Algorithms and Bureaucrats: Evidence from Tax Audit Selection in Senegal

Pierre Bachas, Anne Brockmeyer, Alipio Ferreira and Bassirou Sarr

No 11205, Policy Research Working Paper Series from The World Bank

Abstract: Can algorithms enhance bureaucrats’ work in developing countries? In data-poor environments, bureaucrats often exercise discretion over key decisions, such as audit selection. Exploiting newly digitized micro-data, this study conducted an at-scale field experiment whereby half of Senegal’s annual audit program was selected by tax inspectors and the other half by a transparent risk-scoring algorithm. The algorithm-selected audits were 18 percentage points less likely to be conducted, detected 89% less evasion, were less cost-effective, and did not reduce corruption. Moreover, even a machine-learning algorithm would only have moderately raised detected evasion. These results are consistent with bureaucrats’ expertise, the task complexity, and inherent data limitations.

Date: 2025-09-05
New Economics Papers: this item is included in nep-acc, nep-afr, nep-exp and nep-iue
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