Per capita gross domestic product (GDP) of cities in China varies tremendously, mainly depending on the location of the city. Cities with the highest per capita GDP are mainly to be found in coastal provinces in East China and in South China, like Guangdong province. The poorest cities are located in the still less developed western parts of China, like Gansu province, or in the Chinese rust belt in Northeastern China, like Heilongjiang province.

Since 2000, the share of people living in extreme poverty in rural China has been constantly decreasing. In *************, the Chinese government announced that - based on the current definition of poverty - all residents in China have been relieved from extreme poverty. In the past, extreme poverty had been more common in western and central parts of China, and in these regions the number of poor households is still considerably higher today.

This dataset is used to investigates the local variations, determinants, effects and influence mechanisms of Health Poverty Alleviation Policy in China. The dataset contains policy data at city and provincial levels, city charateristics data, nationally survey data (CHARLS) and qualitative data.

This study investigates the alarming rise of urban poverty in China; in particular the patterns of urban poverty and the institutional causes are examined. The researchers look for evidence of institutional innovations that have emerged as individuals and organisations seek to negotiate more secure access to vital civic goods and services. A case study approach was used due to the complexity of the issue and the size of the Chinese urban population. Six cities were chosen and four neighbourhoods in each city were investigated. These cities were distributed in the costal, central and western region respectively, including Guangzhou, Nanjing, Harbin, Wuhan, Kumin, and Xi’an.

Further information is available from the ESRC Award webpage.

This dataset provides annual data on key economic, social, and environmental indicators affecting poverty levels in Pakistan from the year 2000 to 2023. It includes crucial variables such as GDP growth rate, inflation, unemployment, poverty headcount ratio, agricultural growth, government social spending, external debt, and climate-related disasters. The dataset highlights significant trends, including the economic impact of the 2010 floods, COVID-19 pandemic (2020–2022), and the 2022 economic crisis. It serves as a valuable resource for researchers, policymakers, and analysts studying poverty dynamics and economic development in Pakistan.

This paper used the micro panel data from 2016 to 2019 of 2031 registered poor households in B Town, W County, Lu’an City of Anhui Province in China to analyze the diversified patterns and poverty alleviation effect of paired assistance based on the PSM-DID model. The empirical results show that paired assistance provided by social forces can significantly contribute to the poverty alleviation of poor households, promoting the poverty alleviation rate by 7.8%, which can be concluded through sample matching and control of relevant variables. Furthermore, based on the subsample of poor households with social assistance, we found that external social assistance subject to paired assistance can significantly improve the poverty alleviation rate of poor households by 14.26%, mainly hung on their economic base and strength of poverty alleviation.

In 2024, the average annual per capita disposable income of rural households in China was approximately ****** yuan, roughly ** percent of the income of urban households. Although living standards in China’s rural areas have improved significantly over the past 20 years, the income gap between rural and urban households is still large. Income increase of China’s households From 2000 to 2020, disposable income per capita in China increased by around *** percent. The fast-growing economy has inevitably led to the rapid income increase. Furthermore, inflation has been maintained at a lower rate in recent years compared to other countries. While the number of millionaires in China has increased, many of its population are still living in humble conditions. Consequently, the significant wealth gap between China’s rich and poor has become a social problem across the country. However, in recent years rural areas have been catching up and disposable income has been growing faster than in the cities. This development is also reflected in the Gini coefficient for China, which has decreased since 2008. Urbanization in China The urban population in China surpassed its rural population for the first time in 2011. In fact, the share of the population residing in urban areas is continuing to increase. This is not surprising considering remote, rural areas are among the poorest areas in China. Currently, poverty alleviation has been prioritized by the Chinese government. The measures that the government has taken are related to relocation and job placement. With the transformation and expansion of cities to accommodate the influx of city dwellers, neighboring rural areas are required for the development of infrastructure. Accordingly, land acquisition by the government has resulted in monetary gain by some rural households.

This data provides a comprehensive view into how residents in key Belt and Road Initiative cities perceive China’s influence across multiple aspects of daily life. It captures nuanced sentiment related to economic opportunity, job creation, debt concerns, political influence, and infrastructure improvement, and it also produces an Overall Sentiment Index that brings these perspectives together in a single benchmark score. By incorporating demographic details such as age, income, and household type, the data creates a multidimensional understanding of how different groups within each city view China’s role. What makes this resource especially powerful is that it is not limited to a one-time snapshot. It is designed to be run repeatedly on a weekly, monthly, or quarterly basis so that changes in perception can be tracked over time and interpreted in context.

