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

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  • 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
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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. 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. Montag, Josef, 2015. "Identifying Odometer Fraud: Evidence from the Used Car Market in the Czech Republic," MPRA Paper 65182, University Library of Munich, Germany.
    4. Michalski, Tomasz & Stoltz, Gilles, 2010. "Do countries falsify economic date strategically? Some evidence that they do," HEC Research Papers Series 930, HEC Paris.
    5. 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.
    6. Montag, Josef, 2017. "Identifying odometer fraud in used car market data," Transport Policy, Elsevier, vol. 60(C), pages 10-23.
    7. 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.
    8. Tariq Ahmad Mir, 2012. "The leading digit distribution of the worldwide Illicit Financial Flows," Papers 1201.3432, arXiv.org, revised Nov 2012.
    9. 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.
    10. Lee, Joanne & Judge, George G., 2008. "Identifying falsified clinical data," CUDARE Working Papers 47001, University of California, Berkeley, Department of Agricultural and Resource Economics.
    11. Holz, Carsten A., 2014. "The quality of China's GDP statistics," China Economic Review, Elsevier, vol. 30(C), pages 309-338.
    12. 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.
    13. 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).
    14. 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.
    15. Jesus R Gonzalez-Garcia & Gonzalo C Pastor Campos, 2009. "Benford’s Law and Macroeconomic Data Quality," IMF Working Papers 09/10, International Monetary Fund.
    16. Marcel Ausloos & Roy Cerqueti & Tariq A. Mir, 2017. "Data science for assessing possible tax income manipulation: The case of Italy," Papers 1709.02129, arXiv.org.

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