The article presents the problem of the application of spatial weight matrix based on economic distance in spatial analysis of the intersectoral mobility of labor and wage. The spatial weight matrix expresses potential spatial interactions between the researched regions and forms a basis for further construction of spatial econometric model. Calculations of economic distance were based on the level of chosen measure of labor or wage mobility (respectively), whereas in the spatial model data of their chosen determinants were used (such as the level of unemployment, the average earnings, the level of institutionalism, the index of wage or income inequality). Wide time spectrum of the analysis was obtained thanks to the measure of mobility based on a transition probability matrix estimated with the use of the analysis of Markov processes for aggregated data. Because of the availability of homogeneous, highly aggregated sectoral data only for the period 1994–2010, the analyses were performed for 19 selected OECD countries.
PL
Głównym celem opracowania jest przedstawienie możliwości zastosowania macierzy sąsiedztwa, opartej na odległości ekonomicznej, w prowadzonych przez autora analizach wiążących międzysektorową mobilność płac oraz zatrudnienia. Obliczenia odległości ekonomicznej oparto na poziomie PKB, natomiast w modelu przestrzennym wykorzystano dane dotyczące ich wybranych determinant, np. wskaźnika nierówności płacowych, przeciętnego poziomu płac, stopy bezrobocia, miernika instytucjonalizmu. Przekrój czasowy analizy uzyskano dzięki zastosowaniu mierników mobilności bazujących na macierzy prawdopodobieństw przejść oszacowanych z użyciem procesów Markowa dla danych zagregowanych. Z uwagi na dostępność jednorodnych, wysoce zagregowanych (do poziomu sektora) danych jedynie dla lat 1994–2010 ograniczono się do przeprowadzenia analizy wyłącznie dla 19 wybranych krajów OECD.
Research background: Brands are considered to be the most valuable asset of a company. Some of them achieve spectacular global results. The significance of global brands is proved by the fact that their value is often greater than the sum of all company's net assets. Purpose of the article: The aim of this article is to highlight that brand value does not only create company's value, but also leverages economies. The Authors claim that even though global brands are sold worldwide and are a part of 'global factories', they strongly relate to the development of economies in the countries where these brands' headquarters are located. Methods: Based on 500 Brandirectory, the Most Valuable Global Brands ranking powered by Brand Finance, an analysis of spatial autocorrelation of brand values, GDP per capita was performed and also the interdependency between them was illustrated with the use of the spatial cross-regressive model (SCM). The SCM approach allowed us to include spatial effects of brand values into the final form of the estimated equation. The empirical analysis was performed for 33 countries in 2014. Findings & Value added: Findings confirm the hypothesis that there is a highly statistically significant relationship between brand value and GDP per capita and, what's more, it is observed that spatial dependencies matter for brand values. The evidence is based on the results of spatial cross-regressive model (SCM).
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