China BERD: % of Value Added data was reported at 2.295 % in 2021. This records an increase from the previous number of 2.281 % for 2020. China BERD: % of Value Added data is updated yearly, averaging 1.125 % from Dec 1991 (Median) to 2021, with 31 observations. The data reached an all-time high of 2.295 % in 2021 and a record low of 0.268 % in 1996. China BERD: % of Value Added data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s China – Table CN.OECD.MSTI: Business Enterprise Investment on Research and Development: Non OECD Member: Annual.

Definition of MSTI variables 'Value Added of Industry' and 'Industrial Employment':

R&D data are typically expressed as a percentage of GDP to allow cross-country comparisons. When compiling such indicators for the business enterprise sector, one may wish to exclude, from GDP measures, economic activities for which the Business R&D (BERD) is null or negligible by definition. By doing so, the adjusted denominator (GDP, or Value Added, excluding non-relevant industries) better correspond to the numerator (BERD) with which it is compared to.

The MSTI variable 'Value added in industry' is used to this end:

It is calculated as the total Gross Value Added (GVA) excluding 'real estate activities' (ISIC rev.4 68) where the 'imputed rent of owner-occupied dwellings', specific to the framework of the System of National Accounts, represents a significant share of total GVA and has no R&D counterpart. Moreover, the R&D performed by the community, social and personal services is mainly driven by R&D performers other than businesses.

Consequently, the following service industries are also excluded: ISIC rev.4 84 to 88 and 97 to 98. GVA data are presented at basic prices except for the People's Republic of China, Japan and New Zealand (expressed at producers' prices).In the same way, some indicators on R&D personnel in the business sector are expressed as a percentage of industrial employment. The latter corresponds to total employment excluding ISIC rev.4 68, 84 to 88 and 97 to 98.

Abstract

Different countries have different health outcomes that are in part due to the way respective health systems perform. Regardless of the type of health system, individuals will have health and non-health expectations in terms of how the institution responds to their needs. In many countries, however, health systems do not perform effectively and this is in part due to lack of information on health system performance, and on the different service providers.

The aim of the WHO World Health Survey is to provide empirical data to the national health information systems so that there is a better monitoring of health of the people, responsiveness of health systems and measurement of health-related parameters.

The overall aims of the survey is to examine the way populations report their health, understand how people value health states, measure the performance of health systems in relation to responsiveness and gather information on modes and extents of payment for health encounters through a nationally representative population based community survey. In addition, it addresses various areas such as health care expenditures, adult mortality, birth history, various risk factors, assessment of main chronic health conditions and the coverage of health interventions, in specific additional modules.

The objectives of the survey programme are to: 1. develop a means of providing valid, reliable and comparable information, at low cost, to supplement the information provided by routine health information systems. 2. build the evidence base necessary for policy-makers to monitor if health systems are achieving the desired goals, and to assess if additional investment in health is achieving the desired outcomes. 3. provide policy-makers with the evidence they need to adjust their policies, strategies and programmes as necessary.

Geographic coverage

The survey sampling frame must cover 100% of the country's eligible population, meaning that the entire national territory must be included. This does not mean that every province or territory need be represented in the survey sample but, rather, that all must have a chance (known probability) of being included in the survey sample.

There may be exceptional circumstances that preclude 100% national coverage. Certain areas in certain countries may be impossible to include due to reasons such as accessibility or conflict. All such exceptions must be discussed with WHO sampling experts. If any region must be excluded, it must constitute a coherent area, such as a particular province or region. For example if ¾ of region D in country X is not accessible due to war, the entire region D will be excluded from analysis.

Analysis unit

Households and individuals

Universe

The WHS will include all male and female adults (18 years of age and older) who are not out of the country during the survey period. It should be noted that this includes the population who may be institutionalized for health reasons at the time of the survey: all persons who would have fit the definition of household member at the time of their institutionalisation are included in the eligible population.

