We analyse the influence of 45 regressors that explain the existence of income differentials and other socio-economic spreads in the Italian regions. We use a multivariate adaptive regression splines analysis as a data-driven approach to detect relationships among variables in big data sets. The focus on regional areas allows us to consider economic contexts historically extremely distant in terms of growth and development. The considered time series include the pre-crisis economic period, the negative effects of the crisis and the originated recession. The independent variables chosen are based on the endogenous growth theory according to the knowledge economy, thus focusing on human capital and other intangible assets. Macroeconomic data and entrepreneurial competitiveness are also considered as control variables. The results are consistent with the findings of the recent economic literature.

Regional income differentials in Italy: A MARS analysis

Odoardi I.;
2016

Abstract

We analyse the influence of 45 regressors that explain the existence of income differentials and other socio-economic spreads in the Italian regions. We use a multivariate adaptive regression splines analysis as a data-driven approach to detect relationships among variables in big data sets. The focus on regional areas allows us to consider economic contexts historically extremely distant in terms of growth and development. The considered time series include the pre-crisis economic period, the negative effects of the crisis and the originated recession. The independent variables chosen are based on the endogenous growth theory according to the knowledge economy, thus focusing on human capital and other intangible assets. Macroeconomic data and entrepreneurial competitiveness are also considered as control variables. The results are consistent with the findings of the recent economic literature.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11564/722299
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