[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
IDEAS home Printed from https://ideas.repec.org/a/eee/eneeco/v114y2022ics014098832200439x.html
   My bibliography  Save this article

Are the official national data credible? Empirical evidence from statistics quality evaluation of China's coal and its downstream industries

Author

Listed:
  • Wang, Delu
  • Chen, Fan
  • Mao, Jinqi
  • Liu, Nannan
  • Rong, Fangyu
Abstract
The authenticity and quality of industrial statistical data directly affects all types of systematic research based on it. Considering the limitations of extant data quality evaluation literature on research objects and evaluation methods, we constructed a new data quality comprehensive inspection and evaluation model based on Benford's Law (BL) and the technique for order of preference by similarity to ideal solution (TOPSIS), selected coal-related industries as the research object, and conducted an empirical test along the research path of “Industry→Province→Indicator”. The results showed that, at industry level, the quality of statistical data for China's coal-related industries from 2001 to 2016 was generally poor. Among the eight sample industries selected, the data quality for five industries (including coal, electricity, and steel) was assessed as poor or slightly poor. Furthermore, at the provincial level, there is significant spatial heterogeneity in the quality of statistical data for various industries affected by factors such as economic structure, marketization level, and industrial diversity. Compared with other types of statistical indicators, industry financial indicators are more prone to data quality problems at the indicator level, and the suspicious indicators of different industries show certain common characteristics and some industry differences. To improve the quality of industrial statistical data and reduce the possible adverse impacts of data quality problems, based on the research findings, we propose targeted countermeasures and suggestions on how to prevent data fraud and effectively identify and rationally use suspicious data.

