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Quantitative Assessment of the Dynamics of Changes in the GDP Per Capita Index in the Regions of Bulgaria and Slovakia

References/Literatura: 1. Lewandowski, K. (2011). Ocena polityki regionalnej Unii Europejskiej w świetle badań empirycznych. Gospodarka w teorii i praktyce, 2 (29), 53-64. 2. Łaźniewska, E., Górecki, T., Chmielewski, R. (2011). Konwergencja regionalna . Poznań: Wydawnictwo Uniwersytetu Ekonomicznego w Poznaniu. 3. Puga, D. (2001). European Regional Policies in Light of Recent Location Theories. CEPR Discussion Paper Series , 2767, 14-15. 4. Surówka, A., Prędka, P. (2016). PKB per capita jako wyznacznik rozwoju ekonomicznego regionów

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Models With Varying Parameters as A Tool to Classify Polish Voivodships in 2002-2008

Models With Varying Parameters as A Tool to Classify Polish Voivodships in 2002-2008

One of the often used measures of economic development is gross domestic product per capita. In Poland the Main Statistical Office collects the data on this variable on several levels of aggregation. The paper shows the application of panel data models in order to classify Polish voivodships according to the level of economic development. As explained variable the regional GDP per capita was used and such variables as structure of employees, unemployment rate or retail sales per capita were the explaining variables. As a result the groups of voivodships with similar pattern of economic development were distinguished.

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Exploring Economic Growth and Environment Nexus in Nine Southeastern European Countries

policy , 37 (1), 246-253. doi: 10.1016/j.enpol.2008.08.025 unctadstat.unctad.org. (2017). US Dollars at constant prices (2005) per capita . [online] Available at: http://unctadstat.unctad.org/ [Accessed 6 Sep. 2017]. unfccc.int. (2018). The Paris Agreement - main page . [online] Available at: http://unfccc.int/paris_agreement/items/9485.php [Accessed 8 June 2018].

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Factors Which Influence the Growth of Creative Industries: Cross-section Analysis in China

Factors Which Influence the Growth of Creative Industries: Cross-section Analysis in China

With the more and more important roles of creative economy, its research has become one of the major fields in economic development. The creative economy has the potential to generate income and jobs while promoting social inclusion, cultural diversity and human development. As a developing country, China is also in need of developing the creative economy to adjust the economic structure and realize the sustainable development. In this paper, we examined the factors which influence the growth of creative industries in China through the cross-section analysis of 23 regional data in 2007. Four main factors were examined in this multi-regression model, that is, GDP per capita, the number of higher education institutions, the number of students enrolled in higher education institution and the number of patents.

The statistical analysis found that the model's fit is quite good and 69% of dependent variable (the ratio of value-added of creative industries to the GDP) is explained by the model. Specifically, there are three sub-conclusions. Firstly, there is not a positive relationship between GDP per capita and dependent variable; on the contrary, there is a weak negative relationship in this model. We infer that it is because of the stage of economic development. China heavily depends on the development of the second industry in the process of industrializing. Secondly, there is no linear relationship, in this model, between the number of higher education institutions and the dependent variable. We infer that it is mainly due to the differences between the higher education institutions, such as scales and qualities, etc. Thirdly, there is enough evidence to conclude that the number of students enrolled in higher education institution and the dependent variable are linearly related; the number of patents and the ratio of creative industries are linearly related, as well. They represent the important roles of talents and technology in the development of creative industries.

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Growth Effects of Cross-border Mergers and Acquisitions in European Transition Countries

Abstract

This paper deals with the economic effect of cross-border mergers and acquisitions on GDP per capita in European transition countries for the 2000- 2014 period. Our analysis shows that cross-border mergers and acquisitions have a negative effect on GDP per capita in the current period, whereas their lagged level positively impacts output performance. We found that transition countries characterized by a higher quality of institutional setting have achieved a positive impact on GDP per capita.

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Economic Growth and Environmental Quality in the European Union Countries – Is there Evidence for the Environmental Kuznets Curve?

