The COVID-19 pandemic has spurred controversies related to whether countries manipulate reported data for political gains. We study the association between accuracy of reported COVID-19 data and developmental indicators. We use the Newcomb-Benford law (NBL) to gauge data accuracy. We run an OLS regression of developmental indicators (EIU index, GDP per capita, healthcare expenditures, and universal healthcare coverage index) on goodness-of-fit measures to the NBL. We find that democratic countries, countries with the higher gross domestic product (GDP) per capita, higher healthcare expenditures, and better universal healthcare coverage are less likely to deviate from the Newcomb-Benford law. The relationship holds for the cumulative number of reported deaths and total cases but is more pronounced for the death toll. The findings are robust for second-digit tests, for a sub-sample of countries with regional data, and in relation to the previous swine flu (H1N1) 2009–2010 pandemic. The NBL provides a first screening for potential data manipulation during pandemics. Our study indicates that data from autocratic regimes and less developed countries should be treated with more caution. The paper further highlights the importance of independent surveillance data verification projects.
JEL classification: F5, I10, I18, O1, O57, P52.