Bribery in Tunisia

Bribery puts a heavy burden on low- and middle-income countries, and easing this burden requires policymakers to have better information on how corruption actually works. One underexplored complexity is that bribe-paying firms and bribe-receiving officials often have repeated interactions. These dynamics are very likely to affect how often bribes change hands, the characteristics of those bribes, and downstream economic outcomes like trade volumes and tax revenues. The research team’s paper therefore focuses on a fundamental question: What is the effect of repeated interactions between firms and officials on the frequency and the magnitude of bribery? To answer this question, the research team built an original dataset on Tunisian customs transactions using an audit study to directly observe bribery, to their knowledge the first dataset of its kind in the Middle East and North Africa. They also leveraged a natural experiment in which customs inspectors are randomly assigned to import shipments using a computer algorithm.

The preliminary results suggest that increasing the number of past interactions between clearing agents and customs inspectors decreases both the frequency and the magnitude of bribery. The reason appears to be that, while more past interactions builds trust that makes it easier to engage in tax evasion, the government more aggressively audits those transactions in which the parties involved have longer-term relationships.