Each of the sentiment dimensions tells a different story. Economic opportunity captures whether residents believe Chinese involvement is creating pathways for trade, investment, and business growth. Job creation measures whether these investments translate into employment for locals or remain limited to outside contractors. Debt concerns reflect whether residents feel financing arrangements are sustainable or whether they put their country at risk. Political influence expresses how much China is seen as shaping governance, elections, or policy priorities. Infrastructure improvement reflects the tangible benefits that people associate with new ports, power plants, railways, or digital networks. When these are combined into the Overall Sentiment Index, it becomes possible to see a distilled score for each city at any given time while retaining the ability to drill down into the drivers of that score.

Running this data once is valuable because it shows the present balance of perception. Running it regularly transforms it into a monitoring tool. Over time, it becomes clear whether optimism is building or eroding, whether concerns are intensifying or easing, and whether residents feel more or less positively about China’s role in their city. Weekly runs allow short-term fluctuations to be observed, which is especially important when external events like debt renegotiations or infrastructure launches occur. Monthly runs strike a balance, capturing trends that are still timely but not so volatile that they obscure underlying movement. Quarterly runs provide a strategic rhythm that aligns with government planning cycles, investor reporting, and long-term program design. Whatever cadence is chosen, the ability to compare one wave of sentiment to the next adds an entirely new layer of value.

Consider the implications for economic opportunity. In one quarter, residents may feel optimistic because new trade zones are announced, but by the next quarter that optimism may fade if jobs or contracts do not materialize locally. Debt concerns may remain stable for months and then spike suddenly when repayment deadlines become politically controversial. Infrastructure satisfaction may begin high at the ribbon-cutting of a new port but then decline if maintenance is poor or if local communities feel excluded from its benefits. Political influence sentiment may ebb and flow with election cycles, reflecting moments when Chinese involvement is spotlighted in domestic debates. Without recurring data, these shifts would be invisible or anecdotal. With recurring data, they become measurable, comparable, and actionable.

Demographic segmentation intensifies the usefulness of this time-series view. Younger residents may consistently report higher enthusiasm for economic opportunity, while older residents may be more cautious. Over time, the gap between those groups can widen or narrow, revealing intergenerational dynamics that matter for future policy and business planning. Lower-income households may express higher debt concerns, while wealthier households emphasize infrastructure benefits. Families with children may be focused on long-term job creation, while singles are more attuned to short-term opportunities. Seeing these divergences move over time is more valuable than seeing them once because it highlights whether divisions are hardening, softening, or shifting to new areas.

The geographic coverage of this data spans ten strategically important BRI cities, from Karachi and Colombo to Nairobi, Addis Ababa, Almaty, Athens, Gwadar, Jakarta, Dushanbe, and Belgrade. These cities were selected not only for their individual significance but also because, taken together, they represent a cross-section of the initiative’s global reach. By comparing sentiment across these cities at multiple time points, it becomes possible to identify where China’s influence is gaining legitimacy, where it is facing skepticism, and how those dynamics differ between regions. The standardized structure of the data ensures that these comparisons are meaningful, turning local snapshots into part of a...

Urban Informal Settlements (UIS) denote densely populated areas, which often exhibit a mixture of rural and urban, and are characterized by insufficient urban infrastructure standards. However, UIS mapping products for major cities in China are lacking. To fill this gap, we developed a methodology incorporating very high spatial resolution remote sensing images close to 2013 and 2021 of 37 Chinese megacities and deep learning-based models to map UIS areas (namely the ChinaUIS product). The ChinaUIS product can help promote a new understanding of urban and rural development in major cities of China and facilitate applications such as urban poverty estimation, urban\rural planning, and urban sustainability. The ChinaUIS product is free to use for non-commercial forms including scientific research and science promotion under proper citation.

