Browsing by Subject "Data manipulation"

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    Regime type and data manipulation: evidence from the COVID-19 pandemic
    (Duke University Press, 2024-12) Wigley, Simon
    Context: This study examines whether autocratic governments are more likely than democratic governments to manipulate health data. The COVID-19 pandemic presents a unique opportunity for examining this question because of its global impact. Methods: Three distinct indicators of COVID-19 data manipulation were constructed for nearly all sovereign states. Each indicator was then regressed on democracy and controls for unintended misreporting. A machine learning approach was then used to determine whether any of the specific features of democracy are more predictive of manipulation. Findings: Democracy was found to be negatively associated with all three measures of manipulation, even after running a battery of robustness checks. Absence of opposition party autonomy and free and fair elections were found to be the most important predictors of deliberate undercounting. Conclusions: The manipulation of data in autocracies denies citizens the opportunity to protect themselves against health risks, hinders the ability of international organizations and donors to identify effective policies, and makes it difficult for scholars to assess the impact of political institutions on population health. These findings suggest that health advocates and scholars should use alternative methods to estimate health outcomes in countries where opposition parties lack autonomy or must participate in uncompetitive elections.