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Research analysis of small enterprises are still rare, due to lack of individual level data. Small enterprise failures are connected not only with their financial situation abut also with non-financial factors. In recent research we tend to apply more and more complex models. However, it is not so obvious that increasing complexity increases the effectiveness. In this paper the sample of 806 small enterprises were analyzed. Qualitative factors were used in modeling. Some simple and more complex models were estimated, such as logistic regression, decision trees, neural networks, gradient boosting, and support vector machines. Two hypothesis were verified: (i) not only financial ratios but also non-financial factors matter for small enterprise survival, and (ii) advanced statistical models and data mining techniques only insignificantly increase the prediction accuracy of small enterprise failures. Results show that simple models are as good as more complex model. Data mining models tend to be overfitted. Most important financial ratios in predicting small enterprise failures were: operating profitability of assets, current assets turnover, capital ratio, coverage of short-term liabilities by equity, coverage of fixed assets by equity, and the share of net financial surplus in total liabilities. Among non-financial factors only two of them were important: the sector of activity and employment.

The impact the last financial crisis had on the small- and medium-sized enterprises (SMEs) sector varied across countries, affecting them on different levels and to a different extent. The economic situation in Poland during and after the financial crisis was quite stable compared to other EU member states. SMEs represent one of the most important segments of the economy of every country. Therefore, it is crucial to develop a prediction model which easily adapts to the characteristics of SMEs. Since the Altman Z-Score model was devised, numerous studies on bankruptcy prediction have been written. Most of them involve the application of traditional methods, including linear discriminant analysis (LDA), logistic regression and probit analysis. However, most recent studies in the area of bankruptcy prediction focus on more advanced methods, such as case-based reasoning, genetic algorithms and neural networks. In this paper, the effectiveness of LDA and SVM predictions were compared. A sample of SMEs was used in the empirical analysis, financial ratios were utilised and non-financial factors were taken account of. The hypothesis assuming that multidimensional discrimination was more effective was verified on the basis of the obtained results.

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The total income Y can be expressed as the sum of c components called income sources. The N units of the population can be partitioned into k different subpopulations. In the paper the decompositions by subpopulations as well as by income components of the point ( ) h I Y and the synthetic ( ) I Y inequality indexes proposed by Zenga in 2007 are considered. These indexes can be decomposed into the contributions of income sources and/or into the contributions of subpopulations ,and also the decomposition into within-and between-group inequality can be carried out. In the paper, the two-step approach was used: first of all "additive" decompositions of the point index were derived, and then, by averaging these decompositions, the corresponding decompositions of the synthetic index were obtained. The results were then applied to the analysis of income distributions in Poland and Italy based on the household budget surveys conducted by Statistics Poland and the Bank of Italy, respectively.