Learning about Corruption: A Statistical Framework for working with Audit Reports


  • Laura Sant’Anna Gualda Pereira


26/03/2018 - 16:00


Sala da Congregação FGV/ EMAp (5º andar)


Quantitative studies aiming to disentangle public corruption effects often emphasize the lack of objective information in this research area. The CGU Random Audits Anti-Corruption Program, based on extensive and unadvertised audits of transfers from the federal government to municipalities, arouse as a potential source to try and fill this gap. Reports generated on these audits describe corrupt and mismanagement practices in detail, but reading and coding them manually can be highly inefficient due to time and personal bias. We propose a statistical framework to guide the use of text data to construct objective indicators of corruption and use it in inferential models. It consists of two main steps. In the first one, we use machine learning methods for text classification to create an indicator of corruption based on irregularities from audit reports. In the second step, we use this indicator in a regression model, accounting for the measurement error carried from the first step. To validate this framework, we replicate an empirical strategy presented by Ferraz et al.(2012) to estimate the effect of corruption in educational funds on primary school students' outcomes, between the years of 2006-2015. We achieved an expected accuracy of 92% on the binary classification of irregularities, and our results endorse the findings in Ferraz et al.(2012): students in municipal schools perform significantly worse on standardized tests in municipalities where was found corruption in education.

*Texto enviado pelo aluno. 

Membros da banca: 

  • Eduardo Fonseca Mendes (orientador) - FGV/ EMAp
  • Renato Rocha Souza - FGV / EMAp
  • Marcelo Medeiros - PUC-Rio