4 datasets found
  1. Enterprise Survey 2012 - China

    • catalog.ihsn.org
    • dev.ihsn.org
    • +2more
    Updated Mar 29, 2019
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    World Bank (2019). Enterprise Survey 2012 - China [Dataset]. http://catalog.ihsn.org/catalog/3280
    Explore at:
    Dataset updated
    Mar 29, 2019
    Dataset authored and provided by
    World Bankhttp://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.

  2. S

    Carbon emission data, digital transformation and corporate financial data of...

    • scidb.cn
    Updated Jan 2, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Zhao Sanglin (2025). Carbon emission data, digital transformation and corporate financial data of listed construction companies from 2000 to 2023 [Dataset]. http://doi.org/10.57760/sciencedb.19398
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jan 2, 2025
    Dataset provided by
    Science Data Bank
    Authors
    Zhao Sanglin
    License

    Attribution-NonCommercial-NoDerivs 4.0 (CC BY-NC-ND 4.0)https://creativecommons.org/licenses/by-nc-nd/4.0/
    License information was derived automatically

    Description

    The research data in this article comes from the data of Chinese A-share listed companies from 2000 to 2023; The annual reports of relevant companies are obtained from the official websites of the Shenzhen and Shanghai Stock Exchanges; The relevant data of listed companies comes from the CSMAR database of Guotai An. At the same time, this article conducts a 1% truncation process on non ratio continuous variables to reduce the impact of outliers. (1) Due to the lack of mandatory disclosure of carbon emission data by the Chinese government, there is currently a lack of micro level data on corporate carbon emissions. This study adopted the method of Chapple et al. (2013) to indirectly measure the carbon dioxide emissions of enterprises. Due to the lack of 23 years of carbon emission data, this study borrowed the ARIMA-BP prediction method from Hu Jianbo (2013) and Zhao SL et al. (2024) to fill in the predictions. (2) The degree of digital transformation of listed companies (Digital) The measurement methods for digital transformation of companies are relatively mature, and the measurement method adopts text analysis. This article first constructs numbers Keyword table for transformation; Then use Python software to match the vocabulary with the text of the annual report of the listed company, and use Jieba's method The module can calculate the frequency of relevant keywords appearing in the annual report documents of listed companies; Finally, add 1 to the frequency of the word and perform logarithmic processing Obtain indicators for enterprise digital transformation. Please refer to Wu Fei's (2021) approach (Managing the World) for specific details. (3) Control variables. This study includes enterprise level indicators as control variables: property rights nature of enterprises (SOE), with state-owned enterprises set to 0 and private enterprises set to 0 1) For operating enterprises, the board size (logarithm of the number of board members), the logarithm of the age of the enterprise (age), and the assets and liabilities Rate (lev), return on equity (roe), operating cash flow (CF), sales growth rate (growth), net profit growth rate (gprofit) Proportion of tangible assets (tangibi), proportion of independent directors (indep), proportion of the largest shareholder's shareholding (top 1) The dual role of chairman and general manager, and the nature of property rights (SOE). This study was supported by the Key Support Project for College Students' Innovation and Entrepreneurship in Hunan Province - Research on the Factors and Mechanisms of Digital Transformation of Construction Enterprises in the Digital Economy (S202411532001)

  3. Research on the U-shaped relationship between marketization process and...

    • figshare.com
    txt
    Updated May 27, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    JUNZHOU XU (2025). Research on the U-shaped relationship between marketization process and corporate capital structure: Evidence from Chinese listed companies [Dataset]. http://doi.org/10.6084/m9.figshare.29154239.v1
    Explore at:
    txtAvailable download formats
    Dataset updated
    May 27, 2025
    Dataset provided by
    figshare
    Authors
    JUNZHOU XU
    License

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

    Description

    This study utilizes panel data from Chinese listed firms spanning the period 2012–2021 to examine the U-shaped relationship between marketization and corporate capital structure. The dataset includes financial and institutional variables, capturing firm-level leverage decisions alongside measures of market development across different stages of China's economic reform. Fixed effects models and nonlinear regression techniques are employed to analyze how changes in marketization—ranging from early institutional reforms to advanced capital market development—affect corporate borrowing behavior. The data also incorporates firm characteristics, such as the proportion of fixed assets and state-owned equity ownership, to assess their moderating effects on the marketization-leverage relationship. This comprehensive dataset enables a robust investigation of how institutional transitions shape corporate financing strategies in emerging markets.

  4. f

    Variance inflation factor (VIF).

    • figshare.com
    • plos.figshare.com
    xls
    Updated May 28, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Baoping Liu; Haohao Wei; Jing Liu (2025). Variance inflation factor (VIF). [Dataset]. http://doi.org/10.1371/journal.pone.0324645.t004
    Explore at:
    xlsAvailable download formats
    Dataset updated
    May 28, 2025
    Dataset provided by
    PLOS ONE
    Authors
    Baoping Liu; Haohao Wei; Jing Liu
    License

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

    Description

    Enhancing corporate ESG (Environmental, Social, and Governance) performance is a critical issue garnering widespread attention across various sectors. This research examines the impact of executives with overseas backgrounds on corporate ESG performance, utilizing a two-way fixed-effects model with data from Chinese A-share listed companies in Shanghai and Shenzhen spanning from 2008 to 2022. The findings indicate that: (ⅰ) executives with overseas backgrounds positively influence corporate ESG performance, with this effect amplifying as the proportion of such executives within the executive team increases; (ⅱ)both internal and external pay gaps significantly positively moderate the relationship between overseas background executives and corporate ESG performance; (ⅲ) the influence of overseas executives on ESG performance is more pronounced in state-owned enterprises, large-scale firms, firms in the eastern region, and those in high-pollution industries. This study expands the understanding of factors affecting corporate ESG performance, offers empirical insights for listed companies aiming to enhance their ESG performance, and provides valuable implications for executive team construction, executive compensation strategies, government talent policies, and the pursuit of high-quality economic development.

  5. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
World Bank (2019). Enterprise Survey 2012 - China [Dataset]. http://catalog.ihsn.org/catalog/3280
Organization logo

Enterprise Survey 2012 - China

Explore at:
2 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Mar 29, 2019
Dataset authored and provided by
World Bankhttp://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.

Search
Clear search
Close search
Google apps
Main menu