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The Macroeconomic Impact of Jair Bolsonaro: A Synthetic Control Approach
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Orientador(es)
Resumo(s)
This thesis aims to estimate the macroeconomic impact of the policies and actions of Brazilian
President Jair Bolsonaro on the country's Gross Domestic Product (GDP) using the synthetic control
method (SCM) developed by Abadie et al. (2003). This methodology is a powerful tool for causal
inference as it allows for the estimation of a counterfactual outcome of a treatment group (in this case,
Brazil under Bolsonaro) by constructing a synthetic control group that mimics the pre-treatment trends
of the treatment group. The study finds that for the second part of the 2010 decade, the real outcome
variable couldn't be replicated by the synthetic control. This suggests that there may have been unique
factors at play in the Brazilian economy during this time that were not present in the donor pool and
therefore couldn't be fully captured by the model. The study also finds that the recovery of the
pandemic downturn in GDP was steeper for the synthetic Brazil compared to the real one. This could
be somewhat related to how Bolsonaro handled it. However, despite the statistical significance of the
results, it is not clear that the model can give us great insights in what it comes to have an answer to
our research question – to give us a concrete answer to the question of how Brazil would have
performed without Bolsonaro, further research may be needed.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence
Palavras-chave
Bolsonaro Brazil GDP Synthetic Control Methods