Every year low- and middle-income countries import goods worth more than $7 trillion, and in many states these shipments must first pass through the hands of corrupt customs officials. With such high stakes, policymakers require a deep understanding of both the causes and the effects of customs fraud. In addition, researchers have the opportunity to use trade corruption as a laboratory to discover new insights about corruption as a whole. One previously unexplored complexity is that bribe payers and bribe receivers often have repeated interactions; given corruption’s characteristic contracting frictions, counterparty risks, and information asymmetries, these long-running relationships likely matter for a wide variety of outcomes across a wide variety of contexts. To pursue these learning objectives, we overcome the data and identification challenges inherent to investigating bribery: we build an original dataset on Tunisian customs transactions using an audit study to directly observe bribes, and we leverage a natural experiment in which a computer algorithm randomly assigns customs officials to import shipments. There are three sets of results. First, we show that bribery and tax evasion are widespread, that bribery is collusive (not coercive), and that age (but not gender) predicts officials’ corruptibility. Second, in line with a straightforward Nash bargaining model, we show that the length of official/trader relationships increases tax evasion but decreases bribe amounts. Third, we zoom out to consider the larger macroeconomic implications and show that, in terms of lost tax revenue, bribery costs the Tunisian government 0.7% of GDP or $80 per citizen.
