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Detecting Problems in Military Expenditure Data Using Digital Analysis

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  • Bernhard Rauch
  • Max G�ttsche
  • Stephan Langenegger

Abstract

The UN asks governments to report key figures of their annual military budgets with the aim of creating trust among member states. This goal can only be achieved if the data reported is accurate. However, although there are many reasons for governments to falsify data, the UN does not check for manipulation. In this paper, we apply Benford's law to the military expenditure data of 27 states taken from the UN register. Our analysis of the first digits shows that the states with the greatest deviations from the expected Benford distribution and therefore the lowest data quality are the USA and the UK.

Suggested Citation

  • Bernhard Rauch & Max G�ttsche & Stephan Langenegger, 2014. "Detecting Problems in Military Expenditure Data Using Digital Analysis," Defence and Peace Economics, Taylor & Francis Journals, vol. 25(2), pages 97-111, April.
    • Handle: RePEc:taf:defpea:v:25:y:2014:i:2:p:97-111
    DOI: 10.1080/10242694.2013.763438
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    References listed on IDEAS

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    1. 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.
    2. Jyrki Niskanen & Matti Keloharju, 2000. "Earnings cosmetics in a tax-driven accounting environment: evidence from Finnish public firms," European Accounting Review, Taylor & Francis Journals, vol. 9(3), pages 443-452.
    3. De Ceuster, Marc J. K. & Dhaene, Geert & Schatteman, Tom, 1998. "On the hypothesis of psychological barriers in stock markets and Benford's Law," Journal of Empirical Finance, Elsevier, vol. 5(3), pages 263-279, September.
    4. Beber, Bernd & Scacco, Alexandra, 2012. "What the Numbers Say: A Digit-Based Test for Election Fraud," Political Analysis, Cambridge University Press, vol. 20(2), pages 211-234, April.
    5. Walter Krämer, 2011. "The Cult of Statistical Significance – What Economists Should and Should Not Do to Make their Data Talk," Schmollers Jahrbuch : Journal of Applied Social Science Studies / Zeitschrift für Wirtschafts- und Sozialwissenschaften, Duncker & Humblot, Berlin, vol. 131(3), pages 455-468.
    6. David Giles, 2007. "Benford's law and naturally occurring prices in certain ebaY auctions," Applied Economics Letters, Taylor & Francis Journals, vol. 14(3), pages 157-161.
    7. repec:bla:germec:v:11:y:2010:i::p:397-401 is not listed on IDEAS
    8. Diekmann Andreas & Jann Ben, 2010. "Benford’s Law and Fraud Detection: Facts and Legends," German Economic Review, De Gruyter, vol. 11(3), pages 397-401, August.
    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. Tom Van Caneghem, 2004. "The impact of audit quality on earnings rounding-up behaviour: some UK evidence," European Accounting Review, Taylor & Francis Journals, vol. 13(4), pages 771-786.
    11. 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.
    12. 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).
    13. Christoph Watrin & Ralf Struffert & Robert Ullmann, 2008. "Benford’s Law: an instrument for selecting tax audit targets?," Review of Managerial Science, Springer, vol. 2(3), pages 219-237, November.
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    Cited by:

    1. Rabeea Sadaf, 2017. "Advanced Statistical Techniques For Testing Benford'S Law," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, vol. 1(2), pages 229-238, December.

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