A Multidimensional Analysis of the EU Regional Inequalities

Parente, Francesca

doi:10.1007/s11205-018-2000-6

Cited by 11 publications

(6 citation statements)

References 14 publications

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“…The literature on regional inequality has been mainly based on traditional inequality measures and focused on their geographical decomposition (see among others, Maxwell and Peter 1988;Tsui 1993;Fan and Casetti 1994;Kanbur and Zhang 1999;Nissan and Carter 1999;Azzoni 2001;Akita 2003;Beenstock and Felsenstein 2007). Studies on regional inequality based on composite indices resulting from the combination of multiple socio-economic indicators are also reported in the literature (Majumder et al 1995;Quadrado et al 2001;Parente 2019). Some contributions in the recent literature highlighted some specific limitations of the studies on regional inequality (Rey and Janikas 2005;Portnov and Felsenstein 2010).…”

Section: Introductionmentioning

confidence: 99%

Measuring the Spatial Dimension of Regional Inequality: An Approach Based on the Gini Correlation Measure

Panzera

Postiglione

2019

Soc Indic Res

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Traditional inequality measures fail to capture the geographical distribution of income. The failure to consider such distribution implies that, holding income constant, different spatial patterns provide the same inequality measure. This property is referred to as anonymity and presents an interesting question about the relationship between inequality and space. Particularly, spatial dependence could play an important role in shaping the geographical distribution of income and could be usefully incorporated into inequality measures. Following this idea, this paper introduces a new measure that facilitates the assessment of the relative contribution of spatial patterns to overall inequality. The proposed index is based on the Gini correlation measure and accounts for both inequality and spatial autocorrelation. Unlike most of the spatially based income inequality measures proposed in the literature, our index introduces regional importance weighting in the analysis, thereby differentiating the regional contributions to overall inequality. Starting with the proposed measure, a spatial decomposition of the Gini index of inequality for weighted data is also derived. This decomposition permits the identification of the actual extent of regional disparities and the understanding of the interdependences among regional economies. The proposed measure is illustrated by an empirical analysis focused on Italian provinces.

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Section: Introductionmentioning

confidence: 99%

Measuring the Spatial Dimension of Regional Inequality: An Approach Based on the Gini Correlation Measure

Panzera

Postiglione

2019

Soc Indic Res

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“…Academic and political interests in regional development have grown in recent decades, due to the persistence and growth of spatial inequalities (Wei, 2015). This can be observed not only throughout the empirical literature from developed countries (Gbohoui, Lam, & Lledo, 2019;Heidenreich & Wunder, 2008;Iammarino, Rodríguez-Pose, & Storper, 2018;Martínez-Galarraga, Rosés, & Tirado, 2015;Parente, 2019), but also from developing countries in Asia, Africa, and Latin America (Aroca & Atienza, 2016;Barrios & Strobl, 2009;Cuadrado-Roura & Aroca, 2013;ECLAC, 2010ECLAC, , 2016bKanbur, López Calva, & Venables, 2005;Kanbur, Venables, & Wan, 2006;Milanovic, 2005).…”

Section: Introductionmentioning

confidence: 84%

“…Meanwhile, Acevedo et al (2019), Borensztein et al (2014), Pardo Beltrán (2014), and Tezanos Vázquez (2012 study the development gaps for some Latin American countries. At a sub-national or regional level, we can also find several studies that examine development gaps between regions (Alberdi, Gibaja, & Parrilli, 2016;Czudec, Kata, & Wosiek, 2019;Niembro, 2015;Onofrei & Cigu, 2017;RIMISP, 2012RIMISP, , 2014Windhani & Hardoyono, 2017), while others provide a multidimensional view of spatial inequalities (Aboal et al, 2018;Antunez, Baccaïni, Guérois, & Ysebaert, 2017;Bin, 2016;Meyer, De Jongh, & Meyer, 2016;Niembro, 2020;Parente, 2019;Quadrado, Heijman, & Folmer, 2001;Quadrado, Loman, & Folmer, 2001).…”

Section: Analytical Framework and Empirical Literaturementioning

confidence: 93%

Regional development gaps in Argentina: A multidimensional approach to identify the location of policy priorities

Niembro

Sarmiento

2021

Regional Science Policy & Practice

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Spatial inequalities within Latin American countries have historically attracted the interest of academics, policymakers, and international agencies. This paper aims to provide a multidimensional diagnosis of provincial development gaps in Argentina, in order to identify the location of policy priorities. Therefore, we built a large database, which covers seven development dimensions, and applied multivariate analysis techniques to overcome some analytical limitations of previous studies. Results show the stability of provincial development gaps between 2003 and 2013 and some heterogeneity within geographic regions. Instead, cluster analysis offers a better classification of Argentine provinces according to their development gaps, which can help the government to prioritize the places where development policies are strategic.

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“…This measure has the pro of going beyond the traditional ones for inequality, focusing not only on the income distribution but also on those in additional domains considered fundamental by the HD economics approach (i.e., health and education). Matching Organisation for Economic Co-operation and Development (OECD) and Statistical Office of the European Communities (EUROSTAT) databases with the European Union Statistics on Income and Living Conditions (EU-SILC) survey and following Kovacevic (2010), it was calculated yearly in the period 2000-11 per 205 selected regions comparing the estimated human development index (HDI) with its adjustment to within-region inequalities (Parente, 2018). The adjustment is operated through the estimation of Atkinson measures of inequality in the achievements of each of the three considered domains covered by the HDI (i.e., health, education and income).…”

Section: Methodsmentioning

confidence: 99%

Inequality and social capital in the EU regions: a multidimensional analysis

Parente

2019

Regional Studies, Regional Science

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This paper focuses on the interactions between social capital and production structure in their relation with regional inequality. Combining survey data with available regional databases for the EU-15, it provides a panel data analysis of multidimensional inequalities based on the idea that social capital is a fundamental factor determining its regional levels, along with the economic specialization of regions. Results confirm an inverse relation between inequality and social capital. At the same time, they highlight the positive impact of production clusters on the regional environment. Findings suggest that the joint effect of predictors is ambiguous, and may so be dependent on the different sorts of local specialization and social capital endowment.

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A Multidimensional Analysis of the EU Regional Inequalities

Cited by 11 publications

References 14 publications

Measuring the Spatial Dimension of Regional Inequality: An Approach Based on the Gini Correlation Measure

Measuring the Spatial Dimension of Regional Inequality: An Approach Based on the Gini Correlation Measure

Regional development gaps in Argentina: A multidimensional approach to identify the location of policy priorities

Inequality and social capital in the EU regions: a multidimensional analysis

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