If the randomly selected individual is institutionalized short-term (e.g. a 3-day stay at a hospital) the interviewer must return to the household when the individual will have come back to interview him/her. If the randomly selected individual is institutionalized long term (e.g. has been in a nursing home the last 8 years), the interviewer must travel to that institution to interview him/her.

The target population includes any adult, male or female age 18 or over living in private households. Populations in group quarters, on military reservations, or in other non-household living arrangements will not be eligible for the study. People who are in an institution due to a health condition (such as a hospital, hospice, nursing home, home for the aged, etc.) at the time of the visit to the household are interviewed either in the institution or upon their return to their household if this is within a period of two weeks from the first visit to the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLING GUIDELINES FOR WHS

Surveys in the WHS program must employ a probability sampling design. This means that every single individual in the sampling frame has a known and non-zero chance of being selected into the survey sample. While a Single Stage Random Sample is ideal if feasible, it is recognized that most sites will carry out Multi-stage Cluster Sampling.

The WHS sampling frame should cover 100% of the eligible population in the surveyed country. This means that every eligible person in the country has a chance of being included in the survey sample. It also means that particular ethnic groups or geographical areas may not be excluded from the sampling frame.

The sample size of the WHS in each country is 5000 persons (exceptions considered on a by-country basis). An adequate number of persons must be drawn from the sampling frame to account for an estimated amount of non-response (refusal to participate, empty houses etc.). The highest estimate of potential non-response and empty households should be used to ensure that the desired sample size is reached at the end of the survey period. This is very important because if, at the end of data collection, the required sample size of 5000 has not been reached additional persons must be selected randomly into the survey sample from the sampling frame. This is both costly and technically complicated (if this situation is to occur, consult WHO sampling experts for assistance), and best avoided by proper planning before data collection begins.

All steps of sampling, including justification for stratification, cluster sizes, probabilities of selection, weights at each stage of selection, and the computer program used for randomization must be communicated to WHO

STRATIFICATION

Stratification is the process by which the population is divided into subgroups. Sampling will then be conducted separately in each subgroup. Strata or subgroups are chosen because evidence is available that they are related to the outcome (e.g. health, responsiveness, mortality, coverage etc.). The strata chosen will vary by country and reflect local conditions. Some examples of factors that can be stratified on are geography (e.g. North, Central, South), level of urbanization (e.g. urban, rural), socio-economic zones, provinces (especially if health administration is primarily under the jurisdiction of provincial authorities), or presence of health facility in area. Strata to be used must be identified by each country and the reasons for selection explicitly justified.

Stratification is strongly recommended at the first stage of sampling. Once the strata have been chosen and justified, all stages of selection will be conducted separately in each stratum. We recommend stratifying on 3-5 factors. It is optimum to have half as many strata (note the difference between stratifying variables, which may be such variables as gender, socio-economic status, province/region etc. and strata, which are the combination of variable categories, for example Male, High socio-economic status, Xingtao Province would be a stratum).

Strata should be as homogenous as possible within and as heterogeneous as possible between. This means that strata should be formulated in such a way that individuals belonging to a stratum should be as similar to each other with respect to key variables as possible and as different as possible from individuals belonging to a different stratum. This maximises the efficiency of stratification in reducing sampling variance.

MULTI-STAGE CLUSTER SELECTION

A cluster is a naturally occurring unit or grouping within the population (e.g. enumeration areas, cities, universities, provinces, hospitals etc.); it is a unit for which the administrative level has clear, nonoverlapping boundaries. Cluster sampling is useful because it avoids having to compile exhaustive lists of every single person in the population. Clusters should be as heterogeneous as possible within and as homogenous as possible between (note that this is the opposite criterion as that for strata). Clusters should be as small as possible (i.e. large administrative units such as Provinces or States are not good clusters) but not so small as to be homogenous.

