60 datasets found
  1. T

    China Government Spending

    • tradingeconomics.com
    • fr.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Jul 10, 2025
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    TRADING ECONOMICS (2025). China Government Spending [Dataset]. https://tradingeconomics.com/china/government-spending
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    json, csv, xml, excelAvailable download formats
    Dataset updated
    Jul 10, 2025
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 31, 1952 - Dec 31, 2024
    Area covered
    China
    Description

    Government Spending in China increased to 224396.60 CNY Hundred Million in 2024 from 222474.20 CNY Hundred Million in 2023. This dataset provides - China Government Spending - actual values, historical data, forecast, chart, statistics, economic calendar and news.

  2. T

    China Consumer Spending

    • tradingeconomics.com
    • id.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Jun 17, 2025
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    TRADING ECONOMICS (2025). China Consumer Spending [Dataset]. https://tradingeconomics.com/china/consumer-spending
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    json, csv, excel, xmlAvailable download formats
    Dataset updated
    Jun 17, 2025
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 31, 1952 - Dec 31, 2024
    Area covered
    China
    Description

    Consumer Spending in China increased to 538646.10 CNY Hundred Million in 2024 from 512120.60 CNY Hundred Million in 2023. This dataset provides - China Consumer Spending - actual values, historical data, forecast, chart, statistics, economic calendar and news.

  3. a

    Global China Data

    • aiddata.org
    Updated Nov 6, 2023
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    (2023). Global China Data [Dataset]. https://www.aiddata.org/data/aiddatas-global-chinese-development-finance-dataset-version-3-0
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    Dataset updated
    Nov 6, 2023
    Description

    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).

  4. T

    China GDP

    • tradingeconomics.com
    • it.tradingeconomics.com
    • +13more
    csv, excel, json, xml
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    TRADING ECONOMICS, China GDP [Dataset]. https://tradingeconomics.com/china/gdp
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    csv, json, excel, xmlAvailable download formats
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 31, 1960 - Dec 31, 2024
    Area covered
    China
    Description

    The Gross Domestic Product (GDP) in China was worth 18743.80 billion US dollars in 2024, according to official data from the World Bank. The GDP value of China represents 17.65 percent of the world economy. This dataset provides - China GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

  5. PIIE dataset for PIIE PB 24-14, The rise of US economic sanctions on China:...

    • piie.com
    Updated Dec 4, 2024
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    Martin Chorzempa; Mary E. Lovely; Yuting (Christine) Wan (2024). PIIE dataset for PIIE PB 24-14, The rise of US economic sanctions on China: Analysis of a new PIIE dataset by Martin Chorzempa, Mary E. Lovely, and Christine Wan (2024). [Dataset]. https://www.piie.com/publications/policy-briefs/2024/rise-us-economic-sanctions-china-analysis-new-piie-dataset
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    Dataset updated
    Dec 4, 2024
    Dataset provided by
    Peterson Institute for International Economicshttp://www.piie.com/
    Authors
    Martin Chorzempa; Mary E. Lovely; Yuting (Christine) Wan
    Area covered
    China
    Description

    This data package includes the PIIE dataset to replicate the data and charts presented in The rise of US economic sanctions on China: Analysis of a new PIIE dataset by Martin Chorzempa, Mary E. Lovely, and Christine Wan, PIIE Policy Brief 24-14.

    If you use the dataset, please cite as: Chorzempa, Martin, Mary E. Lovely, and Christine Wan. 2024. The rise of US economic sanctions on China: Analysis of a new PIIE dataset, PIIE Policy Brief 24-14. Washington, DC: Peterson Institute for International Economics.

  6. T

    China Retail Sales YoY

    • tradingeconomics.com
    • ar.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Jul 15, 2025
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    TRADING ECONOMICS (2025). China Retail Sales YoY [Dataset]. https://tradingeconomics.com/china/retail-sales-annual
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    xml, json, excel, csvAvailable download formats
    Dataset updated
    Jul 15, 2025
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 31, 1993 - Jun 30, 2025
    Area covered
    China
    Description

    Retail Sales in China increased 4.80 percent in June of 2025 over the same month in the previous year. This dataset provides - China Retail Sales YoY - actual values, historical data, forecast, chart, statistics, economic calendar and news.

  7. m

    Data for: Political uncertainty and firm entry: Evidence from Chinese...

