Failing to mitigate COVID-19 severity: the case of Brazil

ABSTRACT

Brazilian President Jair Bolsonaro has consistently dismissed the severity of COVID-19, failing to offer guidance and leadership to the governors of federative units responsible for containing the virus and making Brazil an illustrative case of societies most affected by the pandemic due to political negligence and inaction. This article presents a subnational analysis of Brazil’s federative units, examining the effects of policy responsiveness and political capacity on COVID-19 mortality. Specifically, this analysis identifies a conditional relationship between policy effectiveness and governments’ ability to reach and convince their populations to abide by policy recommendations, controlling for other leading explanations of COVID-19 severity (i.e. demographic characteristics, behavioral trends, and preparedness). This work implements subnational measures of political capacity to capture heterogeneity in government responses within the country. Results from random effects regression and a Generalized Additive Model add to recent findings on the role of effective governance in mitigating COVID-19 severity.

RÉSUMÉ

Le président brésilien Jair Bolsonaro n'a cessé de faire abstraction de la gravité de la COVID-19, n'offrant pas de conseils et de direction aux gouverneurs des unités fédératives chargées de contenir le virus et faisant du Brésil un cas illustratif des sociétés les plus touchées par la pandémie en raison de la négligence et de l'inaction politiques. Cet article présente une analyse sous-nationale des unités fédératives du Brésil, examinant les effets de la réactivité et de la capacité politiques concernant la mortalité de la COVID-19. Plus spécifiquement, cette analyse identifie un rapport conditionnel entre l'efficacité politique et la capacité des gouvernements à atteindre et à convaincre leurs populations de se conformer aux recommandations politiques, en contrôlant les autres explications principales de la gravité de la COVID-19 (c'est-à-dire les caractéristiques démographiques, les tendances comportementales et la préparation). Ce travail met en œuvre des mesures sous-nationales de la capacité politique pour saisir l'hétérogénéité des réponses du gouvernement au sein du pays. Les résultats d'une régression à effets aléatoires et d'un modèle additif généralisé viennent s'ajouter aux conclusions récentes de travaux sur le rôle d'une gouvernance efficace dans l'atténuation de la gravité de la COVID-19.

KEYWORDS:

• COVID-19
• political capacity
• Brazilian politics
• policy implementation

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 See Kugler and Tammen (2012) for a detailed review of relative political capacity and its applications.

2 See Arbetman, 1990 and Arbetman, 1994 for a detailed explanation or Relative Political Reach

3 A Breusch-Pagan Lagrange multiplier (LM) test indicates that the variance across the federative units (panels) is different from zero; as such, a random effects regression is a more appropriate econometric model than pooled OLS.

4 With GAM the time variable is modelled by a sum of smooth functions. See Hastie and Tibshirani (1986) for a more detailed discussion.

5 Potential explanatory factors of this discrepancy to consider in future research are the federative units’ location in the Amazon rainforest, where access to healthcare is limited and potable water is scarce (See Castro et al., 2019).

6 Results from panel-data unit-root tests confirm stationarity of the dependent variable. A Wooldridge test for autocorrelation in panel data (Table D) shows evidence of first-order serial correlation, as such a random effects regression with a first-order autoregressive disturbance is used for estimation (xtregar option in Stata). See Baltagi and Wu (1999) for details. See Appendix (Tables E and F) for results of unit-root and serial autocorrelation tests.

7 Using population aged 60 and older as a control instead of 80 and older also yields a negative coefficient and does not affect the robustness of the results (See Table G in Appendix). Population aged 80 and older is preferred since 60 and older is even more highly correlated (0.62) with the measure of bed availability.

8 The direction, and statistical and substantive significance of coefficients are not affected when this variable is removed.

9 See Table H in Appendix.

Additional information

Notes on contributors

Ana Ortiz Salazar

Ana Ortiz Salazar is currently pursuing her doctoral degree in International Politics and Political Science with concentrations in Computational Analytics and World Politics at Claremont Graduate University, from where she also received her master's degree in Applied Data Science and International Studies. She is a Research Fellow at the TransResearch Consortium.