,220.000 RMB for 2023. China Disposable Income per Capita: Upper Middle Income data is updated yearly, averaging 37,850.925 RMB from Dec 2013 (Median) to 2024, with 12 observations. The data reached an all-time high of 53,359.000 RMB in 2024 and a record low of 24,361.249 RMB in 2013. China Disposable Income per Capita: Upper Middle Income data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Household Survey – Table CN.HD: Income by Income Level.

China Income per Capita: Rural Household: Lower Middle data was reported at 6,823.004 RMB in 2012. This records an increase from the previous number of 6,168.249 RMB for 2011. China Income per Capita: Rural Household: Lower Middle data is updated yearly, averaging 3,718.387 RMB from Dec 2002 (Median) to 2012, with 11 observations. The data reached an all-time high of 6,823.004 RMB in 2012 and a record low of 2,288.340 RMB in 2002. China Income per Capita: Rural Household: Lower Middle data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Household Survey – Table CN.HD: Income and Expenditure by Income Level: Rural.

This dataset tracks 123 seaport projects worth $29.9 billion officially financed by China to construct or expand 78 ports in 46 low-income and middle-income countries from 2000-2021.

AimsTo reveal the impact of eleven risk factors on stroke and provide estimates of the prevention potential.MethodsWe completed a multicenter case-control study in Jiangxi, China, a middle-income area. Neuroimaging examination was performed in all cases. Controls were stroke-free adults recruited from the community in the case concentration area. Conditional logistic regression and unconditional logistic regression were used for subgroup analysis of stroke type, and other groups (sex, age and urban-rural area), respectively. Odds ratios (ORs) and their population attributable risks (PARs) were calculated, with 95% confidence intervals.ResultsA total of 43,615 participants (11,735 cases and 31,880 controls) were recruited from February to September 2018, of whom we enrolled 11,729 case-control pairs. Physical inactivity [PAR 69.5% (66.9–71.9%)] and hypertension [53.4% (49.8–56.8%)] were two major risk factors for stroke, followed by high salt intake [23.9% (20.5–27.3%)], dyslipidemia [20.5% (17.1–24.0%)], meat-based diet [17.5% (14.9–20.4%)], diabetes [7.7% (5.9–9.7%)], cardiac causes [5.3% (4.0–6.7%)], alcohol intake [4.7% (0.2–10.0%)], and high homocysteine [4.3% (1.4–7.4%)]. Nine of these factors were associated with ischemic stroke, and five were associated with intracerebral hemorrhage. Collectively, eleven risk factors accounted for 59.9% of the PAR for all stroke (ischemic stroke: 61.0%; intracerebral hemorrhage: 46.5%), and were consistent across sex (men: 65.5%; women: 62.3%), age (≤55: 65.2%; >55: 63.5%), and urban-rural areas (city: 62.2%; county: 65.7%).ConclusionThe 11 risk factors associated with stroke identified will provide an important reference for evidence-based planning for stroke prevention in middle-income areas. There is an urgent need to improve awareness, management and control of behavioral and metabolic risk factors, particularly to promote physical activity and reduce blood pressure.

China Income per Capita: Urban: Upper Middle Income data was reported at 32,758.800 RMB in 2012. This records an increase from the previous number of 29,058.920 RMB for 2011. China Income per Capita: Urban: Upper Middle Income data is updated yearly, averaging 6,673.460 RMB from Dec 1985 (Median) to 2012, with 28 observations. The data reached an all-time high of 32,758.800 RMB in 2012 and a record low of 935.520 RMB in 1985. China Income per Capita: Urban: Upper Middle Income data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Household Survey – Table CN.HD: Income and Expenditure by Income Level: Urban.

Supplementary information files for article Socio-economic disparities in child-to-adolescent growth trajectories in China: Findings from the China Health and Nutrition Survey 1991-2015

