Reducing Corruption in Public Procurement Processes

Fellows: Maria Ines Aran, João Carabetta, Wen Jian, Anna Julià-Verdaguer

Technical Mentor: Pablo Rosado

Project Manager: Josh Sidgwick

Project Partner: Dirección Nacional de Contrataciones Públicas (DNCP)

Public contracts are essential in the delivery of goods and services that people care about and depend on, such as schools, medicines, and roads. Currently, more than 400 public institutions are using Paraguay’s National Directorate of Public Contracting (DNCP) online platform to publish their public tender processes, which results in more than 13,000 processes every year.

Despite the enormity of what is at stake, or perhaps because of it, public contracting might present cases of corruption, mismanagement and secrecy. Scandals from failed contracting processes still persist: cost overruns in the procurement of goods and services, tender documents addressed to companies related to the environment of power, contract breaches, conflict of interest in awarding bids, signs of collusion. 

There is a very rich information that so far is underutilized for public spending design, monitoring, and evaluation. DNCP is looking to apply data science techniques to better formulate evidence-based public procurement policies. The goal of this project is to find and reduce potential anomalies in public tender documents and improve the quality of public procurement.