    • data.mendeley.com
    Updated Apr 26, 2021
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    Hui Mao (2021). Data for: Political uncertainty and firm entry: Evidence from Chinese manufacturing industries [Dataset]. http://doi.org/10.17632/mvfpdcvx3b.2
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    Dataset updated
    Apr 26, 2021
    Authors
    Hui Mao
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    China
    Description

    Our data are mainly from two datasets compiled by the National Bureau of Statistics of China and one dataset we compiled. Industry-related variables are from the Chinese Industrial Enterprises Database, a longitudinal micro-level database based on annual surveys of manufacturing, mining, and construction firms with at least five million yuan in annual sales. This database is the most comprehensive source of information on industrial firms and has been used by some leading academic papers (e.g., Hsieh and Klenow, 2009; Song et al., 2011) . In this study, we focus on the manufacturing sector and construct a panel dataset for 30 industries in 268 cities during 1998–2009. City-related control variables are mainly from the Statistical Bulletin for each city, China City Statistical Yearbook, and Statistical Yearbook for each province. These sources are the most comprehensive statistics on the social and economic development of Chinese prefectural cities. Statistics include, for example, GDP, government expenditure, population, employment, and education. Politician profile data are compiled from www.people.cn, XinhuaNet, and Baidu Encyclopedia . The data contain the names of the municipal party committee secretary and the mayor of 268 cities during 1998–2009. The data also contain the previous position of each secretary and mayor. The data are cross-checked with other sources to ensure quality.

  8. e

    The Serryus Collection - Dataset - B2FIND

    • b2find.eudat.eu
    Updated Dec 5, 2022
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    (2022). The Serryus Collection - Dataset - B2FIND [Dataset]. https://b2find.eudat.eu/dataset/3a1483f5-4459-5166-9006-a21a048006fb
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    Dataset updated
    Dec 5, 2022
    Description

    During a visit to the University of Washington in May 2015, Professor William G. Boltz gave a box filled mainly with Chinese manuscripts to the director of CSMC. It contained a part of the bequest of Father Paul L-M. Serruys, C.I.C.M. (1912-1999) who is remembered as an eminent scholar and professor of early Chinese texts, language and linguistics. Less known is the fact that he spent thirteen years as a missionary in Northern China, most of the time in Shanxi, before he was expelled by the communists in 1949. During this period he developed an interest in anthropology and ethnography which apparently prompted him to have his students write down stories they had heard from relatives or friends. These pieces of ‘homework’ do not only provide sources for the study of folklore in a backward community, but are also of interest to students of Chinese manuscripts, since they represent the full range of writing supports and palaeographical features of the first half of the 20th century. Besides these stories, there were almanacs and other materials relating to Shanxi. – For Father Serruys’ biography see the obituary by his student Weldon South Coblin: ‘Father Paul L-M. Serruys, C.I.C.M. (1912-1999)’, in Monumenta Serica 47, 1999, 505-514. The Structure of the Collection For transport to Hamburg the contents of the box were repacked in several bags seeing to it that the folders containing the loose leaves were not disturbed. After arrival at CSMC, Nadine Bregler and Tim Bockholt scanned them according to what might be considered as codicological units – the folders. For their transport to Hamburg the manuscripts were distributed into four bags. Thus, the initial naming of the image files was based on the respective bag they were found in. The first bag to be scanned was labeled with the letter A, the second with B, and so forth. These letters are then followed by a number which reflects the order in which the sheets were found inside the bags.

  9. f

    Table_1_Health Care Utilization and Costs of Patients With Prostate Cancer...

    • frontiersin.figshare.com
    docx
    Updated Jun 3, 2023
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    Lin Bai; Haishaerjiang Wushouer; Cong Huang; Zhenhuan Luo; Xiaodong Guan; Luwen Shi (2023). Table_1_Health Care Utilization and Costs of Patients With Prostate Cancer in China Based on National Health Insurance Database From 2015 to 2017.docx [Dataset]. http://doi.org/10.3389/fphar.2020.00719.s001
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    docxAvailable download formats
    Dataset updated
    Jun 3, 2023
    Dataset provided by
    Frontiers
    Authors
    Lin Bai; Haishaerjiang Wushouer; Cong Huang; Zhenhuan Luo; Xiaodong Guan; Luwen Shi
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    China
    Description