In cluster sampling, a number of clusters are randomly selected from a list of clusters. Then, either all members of the chosen cluster or a random selection from among them are included in the sample. Multistage sampling is an extension of cluster sampling where a hierarchy of clusters are chosen going from larger to smaller.

In order to carry out multi-stage sampling, one needs to know only the population sizes of the sampling units. For the smallest sampling unit above the elementary unit however, a complete list of all elementary units (households) is needed; in order to be able to randomly select among all households in the TSU, a list of all those households is required. This information may be available from the most recent population census. If the last census was >3 years ago or the information furnished by it was of poor quality or unreliable, the survey staff will have the task of enumerating all households in the smallest randomly selected sampling unit. It is very important to budget for this step if it is necessary and ensure that all households are properly enumerated in order that a representative sample is obtained.

It is always best to have as many clusters in the PSU as possible. The reason for this is that the fewer the number of respondents in each PSU, the lower will be the clustering effect which

The Chinese Big Data market presents a compelling investment landscape, projected to experience robust growth. With a Compound Annual Growth Rate (CAGR) of 30% from 2019 to 2033, the market's value is expected to surge significantly. Several key drivers fuel this expansion. The burgeoning digital economy in China, coupled with increasing government initiatives promoting data-driven decision-making across sectors, is creating substantial demand for big data solutions. Furthermore, advancements in artificial intelligence (AI) and machine learning (ML) are inextricably linked to big data, fostering innovation and creating new applications across diverse industries, including BFSI, healthcare, retail, and manufacturing. The adoption of cloud-based big data solutions is accelerating, offering scalability and cost-effectiveness for businesses of all sizes. However, challenges remain, including data security concerns, a lack of skilled professionals, and the need for robust data governance frameworks. These restraints, while present, are not expected to significantly impede the overall market trajectory given the substantial opportunities and government support.
The market segmentation reveals diverse investment avenues. The cloud deployment model is projected to dominate due to its advantages, while the large enterprise segment presents the largest revenue pool. Within solutions, customer analytics, fraud detection, and predictive maintenance are currently high-growth areas, offering attractive ROI. Geographically, China itself represents a significant portion of the market, although international players are also gaining traction. Considering the robust CAGR and the diverse segments, strategic investments targeting cloud-based solutions, AI-powered analytics, and specific industry verticals (like BFSI and healthcare) hold significant promise for high returns. Careful consideration of regulatory landscapes and data privacy regulations is crucial for successful investment strategies within this dynamic market. Investment Opportunities of Big Data Technology in China This comprehensive report analyzes the burgeoning investment opportunities within China's Big Data Technology sector, offering a detailed forecast from 2019-2033. The report utilizes 2025 as its base and estimated year, covering the historical period (2019-2024) and forecasting market trends from 2025-2033. It delves into market dynamics, key players, and emerging trends shaping this rapidly expanding industry. This report is crucial for investors, businesses, and analysts seeking to understand and capitalize on the immense potential of China's big data market. Recent developments include: November 2022 - Alibaba announced the Innovative upgrade, and Greener 11.11 runs wholly on Alibaba Cloud, whereas Alibaba Cloud's dedicated processing unit powered 11.11 for the Apsara Cloud operating system. The upgraded infrastructure system significantly improved the efficiency of computing, storage, etc., October 2022 - Huawei Technologies Co.has unveiled its 4-in-1 hyper-converged enterprise gateway NetEngine AR5710, delved into the latest CloudCampus 3.0 + Simplified Solution, and launched a series of products for large enterprises and Small- and Medium-Sized Enterprises (SMEs). With these new offerings, Huawei aims to help enterprises simplify their campus networks and maximize digital productivity.. Key drivers for this market are: 6.1 Data Explosion: Unstructured, Semi-structured and Complex6.2 Improvement in Algorithm Development6.3 Need for Customer Analytics. Potential restraints include: 7.1 Lack of General Awareness And Expertise7.2 Data Security Concerns. Notable trends are: Need for Customer Analytics to Increase Exponentially Driving the Market Growth.