Suggested Citation

  • Wang, Delu & Chen, Fan & Mao, Jinqi & Liu, Nannan & Rong, Fangyu, 2022. "Are the official national data credible? Empirical evidence from statistics quality evaluation of China's coal and its downstream industries," Energy Economics, Elsevier, vol. 114(C).
    • Handle: RePEc:eee:eneeco:v:114:y:2022:i:c:s014098832200439x
    DOI: 10.1016/j.eneco.2022.106310
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S014098832200439X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.eneco.2022.106310?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Fewster, R. M., 2009. "A Simple Explanation of Benford's Law," The American Statistician, American Statistical Association, vol. 63(1), pages 26-32.
    2. Yang Bao & Bin Ke & Bin Li & Y. Julia Yu & Jie Zhang, 2020. "Detecting Accounting Fraud in Publicly Traded U.S. Firms Using a Machine Learning Approach," Journal of Accounting Research, Wiley Blackwell, vol. 58(1), pages 199-235, March.
    3. Wang, Xiaolei & Wen, Xiaohui & Xie, Chunping, 2018. "An evaluation of technical progress and energy rebound effects in China's iron & steel industry," Energy Policy, Elsevier, vol. 123(C), pages 259-265.
    4. Micha Kaiser, 2019. "Benford'S Law As An Indicator Of Survey Reliability—Can We Trust Our Data?," Journal of Economic Surveys, Wiley Blackwell, vol. 33(5), pages 1602-1618, December.
    5. Huang, Yasheng & Niu, Zhiyong & Yang, Clair, 2020. "Testing firm-level data quality in China against Benford’s Law," Economics Letters, Elsevier, vol. 192(C).
    6. Schlag, Christian & Zeng, Kailin, 2019. "Horizontal industry relationships and return predictability," Journal of Empirical Finance, Elsevier, vol. 53(C), pages 310-330.
    7. Tam Cho, Wendy K. & Gaines, Brian J., 2007. "Breaking the (Benford) Law: Statistical Fraud Detection in Campaign Finance," The American Statistician, American Statistical Association, vol. 61, pages 218-223, August.
    8. Serrano-Cinca, Carlos & Gutiérrez-Nieto, Begoña & Bernate-Valbuena, Martha, 2019. "The use of accounting anomalies indicators to predict business failure," European Management Journal, Elsevier, vol. 37(3), pages 353-375.
    9. Xia Chen & Qiang Cheng & Ying Hao & Qiang Liu, 2020. "GDP growth incentives and earnings management: evidence from China," Review of Accounting Studies, Springer, vol. 25(3), pages 1002-1039, September.
    10. Abboud, Ali & Betz, Michael R., 2021. "The local economic impacts of the oil and gas industry: Boom, bust and resilience to shocks," Energy Economics, Elsevier, vol. 99(C).
    11. Cludius, Johanna & de Bruyn, Sander & Schumacher, Katja & Vergeer, Robert, 2020. "Ex-post investigation of cost pass-through in the EU ETS - an analysis for six industry sectors," Energy Economics, Elsevier, vol. 91(C).
    12. Robert Garlick & Kate Orkin & Simon Quinn, 2020. "Call Me Maybe: Experimental Evidence on Frequency and Medium Effects in Microenterprise Surveys," The World Bank Economic Review, World Bank, vol. 34(2), pages 418-443.
    13. Aaron D Slepkov & Kevin B Ironside & David DiBattista, 2015. "Benford’s Law: Textbook Exercises and Multiple-Choice Testbanks," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-13, February.
    14. Horton, Joanne & Krishna Kumar, Dhanya & Wood, Anthony, 2020. "Detecting academic fraud using Benford law: The case of Professor James Hunton," Research Policy, Elsevier, vol. 49(8).
    15. Fernandez, Viviana, 2021. "Copper mining in Chile and its regional employment linkages," Resources Policy, Elsevier, vol. 70(C).
    16. Demir, Banu & Javorcik, Beata, 2020. "Trade policy changes, tax evasion and Benford's law," Journal of Development Economics, Elsevier, vol. 144(C).
    17. Liu, Xianbing & Wang, Can & Zhang, Weishi & Suk, Sunhee & Sudo, Kinichi, 2013. "Company's affordability of increased energy costs due to climate policies: A survey by sector in China," Energy Economics, Elsevier, vol. 36(C), pages 419-430.
    18. Schlag, Christian & Zeng, Kailin, 2019. "Horizontal industry relationships and return predictability," SAFE Working Paper Series 256, Leibniz Institute for Financial Research SAFE.
    19. George Judge & Laura Schechter, 2009. "Detecting Problems in Survey Data Using Benford’s Law," Journal of Human Resources, University of Wisconsin Press, vol. 44(1).
    20. Zhang, Yanfang & Zhang, Ming & Liu, Yue & Nie, Rui, 2017. "Enterprise investment, local government intervention and coal overcapacity: The case of China," Energy Policy, Elsevier, vol. 101(C), pages 162-169.
    21. Wang, Zheng-Xin & Jv, Yue-Qi, 2021. "A non-linear systematic grey model for forecasting the industrial economy-energy-environment system," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    22. M Lesperance & W J Reed & M A Stephens & C Tsao & B Wilton, 2016. "Assessing Conformance with Benford’s Law: Goodness-Of-Fit Tests and Simultaneous Confidence Intervals," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-20, March.
    23. Ausloos, Marcel & Cerqueti, Roy & Mir, Tariq A., 2017. "Data science for assessing possible tax income manipulation: The case of Italy," Chaos, Solitons & Fractals, Elsevier, vol. 104(C), pages 238-256.
    24. Riccioni, Jessica & Cerqueti, Roy, 2018. "Regular paths in financial markets: Investigating the Benford's law," Chaos, Solitons & Fractals, Elsevier, vol. 107(C), pages 186-194.