    BackgroundIn terms of medical costs, prostate cancer is on the increase as one of the most costly cancers, posing a tremendous economic burden, but evidence on the health care utilization and medical expenditure of prostate cancer has been absent in China.ObjectiveThis study aimed to analyze health care utilization and direct medical costs of patients with prostate cancer in China.MethodsHealth care service data with a national representative sample of basic medical insurance beneficiaries between 2015 and 2017 were obtained from the China Health Insurance Association database. We conducted descriptive and statistical analyses of health care utilization, annual direct medical costs, and composition based on cancer-related medical records. Health care utilization was measured by the number of hospital visits and the length of stay.ResultsA total of 3,936 patients with prostate cancer and 24,686 cancer-related visits between 2015 and 2017 were identified in the database. The number of annual outpatient and inpatient visits per patient differed significantly from 2015 to 2017. There was no obvious change in length of stay and annual direct medical costs from 2015 to 2017. The number of annual visits per patient (outpatient: 3.0 vs. 4.0, P < 0.01; inpatient: 1.5 vs. 2.0, P < 0.001) and the annual medical direct costs per patient (US$2,300.1 vs. US$3,543.3, P < 0.001) of patients covered by the Urban Rural Resident Basic Medical Insurance (URRBMI) were both lower than those of patients covered by the Urban Employee Basic Medical Insurance (UEBMI), and the median out-of-pocket expense of URRBMI was higher than that of UEBMI (US$926.6 vs. US$594.0, P < 0.001). The annual direct medical costs of patients with prostate cancer in Western regions were significantly lower than those of patients in Eastern and Central regions (East: US$4011.9; Central: US$3458.6; West: US$2115.5) (P < 0.001).ConclusionsThere was an imbalanced distribution of health care utilization among regions in China. The direct medical costs of Chinese patients with prostate cancer remained stable, but the gap in health care utilization and medical costs between two different insurance schemes and among regions still needed to be further addressed.

  10. China Building Construction: Value Completed: Total

    • ceicdata.com
    Updated Dec 15, 2024
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    CEICdata.com (2024). China Building Construction: Value Completed: Total [Dataset]. https://www.ceicdata.com/en/china/construction-building-construction-value-completed/building-construction-value-completed-total
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    Dataset updated
    Dec 15, 2024
    Dataset provided by
    CEIC Data
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 1, 2013 - Dec 1, 2024
    Area covered
    China
    Variables measured
    Construction Completed
    Description

    China Building Construction: Value Completed: Total data was reported at 7,863,563.000 RMB mn in 2024. This records a decrease from the previous number of 8,182,805.000 RMB mn for 2023. China Building Construction: Value Completed: Total data is updated yearly, averaging 6,104,224.970 RMB mn from Dec 2003 (Median) to 2024, with 22 observations. The data reached an all-time high of 8,182,805.000 RMB mn in 2023 and a record low of 901,173.500 RMB mn in 2003. China Building Construction: Value Completed: Total data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Construction Sector – Table CN.ED: Construction: Building Construction: Value Completed.

  11. f

    Dataset.

    • plos.figshare.com
    bin
    Updated Aug 9, 2023
    + more versions
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    Bao Bing (2023). Dataset. [Dataset]. http://doi.org/10.1371/journal.pone.0289817.s001
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    binAvailable download formats
    Dataset updated
    Aug 9, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Bao Bing
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    In the context of China’s new stage of economic development, this study examines the role of higher education in China’s high quality economic development (HQED) strategy from a digital perspective. Using panel data of 30 Chinese provinces (municipalities and autonomous regions) collected from 2012–2020, comprehensive evaluations of the level of higher education and HQED are conducted through the entropy method, and a regression analysis is carried out with the fixed effect model. The results show that the level of higher education is positively associated with HQED and is able to achieve this effect through mechanisms that actively promote digital innovation and development. Further, the structure and quality of higher education plays a greater part in facilitating digital development than the scale and quantity. The heterogeneity analysis demonstrates that the impact of higher education on HQED is more significant in the eastern region of China than in the western region. An increase in the proportion of fiscal expenditure to GDP diminishes the impact of higher education on HQED, while an improvement in digital governance level enhances its influence.