Contact: Jia XIng, School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China. This dataset is not publicly accessible because: Model simulations were performed at Tsinghua University, China. EPA does not have any data associated with the article. It can be accessed through the following means: Contact: Jia Xing, Tsinghua University, email: xingjia@mail.tsinghua.edu.cn. Format: Model simulations were performed at Tsinghua University, China. EPA does not have any data associated with the article. This dataset is associated with the following publication: Zhang, S., G. Sarwar, J. Xing, B. Chu, C. Xue, A. Sarav, D. Ding, H. Zheng, Y. Mu, F. Duan, T. Ma, and H. He. Improving the representation of HONO chemistry in CMAQ and examining its impact on haze over China. Atmospheric Chemistry and Physics. Copernicus Publications, Katlenburg-Lindau, GERMANY, 21(20): 15809-15826, (2021).

China is at a critical moment of transforming high-speed development to high-quality development, and it is significant to improve the efficiency of green technological innovation. In this paper, under the perspective of two-stage innovation value chain, we construct the evaluation index system of green technology innovation efficiency, adopt the super efficiency SBM model to measure the green technology innovation efficiency of China’s high-tech industries, and based on the results obtained, we assume the fuzzy set qualitative comparative analysis method (fs-QCA) based on the group theory to explore the complex causal mechanism and grouping paths of the interaction between enterprises, government and market that affects the green technology innovation efficiency Mechanism and group path. The study results show that (1) enterprise, government, and market are not necessary conditions to influence the efficiency of green technological innovation, and even if a particular party plays a central role, it needs the assistance of other parties. (2) The improvement of green technological innovation efficiency requires the interaction of enterprises, government, and market, and even if any party does not have the core conditions, it can still produce high green technological innovation efficiency. (3) The path of the "innovative compensation" effect is identified, which indicates that enterprises will generate a high level of green innovation efficiency under sufficient investment brought about by the enterprise scale effect and matched with a good level of economic development. (4) The market economy-led pathway suggests that when the market economy is highly developed, firms do not need environmental regulation and government support to generate efficient levels of green technological innovation.

About this dataset

Context

The dataset tabulates the China median household income by race. The dataset can be utilized to understand the racial distribution of China income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• China, TX median household income breakdown by race betwen 2011 and 2021
• Median Household Income by Racial Categories in China, TX (2021, in 2022 inflation-adjusted dollars)

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of China median household income by race. You can refer the same here

China administrative level 0-2 boundaries (COD-AB) dataset.

The date that these administrative boundaries were established is unknown.

NOTE: COD-AB source is unofficial. COD-AB reflects Chinese and UN recognition of 'Taiwan' as a Chinese ADM1 feature.

This COD-AB was most recently reviewed for accuracy and necessary changes in October 2024. The COD-AB does not require any update.

Sourced from publicly available online sources

Vetting by Information Technology Outreach Services (ITOS) with funding from USAID.

There is no suitable population statistics dataset (COD-PS) for linkage to this COD-AB..

No edge-matched (COD-EM) version of this COD-AB has yet been prepared.

Please see the COD Portal.

Administrative level 1 contains 34 feature(s). The normal administrative level 1 feature type is ""currently not known"".

Administrative level 2 contains 361 feature(s). The normal administrative level 2 feature type is ""currently not known"".

"China administrative level 0-2 boundaries (COD-AB) dataset.

The date that these administrative boundaries were established is unknown.

NOTE: COD-AB source is unofficial. COD-AB reflects Chinese and UN recognition of 'Taiwan' as a Chinese ADM1 feature.

This COD-AB was most recently reviewed for accuracy and necessary changes in October 2024. The COD-AB does not require any update.

Sourced from publicly available online sources

Vetting by Information Technology Outreach Services (ITOS) with funding from USAID.