Backgrounds: Socio-economic disparities in growth trajectories of children from low-/middle-income countries are poorly understood, especially those experiencing rapid economic growth. We investigated socio-economic disparities in child growth in recent decades in China. Methods: Using longitudinal data on 5,095 children/adolescents (7-18y) from the China Health and Nutrition Survey (1991-2015), we estimated mean height and BMI trajectories by socio-economic position (SEP) and sex for cohorts born in 1981-85, 1986-90, 1991-95, 1996-2000, using random-effects models. We estimated differences between high (urbanization index ≥median, household income per capita ≥median, parental education ≥high school, or occupational classes I-IV) and low SEP groups. Findings: Mean height and BMI trajectories have shifted upwards across cohorts. In all cohorts, growth trajectories for high SEP groups were above those for low SEP groups across SEP indicators. For height, socio-economic differences persisted across cohorts (e.g. 3.8cm and 2.9cm in earliest and latest cohorts by urbanization index for boys at 10y, and 3.6cm and 3.1cm respectively by household income). For BMI, trends were greater in high than low SEP groups, thus socio-economic differences increased across cohorts (e.g. 0.5 to 0.8kg/m2 by urbanization index, 0.4 to 1.1kg/m2 by household income for boys at 10y). Similar trends were found for stunting and overweight/obesity by SEP. There was no association between SEP indicators and thinness. Interpretation: Socio-economic disparities in physical growth persist among Chinese youth. Short stature was associated with lower SEP, but high BMI with higher SEP. Public health interventions should be tailored by SEP, in order to improve children’s growth while reducing overweight/obesity.

Abstract (en): The Research on Early Life and Aging Trends and Effects (RELATE) study compiles cross-national data that contain information that can be used to examine the effects of early life conditions on older adult health conditions, including heart disease, diabetes, obesity, functionality, mortality, and self-reported health. The complete cross sectional/longitudinal dataset (n=147,278) was compiled from major studies of older adults or households across the world that in most instances are representative of the older adult population either nationally, in major urban centers, or in provinces. It includes over 180 variables with information on demographic and geographic variables along with information about early life conditions and life course events for older adults in low, middle and high income countries. Selected variables were harmonized to facilitate cross national comparisons. In this first public release of the RELATE data, a subset of the data (n=88,273) is being released. The subset includes harmonized data of older adults from the following regions of the world: Africa (Ghana and South Africa), Asia (China, India), Latin America (Costa Rica, major cities in Latin America), and the United States (Puerto Rico, Wisconsin). This first release of the data collection is composed of 19 downloadable parts: Part 1 includes the harmonized cross-national RELATE dataset, which harmonizes data from parts 2 through 19. Specifically, parts 2 through 19 include data from Costa Rica (Part 2), Puerto Rico (Part 3), the United States (Wisconsin) (Part 4), Argentina (Part 5), Barbados (Part 6), Brazil (Part 7), Chile (Part 8), Cuba (Part 9), Mexico (Parts 10 and 15), Uruguay (Part 11), China (Parts 12, 18, and 19), Ghana (Part 13), India (Part 14), Russia (Part 16), and South Africa (Part 17). The Health and Retirement Study (HRS) was also used in the compilation of the larger RELATE data set (HRS) (N=12,527), and these data are now available for public release on the HRS data products page. To access the HRS data that are part of the RELATE data set, please see the collection notes below. The purpose of this study was to compile and harmonize cross-national data from both the developing and developed world to allow for the examination of how early life conditions are related to older adult health and well being. The selection of countries for this study was based on their diversity but also on the availability of comprehensive cross sectional/panel survey data for older adults born in the early to mid 20th century in low, middle and high income countries. These data were then utilized to create the harmonized cross-national RELATE data (Part 1). Specifically, data that are being released in this version of the RELATE study come from the following studies: CHNS (China Health and Nutrition Study) CLHLS (Chinese Longitudinal Healthy Longevity Survey) CRELES (Costa Rican Study of Longevity and Healthy Aging) PREHCO (Puerto Rican Elderly: Health Conditions) SABE (Study of Aging Survey on Health and Well Being of Elders) SAGE (WHO Study on Global Ageing and Adult Health) WLS (Wisconsin Longitudinal Study) Note that the countries selected represent a diverse range in national income levels: Barbados and the United States (including Puerto Rico) represent high income countries; Argentina, Cuba, Uruguay, Chile, Costa Rica, Brazil, Mexico, and Russia represent upper middle income countries; China and India represent lower middle income countries; and Ghana represents a low income country. Users should refer to the technical report that accompanies the RELATE data for more detailed information regarding the study design of the surveys used in the construction of the cross-national data. The Research on Early Life and Aging Trends and Effects (RELATE) data includes an array of variables, including basic demographic variables (age, gender, education), variables relating to early life conditions (height, knee height, rural/urban birthplace, childhood health, childhood socioeconomic status), adult socioeconomic status (income, wealth), adult lifestyle (smoking, drinking, exercising, diet), and health outcomes (self-reported health, chronic conditions, difficulty with functionality, obesity, mortality). Not all countries have the same variables. Please refer to the technical report that is part of the documentation for more detail regarding the variables available across countries. Sample weights are applicable to all countries exc...

