Statistical footprints in corruption measurement: general approach, with application to automobile license plates in Russia

We offer a novel big data approach to corruption measurement by using statistical anomalies in publicly observable allocations affected by corruption. While individual corruption incidents remain undetectable, systemic corruption leaves measurable statistical footprints by affecting distributions of observable outcomes. We apply our approach to measuring corruption in the Russian traffic police, which issues automobile licence plates. Plates that signal status are found to be excessively concentrated among upmarket and luxurious classes and brands. We use these discrepancies to measure traffic police corruption, and discuss the potential of such approach in assessing the scale of abuse of power for private gains.