There is no suitable population statistics dataset (COD-PS) for linkage to this COD-AB..

No edge-matched (COD-EM) version of this COD-AB has yet been prepared.

Please see the COD Portal.

Administrative level 1 contains 34 feature(s). The normal administrative level 1 feature type is 'currently not known'.

Administrative level 2 contains 361 feature(s). The normal administrative level 2 feature type is 'currently not known'.

Recommended cartographic projection: Asia South Albers Equal Area Conic

This metadata was last updated on January 18, 2025.

China Total Business Enterprise R&D Personnel: % of National Total data was reported at 78.090 % in 2021. This records an increase from the previous number of 77.569 % for 2020. China Total Business Enterprise R&D Personnel: % of National Total data is updated yearly, averaging 65.747 % from Dec 1991 (Median) to 2021, with 31 observations. The data reached an all-time high of 78.166 % in 2018 and a record low of 30.723 % in 1991. China Total Business Enterprise R&D Personnel: % of National Total data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s China – Table CN.OECD.MSTI: Number of Researchers and Personnel on Research and Development: Non OECD Member: Annual.

China Required Reserve Ratio data was reported at 6.200 % in 15 May 2025. This records a decrease from the previous number of 6.600 % for 14 May 2025. China Required Reserve Ratio data is updated daily, averaging 9.400 % from Sep 2003 (Median) to 15 May 2025, with 7908 observations. The data reached an all-time high of 18.000 % in 05 Sep 2015 and a record low of 6.200 % in 15 May 2025. China Required Reserve Ratio data remains active status in CEIC and is reported by The People's Bank of China. The data is categorized under China Premium Database’s Money Market, Interest Rate, Yield and Exchange Rate – Table CN.MA: Required Reserve Ratio . In view of the fact that the cut of the RRR effective from 15 Oct 2018 was not for all depository institutions, it could not be updated accordingly. Afterwards, if the official has disclosed the exact data of the ratio, it will be updated accordingly. Release date: 07 Oct 2018 In view of the fact that the cut of the RRR effective from 5 July 2018 was not for all depository institutions, it could not be updated accordingly. Afterwards, if the official has disclosed the exact data of the ratio, it will be updated accordingly. Release date: 24 June 2018 In view of the fact that the cut of the RRR effective from 25 April 2018 was not for all depository institutions, it could not be updated accordingly. Afterwards, if the official has disclosed the exact data of the ratio, it will be updated accordingly. Release date: 17 April 2018

With the deepening of population aging, the expenditure of basic endowment insurance in China is increasing. The urban employees’ basic endowment insurance(UEBEI) system for is an important part of China’s basic social endowment insurance system, which is the most important institutional guarantee for the basic needs of employees after retirement. It not only relates to the living standards of retired employees but also relates to the stability of the whole society. Especially considering the acceleration of urbanization process, the financial sustainability of the basic endowment insurance for employees is of great significance for safeguarding the pension rights of retired employees and realizing the normal operation of the whole system, and the operation efficiency of urban employees’ basic endowment insurance(UEBEI) fund inevitably becomes the focus of increasing attention. Based on the panel data of 31 provinces in China from 2016 to 2020, this paper established a three-stage DEA-SFA model, and compared the differences of comprehensive technical efficiency, pure technical efficiency and scale efficiency with radar chart, aiming to explore the operating efficiency of the UEBEI in China and how environmental factors affect it. The empirical results show that at present, the overall level of the expenditure efficiency of the UEBEI fund for urban workers is not high, and all provinces have not reached the efficiency frontier level, and there is still a certain space for efficiency improvement. Fiscal autonomy and elderly dependency ratio are negatively correlated with fund expenditure efficiency, while urbanization level and marketization level are positively correlated with fund expenditure efficiency. The regional difference of fund operation efficiency is significant, from high to low, it is East China, Central China and West China. Reasonable control of environmental variables and narrowing of regional economic development and fund expenditure efficiency differences can provide some enlightenment for better realization of common prosperity.