Average Wage: On Duty: Liaoning: Anshan data was reported at 79,137.000 RMB in 2022. This records an increase from the previous number of 76,692.000 RMB for 2021. Average Wage: On Duty: Liaoning: Anshan data is updated yearly, averaging 30,432.650 RMB from Dec 1996 (Median) to 2022, with 27 observations. The data reached an all-time high of 79,137.000 RMB in 2022 and a record low of 5,451.000 RMB in 1996. Average Wage: On Duty: Liaoning: Anshan data remains active status in CEIC and is reported by Anshan Municipal Bureau of Statistics. The data is categorized under China Premium Database’s Labour Market – Table CN.GG: Average Wage: On Duty: Prefecture Level City.

This graph shows what respondents in selected Chinese cities saw as sources of dissatisfaction in hospital care. In Shanghai, **** percent of respondents said that they regarded a poor physical environment as a source of dissatisfaction.

Number of Household: Yunnan: Kunming data was reported at 3,514.800 Unit th in 2020. This records an increase from the previous number of 2,100.000 Unit th for 2019. Number of Household: Yunnan: Kunming data is updated yearly, averaging 1,981.500 Unit th from Dec 2005 (Median) to 2020, with 16 observations. The data reached an all-time high of 3,514.800 Unit th in 2020 and a record low of 1,531.900 Unit th in 2005. Number of Household: Yunnan: Kunming data remains active status in CEIC and is reported by Kunming Municipal Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GE: Population: Prefecture Level City: No of Household.

In 2024, approximately 67 percent of the total population in China lived in cities. The urbanization rate has increased steadily in China over the last decades. Degree of urbanization in China Urbanization is generally defined as a process of people migrating from rural to urban areas, during which towns and cities are formed and increase in size. Even though urbanization is not exclusively a modern phenomenon, industrialization and modernization did accelerate its progress. As shown in the statistic at hand, the degree of urbanization of China, the world's second-largest economy, rose from 36 percent in 2000 to around 51 percent in 2011. That year, the urban population surpassed the number of rural residents for the first time in the country's history.The urbanization rate varies greatly in different parts of China. While urbanization is lesser advanced in western or central China, in most coastal regions in eastern China more than two-thirds of the population lives already in cities. Among the ten largest Chinese cities in 2021, six were located in coastal regions in East and South China. Urbanization in international comparison Brazil and Russia, two other BRIC countries, display a much higher degree of urbanization than China. On the other hand, in India, the country with the worlds’ largest population, a mere 36.3 percent of the population lived in urban regions as of 2023. Similar to other parts of the world, the progress of urbanization in China is closely linked to modernization. From 2000 to 2024, the contribution of agriculture to the gross domestic product in China shrank from 14.7 percent to 6.8 percent. Even more evident was the decrease of workforce in agriculture.

Abstract

The World Values Survey (www.worldvaluessurvey.org) is a global network of social scientists studying changing values and their impact on social and political life, led by an international team of scholars, with the WVS association and secretariat headquartered in Stockholm, Sweden. The survey, which started in 1981, seeks to use the most rigorous, high-quality research designs in each country. The WVS consists of nationally representative surveys conducted in almost 100 countries which contain almost 90 percent of the world’s population, using a common questionnaire. The WVS is the largest non-commercial, cross-national, time series investigation of human beliefs and values ever executed, currently including interviews with almost 400,000 respondents. Moreover the WVS is the only academic study covering the full range of global variations, from very poor to very rich countries, in all of the world’s major cultural zones. The WVS seeks to help scientists and policy makers understand changes in the beliefs, values and motivations of people throughout the world. Thousands of political scientists, sociologists, social psychologists, anthropologists and economists have used these data to analyze such topics as economic development, democratization, religion, gender equality, social capital, and subjective well-being. These data have also been widely used by government officials, journalists and students, and groups at the World Bank have analyzed the linkages between cultural factors and economic development.