  12. Replication dataset and calculations for PIIE WP 24-7 Lessons from China's...

    • piie.com
    Updated Mar 19, 2024
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    Tianlei Huang (2024). Replication dataset and calculations for PIIE WP 24-7 Lessons from China's fiscal policy during the COVID-19 pandemic by Tianlei Huang (2024). [Dataset]. https://www.piie.com/publications/working-papers/2024/lessons-chinas-fiscal-policy-during-covid-19-pandemic
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    Dataset updated
    Mar 19, 2024
    Dataset provided by
    Peterson Institute for International Economicshttp://www.piie.com/
    Authors
    Tianlei Huang
    Area covered
    China
    Description

    This data package includes the underlying data to replicate the charts presented in Lessons from China's fiscal policy during the COVID-19 pandemic, PIIE Working Paper 24-7.

    If you use the data, please cite as: Huang, Tianlei. 2024. Lessons from China's fiscal policy during the COVID-19 pandemic. PIIE Working Paper 24-7. Washington: Peterson Institute for International Economics.

  13. e

    Social Capital and the Effectiveness of Land Use Policies: Evidence from...

    • b2find.eudat.eu
    Updated Mar 23, 2024
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    (2024). Social Capital and the Effectiveness of Land Use Policies: Evidence from Rural China, 2016 - Dataset - B2FIND [Dataset]. https://b2find.eudat.eu/dataset/ead59467-21f3-511e-9d27-34eeff0d2fa0
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    Dataset updated
    Mar 23, 2024
    Area covered
    China
    Description

    The dataset underpins a study on "Social Capital and the Effectiveness of Land Use Policies: Evidence from Rural China," drawing from the 17 Provinces Rural Land Survey by Renmin University of China. This research navigates the intricacies of land use policy effectiveness in rural China, underpinned by the significant reforms initiated by the 1986 Constitution allowing transactions of land use rights. These reforms enabled local governments to lease land use rights to the private sector, significantly contributing to fiscal revenues and fostering economic development and urban expansion at an impressive rate. However, this rapid transformation introduced several challenges, including legal, social, and environmental issues centered around land use policies. The study delves into the consequences of these reforms, such as the technical efficiency impacts on livestock grazing in Tibet versus the degradation of ecosystem services in Inner Mongolia, and the negative effects of full-scale land relocation practices on organic fertilizer usage. The complexity of redeveloping brownfields in rural areas and the crucial role of rural land tenure in investment, productivity, and participation in the land rental market are also highlighted. The effectiveness of land use policies has thus become a focal point for scholarly investigation, particularly regarding the impact on rural residents, who are critical stakeholders in the reform process. Central to this exploration is the concept of social capital, defined as the network of relationships among people who live and work in a particular society, enabling society to function effectively. Social capital, encompassing elements such as trust, social networks, and norms, plays a pivotal role in encouraging environmental restoration and climate change adaptation efforts. This has been observed not only in China but globally, suggesting a move towards behavioral land use policies that leverage social capital for cost-effective and sustainable outcomes. These policies aim to influence behaviors through intrinsic motivations rather than through monetary incentives or legal mandates, which often entail significant public expenditure and administrative costs. The data seeks to advance the discourse on land use policy by proposing a comprehensive analytical framework that includes various forms of social capital and measures policy outcomes both in the short and long term. Employing an innovative empirical strategy, the study addresses endogeneity issues and aims to provide a nuanced understanding of the relationship between social capital and land use policy outcomes. The findings suggest that social capital has a contextually dependent effect on policy effectiveness, varying across different policy objectives and stages of policy evaluation. This research underscores the importance of incorporating multiple dimensions of social capital into policy analysis and design, offering insights that could guide sustainable urbanization and rural development efforts.Although China has almost eliminated urban poverty, the total number of Chinese citizens in poverty remains at 82 million, most of which are rural residents. The development of rural finance is essential to preventing the country from undergoing further polarization because of the significant potential of such development to facilitate resource interflows between rural and urban markets and to support sustainable development in the agricultural sector. However, rural finance is the weakest point in China's financial systems. Rural households are more constrained than their urban counterparts in terms of financial product availability, consumer protection, and asset accumulation. The development of the rural financial system faces resistance from both the demand and the supply sides. The proposed project addresses this challenge by investigating the applications of a proven behavioural approach, namely, Libertarian Paternalism, in the development of rural financial systems in China. This approach promotes choice architectures to nudge people into optimal decisions without interfering with the freedom of choice. It has been rigorously tested and warmly received in the UK public policy domain. This approach also fits the political and cultural background in China, in which the central government needs to maintain a firm control over financial systems as the general public increasingly demands more freedom. Existing behavioural studies have been heavily reliant on laboratory experiments. Although the use of field studies has been increasing, empirical evidence from the developing world is limited. Meanwhile, the applications of behavioural insights in rural economic development in China remains an uncharted territory. Rural finance studies on the household level are limited; evidence on the role of psychological and social factors in rural households' financial decisions is scarce. The proposed project will bridge this gap in the literature. The overarching research question of this project is whether and how behavioural insights can be used to help rural residents in China make sound financial decisions, which will ultimately contribute to the sustainable economic development in China. The research will be conducted through field experiments in rural China. By relying on field evidences, the project team will develop policy tools and checklists for policy makers to help rural households make sound financial decisions. Two types of tools will be developed for policy makers, namely, "push" tools that aim to achieve short-term policy compliance among rural households so that they can break out of the persistent poverty cycle and "pull" tools that can reduce fraud, error, and debt among rural households to prevent them from falling back into poverty. Finally, the project team will also use the research activities and findings as vehicles to engage and educate rural residents, local governments, regulators, and financial institutions. Standard and good practice will be proposed to interested parties for the designs of good behavioural interventions; ethical guidelines will be provided to encourage good practice. This important step ensures that the findings of this project will benefit academia and practice, with long-lasting, positive impacts. The findings will benefit researchers in behavioural finance and economics, rural economics, development economics, political sciences, and psychology. The findings of and the engagement in this project will help policy makers to develop cost-effective behavioural change policies. Rural households will benefit by being nudged into sound financial decisions and healthy financial habits. The project will provide insights on how to leverage behavioural insights to overcome persistent poverty in the developing world. Therefore, the research will be of interest to communities in China and internationally. We collected data by including a special module in the 17 Provinces Rural Land Survey administrated by Renmin University of China. This survey is a joint research project between Renmin University of China and the Rural Development Institute (RDI) in the US conducted since 1999. A total of seven rounds of surveys have been conducted since then, and we obtained our data from the latest round completed in 2016.

