The aim of the article is to identify the main tendencies in the dynamics of interregional disparities in the level of socio-economic development in Russia during periods of economic growth and crisis. These trends have been identified on the basis of an analysis of the regional coefficient of asymmetry of key per-capita indicators (GDP, investment in fixed capital etc.) as well as indicators of spatial concentration in Russia and deviations from the average (for GDP, per capita GDP and investments) at the federal district and regional levels. The main factors driving the dynamics of these disparities were the economic crisis, government anti-crisis measures and measures of social support. Comparison of the level of interregional disproportions in Russia and abroad indicated that the differences between levels of socio-economic development at the federal level are comparable with differences in EU countries, but at the level of regions - with countries of the world.
Poland joined the European Union on 1 May 2004. By 2007, this had resulted in Poland being the greatest beneficiary of the European cohesion policy due to its low per capita GDP by purchasing power parity at the voivodeship level. The scale of European Structural and Investment Funds brought the possibility for a fundamental acceleration of socio-economic growth in Polish regions. The European Union gradually modified the directions of intervention under the framework of the European cohesion policy, initially orienting this activity principally towards cohesion, but from 2010 directing it mainly towards competitiveness. Of particular significance was the Europe 2020 strategy (2010). In Poland its arrangements were deferred until the signing of the Partnership Agreement for the period 2014-2020, which established extensive support for innovation, competitiveness and the R&D sector. In the final part of the paper, conclusions and recommendations for regional policy are elaborated.
considerably differentiated natural conditions. There is a negative cementing element of tremendous discrepancies between core and peripheral areas, which implicates discrepancies between rich and poor inhabitants. There is a relatively low value of GDPpercapita and, in particular, a low life expectancy, especially for men (only 65 years). Other problems include large transport distances and the low value added of industrial production, as well as the focus on the export of raw materials and arms systems.
The Australian - Oceanic macro-region has by far the
Cuaresma et al. (2014) tested the impact of 48 variables on the economic growth of 255 NUTS2 regions in the European Union between 1995 and 2005, using econometric models. The results of their research indicate the importance of education (10% increase in the share of workers with higher education is associated with a 0.4% higher growth rate of annual GDPpercapita) and location of the capital city within the region. The latter factor is particularly important in CEE countries (regions with capital cities grew on average 1.8 percentage points faster than
, data about tourists’ movements was obtained from the World Tourism Organization (UNWTO). Data about changes in GDP was acquired mainly from the World Bank Database, while data concerning the Human Development Index (HDI) came from the UN.
Figure 1 presents the selected island territories that have more than 10% of their GDP coming from tourism expenditure for 2012. Figure 2 presents the gross domestic product percapita in 2012 for the selected island territories.
Selected island territories with more than 10% of GDP coming from tourism
Lubuskie). It should be emphasized that the regional differentiation of GDPpercapita in Poland is stable long-term, and for many years the Mazowieckie voivodeship has been in first place, while in last place is Eastern Poland (the Lubelskie, Podkarpackie, Podlaskie, Świętokrzyskie and Warmińsko-mazurskie voivodeships) ( Map 3 ).
Number of migrated companies per 1,000 inhabitants in 2014. Source: own analysis on the basis of the data from GUS (Central Statistical Office)
Spatial distribution of companies that, following changes in 2014, had