Accounting for partisanship and politicization: Employing Benford's Law to examine misreporting of COVID-19 infection cases and deaths in the United States

Abstract

The unprecedented contagion of the SARS-CoV-2 virus, causative of COVID-19, has spawned watershed economic, social, ethical, and political upheaval—catalyzing severe polarization among the global populace. Ostensibly, to demonstrate the most appropriate path towards responding to the virus outbreak, public officials in the United States (“U.S.”), representing both Democratic and Republican parties, stand accused of unduly influencing COVID-19 records in their respective jurisdictions. This study investigates the role political partisanship may have played in decreasing the accuracy of publicly reported COVID-19 data in the U.S. Leveraging social identity theory, we contend that public officials may have manipulated the reporting records in accounting for COVID-19 infection cases and deaths to validate the effectiveness of political party objectives. We employ Benford's Law to assess misreporting and evaluate the integrity of county-level COVID-19 reporting data through the construction of four distinct political party classifications. Specifically, we cross the county voting majority for the 2016 presidential candidate for each U.S. state (Democratic and Republican) with the 2020 gubernatorial political party (Democratic and Republican) in which each county resides. For the sample period of January 21, 2020 through November 3, 2020 (Election Day), the study's results suggest that the reported COVID-19 infection cases and deaths in the U.S. violate Benford's Law in a manner consistent with underreporting. Our analysis reveals that Democratic counties demonstrate the smallest departures from Benford's Law while Republican counties demonstrate the greatest departures.