Fabrizio Muratore

Looking for Regional Convergence: Evidence from the Italian Case with Multivariate Adaptive Regression Splines

This paper examines the role of data mining analysis in explaining the Italian regional dualism with the aim of suggesting economic policies to fill the existing socio-economic gaps. We analyze the 2004–2014 period exploiting the capacity of MARS model in finding relationships among data. In Italy, the presence of a North-South divide is well-known for decades and present for several social and economic aspects. Recent studies prove that strong differences exist also in the regional human capital. Thus, we search for the causes of the local differences, also considering the entrepreneurial vitality and the international trade leverage. Among several variables, MARS is useful in showing the actual determinants on which to intervene. This is possible by comparing regions grouped homogeneously into clusters using recent data. MARS results are used for policy suggestions with the aim of filling the income gap.

The North-South Divergence in Italy during the Great Recession

The economic recession that followed the 2007 crisis has widened the economic gaps between the wealthiest and the relatively poorer regions in Italy. The Great Recession has changed the importance of local economic strengths and hindered the possibilities of economic recovery, especially in the Mezzogiorno of Italy. We seek the local strengths present in Italian regions in the post‐crisis period by comparing two macro areas to observe strong and weak points for intervention. A first analysis using multivariate adaptive regression splines (MARS) is used to filter the relevant determinants in a large dataset, and a panel data analysis serves to obtain group‐specific results. Some effects of the prolonged recession are confirmed in all regions, while some weaknesses of the South, such as financial markets, play an increasing role in the regional development scenarios.

A Data Mining Analysis of the Chinese Inland-Coastal Inequality

As in many countries, even in China the socio-economic changes have affected income inequality in recent decades. The various economic opportunities have led to different paths of development causing severe disparities in GDP per capita level. In addition to the well-known Chinese rural/urban inequality, in this work we study the inland/coastal differences. There are many known causes of inequality, but we aim to discover the actual determinants of the local GDP and, therefore, of income in a period that includes the international economic crisis started in 2007. With this aim, we use different variables to obtain clusters of the Chinese provinces in the period 2004–2015 and, subsequently, we investigate the determinants of income with a multivariate adaptive regression splines (MARS). There is an extensive economic literature on the Chinese case: MARS allows us to integrate this literature enabling us to find which GDP determinants are the most relevant in the certain areas of China.

A Comparative Analysis of Literacy Rate in Contributing to Social Exclusion Insights

Our contribution aims to analyse across countries the relationship between the phenomena of social exclusion and literacy levels, and to consider the implications of this relationship on economic growth. A low level of literacy, affecting the employment status, precludes the possibility to enter and operate freely in society, while poverty and persistent social exclusion of a person or family make difficult to address appropriate educational and training paths. Therefore, our study has rejoined two issues which influence almost all decisions adopted by policy makers, especially in the Western world. The opening issue is the level of education, which should constitute the human capital of a country, through appropriate investment, and the second one is the relational conditions of social dynamics, which highlight the so-called social capital. Together these two types of intangible capitals constitute a strong support for the long-term development of a country. Our quantitative analysis is also addressed to detect differences and peculiarities among the different national realities, with the ultimate purpose to recognize which socio-economic variables affect more directly the processes of education.