  14. China CN: Total Employment

    • ceicdata.com
    Updated Dec 15, 2019
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    CEICdata.com (2019). China CN: Total Employment [Dataset]. https://www.ceicdata.com/en/china/population-labour-force-and-employment-non-oecd-member-annual/cn-total-employment
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    Dataset updated
    Dec 15, 2019
    Dataset provided by
    CEIC Data
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 1, 2011 - Dec 1, 2022
    Area covered
    China
    Description

    China Total Employment data was reported at 733,510.000 Person th in 2022. This records a decrease from the previous number of 746,520.000 Person th for 2021. China Total Employment data is updated yearly, averaging 746,470.000 Person th from Dec 1990 (Median) to 2022, with 33 observations. The data reached an all-time high of 763,490.000 Person th in 2014 and a record low of 647,490.000 Person th in 1990. China Total Employment 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: Population, Labour Force and Employment: Non OECD Member: Annual.

    The national breakdown by source of funds does not fully match with the classification defined in the Frascati Manual. The R&D financed by the government, business enterprises, and by the rest of the world can be retrieved but part of the expenditure has no specific source of financing, i.e. self-raised funding (in particular for independent research institutions), the funds from the higher education sector and left-over government grants from previous years.

    The government and higher education sectors cover all fields of NSE and SSH while the business enterprise sector only covers the fields of NSE. There are only few organisations in the private non-profit sector, hence no R&D survey has been carried out in this sector and the data are not available.

    From 2009, researcher data are collected according to the Frascati Manual definition of researcher. Beforehand, this was only the case for independent research institutions, while for the other sectors data were collected according to the UNESCO concept of “scientist and engineer”.

    In 2009, the survey coverage in the business and the government sectors has been expanded.

    Before 2000, all of the personnel data and 95% of the expenditure data in the business enterprise sector are for large and medium-sized enterprises only. Since 2000 however, the survey covers almost all industries and all enterprises above a certain threshold. In 2000 and 2004, a census of all enterprises was held, while in the intermediate years data for small enterprises are estimated.

    Due to the reform of the S&T system some government institutions have become enterprises, and their R&D data have been reflected in the Business Enterprise sector since 2000.

  15. f

    Data_Sheet_1_Code-switching costs from Chinese-English relative clauses...

