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Testing firm-level data quality in China against Benford’s Law

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  • Huang, Yasheng
  • Niu, Zhiyong
  • Yang, Clair

Abstract

The authenticity of China’s economic data has long been questioned. We use a new statistical method, Benford’s test, to evaluate data quality of the Chinese Industrial Census (CIC). We show that the method is effective to uncover data irregularities. Based on predicted industrial output by variables that are less manipulatable, such as employment and electricity, we further demonstrate that firms of different ownership types display different behavior in terms of the direction of data manipulation. We find no conclusive evidence of data manipulation by state-owned enterprises (SOEs), whereas private firms tend to under-report performance.

Suggested Citation

  • Huang, Yasheng & Niu, Zhiyong & Yang, Clair, 2020. "Testing firm-level data quality in China against Benford’s Law," Economics Letters, Elsevier, vol. 192(C).
    • Handle: RePEc:eee:ecolet:v:192:y:2020:i:c:s0165176520301361
    DOI: 10.1016/j.econlet.2020.109182
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    References listed on IDEAS

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    1. Bernhard Rauch & Max Göttsche & Gernot Brähler & Stefan Engel, 2011. "Fact and Fiction in EU‐Governmental Economic Data," German Economic Review, Verein für Socialpolitik, vol. 12(3), pages 243-255, August.
    2. Mr. Jesus R Gonzalez-Garcia & Mr. Gonzalo C Pastor Campos, 2009. "Benford’s Law and Macroeconomic Data Quality," IMF Working Papers 2009/010, International Monetary Fund.
    3. Deckert, Joseph & Myagkov, Mikhail & Ordeshook, Peter C., 2011. "Benford's Law and the Detection of Election Fraud," Political Analysis, Cambridge University Press, vol. 19(3), pages 245-268, July.
    4. Tomasz Kamil Michalski & Guillaume Stoltz, 2010. "Do countries falsify economic data strategically? Some evidence that they do," Post-Print hal-00543490, HAL.
    5. Hongbin Cai & Qiao Liu, 2009. "Competition and Corporate Tax Avoidance: Evidence from Chinese Industrial Firms," Economic Journal, Royal Economic Society, vol. 119(537), pages 764-795, April.
    6. Steven J. Miller, 2015. "Benford's Law: Theory and Applications," Economics Books, Princeton University Press, edition 1, number 10527.
    7. Tomasz Michalski & Gilles Stoltz, 2013. "Do Countries Falsify Economic Data Strategically? Some Evidence That They Might," The Review of Economics and Statistics, MIT Press, vol. 95(2), pages 591-616, May.
    8. Gregory Chow, 2006. "Are Chinese Official Statistics Reliable?," CESifo Economic Studies, CESifo Group, vol. 52(2), pages 396-414, June.
    9. Rauch Bernhard & Göttsche Max & Engel Stefan & Brähler Gernot, 2011. "Fact and Fiction in EU-Governmental Economic Data," German Economic Review, De Gruyter, vol. 12(3), pages 243-255, August.
    10. Nuno Fernandes & José Guedes, 2010. "Keeping Up with the Joneses: A Model and a Test of Collective Accounting Fraud," European Financial Management, European Financial Management Association, vol. 16(1), pages 72-93, January.
    11. 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.
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    Cited by:

    1. 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).
    2. Jaroslav Petráš & Marek Pavlík & Ján Zbojovský & Ardian Hyseni & Jozef Dudiak, 2023. "Benford’s Law in Electric Distribution Network," Mathematics, MDPI, vol. 11(18), pages 1-27, September.

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    More about this item

    Keywords

    Data quality; China; Benford’s Law;
    All these keywords.

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • P20 - Political Economy and Comparative Economic Systems - - Socialist and Transition Economies - - - General

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