China Net Income per Capita: Rural Household: Upper Middle Income data was reported at 11,373.030 RMB in 2013. This records an increase from the previous number of 10,142.080 RMB for 2012. China Net Income per Capita: Rural Household: Upper Middle Income data is updated yearly, averaging 4,788.186 RMB from Dec 2000 (Median) to 2013, with 14 observations. The data reached an all-time high of 11,373.030 RMB in 2013 and a record low of 2,767.000 RMB in 2000. China Net Income per Capita: Rural Household: Upper Middle Income data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Household Survey – Table CN.HD: Income and Expenditure by Income Level: Rural.

This paper uses a large-scale nationally representative dataset to examine the nonlinear effect of income on mental health. To investigate their causal relationship, the exogenous impact of automation on income is utilized as the instrument variable (IV). In addition, to explore their nonlinear relationship, both income and its quadratic term are included in regressions. It is found that the impact of income on mental health is U-shaped rather than linear. The turning point (7.698) of this nonlinear relation is near the midpoint of the income interval ([0, 16.113]). This suggests that depression declines as income increases at the lower-income level. However, beyond middle income, further increases in income take pronounced mental health costs, leading to a positive relationship between the two factors. We further exclude the possibility of more complex nonlinear relationships by testing higher order terms of income. In addition, robustness checks, using other instrument variables and mental health indicators, different IV models and placebo analysis, all support above conclusions. Heterogeneity analysis demonstrates that males, older workers, ethnic minorities and those with lower health and socioeconomic status experience higher levels of depression. Highly educated and urban residents suffer from greater mental disorders after the turning point. Religious believers and Communist Party of China members are mentally healthier at lower income levels, meaning that religious and political beliefs moderate the relationship between income and mental health.

Purpose:
The multi-country Study on Global Ageing and Adult Health (SAGE) is run by the World Health Organization's Multi-Country Studies unit in the Innovation, Information, Evidence and Research Cluster. SAGE is part of the unit's Longitudinal Study Programme which is compiling longitudinal data on the health and well-being of adult populations, and the ageing process, through primary data collection and secondary data analysis. SAGE baseline data (Wave 0, 2002/3) was collected as part of WHO's World Health Survey http://www.who.int/healthinfo/survey/en/index.html (WHS). SAGE Wave 1 (2007/10) provides a comprehensive data set on the health and well-being of adults in six low and middle-income countries: China, Ghana, India, Mexico, Russian Federation and South Africa.
Objectives:
To obtain reliable, valid and comparable health, health-related and well-being data over a range of key domains for adult and older adult populations in nationally representative samples
To examine patterns and dynamics of age-related changes in health and well-being using longitudinal follow-up of a cohort as they age, and to investigate socio-economic consequences of these health changes
To supplement and cross-validate self-reported measures of health and the anchoring vignette approach to improving comparability of self-reported measures, through measured performance tests for selected health domains
To collect health examination and biomarker data that improves reliability of morbidity and risk factor data and to objectively monitor the effect of interventions

Additional Objectives:
To generate large cohorts of older adult populations and comparison cohorts of younger populations for following-up intermediate outcomes, monitoring trends, examining transitions and life events, and addressing relationships between determinants and health, well-being and health-related outcomes
To develop a mechanism to link survey data to demographic surveillance site data
To build linkages with other national and multi-country ageing studies
To improve the methodologies to enhance the reliability and validity of health outcomes and determinants data
To provide a public-access information base to engage all stakeholders, including national policy makers and health systems planners, in planning and decision-making processes about the health and well-being of older adults

Methods:
SAGE's first full round of data collection included both follow-up and new respondents in most participating countries. The goal of the sampling design was to obtain a nationally representative cohort of persons aged 50 years and older, with a smaller cohort of persons aged 18 to 49 for comparison purposes. In the older households, all persons aged 50+ years (for example, spouses and siblings) were invited to participate. Proxy respondents were identified for respondents who were unable to respond for themselves. Standardized SAGE survey instruments were used in all countries consisting of five main parts: 1) household questionnaire; 2) individual questionnaire; 3) proxy questionnaire; 4) verbal autopsy questionnaire; and, 5) appendices including showcards. A VAQ was completed for deaths in the household over the last 24 months. The procedures for including country-specific adaptations to the standardized questionnaire and translations into local languages from English follow those developed by and used for the World Health Survey.

