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Benford’s Law and Macroeconomic Data Quality

Author

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  • Jesus R Gonzalez-Garcia
  • Gonzalo C Pastor Campos

Abstract

This paper examines the usefulness of testing the conformity of macroeconomic data with Benford's law as indicator of data quality. Most of the macroeconomic data series tested conform with Benford's law. However, questions emerge on the reliability of such tests as indicators of data quality once conformity with Benford's law is contrasted with the data quality ratings included in the data module of the Reports on the Observance of Standards and Codes (data ROSCs). Furthermore, the analysis shows that rejection of Benford's law may be unrelated to the quality of statistics, and instead may result from marked structural shifts in the data series. Hence, nonconformity with Benford's law should not be interpreted as a reliable indication of poor quality in macroeconomic data.

Suggested Citation

  • Jesus R Gonzalez-Garcia & Gonzalo C Pastor Campos, 2009. "Benford’s Law and Macroeconomic Data Quality," IMF Working Papers 09/10, International Monetary Fund.
  • Handle: RePEc:imf:imfwpa:09/10
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    File URL: http://www.imf.org/external/pubs/cat/longres.aspx?sk=22581
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    References listed on IDEAS

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    1. Eduardo Ley, 1995. "On the Peculiar Distribution of the U.S. Stock Indeces' Digits," Finance 9503002, EconWPA.
    2. Andreas Diekmann, 2007. "Not the First Digit! Using Benford's Law to Detect Fraudulent Scientif ic Data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 34(3), pages 321-329.
    3. 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).
    4. Nye John & Moul Charles, 2007. "The Political Economy of Numbers: On the Application of Benford's Law to International Macroeconomic Statistics," The B.E. Journal of Macroeconomics, De Gruyter, vol. 7(1), pages 1-14, July.
    5. Hill, Theodore P. & Schürger, Klaus, 2005. "Regularity of digits and significant digits of random variables," Stochastic Processes and their Applications, Elsevier, vol. 115(10), pages 1723-1743, October.
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    Cited by:

    1. T. Mir, 2016. "The leading digit distribution of the worldwide illicit financial flows," Quality & Quantity: International Journal of Methodology, Springer, vol. 50(1), pages 271-281, January.
    2. T. A. Mir, 2016. "The leading digit distribution of the worldwide illicit financial flows," Quality & Quantity: International Journal of Methodology, Springer, vol. 50(1), pages 271-281, January.
    3. Holz, Carsten A., 2014. "The quality of China's GDP statistics," China Economic Review, Elsevier, vol. 30(C), pages 309-338.

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