Search

This study explores the dynamic relationship between income inequality and economic growth by using a non-parametric approach and numerical taxonomy as a research method based on data symbolization and clustering methods. The study uses annual data of the GINI index (considering two databases, i.e. the Standardized World Income Inequality Database (SWIID) and the Estimated Household Income Inequality Data Set (EHII)) and the Per Capita GDP Growth Rates (economic growth variable) for two samples, i.e. 38 countries between 1980 and 2015, and 23 countries during the period 1980–2010. This novel methodology is used to detect the existence of clusters of countries sharing similar economic performance that are characterized by the income inequality variable. Once the homogeneous clusters are fixed, using a dynamic econometric approach, we study the Granger causal relationship between economic growth and income inequality, and the positive or negative long-run effects. Our results show that in advanced economies there is an economic growth policy supporting income distribution, while in poor or developing economies economic growth is enhanced by income concentration. [ABSTRACT FROM AUTHOR]

In this study, we explore the dynamic relationship between public debt and economic growth by using a non-parametric approach based on data symbolization and clustering methods. The study uses annual data of general government consolidated gross debt-to-GDP ratio and gross domestic product for sixteen countries between 1977 and 2015. Using symbolic sequences, we introduce a notion of distance between the dynamical paths of different countries. Then, a Minimal Spanning Tree and a Hierarchical Tree are constructed from time series to help detecting the existence of groups of countries sharing similar economic performance. The main finding of the study appears for the period 2008–2016 when several countries surpassed the 90% debt-to-GDP threshold. During this period, three groups (clubs) of countries are obtained: high, mid and low indebted countries, suggesting that the employed debt-to-GDP threshold drives economic dynamics for the selected countries. [ABSTRACT FROM AUTHOR]

This study applies network analysis to analyze the structure of the Euro Stoxx market during the long period from 2002 up to 2014. The paper generalizes previous research on stock market networks by including asset returns and volume trading as the main variables to study the financial market. A multidimensional generalization of the minimal spanning tree (MST) concept is introduced, by adding the role of trading volume to the traditional approach which only includes price returns. Additionally, we use symbolization methods to the raw data to study the behavior of the market structure in different, normal and critical , situations. The hierarchical organization of the network is derived, and the MST for different sub-periods of 2002–2014 is created to illustrate how the structure of the market evolves over time. From the structural topologies of these trees, different clusters of companies are identified and analyzed according to their geographical and economic links. Two important results are achieved. Firstly, as other studies have highlighted, at the time of the financial crisis after 2008 the network becomes a more centralized one. Secondly and most important, during our second period of analysis, 2008–2014, we observe that hierarchy becomes more country-specific where different sub-clusters of stocks belonging to France, Germany, Spain or Italy are found apart from their business sector group. This result may suggest that during this period of time financial investors seem to be worried most about country specific economic circumstances. [ABSTRACT FROM AUTHOR]