    25. Lukas Linsi & Daniel K. Mügge, 2019. "Globalization and the growing defects of international economic statistics," Review of International Political Economy, Taylor & Francis Journals, vol. 26(3), pages 361-383, May.
    26. Lucio Barabesi & Andrea Cerasa & Andrea Cerioli & Domenico Perrotta, 2018. "Goodness-of-Fit Testing for the Newcomb-Benford Law With Application to the Detection of Customs Fraud," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(2), pages 346-358, April.
    27. Wu, Yunna & Xiao, Xinli & Song, Zongyun, 2017. "Competitiveness analysis of coal industry in China: A diamond model study," Resources Policy, Elsevier, vol. 52(C), pages 39-53.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sun, Guanglin & Wang, Jiawei & Ai, Yongfang, 2024. "The impact of government green subsidies on stock price crash risk," Energy Economics, Elsevier, vol. 134(C).
    2. Chen, Fan & Yu, Lan & Mao, Jinqi & Yang, Qing & Wang, Delu & Yu, Chenghao, 2024. "A novel data-characteristic-driven modeling approach for imputing missing value in industrial statistics: A case study of China electricity statistics," Applied Energy, Elsevier, vol. 373(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hao, Zhuang & Zhang, Xudong & Wang, Yuze, 2024. "Assessing the accuracy of self-reported health expenditure data: Evidence from two public surveys in China," Social Science & Medicine, Elsevier, vol. 356(C).
    2. Solanki Gupta & Vivek Kumar Singh & Sumit Kumar Banshal, 2024. "Altmetric data quality analysis using Benford’s law," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(7), pages 4597-4621, July.
    3. Roy Cerqueti & Claudio Lupi, 2023. "Severe testing of Benford’s law," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(2), pages 677-694, June.
    4. Matthew A. Cole & David J. Maddison & Liyun Zhang, 2020. "Testing the emission reduction claims of CDM projects using the Benford’s Law," Climatic Change, Springer, vol. 160(3), pages 407-426, June.
    5. Karlo Kauko, 2024. "How to detect what drives deviations from Benford’s law? An application to bank deposit data," Empirical Economics, Springer, vol. 67(3), pages 1045-1061, September.
    6. Montag, Josef, 2017. "Identifying odometer fraud in used car market data," Transport Policy, Elsevier, vol. 60(C), pages 10-23.
    7. Ausloos, Marcel & Ficcadenti, Valerio & Dhesi, Gurjeet & Shakeel, Muhammad, 2021. "Benford’s laws tests on S&P500 daily closing values and the corresponding daily log-returns both point to huge non-conformity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 574(C).
    8. Cerqueti, Roy & Maggi, Mario, 2021. "Data validity and statistical conformity with Benford’s Law," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    9. Shi, Jing & Ausloos, Marcel & Zhu, Tingting, 2018. "Benford’s law first significant digit and distribution distances for testing the reliability of financial reports in developing countries," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 878-888.
    10. Vadim S. Balashov & Yuxing Yan & Xiaodi Zhu, 2020. "Who Manipulates Data During Pandemics? Evidence from Newcomb-Benford Law," Papers 2007.14841, arXiv.org, revised Jan 2021.
    11. Willis A. Jones, 2020. "A Benford Analysis of National Collegiate Athletic Association Division I Finance Data," Journal of Sports Economics, , vol. 21(3), pages 234-255, April.
    12. Parnes, Dror, 2022. "Banks' off-balance sheet manipulations," The Quarterly Review of Economics and Finance, Elsevier, vol. 86(C), pages 314-331.
    13. Ronelle Burger & Canh Thien Dang & Trudy Owens, 2017. "Better performing NGOs do report more accurately: Evidence from investigating Ugandan NGO financial accounts," Discussion Papers 2017-10, University of Nottingham, CREDIT.
    14. Sitsofe Tsagbey & Miguel de Carvalho & Garritt L. Page, 2017. "All Data are Wrong, but Some are Useful? Advocating the Need for Data Auditing," The American Statistician, Taylor & Francis Journals, vol. 71(3), pages 231-235, July.
    15. Zeng, Kailin & Tang, Ting & Liu, Fangbiao & Atta Mills, Ebenezer Fiifi Emire, 2022. "Innovation links, information diffusion, and return predictability: Evidence from China," International Review of Financial Analysis, Elsevier, vol. 83(C).
    16. Philip E Hulme & Danish A Ahmed & Phillip J Haubrock & Brooks A Kaiser & Melina Kourantidou & Boris Leroy & Shana M Mcdermott, 2024. "Widespread imprecision in estimates of the economic costs of invasive alien species worldwide," Post-Print hal-04633043, HAL.
    17. Abate, Gashaw T. & de Brauw, Alan & Hirvonen, Kalle & Wolle, Abdulazize, 2023. "Measuring consumption over the phone: Evidence from a survey experiment in urban Ethiopia," Journal of Development Economics, Elsevier, vol. 161(C).
    18. Stefan Günnel & Karl-Heinz Tödter, 2009. "Does Benford’s Law hold in economic research and forecasting?," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 36(3), pages 273-292, August.
    19. Holz, Carsten A., 2014. "The quality of China's GDP statistics," China Economic Review, Elsevier, vol. 30(C), pages 309-338.
    20. Roy Cerqueti & Claudio Lupi, 2021. "Some New Tests of Conformity with Benford’s Law," Stats, MDPI, vol. 4(3), pages 1-17, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:eneeco:v:114:y:2022:i:c:s014098832200439x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eneco .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.