Arthur J. Villasanta – Fourth Estate Contributor
Valladolid, Spain (4E) – Scientists from the University of Valladolid in Spain and the Higher School of Economics (HSE) in Russia have developed a neural network prediction model of corruption based on economic and political factors.
Researchers contend that corruption must be detected as soon as possible in order to take corrective and preventive measures.
They propose an early warning corruption model to predict whether corruption cases are likely to emerge in Spanish regions given certain macroeconomic and political determinants. The model provides different profiles of corruption risk depending on the economic conditions of a region conditional on the timing of the prediction.
HSE and University of Valladolid scientists used self-organizing maps (SOMs), a neural network approach, to predict corruption cases in different time horizons. SOMs are a kind of artificial neural network that aims to mimic brain functions.
They have the ability to extract patterns from large data sets without an explicit understanding of the underlying relationships. SOMs convert nonlinear relations among high dimensional data into simple geometric connections.
These properties have made SOMs a useful tool to detect patterns and obtain visual representations of large amounts of data. Consequently, predicting corruption is a field where SOMs can become a powerful tool.
The results show that economic factors prove to be relevant predictors of corruption.
Researchers find that the taxation of real estate, economic growth, increased house prices, and the growing number of deposit institutions and non-financial firms might induce public corruption. They also find that the same ruling party remaining in power too long is positively related to public corruption.
In some cases, corruption cases can be predicted well before they occur and thus allow preventive measures to be implemented. But in other cases, the prediction period is much shorter and urgent corrective political measures are required.
The method consists of a sophisticated algorithm with multiple non-linear relations according to which the determinants of the propensity to corruption change throughout the time.
“Our research develops a novel approach with three differential characteristics,” said s Felix J. Lopez-Iturriaga, Leading Research Fellow at the International Laboratory of Intangible-driven Economy of HSE, and one of the study authors.
“First, unlike previous research, which is mainly based on the perception of corruption, we use data on actual cases of corruption. Second, we use the neural network approach, a particularly suitable method since it does not make assumptions about data distribution. Neural networks are quite powerful and flexible modeling devices that do not make restrictive assumptions on the data-generating process or the statistical laws concerning the relevant variables.
“Third, we report the probability of corruption cases on different time scenarios, so that anti-corruption measures can be tailored depending on the immediacy of such corrupt practices. Our model allows patterns of corruption to be identified on different time horizons.”
The model can be applied to countries, as well. Of course, it could be improved if country or region-specific factors are taken into account.
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