Content
Household questionnaire
0000 Coversheet
0100 Sampling Information
0200 Geocoding and GPS Information
0300 Recontact Information
0350 Contact Record
0400 Household Roster
0450 Kish Tables and Household Consent
0500 Housing
0600 Household and Family Support Networks and Transfers
0700 Assets and Household Income
0800 Household Expenditures
0900 Interviewer Observations

Individual questionnaire
1000 Socio-Demographic Characteristics
1500 Work History and Benefits
2000 Health State Descriptions and Vignettes
2500 Anthropometrics, Performance Tests and Biomarkers
3000 Risk Factors and Preventive Health Behaviours
4000 Chronic Conditions and Health Services Coverage
5000 Health Care Utilization
6000 Social Cohesion
7000 Subjective Well-Being and Quality of Life (WHOQoL-8 and Day Reconstruction Method)
8000 Impact of Caregiving
9000 Interviewer Assessment

BackgroundParental household wealth has been shown to be associated with offspring health conditions, while inconsistent associations were reported among generally healthy population especially in low- and middle- income countries (LMICs). Whether the household wealth upward mobility in LMICs would confer benefits to child health remains unknown.MethodsWe conducted a prospective birth cohort of children born to mothers who participated in a randomized trial of antenatal micronutrient supplementation in rural western China. Household wealth were repeatedly assessed at pregnancy, mid-childhood and early adolescence using principal component analysis for household assets and dwelling characteristics. We used conditional gains and group-based trajectory modeling to assess the quantitative changes between two single-time points and relative mobility of household wealth over life-course, respectively. We performed generalized linear regressions to examine the associations of household wealth mobility indicators with adolescent height- (HAZ) and body mass index-for-age and sex z score (BAZ), scores of full-scale intelligent quotient (FSIQ) and emotional and behavioral problems.ResultsA total of 1,188 adolescents were followed, among them 59.9% were male with a mean (SD) age of 11.7 (0.9) years old. Per SD conditional increase of household wealth z score from pregnancy to mid-childhood was associated with 0.11 (95% CI 0.04, 0.17) SD higher HAZ and 1.41 (95% CI 0.68, 2.13) points higher FSIQ at early adolescence. Adolescents from the household wealth Upward trajectory had a 0.25 (95% CI 0.03, 0.47) SD higher HAZ and 4.98 (95% CI 2.59, 7.38) points higher FSIQ than those in the Consistently low subgroup.ConclusionHousehold wealth upward mobility particularly during early life has benefits on adolescent HAZ and cognitive development, which argues for government policies to implement social welfare programs to mitigate or reduce the consequences of early-life deprivations. Given the importance of household wealth in child health, it is recommended that socioeconomic circumstances should be routinely documented in the healthcare record in LMICs.

BackgroundSince the reform and opening up, China’s urban and rural economic development has exhibited characteristics of imbalance, with the urban-rural income gap being the largest and most noticeable issue facing China’s socio-economic landscape. Alleviating and effectively resolving the urban-rural income disparity is crucial for achieving overall common prosperity. Therefore, this study provides insights for strategically narrowing the urban-rural income gap from the perspective of higher education investment.MethodsWe employ a panel fixed effects model to examine the basic regression, heterogeneity, mediating effects, and threshold effects. Simultaneously, we address the endogeneity issues in basic regression and mediating effects using the instrumental variable method. Additionally, we adopt the substitution of variables to ensure the robustness of the results.ResultsThis paper selects panel data from China’s eight major comprehensive economic zones from 2003 to 2021 for analysis. The findings reveal that, overall, higher education investment in China’s eight major comprehensive economic zones can narrow the urban-rural income gap. Specifically, higher education investment in 50% of these comprehensive economic zones—namely, the Northern Coastal Comprehensive Economic Zone, Eastern Coastal Comprehensive Economic Zone, Northeast Comprehensive Economic Zone, and Middle Yangtze River Comprehensive Economic Zone—can reduce the urban-rural income disparity. Conversely, higher education investment in the Middle Yellow River Comprehensive Economic Zone, Southern Coastal Comprehensive Economic Zone, Greater Southwest Comprehensive Economic Zone, and Greater Northwest Comprehensive Economic Zone has widened the urban-rural income gap. Additionally, higher education investment can affect the urban-rural income disparity through technological innovation. Overall, the impact of higher education investment on the urban-rural income gap in China’s eight major comprehensive economic zones is also influenced by the level of economic development, exhibiting an “inverted U-shaped” characteristic. This nonlinear impact varies across regions.ConclusionsIn conclusion, to narrow the urban-rural income gap across China’s eight major integrated economic zones, it is necessary to improve the mechanism for higher education investment in these zones. Strategies should be based on regional differences, tailored to local conditions, and implemented with a differentiated and precise approach to higher education development across regions. Emphasis should also be placed on the research and application of innovative technologies to achieve deep integration between urban and rural areas within China’s eight major integrated economic zones.