Forthcoming Transatlantic Trade and Investment Partnership may become a crucial element in the post-crisis world of economics and politics. Consequently, it is worth scrutinizing the economic performances of the United States and the European Union. This paper is aimed at answering the question of whether absolute GDP per capita beta-convergence exists in the case of regions of the United States (US counties) and in the case of regions of the EU-28 (NUTS 3) during the period of 2000-2011 and where (in the United States or in the EU-28) speed of convergence used to be faster. The research is based on econometric models, namely on the spatial lagged model (SLM) and spatial error (SEM) which contrary to ordinary the least squares (OLS) model include spatial dependencies. The SLM and SEM models detect the absolute GDP per capita beta-convergence among regions of US and among regions of the EU-28.During the period of 2000-2011 the average annual speed of convergence among regions in US was faster than between the regions of the EU-28. Moreover, according to the statistical analysis the EU-28 regions are characterized by stronger spatial dependencies than regions of the United States. But disparities (according to the results of general sigma-convergence analysis) among the EU-28 regions are bigger than among the US regions.
References
Arbia, G., 2006, Spatial Econometrics. Statistical Foundations and Applications to Regional Convergence, Springer, Berlin.
Baumol, W., 1986, Productivity Growth, Convergence and Welfare: What the Long-run Data Show?, American Economic Review, vol. 76, no. 5, s. 1072-1085.
Genc, I.H., Miller, J.R., Rupasingha, A., 2011, Stochastic Convergence Tests for US Regional per capita Personal Income; Some Further Evidence: a Research Note, The Annals of Regional Science, vol. 46, no. 2, s. 369-377.
Higgins, M.J., Levy, D., Young, A.T., 2006, Growth and Convergence across the U.S.: Evidence from County Level Data, The Review of Economics and Statistics, vol. 88, no. 4, s. 671-681.
Kopczewska, K., 2006, Ekonometria i statystyka przestrzenna z wykorzystaniem program R CRAN, CeDeWu, Warszawa.
Mikulić, D., Lovrinčević, Ž., Nagyszombaty, A.G., 2013, Regional Convergence in the European Union, New Member States and Croatia, South East European Journal of Economics and Business, vol. 8, no. 1, s. 7-19.
Misiak, T., Jabłoński, Ł., 2013, Realna konwergencja między regionami Unii Europejskiej w latach 1995-2008, Studia Prawno-Ekonomiczne, nr 88, s. 267-292.
Paas, T., Kuusk, A., Schlitte, F., Vork, A., 2007, Econometric Analysis of Income Convergence in Selected EU Countries and Their NUTS 3 Level Regions, University of Tartu - Faculty of Economics and Business Administration Working Paper Series, no. 60, s. 1-56.
Pelkmans, J., Hamilton, D.S. (eds.), 2015, Rule-Makers or Rule-Takers? Exploring the Transatlantic Trade and Investment Partnership, Rowman & Littlefield International, Ltd., London.
Rey, S.J., Montouri, B.D., 1999, US Regional Income Convergence: A Spatial Econometric Perspective, Regional Studies, vol. 33, no. 2, s. 143-156.
Suchecki, B. (red.), 2010, Ekonometria przestrzenna. Metody i modele analizy przestrzennych, Wydawnictwo C.H. Beck, Warszawa.
Supińska, J., 2013, Does Human Factor Matter for Economic Growth? Determinants of Economic Growth Proces in CEE Countries in Light of Spatial Theory, Bank i Kredyt, vol. 44, no. 5, s. 505-532.