    • frontiersin.figshare.com
    pdf
    Updated May 30, 2023
    + more versions
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    Wanying Hu; Yang Zhao (2023). Data_Sheet_1_Code-switching costs from Chinese-English relative clauses processing.pdf [Dataset]. http://doi.org/10.3389/fpsyg.2023.1144530.s001
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    pdfAvailable download formats
    Dataset updated
    May 30, 2023
    Dataset provided by
    Frontiers
    Authors
    Wanying Hu; Yang Zhao
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    IntroductionThe source of costs is a primary concern in code-switching, yet a consensus has not yet been reached. This study investigates whether code-switching during syntactic processing in Chinese-English dual languages results in a cost.MethodsWe use Chinese and English relative clauses in either object (Experiment 1) or subject (Experiment 2, which has a more complex structure) positions to test the costs in syntactic processing. Forty-seven Chinese-English bilinguals and 17 English-Chinese bilinguals participated in acceptability judgment tests and self-paced reading experiments.ResultsThe statistical findings indicate that syntactic processing is a source of the costs incurred in code-switching, as evidenced by the code-switching costs observed in the head movement during relative clause comprehension.DiscussionThe outcomes are consistent with the implications of the 4-Morpheme Model and the Matrix Language Framework. Additionally, the experiment shows that the processing of relative clauses depends on the underlying structures, which is consistent with the Dependency Locality Theory.

  16. T

    China Fixed Asset Investment

    • tradingeconomics.com
    • pt.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Jul 15, 2025
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    TRADING ECONOMICS (2025). China Fixed Asset Investment [Dataset]. https://tradingeconomics.com/china/fixed-asset-investment
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    json, xml, excel, csvAvailable download formats
    Dataset updated
    Jul 15, 2025
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Feb 29, 1996 - Jun 30, 2025
    Area covered
    China
    Description

    Fixed Asset Investment in China decreased to 2.80 percent in June from 3.70 percent in May of 2025. This dataset provides - China Fixed Asset Investment- actual values, historical data, forecast, chart, statistics, economic calendar and news.

  17. D

    Data from: Body image and acceptance of cosmetic surgery in China and the...

    • dataverse.nl
    • explore.openaire.eu
    zip
    Updated Jun 7, 2022
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    Yi Wu; Yi Wu; Sandra Mulkens; Sandra Mulkens; Jessica M. Alleva; Jessica M. Alleva (2022). Body image and acceptance of cosmetic surgery in China and the Netherlands: A qualitative study on cultural differences and similarities [Dataset]. http://doi.org/10.34894/NDIZI1
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    zip(880283)Available download formats
    Dataset updated
    Jun 7, 2022
    Dataset provided by
    DataverseNL
    Authors
    Yi Wu; Yi Wu; Sandra Mulkens; Sandra Mulkens; Jessica M. Alleva; Jessica M. Alleva
    License

    https://dataverse.nl/api/datasets/:persistentId/versions/1.0/customlicense?persistentId=doi:10.34894/NDIZI1https://dataverse.nl/api/datasets/:persistentId/versions/1.0/customlicense?persistentId=doi:10.34894/NDIZI1

    Area covered
    Netherlands, China
    Description

    There is an established relationship between acceptance of cosmetic surgery and psychological factors, including body image. However, qualitative research among diverse cultural groups is needed to provide a more fine-grained understanding of the influences on women’s attitudes towards cosmetic surgery. In this study, 20 Chinese and 20 Dutch women aged 18-50 years (MChinese = 34.20; MDutch = 34.70) participated in one-on-one semi-structured interviews. Data were analyzed using reflexive thematic analysis. We identified three themes that captured the factors that women perceived to foster favorable attitudes towards cosmetic surgery: (a) sociocultural pressures (e.g., normalization of cosmetic surgery, appearance-focused peers); (b) intrapersonal characteristics (e.g., beauty-ideal internalization, social comparison); and (c) benefits of beauty (e.g., attracting men, socioeconomic benefits). Conversely, two themes captured the factors perceived to reduce favorable attitudes towards cosmetic surgery: (a) intrapersonal characteristics (e.g., unconditional body acceptance, self-confidence); and (b) external considerations (e.g., health risks, financial costs). Overall, Chinese and Dutch participants shared many similarities in their opinions about what might affect cosmetic surgery consideration. The most striking cross-cultural differences concerned perceived socioeconomic benefits of beauty (mainly Chinese women) and women’s conceptualization of body appreciation. This study may enable a more comprehensive understanding about the factors influencing Chinese and Dutch women’s attitudes towards cosmetic surgery, and the nuances in these relationships across these cultures.