Geographic coverage

China

Analysis unit

Household Individual

Universe

National Population, Both sexes,18 and more years

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample size: 1000

The sample is a representative national sample of China containing 40 county/city sample units to collect individual level data of, from a political cultural perspective, the values and attitudes currently held by Chinese citizens. With considerations of representativeness, feasibility, and budgetary constrains, it was decided this project would draw a subsidiary probability sample out of a master sample that RCCC created based on its previous national survey on environmental awareness of the general public in China conducted in 1998. The Environmental Awareness Survey, which was used as a master sample, was a national survey conducted through out the entire country. The target population was the same as the one defined for this survey. Through the stratification, the proportionally allocated multi-stage PPS (probability proportional to size) technique was employed in order to obtain the self-weighted household samples. There were different stages in the sampling procedure: Counties and county-level cities are taken as primary sampling units (PSUs). Family households are the basic sampling unit. Demographic data at all levels was obtained from The Demographic Data for Chinese Cities and Counties, 1997, published by the State Bureau of Statistics.

Nation wide, there were 2,860 county-level units for the first stage sampling (including 1,689 counties, 436 county-level cities, and 735 urban district--with administrative rank equivalent to county--in large cities). The total households were 337,659,447. This was the base for establishing the sampling frames. Some readjustments: Taking into account of cost and accessibility, only the provincial capitals (Lhasa and Urumchi) and their surrounding areas in Tibet and Sinkiang were included in the sampling frame; in other remote western provinces, a few areas that are extremely hard to access were left out as well. After such readjustment the sampling frame then includes 2,708 county-level units, of which the total households are 322,002,173. Compared to the target population, there was a 5.3% reduction (152 units) in the first stage sampling units. However, since the population density in the remote areas of the western provinces is very low, the reduction counts merely 1.4% of the total households in the sampling frame. Geographical administrative divisions of China were regarded as the primary labels of stratification, that is, each province was treated as an independent stratum. Allocation of target sampling units among the sampling stages was designed as following: 135 PSUs out of the first sampling (county-level) units; 2 secondary sampling (townshiplevel) units in each of the PSUs; then 2 third sampling (village-level) units in each of the SSUs; 25 households in each of the third sampling units, on average. Based on the proportional stratification principle, sample allocation to strata was proportional to the size of each stratum, by an equal probability of f = .0042%. Within each stratum (province), sample sizes were calculated and allocated proportionally to each of the sampling stages. A self-weighted national sample thus was obtained.

Multi-stage PPS: -The first stage: equidistance PPS was employed to draw the county sample. -The second stage: in each of the chosen county-level units, a sampling frame was created based on the data of townships/ward and size measurement; then the equidistance PPS is employed to choose the township/streets sample. -The third stage: a third sampling frame was obtained from each of the chosen township-level units (neighbourhoods, villages and size measurement), and, again, the equidistance PPS is employed to choose the village/neighbourhood sample. -The fourth stage: in each of the chosen village/neighbourhood units, the official list of households registration was obtained; using the size measurement of this unit and the desired number of households to count the sampling distance, then households were selected according to the sampling interval. Since the household registration also listed all family members of each of the household, respondents were drawn randomly immediately after the household drawing. The WVS-China sample was drawn out of the above described master sample.

Some readjustments: Primarily because of the budgetary constrains of the WVS project, six remote provinces in the master sample were excluded. They were: Hainan, Tibet, Gansu, Qinghai, Ningxia, and Sinkiang. These provinces are all with very low population density, and all together they count 5.1% of the total population and 4.6% of total households of the country. After the adjustments, seven of the 139 county-level units of the master sample were removed. Therefore, the target 40 PSUs were to be drawn out of the remaining 132 units.

Sampling Stages: -The first stage: 40 units were drawn from 132 county-level units of the master sample were removed. Therefore, the 40 PSUs were to be drawn out of the remaining 132 units. -The second stage: one unit was chosen randomly out of the 2 original township-level units (SSUs) in each of the 40 selected PSUs. -The third stage: one unit was chosen randomly out of the 2 original village-level units in each of the selected SSUs. -The fourth stage: from each of the chosen village-level units, 35 households were drawn out of the household registration list with equidistance, along with one respondent in each selected household.

Remarks about sampling: -Sample unit from office sampling: Housing

Mode of data collection

Face-to-face [f2f]

Research instrument

As a participating country-team of the World Values Survey (WVS), the Research Center of Contemporary China (RCCC) at Peking University implemented the WVS-China survey in 2001. The target population covers those who are between 18 and 65 of age (born between July 2, 1935 and July 1, 1982), formally registered and actually reside in dowelings within the households in China when the survey is conducted.

