fiscal flows, this study has not been paralleled
until today. See report in Zimmermann 2011 , p. 664. Table 2 therefore shows the results of that earlier study, which took the Land of Rheinland-Pfalz as an example of differences between rich and poor regions and cities. Trier and its surroundings were at the time an example of relatively poor German areas, whereas Ludwigshafen, dominated by a large chemical complex, was and is among the rich regions. This can be seen by the difference in GDPpercapita at that time, which was more than 1 to 2.
EUROSTAT (2016a): Final consumption expenditure of households, by consumption purpose , code: tsdpc520, [online] [Retrieved November 12, 2016]. Available at: http://ec.europa.eu/eurostat/en/web/products-datasets/-/TSDPC520
EUROSTAT (2016b): GDPpercapita in PPS , code: tec00114, [online] [Retrieved November 12, 2016], Available at: http://ec.europa.eu/eurostat/web/products-datasets/-/tec00114
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that there is dependence between government military expenditure and the shadow economy. This effect is very robust and it remains significant in many alternative specifications of the model after a number of socioeconomic factors are controlled for, such as level of corruption, rule of law and GDPpercapita. There are several possible explanations for these results. First, military-controlled property, such as land and real estate, can be misused for illegal economic activities ( Gupta et al., 2001 ). Furthermore, military expenditures are the most opaque
products, followed by the introduction of less costly methods of production and new or improved products. These responses led eventually to an increase in the rate of growth of percapitaGDP, substantial enough to resume the process of catching up with the percapitaGDP levels of the technologically advanced economies.
3 The unexpected developments in Poland
During the last 25 years the rate of growth of percapitaGDP in Poland was about twice as high as in the part of the world economy which I call the Technology Frontier Area (TFA). The TFA includes, above all
-level, which refers to the states as a whole, the statistical units range from Germany with more than 82 million inhabitants to Luxembourg with less than half a million. The same is true for the regional units: The NUTS3-level subsumes regions with a population size between about 20 000 and 5 million. Because of these differences the empirical findings on national and regional disparities should be handled with care and interpreted thoroughly.
Regarding the national differences in GDPpercapita the gap between the rich and the poor member states of the EU15 has clearly
This article analyses the convergence across Polish regions between 2005-2011. Its theoretical and empirical character determined the choice of research methods. The theoretical part includes an analysis of the literature devoted to the convergence theory, and the empirical part is based on statistical surveys. Statistical data used in the article was taken from the following databases: for the United Kingdom - Office for National Statistics; for Finland - Statistic Finland; for Poland and the rest of the countries - Statistical Yearbook of the Regions - Poland from 2005 to 2013. The studies confirmed that in Poland a strong concentration of economic activity took place in analyzed period. The convergence of per capita GDP did not apply. Rich regions grew faster than poor ones. The convergence of labour productivity did not apply either. The divergence of the K/L relation determined the divergence of labour productivity in the analyzed period. In the last part of the article the author analyzed the convergence across regions in EU countries. In case of countries that gained the accession to the EU on 1 May 2004, convergence did not apply. On the other hand, rich countries of EU like Austria, Belgium or the Netherlands confirmed the phenomenon of convergence at the NUTS level in analyzed period.
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