Abstract

The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds.

The Global Findex is the world's most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of about 128,000 adults in more than 120 economies. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments.

The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals.

Geographic coverage

Tibet was excluded from the sample. The excluded areas represent less than 1 percent of the total population of China.

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19 related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Phone surveys were not a viable option in 17 economies that had been part of previous Global Findex surveys, however, because of low mobile phone ownership and surveying restrictions. Data for these economies will be collected in 2022 and released in 2023.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households. Each eligible household member is listed, and the hand-held survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used.

The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent).

For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Sample size for China is 3500.

Mode of data collection

Mobile telephone

Research instrument

Questionnaires are available on the website.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

The number of smartphone users in China was forecast to increase between 2024 and 2029 by in total 357.5 million users (+41.6 percent). This overall increase does not happen continuously, notably not in 2029. The smartphone user base is estimated to amount to 1.2 billion users in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

About this dataset

Context

The dataset tabulates the population of China by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of China across both sexes and to determine which sex constitutes the majority.

Key observations

There is a majority of female population, with 62.22% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the China is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of China total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for China Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the China population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of China across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2022, the population of China was 1,273, a 0.55% decrease year-by-year from 2021. Previously, in 2021, China population was 1,280, a decline of 0.62% compared to a population of 1,288 in 2020. Over the last 20 plus years, between 2000 and 2022, population of China increased by 111. In this period, the peak population was 1,288 in the year 2020. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the China is shown in this column.
• Year on Year Change: This column displays the change in China population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for China Population by Year. You can refer the same here

These data are not available through ICPSR. To apply for access to the data please visit the China Family Panel Studies Web site. The China Family Panel Studies (CFPS) is a nationally representative, annual longitudinal general social survey project designed to document changes in Chinese society, economy, population, education, and health. The CFPS was launched in 2010 by the the Institute of Social Science Survey (ISSS) of Peking University, China. The data were collected at the individual, family, and community levels and are targeted for use in academic research and public policy analysis. All members over age 9 in a sampled household are interviewed. These individuals constitute core members of the CFPS and follow-up of all core members of the CFPS is designed to take place on a yearly basis. CFPS focuses on the economic and non-economic well-being of the Chinese people, and covers topics such as economic activities, educational attainment, family relationships and dynamics, migration, and physical and mental health.

This study identifies 37 central banks that added China’s renminbi (RMB) to their reserve portfolio since 2010. Why do some states diversify into new reserve currencies at an early stage while most continue to take a wait-and-see approach? We argue that state preferences regarding international order influence decisions to invest in RMB. While some states support the liberal, US-led status quo, others prefer an emerging Chinese alternative order. We contend that as state preferences for international order move away from the US model (and toward China), the likelihood of diversifying reserves into RMB should increase. Thus, the decision to invest in RMB is not simply an economic choice. It is also a political act that signals and symbolizes a state’s preferences for a diminution of American global influence and support for a revised order. Employing new United Nations General Assembly (UNGA) ideal points data, we find that states with larger (smaller) ideal point distance with the United States (China) are more likely to adopt RMB as a reserve currency. Furthermore, political consideration—rather than economic concerns about transaction needs, optimal portfolio considerations, or instrumental calculations—best explains emergent demand for the RMB as a reserve currency.

