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The leading digit distribution of the worldwide illicit financial flows

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  • T. Mir

Abstract

The illicit financial flows (IFFs) exiting the developing countries are frequently discussed as hidden resources which could have been otherwise properly utilized for their development. Further, in the context of overhaul of the global financial system, necessitated by the current financial crisis, the IFFs have generated a lot of media and public interest which in turn has however also triggered a debate on the validity of these estimates. To look for completeness or rather for possible manipulation of financial data, forensic analysts routinely use a statistical tool called Benford’s law which states that in data sets from different phenomena leading digits tend to be distributed logarithmically such that the numbers beginning with smaller digits occur more often than those with larger ones. In order to gain some insight on their validity we investigate here the recent data on estimates of IFFs for conformity to Benford’s law. We find the patterns in the distribution of the leading digits in the IFFs data similar as predicted by Benford’s law and thereby establish that the frequency of occurrence of the leading digits in these estimates does closely follow the law. Copyright Springer Science+Business Media Dordrecht 2016

Suggested Citation

  • 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.
    • Handle: RePEc:spr:qualqt:v:50:y:2016:i:1:p:271-281
    DOI: 10.1007/s11135-014-0147-z
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    References listed on IDEAS

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    Cited by:

    1. Bogdan Vasile Ileanu & Marcel Ausloos & Claudiu Herteliu & Marian Pompiliu Cristescu, 2019. "Intriguing behavior when testing the impact of quotation marks usage in Google search results," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(5), pages 2507-2519, September.
    2. 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.
    3. 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.
    4. Yan Bao & Chuo-Hsuan Lee & Frank Heilig & Edward J. Lusk, 2018. "Empirical Information on the Small Size Effect Bias Relative to the False Positive Rejection Error for Benford Test-Screening," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 10(2), pages 1-9, February.
    5. 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).
    6. Cerqueti, Roy & Maggi, Mario, 2021. "Data validity and statistical conformity with Benford’s Law," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).

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