Suchergebnisse

The paper focuses on quantitative assessment of the innovation's role in explaining regional disparities and convergence in Europe. The empirical part of the study bases on the regional GDP pc and innovation indicators on the EU-27 NUTS2 level regions. Based on the selected set of initial innovation indicators for the 262 EU NUTS2 level regions and using the principal components factor analysis method, three composite indicators of regional innovation capacity are extracted. They explain around 80 % of the variation of the initial innovation indicators. The preliminary research results show that around 60% of variability of regional GDP per capita is explained by composite indicators of regional innovation performance and additional 20% are country specific factors. Estimating convergence equations, we noticed that regional innovations tend to increase inter-regional differences, at least during the short-run period. Thus, if regional income convergence is a policy target, additional policy measures beside innovation activities should be effectively implemented

Using data from the OECD Regional Well-Being Index - a set of quality-of-life indicators measured at the sub-national level, we construct a set of composite well-being indices. We analyse the extent to which the choice of five alternative aggregation methods affects the well-being ranking of regions. We find that regional inequality in these composite measures is lower than regional inequality in gross-domestic product (GDP) per capita. For most aggregation methods, the rank correlation across regions appears to be quite high. It is also shown that using alternative indicators instead of GDP per capita would only have a small effect on the set of regions eligible for aid from EU Structural Funds. The exception appears to be an aggregation based on how individual dimensions of welfare relate to average life satisfaction across regions, which would substantially change both the ranking of regions and which regions would receive EU funds.

AbstractUsing data from the OECD Regional Well‐Being Index – a set of quality‐of‐life indicators measured at the sub‐national level – we construct a set of composite well‐being indices. We analyze the extent to which the choice of five alternative aggregation methods affects the well‐being ranking of regions. We find that regional inequality in these composite measures is lower than regional inequality in real GDP per capita. For most aggregation methods, the rank correlation across regions appears to be quite high. It is also shown that using alternative indices instead of GDP per capita would only have a small effect on the set of regions eligible for aid from EU Structural Funds. The exception appears to be an aggregation based on how individual dimensions relate to average life satisfaction across regions, which would substantially change both the ranking of regions and which regions would be eligible for EU funds.

Using data from the OECD Regional Well-Being Index - a set of quality-of-life indicators measured at the sub-national level, we construct a set of composite well-being indices. We analyse the extent to which the choice of five alternative aggregation methods affects the well-being ranking of regions. We find that regional inequality in these composite measures is lower than regional inequality in gross-domestic product (GDP) per capita. For most aggregation methods, the rank correlation across regions appears to be quite high. It is also shown that using alternative indicators instead of GDP per capita would only have a small effect on the set of regions eligible for aid from EU Structural Funds. The exception appears to be an aggregation based on how individual dimensions of welfare relate to average life satisfaction across regions, which would substantially change both the ranking of regions and which regions would receive EU funds.

The paper presents a legislative and institutional framework of regional development in Serbia, as well as the analysis of regional disparities across different geography, elaborated through six selected indicators: population and population density (analyzed as a single indicator), Regional GDP, Employment, Unemployment, Business Demography and Budgetary Revenues per capita. Indicators are analyzed at the all three NUTS, as defined by the Law on Regional Development, and at the local level (cities and municipalities). In the analysis, the paper applies the unweighted Gini index of regional disparity that was used in calculating disparities within each observed indicator. The results are presented within the tables and figures that show changes of the Gini index across years of observation. The paper showed that highest regional disparities in Serbia are at the local and NUTS 3 level. Disparities at those two levels are stable across the years. Disparities at NUTS 1 and NUTS 2 levels are also high, yet lower than at the bellow territorial levels (NUTS 3 and the local level). However, disparity at these two levels grows much faster than at the NUTS 3 and local level.

The book is aimed at a wide audience, including academics, economic geography, spatial planning and regional policy researchers, institutional leaders and managers, national and institutional policy makers, practitioners, administrators, master's and senior bachelor's students on related courses, general readers. A list of courses and corresponding programmes in Geography, Planning, Economics and Management will be prepared later.