Abstract
Corruption is a major global problem. It undermines economic growth, government efficiency
and public trust in institutions. Therefore, predicting the evolution of the corruption
situation is a very important goal. However, choosing which indicators to use is difficult.
The perception of corruption either measures a subjective corruption perception index
or is based on objective, statistical numbers. However, these latter statistics do
not show undetected cases of corruption, and therefore underestimate the frequency
of the phenomenon. This study uses a machine learning method to select economic and
socio-statistical indicators that can be used to estimate corruption. Based on the
method, the trend of the relationship between the indicators included in the research
and the perception of corruption, or the lack thereof, enriches the knowledge on the
subject with a lot of information. The results also bring science closer to developing
a reliable forecasting method.
