The Analysis of Some Determinants in the Efficiency of Social Inclusion Policies Using Data Mining Techniques

Assessing the effectiveness of transfers in social inclusion programs is a major concern that can lead to the design of coherent public policies. The complexity of specific elements and peculiarities makes the process of assessing effectiveness a difficult one, considering the general objective of the state to facilitate the optimal redistribution of income within the modern capitalist society. Analyzes made on data sources such as OECD, Eurostat, Word Bank or SGI Network can also be developed by using advanced statistical techniques and machine learning. The paper uses classification algorithms to determine important factors in a social-specific transfer policy. The results of the study reconfirm the importance of factors such as the role of the rule of law, GDP/capita, maintaining income inequality at an acceptable level, but also the significance of other indirect factors, such as the quality of information dissemination through the media, and the development of entrepreneurship and innovation. In addition, the indirect factors determined in the theoretical model offer the possibility to approach other levers which can be considered by the social policy initiators and also by the private environment through the corporate social responsibility projects.