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Ensminger, J and Leder-Luis, J (2022)

Detecting Fraud in Development Aid


ISSN/ISBN: Not available at this time. DOI: Not available at this time.

Abstract: In developing countries, traditional antifraud measures including auditing may face barriers due to institutional resistance and practical difficulties on the ground. This is especially true in development aid, where aid organizations face incentives to suppress information about misappropriated funds and may operate with limited transparency and accountability. We develop new statistical tests to uncover strategic data manipulation consistent with fraud. These tests detect falsified cost reports and facilitate monitoring in difficult-to-audit circumstances, relying only on mandated reporting of data. While the digits of naturally-occurring data follow the Benford’s Law distribution, humanly-produced data instead reflect behavioral biases and incentives to misreport. Our new tests distinguish intentional manipulation from benign misreporting and improve the statistical power of digit analysis. We apply this method to a World Bank development project in Kenya. Our evidence is consistent with higher levels of fraud in harder to monitor sectors and in a Kenyan election year when graft also had political value. The results are validated by a forensic audit conducted by the World Bank. We produce simulations that demonstrate the superiority of our new tests to the standards in the field, and provide evidence of the broad generalizability of Benford’s Law.

@misc{, author = {Jean Ensminger and Jetson Leder-Luis}, title = {Detecting Fraud in Development Aid}. year = {2022}, url = {}, }

Reference Type: Preprint

Subject Area(s): Accounting, Statistics