I study the impact of corona populism -- politics aimed at denying or downplaying the danger posed by COVID-19 for strategic reasons -- on the evolution of the pandemic using regional data from Austria. The right-wing FPOE first vocalized strong support for strict lockdown measures, but made a corona populist turn at the end of the first wave of infections. Using panel regression analysis, I show that the vote share of the FPOE at the last national parliamentary elections is a strong predictor for the number of COVID-19 deaths per capita after the FPOE switched their policy stance, while there is no correlation before the policy switch. Interestingly, I do not find a statistically significant correlation between the FPOE vote share and the reported number of infections. I hypothesize that this can be traced back to a self-selection bias in testing . To explore this hypothesis , I extend the classical SIRD model to incorporate conditional quarantine and  two groups of agents : the majority and the corona sceptics, where the latter are less inclined to get tested and engage in social distancing. Such a model can explain the puzzling empirics: if mixing is sufficiently homophilic , an increase in the share of corona sceptics can cause an increase in the number of deaths without increasing the number of reported infections . I finally discuss the partly non-trivial implications of the interplay between group sizes, behavioral differences and the degree of homophily on public health outcomes within the theoretical model .
I study the impact of opposition politics aimed at downplaying the threat of Covid-19. Exploiting a policy U-turn of a major Austrian right-wing party (FPOE), I show that Covid-19 deaths per capita are significantly positively correlated with support for the FPOE only after the turn using panel regressions. Paradoxically, there is no statistically significant correlation with the reported number of infections. I hypothesize that this can be traced back to a self-selection bias in testing , which causes a higher dark figure in FPOE strongholds. I find empirical support for this hypothesis in individual-level data from a Covid-19 prevalence study showing a much higher share of (undetected) cases among "corona skeptics". I finally extend the classical SIRD model to incorporate conditional quarantine and heterogeneous mixing of two groups of agents with behavioral differences and explore its partly non-trivial properties using thousands of numerical simulations. This model can explain the puzzling empirics: if the behavioral differences between the two groups are sufficiently different , an increase in the share of corona skeptics can cause an increase in the number of deaths without increasing the number of reported infections .
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corona populism --
opposition
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denying or downplaying the danger posed by COVID-19 for strategic reasons -- on the evolution of the pandemic using regional data from Austria. The
downplaying the threat of Covid-19. Exploiting a policy U-turn of a major Austrian
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FPOE first vocalized strong support for strict lockdown measures, but made a corona populist turn at the end of the first wave of infections. Using panel regression analysis,
party (FPOE),
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the vote share of the FPOE at the last national parliamentary elections is a strong predictor for the number of COVID-19
Covid-19
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after the FPOE switched their policy stance, while
are significantly positively correlated with support for the FPOE only after the turn using panel regressions. Paradoxically,
D
correlation before the policy switch. Interestingly, I do not find a
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between the FPOE vote share and the
with the
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. To explore this hypothesis , I
, which causes a higher dark figure in FPOE strongholds. I find empirical support for this hypothesis in individual-level data from a Covid-19 prevalence study showing a much higher share of (undetected) cases among "corona skeptics". I finally
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heterogeneous mixing of
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: the majority and the corona sceptics, where the latter are less inclined to get tested and engage in social distancing. Such a
with behavioral differences and explore its partly non-trivial properties using thousands of numerical simulations. This
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mixing is sufficiently homophilic
the behavioral differences between the two groups are sufficiently different
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sceptics
skeptics
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. I finally discuss the partly non-trivial implications of the interplay between group sizes, behavioral differences and the degree of homophily on public health outcomes within the theoretical model