  18. Enterprise Survey 2012 - China

    • catalog.ihsn.org
    • dev.ihsn.org
    • +2more
    Updated Mar 29, 2019
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    World Bank (2019). Enterprise Survey 2012 - China [Dataset]. http://catalog.ihsn.org/catalog/3280
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    Dataset updated
    Mar 29, 2019
    Dataset authored and provided by
    World Bankhttps://www.worldbank.org/
    Time period covered
    2011 - 2013
    Area covered
    China
    Description

    Abstract

    This research was carried out in China between December 2011 and February 2013. Data was collected from 2,700 privately-owned and 148 state-owned firms.

    The objective of Enterprise Surveys is to obtain feedback from businesses on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries.

    Usually Enterprise Surveys focus only on private companies, but in China, a special sample of fully state-owned establishments was included as this is an important part of the economy. Data on 148 state-owned enterprises is provided separately from the data of 2,700 private sector firms. To maintain comparability of the China Enterprise Surveys to surveys conducted in other countries, only the dataset of privately sector firms should be used.

    Geographic coverage

    Twenty-five metro areas: Beijing (municipalities), Chengdu City, Dalian City, Dongguan City, Foshan City, Guangzhou City, Hangzhou City, Hefei City, Jinan City, Luoyang City, Nanjing City, Nantong City, Ningbo City, Qingdao City, Shanghai (municipalities), Shenyang City, Shenzhen City, Shijiazhuang City, Suzhou City, Tangshan City, Wenzhou City, Wuhan City, Wuxi City, Yantai City, Zhengzhou City.

    Analysis unit

    The primary sampling unit of the study is an establishment.The establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

    Universe

    The whole population, or universe of the study, is the non-agricultural economy of firms with at least 5 employees and positive amounts of private ownership. The non-agricultural economy comprises: all manufacturing sectors according to the group classification of ISIC Revision 3.1: (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities sectors.

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    The sample for China ES was selected using stratified random sampling. Three levels of stratification were used in this country: industry, establishment size, and region.

    Industry stratification was designed in the following way: the universe was stratified into 11 manufacturing industries and 7 services industries as defined in the sampling manual. Each manufacturing industry had a target of 150 interviews. Sample sizes were inflated by about 20% to account for potential non-response cases when requesting sensitive financial data and also because of likely attrition in future surveys that would affect the construction of a panel. Note that 100% government owned firms are categorized independently of their industrial classification. The 148 surveyed state-owned enterprises were categorized as a separate sector group to preserve the representativeness of other sector groupings for the private economy.

    Size stratification was defined following the standardized definition for the rollout: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposes, the number of employees was defined on the basis of reported permanent full-time workers. This seems to be an appropriate definition of the labor force since seasonal/casual/part-time employment is not a common practice, except in the sectors of construction and agriculture.

    Regional stratification was defined in twenty-five metro areas: Beijing (municipalities), Chengdu City, Dalian City, Dongguan City, Foshan City, Guangzhou City, Hangzhou City, Hefei City, Jinan City, Luoyang City, Nanjing City, Nantong City, Ningbo City, Qingdao City, Shanghai (municipalities), Shenyang City, Shenzhen City, Shijiazhuang City, Suzhou City, Tangshan City, Wenzhou City, Wuhan City, Wuxi City, Yantai City, Zhengzhou City.

    The sample frame was obtained by SunFaith from SinoTrust.

    The enumerated establishments were then used as the frame for the selection of a sample with the aim of obtaining interviews at 3,000 establishments with five or more employees. The quality of the frame was assessed at the onset of the project through calls to a random subset of firms and local contractor knowledge. The sample frame was not immune from the typical problems found in establishment surveys: positive rates of non-eligibility, repetition, non-existent units, etc.

    Given the impact that non-eligible units included in the sample universe may have on the results, adjustments are needed when computing the appropriate weights for individual observations. The percentage of confirmed non-eligible units as a proportion of the total number of sampled establishments contacted for the survey was 31% (6,485 out of 20,616 establishments).

    Mode of data collection

    Face-to-face [f2f]

    Research instrument

    The following survey instruments are available: - Services Questionnaire, - Manufacturing Questionnaire, - Screener Questionnaire.

    The Services Questionnaire is administered to the establishments in the services sector. The Manufacturing Questionnaire is built upon the Services Questionnaire and adds specific questions relevant to manufacturing.

    The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90% of the questions objectively ascertain characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance.