The interview data were collected by Dr Zinian Zhang from his fieldwork conducted in Hangzhou, the capital city of the Zhejiang Province, the People's Republic of China in May 2017. The interviews focused on the question of how the Chinese court conducts equal distribution in commercial judgment enforcements to deliver fairness between competing creditors. In total, there were sixteen law practitioners, including four judges and twelve lawyers, interviewed. Among twelve lawyers, nine once represented judgment creditors seeking equal distribution, and three represented clients who have to share. The data reveal that fair distribution is not as often used as thought, and that fair distribution is unable to fill the gap left by a corporate bankruptcy system.This application demonstrates that the quality of legal institutions can matter for economic development and that important policy lessons can be learned by China from the UK in this regard. This application recognises that China has been a remarkable economic success story but the country also faces new challenges as its economy enters a more mature phase. In particular, it needs to avoid the 'middle income trap' i.e. where a country has costs that are now too high to compete with low-income countries but where productivity does not match those in high-income countries. There are economies in Asia including Singapore and Hong Kong SAR that have emerged successfully from middle income status. Both these economies are built on UK law and are renowned for the quality of their legal infrastructure in supporting development of the financial system. The application suggests how China might also benefit from the UK experience in building its legal infrastructure. But the application recognises China's singular journey and avoids simplistic conclusions that certain consequences will inevitably follow form certain formal changes. It recognises the need for a continuous process of adaptation and development; learning appropriately from experience and responding sensitively to local conditions. The application demonstrates in particular how legal reforms can support economic growth through - enhancing the protections available to minority investors - supporting the availability of credit and contributing to lower-cost credit - supporting the restructuring of ailing businesses. n these areas we seek to provide options for enhancing and reforming the legal and financial system in China that are based upon the UK and other experience. We acknowledge that there are choices to be made between means and ends and that the relationship between means and ends is contingent and uncertain. The data we rely on will come principally from the World Bank Doing Business (DB) reports and rankings which are grounded on the notion that smarter business regulation promotes economic growth. The DB rankings have been issued annually since 2004 and the 2016 rankings includes 11 sets of indicators for 189 economies. Each economy is ranked on the individual indicators and also in an overall table. Currently, the UK is 6th in this table and China 84th but Singapore is 1st and Hong SAR is 5th which shows that it is possible for Asian economies to rank highly. In our project, we will explore deep into the detail underlying the Protecting Minority Investors, Getting credit and Resolving Insolvency indicators. These 3 indicators appear particularly pertinent to the development of a mature financial system and in relation to them all China ranks far below the UK. On protecting investors, China is ranked as 134th whereas the UK is 4th. We show how the gap can be bridged and how China can learn from the UK experience by examining critically how the UK has protected minority investors and ascertaining what measures of protection might work most effectively in Chinese conditions. Our approach takes the relevant DB rankings as a guide but subjects them to critical scrutiny and engaging systematically with the methodology underpinning the rankings; addressing the robustness of this methodology and considering alternative approaches. For instance, we will test the robustness and limitations of the DB 'resolving insolvency' data on China using Jiande Municipal People's Court in Zhejiang Province as a case study. This makes the process of data collection and analysis more manageable. 20 interviews with creditors and practitioners will be undertaken in Zhejiang Province and data on business closures from the local branches of the China Business Registration Authorities and the China Pension Management Authorities will also be collected. We will also use econometric analyses based on detailed micro data from other data sources

Country Annual Loan Value by Year

1. CARI LOAN DATA OVERVIEW

In 2007, CARI researchers began collection, cleaning, and analyzing China’s African loans.

• From 2000 to 2017, the Chinese government, banks and contractors extended US $143 billion in loans to African governments and their state-owned enterprises (SOEs).
• Angola is the top recipient of Chinese loans, with $42.8 billion disbursed over 17 years.
• Chinese loan finance is varied. Some government loans qualify as "official development aid." But other Chinese loans are export credits, suppliers' credits, or commercial, not concessional in nature. China is not Africa's largest "donor". That honor still belongs to the United States.

2. WHY CARI DATA?

While CARI does not have access to any special information not available to any other research efforts, CARI’s experienced research team and methodology allows us to compile a more rigorous database.

Our loan database builds on previous work by Brautigam tracking Chinese aid finance. All data collection and cleaning is done by master’s or Ph.D. students and post-docs. Team members use Chinese, French, Portuguese, and Arabic language skills and follow a rigorous set of steps in triangulating and cross-checking reports of loans, emphasizing official websites of central banks and ministries of finance, Chinese contractors, and our own personal contacts in China and in African countries.

