Is there more corruption in countries less opened to international markets? Application of a predictive classification model based on neural networks.


  • Cristian Picón Universidad del Atlántico


Economic Integration, international trade, international finances, corruption, neuronal networks, Multilayer Perceptron (MLP)


The most common approaches in favor of liberalizing international trade and international finances suggest that opening policies will have a positive impact in reducing corruption. In despite of the complexity of studying about these relationships, most research in this field are limited to correlational studies or deterministic. In this paper we applied a predictive model of classification based on neural networks called Multilayer Perceptron (MLP) that meets a set of qualities desired statistics, with the purpose of estimate the characteristics or "symptoms" that presents a countries categorized as more or less corrupt.
Of the variables used, the levels of human development (HDI) and levels of economic openness are the common characteristics shared by countries with similar levels of corruption, allowing classified correctly. We found evidence that show us if opening economic level of a country is lower, the chance of being classified at a higher level of corruption will be higher.





Science article