Fighting Corruption in Peru: Detecting bias in prioritizing whistleblower complaints

As part of its battle against corruption, the Peruvian government's Contraloría General de la República (CGR)---the Comptroller General---invites and receives a large number of anonymous complaints from citizens about misconduct by government officials. However, CGR receives a very large volume of whistleblower complaints, and has a small team that is tasked with receiving the complaints. As a result, CGR is forced to make triaging decisions about which complaints to prioritize and which to discard. These triage decisions are made by analysts who, as in any case where human judgement is involved, will suffer from inefficiency and potential bias. In the Peruvian context, the main candidate for potential bias is bias against indigenous people. This study seeks to measure this bias and discern its causes by differentiating between competing theories of discrimination.

This study is a lab-in-the-field experiment. We will hold sessions in which participants take part in a range of tasks under laboratory conditions, but without physically coming to a laboratory but rather taking part virtually from their home or office. The participants will perform two types of tasks. In the first part of the study, we aim to simulate under laboratory conditions, the triage decisions the analysts make. They will read examples of whistleblower complaints and then assign each complaint a priority score reflecting their impression of how likely it is that this case is worthy of deeper investigation. The complaints they read will be drawn from real, historical, examples of whistleblower complaints from the last 3 years. For a random subset of the complaints, the name of the accused individual will be switched from a Spanish-sounding name to an indigenous (Quechua or Aymara) sounding name, or vice versa. This technique will allow us to measure the extent of ethnic bias in triage decisions among respondents. Combining it with administrative data on the outcomes of the investigations of the cases that were, actually, investigated will allow us to study the sources of this bias. In the second part of the study we elicit a series of behavioral traits of the subjects that we can then correlate with the biases measured in the first part of the study. The subjects will be grouped into sessions with groups of 20 participants and asked to play a series of behavioral games designed to measure prosocial outcomes, such as their level of trust and cooperation. After finishing the tasks that require interaction between players, they will be presented with individual surveys regarding their personality and cognitive skills.