The desk work was supplemented by in-country interviews and meetings with Chinese and African officials. The “forensic internet sleuthing” methods that we employ cannot easily be replicated. The work is more akin to investigative reporting or detective work than accounting. Some sources are more reliable than others. Learning to judge information appropriately takes time, and depends deeply on experience, personal contacts, perseverance and inclination.

3.DATA

3.1 Official Data

There is no official Chinese data on loans. China is not a member of the OECD and they do not participate in the OECD’s Creditor Reporting System, the source for much of the data we have on official flows from the wealthier countries. As is the case with the United States Export Import Bank and other export credit agencies, Chinese banks also rarely publish information regarding specific financing agreements. It is also uncommon for the recipients of such financing to fully disclose the details of the finance they receive. This creates an information gap that CARI is filling.

Tajikistan Exports of cotton, not carded or combed to China was US$41.12 Million during 2023, according to the United Nations COMTRADE database on international trade. Tajikistan Exports of cotton, not carded or combed to China - data, historical chart and statistics - was last updated on July of 2025.

China boasts the fastest growing GDP of all developed nations. Neighboring regions will have the largest middle class in history. China is building transport infrastructure to take advantage. Companies that capture market share in this region will be the largest and best performing over the next decade.

Macro Tailwinds

1) China GDP is the fastest growing of any major country with expected 5-6% over the next decade. If businesses (Alibaba, Tencent, etc..) maintain flat market share, that alone will drive 5-6% over the next decade. This is already higher than JP Morgans expectation (from their 13f filings) that the US market will perform between -5% and +5% over this coming decade.

2) The Southeast Asia Region contains about 5 billion people. China is constructing the One Best One Road which will be completed by 2030. This will grant their businesses access to the fastest and largest growing middle class in human history. Over the next 10+ years this region will be home to the largest middle class in history, potentially over 10x that of North America and Europe, based on stock price in Google Sheets.

Increasing average Chinese income.

Chinese average income has more than doubled over the last decade. Having sustained the least economic damage from the virus, this trend is expected to continue. At this pace the average Chinese citizen salary will be at 50% of the average US by 2030 (with stock price in Excel provided by Finsheet via Finnhub Stock Api), with the difference being there are 4x more Chinese. Thus a market potential of almost 2x the US over the next decade.

The Southeast Asia Region now contains the largest total number of billionaires, this number is expected to increase at an increasing rate as the region continues to develop. Over the next 10 years the largest trading route ever assembled will be completed, and China will be the primary provider of goods to 5b+ people

2013 North America was home to the largest number of billionaires. This reversed with Asia over the following 5 years. This separation is expected to continue at an increasing rate. Why does this matter? Over the next 10 years the largest trading route ever assembled will be completed, and China will be the primary provider of goods to 5b+ people

Companies that can easily access all customers in the world will perform best. This is good news for Apple, Microsoft, and Disney. Disney stock price in Excel right now is $70. But not for Amazon or Google which at first may sound contrary as the expectation is that Amazon "will take over the world". However one cannot do that without first conquering China. Firms like Alibaba and Tencent will have easy access to the global infrastructure being built by China in an attempt to speed up and ease trade in that region. The following guide shows how to get stock price in Excel.

We will explore companies using a:

1) Past

2) Present (including financial statements)

3) Future

4) Story/Tailwind

Method to find investing ideas in these regions. The tailwind is currently largest in the Asia region with 6%+ GDP growth according to the latest SEC form 4 from Edgar Company Search. This is relevant as investments in this region have a greater margin of safety; investing in a company that maintains flat market share should increase about 6% per year as the market growth size is so significant. The next article I will explore Alibaba (NYSE: BABA), and why I recently purchased a large position during the recent Ant Financial Crisis.