Social Indicators Research

, Volume 140, Issue 3, pp 975–998 | Cite as

Predicting Public Corruption with Neural Networks: An Analysis of Spanish Provinces

  • Félix J. López-IturriagaEmail author
  • Iván Pastor Sanz


We contend that corruption must be detected as soon as possible so that corrective and preventive measures may be taken. Thus, we develop an early warning system based on a neural network approach, specifically self-organizing maps, to predict public corruption based on economic and political factors. Unlike previous research, which is based on the perception of corruption, we use data on actual cases of corruption. We apply the model to Spanish provinces in which actual cases of corruption were reported by the media or went to court between 2000 and 2012. We find that the taxation of real estate, economic growth, the increase in real estate prices, the growing number of deposit institutions and non-financial firms, and the same political party remaining in power for long periods seem to induce public corruption. Our model provides different profiles of corruption risk depending on the economic conditions of a region conditional on the timing of the prediction. Our model also provides different time frameworks to predict corruption up to 3 years before cases are detected.


Corruption Prediction Early warning system Neural networks Self-organizing maps 

JEL Classification

C45 D73 



We are grateful to Alisa Larson and Philip Jaggs for their comments on previous versions. All the remaining errors are the authors’ sole responsibility. We acknowledge the Spanish Ministry of Economy and Competitiveness for financial support (Project ECO2014-56102-P). This paper was prepared also within the framework of the Basic Research Program at the National Research University Higher School of Economics (HSE) and supported within the framework of a subsidy granted to the HSE by the Government of the Russian Federation for the implementation of the Global Competitiveness Program.


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Authors and Affiliations

  1. 1.School of Business and EconomicsUniversity of ValladolidValladolidSpain
  2. 2.Higher School of EconomicsMoscowRussia

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