Response rate

The sample size was determined to be approximately 1,000 -- eligible individuals are to be drawn out of the above defined target population in China. Based on previous experience of response rate, it was decided to increase the target sample to 1,400 in order to reach a satisfied response rate. The final results are summarized as follows: - Target sample size: 1,400 - Sample drawn in the field: 1,385 - Completed, valid interviews: 1,000 - Response rate: 72.2% Summary of Non-Responses Types of Non-Responses (missing cases) % - Be away/not seen for several times: 145-37.7% - Be away for long time/be on a business trip/go abroad/travel:138-35.8% - The interviewer didnt write the reason: 23-6.0% - Rejection: 19-4.9% - Move/investigation reveals no this person: 15-3.9% - Impediments in body or language/at variance with qualification: 12-3.1% - Useless: 11-2.9% - Address is nor clear/cant find the address: 10-2.6% - A vacant house: 6-1.6% - Tenant: 6-1.6% - Total: 385-100%

Sampling error estimates

Estimated Error: 3,2

Unemployment Rate in China decreased to 5.10 percent in October from 5.20 percent in September of 2025. This dataset provides - China Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The data has been generated by ethnographic observations, interviews and interactions with migrant workers in two sites in Shanghai in 2017/2018: Songjiang District on the south-western outskirts, and the inner-city Huangpu District, in proximity to some of the city’s most famous tourist attractions, such as the Bund or Nanjing Road. Ethnography, with its focus on everyday experience, can yield significant insights into understanding migrant mental health in contexts where signs of severe mental distress remain largely imperceptible, and more generally, into how stresses and strains are lived through the spaces, times and affective atmospheres of the city. Migrant ethnography can help us reconsider the oft-made connection between everyday stress and mental ill health. In this research, drawing on field evidence in central and peripheral Shanghai, we highlight the importance of attending to the forms of spatial and temporal agency through which migrants actively manage the ways in which the city affects their subjectivity. These everyday subjective practices serve to problematize the very concept of ‘mental health’, enabling us to engage in a critical dialogue with sociological and epidemiological research that assesses migrant mental health states through the lens of the vulnerability or resilience of this social group, often reducing citiness to a series of environmental ‘stressors’.We have known, since at least the early twentieth century, that there is an association between living in a city and being diagnosed with a mental illness. But questions around the specificity of relationship between urban life and have continued well into the twenty-first century. We still don't know, for example, exactly why mental illness clusters in cities; we don't know how it relates to experiences of urban poverty, deprivation, overcrowding, social exclusion, and racism; and we don't know the precise biological and sociological mechanisms that turn difficult urban lives into diagnosable mental health conditions. What we do know is that migrants into cities bear a disproportionately large share of the burden of urban mental illness; we know that dense living conditions seem to exacerbate the problem; and we know that the general stress, tumult and precarity of urban living can, sometimes, create the basis for the development of clinical problems. If there are unanswered questions around the relationship between mental health and the city, these questions are particularly acute in contemporary China: China has urbanised at an unprecedented rate in the last decade, and has now become a majority urban society. But whereas in nineteenth-century Europe urbanization came from a growth in population, in twenty-first century China the situation is different: most of the growth is from rural migrants coming into the cities. In China, then, the link between urban transformation and mental illness is a critical issue: (1) Development in China is related to migration from the countryside into the cities; (2) Unrecognized and untreated mental disorder is a key factor in casting individuals and families into poverty and social exclusion; (3) Effective development of urban mental health policu requires far greater understanding of the related problems of urban stress, precarious living conditions and mental disorder. This project is an attempt to understand the relationship between migration and mental health in one Chinese mega-city: Shanghai. Given what we know about the relationship between urban mental health and particular patterns of social life (poverty, migration, dense housing, and so on), it starts from the position that this question requires new input from the social sciences. At the heart of the project is an attempt to mix what we know about mental health in contemporary Shanghai with a new kind of close-up, street-level data on what the daily experience of being a migrant on Shanghai is actually life - especially with regard to stress, housing, and access to services. We will then connect these two forms of knowledge to produce a new kind of survey for getting a new sociological deep surveying instrument for mapping migrant mental health in Shanghai. The project, which is split between researchers in the UK and China, asks: (1) How is mental disorder actually patterned in Shanghai, and how is that pattern affected by recent migration? (2) How are immigrants absorbed in Shanghai, and what is daily life actually like in Shanghai's migrant communities? (3) What policies, services, or laws might alleviate mental health among migrants in Shanghai? (4) What can be learned in Shanghai for similar problems in other developing mega-cities (such as Sao Paolo or Lagos).

