EDUCAÇÃO FINANCEIRA E SUPERENDIVIDAMENTO: UM ESTUDO DE CASO
Mots-clés :
educação financeira, alfabetização financeira, machine learning, random forrestRésumé
Em meio à pandemia do covid-19, foram investigados de forma aleatória servidores públicos brasileiros, por meio de um formulário enviado por e-mail. A pesquisa teve uma fase exploratória, com o emprego da Análise Fatorial Exploratória para identificação das dimensões. Além de validar um questionário com 63 variáveis, a pesquisa criou um modelo preditivo para a educação financeira. Um cluster significativo foi identificado a partir das análises de Regressão Logísitca: as pessoas realizam comparações prévias entre as opções de crédito disponíveis; as pessoas estão satisfeitas com a forma com que controlam os seus gastos; a vida seria melhor se as pessoas possuíssem coisas que não possuem. O modelo de Regressão Logística previu com exatidão cerca de 90% das respostas, apresentando-se de forma robusta para efeitos de previsão. A variável dependente “endividamento” foi explicada com sucesso por variáveis importantes “faço escolhas” mas “não controlo minhas despesas mensais” Esse modelo, além do questionário validado, pode ser empregado para programas de alfabetização e educação financeiras.
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