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Authoritarian Governments Appear to Manipulate COVID Data

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  • Mudit Kapoor
  • Anup Malani
  • Shamika Ravi
  • Arnav Agrawal

Abstract

Because SARS-Cov-2 (COVID-19) statistics affect economic policies and political outcomes, governments have an incentive to control them. Manipulation may be less likely in democracies, which have checks to ensure transparency. We show that data on disease burden bear indicia of data modification by authoritarian governments relative to democratic governments. First, data on COVID-19 cases and deaths from authoritarian governments show significantly less variation from a 7 day moving average. Because governments have no reason to add noise to data, lower deviation is evidence that data may be massaged. Second, data on COVID-19 deaths from authoritarian governments do not follow Benford's law, which describes the distribution of leading digits of numbers. Deviations from this law are used to test for accounting fraud. Smoothing and adjustments to COVID-19 data may indicate other alterations to these data and a need to account for such alterations when tracking the disease.

Suggested Citation

  • Mudit Kapoor & Anup Malani & Shamika Ravi & Arnav Agrawal, 2020. "Authoritarian Governments Appear to Manipulate COVID Data," Papers 2007.09566, arXiv.org.
  • Handle: RePEc:arx:papers:2007.09566
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    References listed on IDEAS

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    3. 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.
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