In 2024, about 943.5 million people lived in urban regions in China and 464.8 million in rural. That year, the country had a total population of approximately 1.41 billion people. As of 2024, China was the second most populous country in the world. Urbanization in China Urbanization refers to the process by which people move from rural to urban areas and how a society adapts to the population shift. It is usually seen as a driving force in economic growth, accompanied by industrialization, modernization and the spread of education. Urbanization levels tend to be higher in industrial countries, whereas the degree of urbanization in developing countries remains relatively low. According to World Bank, a mere 19.4 percent of the Chinese population had been living in urban areas in 1980. Since then, China’s urban population has skyrocketed. By 2024, about 67 percent of the Chinese population lived in urban areas. Regional urbanization rates In the last decades, urbanization has progressed greatly in every region of China. Even in most of the more remote Chinese provinces, the urbanization rate surpassed 50 percent in recent years. However, the most urbanized areas are still to be found in the coastal eastern and southern regions of China. The population of Shanghai, the largest city in China and the world’s seventh largest city ranged at around 24 million people in 2023. China’s urban areas are characterized by a developing middle class. Per capita disposable income of Chinese urban households has more than doubled between 2010 and 2020. The emerging middle class is expected to become a significant driver for the continuing growth of the Chinese economy.

Housing Index in China remained unchanged at -2.20 percent in October. This dataset provides the latest reported value for - China Newly Built House Prices YoY Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

BackgroundIt introduced an artefactual field experiment to analyze the influence of incentives from fee-for-service (FFS) and diagnosis-intervention package (DIP) payments on physicians’ provision of medical services.MethodsThis study recruited 32 physicians from a national pilot city in China and utilized an artefactual field experiment to examine medical services provided to patients with different health status.ResultsIn general, the average quantities of medical services provided by physicians under the FFS payment were higher than the optimal quantities, the difference was statistically significant. While the average quantities of medical services provided by physicians under the DIP payment were very close to the optimal quantities, the difference was not statistically significant. Physicians provided 24.49, 14.31 and 5.68% more medical services to patients with good, moderate and bad health status under the FFS payment than under the DIP payment. Patients with good, moderate and bad health status experienced corresponding losses of 5.70, 8.10 and 9.42% in benefits respectively under the DIP payment, the corresponding reductions in profits for physicians were 10.85, 20.85 and 35.51%.ConclusionIt found patients are overserved under the FFS payment, but patients in bad health status can receive more adequate treatment. Physicians’ provision behavior can be regulated to a certain extent under the DIP payment and the DIP payment is suitable for the treatment of patients in relatively good health status. Doctors sometimes have violations under DIP payment, such as inadequate service and so on. Therefore, it is necessary to innovate the supervision of physicians’ provision behavior under the DIP payment. It showed both medical insurance payment systems and patients with difference health status can influence physicians’ provision behavior.

In 2024, the annual per capita gross domestic product (GDP) in different provinces, municipalities, and autonomous regions in China varied from approximately 228,200 yuan in Beijing municipality to roughly 52,800 yuan in Gansu province. The average national per capita GDP crossed the threshold of 10,000 U.S. dollars in 2019 and reached around 95,700 yuan in 2024. Regional economic differences in China The level of economic development varies considerably in different parts of China. Four major geographic and economic regions can be discerned in the country: The economically advanced coastal regions in the east, less developed regions in Northeast and Central China, and the developing regions in the west. This division has deep historical roots reflecting the geography of each region and their political past and present. Furthermore, regional economic development closely correlates with regional urbanization rates, which closely resembles the borders of the four main economic regions. Private income in different parts of China Breaking the average income figures further down by province, municipality, or autonomous region reveals that the average disposable income in Shanghai or Beijing is on average more than three times higher than in Tibet or Gansu province. In rural areas, average disposable income is often only between one third and one half of that in urban areas of the same region. Accordingly, consumer expenditure per capita in urban areas reaches the highest levels in Shanghai, Beijing, and the coastal regions of China.