    Cleaning operations

    Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

    Response rate

    The number of contacted establishments per realized interview was 7.24. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The number of rejections per contact was 0.55.

    Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect the refusal to respond as a different option from don’t know. b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary.

    Survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals.

  19. f

    Data from: S1 Dataset -

    • plos.figshare.com
    xls
    Updated Jun 11, 2024
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    Xiaolie Qi; Wen Qin; Baojun Lin (2024). S1 Dataset - [Dataset]. http://doi.org/10.1371/journal.pone.0304393.s001
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    xlsAvailable download formats
    Dataset updated
    Jun 11, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Xiaolie Qi; Wen Qin; Baojun Lin
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This study investigates the dynamic relationship between cross-border e-commerce and logistics co-development strategies within the context of small and medium-sized enterprises (SMEs) in China. The primary objective is to formulate an empirical model capable of estimating the utility and flexibility of cross-border e-commerce logistics, specifically focusing on its role in achieving competitive advantage for SMEs. The research employs a comprehensive approach, considering various factors that influence the formulation and implementation of cross-border e-commerce logistics strategies. Factors such as scale, quality, potential, and infrastructure are scrutinized to provide a nuanced understanding of the dynamic interplay. Real-world data is analyzed using advanced statistical techniques to derive meaningful insights. The data of CBEC and logistics planning in Guangdong from 2013 to 2018 was used as a case example to demonstrate its and flexibility and usefulness of empirically proposed model estimation and develop a holistic indicator system for the evaluation of the synergistic development of CBEC and logistics. This research focus on the modelling of the CBEC for the synergistic effect analysis and evaluate the usefulness and flexibility of CBEC to achieve competitive advantage. The study aims to uncover insights into the most effective approaches for Chinese SMEs in navigating the landscape of cross-border e-commerce. By examining the results derived from the empirical model, the research sheds light on the impact of cross-border e-commerce logistics on competitive advantage. The findings contribute to a deeper understanding of how SMEs can strategically position themselves in the cross-border e-commerce arena. Through the analysis of real-world data and the application of advanced statistical methods, this research offers valuable insights for Chinese SMEs. The insights generated from the study not only illuminate the intricacies of cross-border e-commerce logistics but also provide practical recommendations to reduce associated costs. Ultimately, the study aims to equip SMEs with the knowledge necessary to thrive in the evolving landscape of cross-border e-commerce.

  20. p

    Pioneer Valley Chinese Immersion Charter School District

    • publicschoolreview.com
    json, xml
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    Public School Review, Pioneer Valley Chinese Immersion Charter School District [Dataset]. https://www.publicschoolreview.com/massachusetts/pioneer-valley-chinese-immersion-charter-school-district/2500517-school-district
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    json, xmlAvailable download formats
    Dataset authored and provided by
    Public School Review
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 1, 2009 - Dec 31, 2025
    Area covered
    Pioneer Valley
    Description

    Historical Dataset of Pioneer Valley Chinese Immersion Charter School District is provided by PublicSchoolReview and contain statistics on metrics:Comparison of Diversity Score Trends,Total Revenues Trends,Total Expenditure Trends,Average Revenue Per Student Trends,Average Expenditure Per Student Trends,Reading and Language Arts Proficiency Trends,Math Proficiency Trends,Science Proficiency Trends,Graduation Rate Trends,Overall School District Rank Trends,Asian Student Percentage Comparison Over Years (2009-2023),Hispanic Student Percentage Comparison Over Years (2009-2023),Black Student Percentage Comparison Over Years (2009-2023),White Student Percentage Comparison Over Years (2009-2023),Two or More Races Student Percentage Comparison Over Years (2009-2023),Comparison of Students By Grade Trends

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TRADING ECONOMICS (2025). China Government Spending [Dataset]. https://tradingeconomics.com/china/government-spending

China Government Spending

China Government Spending - Historical Dataset (1952-12-31/2024-12-31)

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47 scholarly articles cite this dataset (View in Google Scholar)
json, csv, xml, excelAvailable download formats
Dataset updated
Jul 10, 2025
Dataset authored and provided by
TRADING ECONOMICS
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Time period covered
Dec 31, 1952 - Dec 31, 2024
Area covered
China
Description

Government Spending in China increased to 224396.60 CNY Hundred Million in 2024 from 222474.20 CNY Hundred Million in 2023. This dataset provides - China Government Spending - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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