., Krueger, A. B. (1991), Environmental impacts of a North American free trade agreement, Working Paper, 3914, National Bureau of Economic Research. Grossman, G. M., Krueger, A. B. (1993), Environmental impacts of the North American free trade agreement, in Peter Garber (ed.), The U. S.‑Mexico Free Trade Agreement, MIT Press, Cambridge. Hannesson, R. (2009), Energy and GDP growth, International Journal of Energy Sector Management, 3 (2), pp. 157-170. Hausman, J. A. (1978), Specification tests in econometrics, Econometrica, 46

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The Importance Of Human Capital For The Economic Development Of EU Regions

Abstract

The EU designs its cohesion policy with the primary purpose of reducing disparities in regional development. The success of the policy is largely determined by the identification of factors that contribute to such disparities. One of the key determinants of economic success is human capital. This article examines the relationship between the quality of human capital and economic development of EU’s regions. Using spatial analysis methods, the spatial dependencies between the growth of human capital and GDP per capita are investigated.

According to the research results, the highest levels of human capital are typical of the most affluent regions in Western Europe, while its lowest levels are found in the poorest countries that became EU members only recently and in countries in southern Europe, including Greece. The spatial correlation measures confirm that spatial relationships have effect on the regional resources of human capital, showing that regions rich in human capital border on regions that are similar to them in that respect. The results of the spatial growth regression indicate that the amount of human capital in the region has a significant and positive effect on its GDP per capita.

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Convergence in GDP per capita across the EU regions— spatial effects

Abstract

The aim of this paper is to offer an empirical insight into the spatial effects of growth of regional income and disparities across EU regions (NUTS 2). Since regions are spatial units and there are interrelated standard linear regression is not sufficient to evidence the convergence process. Two models (Spatial Lag Model – SLM and Spatial Error model – SEM), derived from spatial econometrics, have been used to identify and explain spatial effects in convergence clubs—all EU countries (EU-28), countries that entered the EU in 2004 (EU-13) and countries that were in EU prior to 2004 (EU-15). Unconditional and conditional β-convergence has been examined in the period 2000-2015 thus covering two financial perspectives (including n + 2 rule3). Dummy variables have been also applied to catch the country-specific effects, such as national policies, legislation, technology progress, etc.

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Forecast of Carbon Dioxide Emissions from Energy Consumption in Industry Sectors in Thailand

Abstract

The aim of this research is to forecast CO2emissions from consumption of energy in Industry sectors in Thailand. To study, input-output tables based on Thailand for the years 2000 to 2015 are deployed to estimate CO2emissions, population growth and GDP growth. Moreover, those are also used to anticipate the energy consumption for fifteen years and thirty years ahead. The ARIMAX Model is applied to two sub-models, and the result indicates that Thailand will have 14.3541 % on average higher in CO2emissions in a fifteen-year period (2016-2030), and 31.1536 % in a thirty-year period (2016-2045). This study hopes to be useful in shaping future national policies and more effective planning. The researcher uses a statistical model called the ARIMAX Model, which is a stationary data model, and is a model that eliminates the problems of autocorrelations, heteroskedasticity, and multicollinearity. Thus, the forecasts will be made with minor error.

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The role of entrepreneurship in explaining the real Gross Domestic Product per capita: Regression model selection

and GDP per capita. In Bendekovic, M., Klacmer Calopa, M. and Filipovic, D. (Eds.) “Economic and Social Development”, Book of Proceedings of the 6th International Scientific Conference on Economic and Social Development and 3rd Eastern European ESD Conference: Business Continuity, Vienna, 25-25 April 2014.Varazdin, Croatia: Varazdin Development and Entrepreneurship Agency. ISBN: 978-953-6125-10-4. pp. 507-516. 16. Harris, R. (1995). Using Cointegration Analysis in Econometrics Modelling. Prentice Hall/Harvester Wheatsheaf, Upper Saddle River

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