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The Political Economy of Numbers: On the Application of Benford's Law to International Macroeconomic Statistics

Author

Listed:
  • Nye John

    (Washington University in St. Louis)

  • Moul Charles

    (Washington University in St. Louis)

Abstract

In this paper we present a technique for assessing data quality based on conformity with Benford's Law, which states that the first digits of numbers generated from natural phenomena do not occur with equal frequency. If data do not conform to the Benford distribution, then questions arise about the process that generated it. Because neutral transformations should preserve conformity to Benford's Law, any macroeconomic adjustment that destroys this conformity should make those calculations suspect.Benford's Law is applied to one of the most commonly used data sets in economics: international macroeconomic statistics. We find that the World Bank international GDP data and purchasing power parity (PPP) corrected Penn World tables for OECD countries conform well to Benford's Law. But some subsets of the data - particularly GDP figures from the developing world -- show non-conformity consistent with deliberate manipulation of the underlying series. The test also flags potential problems with a variety of standard macro transformations of the data.

Suggested Citation

  • 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.
  • Handle: RePEc:bpj:bejmac:v:7:y:2007:i:1:n:17
    DOI: 10.2202/1935-1690.1449
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    Citations

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    Cited by:

    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. Montag, Josef, 2015. "Identifying Odometer Fraud: Evidence from the Used Car Market in the Czech Republic," MPRA Paper 65182, University Library of Munich, Germany.
    3. Tomasz Kamil Michalski & Guillaume Stoltz, 2010. "Do countries falsify economic date strategically? Some evidence that they do," Working Papers hal-00540794, HAL.
    4. 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.
    5. 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.
    6. Ioana Sorina Deleanu, 2017. "Do Countries Consistently Engage in Misinforming the International Community about Their Efforts to Combat Money Laundering? Evidence Using Benford’s Law," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-19, January.
    7. 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.
    8. 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.
    9. Theoharry Grammatikos & Nikolaos I. Papanikolaou, 2021. "Applying Benford’s Law to Detect Accounting Data Manipulation in the Banking Industry," Journal of Financial Services Research, Springer;Western Finance Association, vol. 59(1), pages 115-142, April.
    10. Tariq Ahmad Mir & Marcel Ausloos & Roy Cerqueti, 2014. "Benford's law predicted digit distribution of aggregated income taxes: the surprising conformity of Italian cities and regions," Papers 1410.2890, arXiv.org.
    11. Tariq Ahmad Mir, 2012. "The leading digit distribution of the worldwide Illicit Financial Flows," Papers 1201.3432, arXiv.org, revised Nov 2012.
    12. Lee, Joanne & Judge, George G, 2008. "Identifying falsified clinical data," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt8x00h1c1, Department of Agricultural & Resource Economics, UC Berkeley.
    13. Holz, Carsten A., 2014. "The quality of China's GDP statistics," China Economic Review, Elsevier, vol. 30(C), pages 309-338.
    14. Mr. Gonzalo C Pastor Campos & Mr. Jesus R Gonzalez-Garcia, 2009. "Benford’s Law and Macroeconomic Data Quality," IMF Working Papers 2009/010, International Monetary Fund.
    15. Mir, T.A., 2012. "The law of the leading digits and the world religions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(3), pages 792-798.
    16. McDonald, Bruce D. III & Goodman, Christopher B, 2020. "The Truth about Honesty in the Nonprofit Sector," SocArXiv 48g5c, Center for Open Science.
    17. Koch, Christoffer & Okamura, Ken, 2020. "Benford’s Law and COVID-19 reporting," Economics Letters, Elsevier, vol. 196(C).
    18. Montag, Josef, 2017. "Identifying odometer fraud in used car market data," Transport Policy, Elsevier, vol. 60(C), pages 10-23.
    19. 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.
    20. Hansen, Bradley A. & Hansen, Mary Eschelbach, 2016. "The historian's craft and economics," Journal of Institutional Economics, Cambridge University Press, vol. 12(2), pages 349-370, June.
    21. Dang, Canh Thien & Owens, Trudy, 2020. "Does transparency come at the cost of charitable services? Evidence from investigating British charities," Journal of Economic Behavior & Organization, Elsevier, vol. 172(C), pages 314-343.
    22. Cerqueti, Roy & Maggi, Mario, 2021. "Data validity and statistical conformity with Benford’s Law," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    23. 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).

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