The aim of this paper is to present results of spatio-temporal analysis ofunemployment rate in Poland, with the usage of advanced spatial econometricmethods. The analysis was done on data collected for ‘powiat’ level between2006 and 2010. GlS and ESDA tools were applied for visualization of the spatiotemporaldata and identification of spatial interactions between polish countieson labor market. Multi-equation spatial econometric models were used todescribe unemployment rate in relation to selected social-economic variables.
The aim of the paper is to apply the spatio-temporal Environmental Kuznets Curve (SpEKC) to test the relationship between economic growth and the amount of collected mixed municipal waste. The analysis was conducted at the level of sixty-six Polish sub-regions. The study contained selected environmental indicators. The dependent variable - the amount of municipal waste generated in kilograms per capita characterized the state of the environment. The GDP per capita in constant prices (as an explanatory variable) presented the level of economic development of the sub-regions. In the empirical part of the research there were used spatial panel data models based on EKCs. It determined the levels of economic development, at which the amount of produced wastes has fallen or increased, depending on the wealth of the region. The application of different types of spatial weight matrices was an important element of this modelling. Data obtained the years 2005-2012. Models were estimated in the RCran package.
The European Commission (EC) has identified active and healthy ageing (AHA) as a major societal challenge mutual to European countries. This issue has increased in importance due to the progressive ageing observed in European societies, that force authorities to take initiatives for support the activity of the elderly. One of the initiatives, widely recognised is The European Innovation Partnership on Active and Healthy Ageing, which strive to enabling EU citizens to lead healthy, active and independent lives while ageing.
The positive effect of actions for the AHA will be extension of the life in good health duration of EU citizens by two years by 2020. This is an important issue, as in 2013, women who have reached the age of 65 years in UE28 were facing on average 21.3 years of further life years and only 8.6 years (on average this amounted for 40.4 % of life expectancy) accounted for living in health, whereas for males, this ratio was estimated on 8.5 years in health of the anticipated further 17.9 years (47.5% of further life duration).
Life expectancy in good health in older age is influenced by many different factors, i.e. cultural, social, economic and accessibility to health services and the quality of provided treatment. The last aspect is related to both the economic development of the country and the health care system management. The significant factor that has been increasingly emphasised in documentation of World Health Organisation or European Commission, concerns the investment in public and individual health.
Taking into account the multivariate impact of objective and subjective factors on life expectancy in good health of elderly, the Authors decided to conduct the multidimensional comparative analysis for EU countries, including Norway, Switzerland and Iceland as well. Among the objective factors Authors distinguished: proportion of population (men and women) aged 65 years and more, economic development of the countries measured by GDP per capita, healthy life years expectancy in absolute values for males and females at 65 years, health care expenditures in PPS per inhabitant aged 65+, whereas the group of subjective characteristics consisted of: self-perceived health for people aged 65+ and self-reported unmet needs for medical services.
The article aims to investigate the relationship between the length of the further life in healthy for men and women aged 65 years and selected factors in European countries in the period 2005-2012. For this purpose, following methods were used: 1/ spatial distribution of characteristics - rates of change in selected periods: 2005 and 2012, 2/ tests for dependencies using correlograms and Spearman’s rank correlation coefficients, 3/ cluster analysis: on the basis of Ward’s methods spatial similarities (among countries) were indicated. As the source of data the Eurostat database were used.
Regional Innovation Strategies (RIS) have existed in Poland for almost ten years and in this period they have been developed, accepted and implemented in all provinces. The basic aim of Regional Innovativeness Strategies is to support regional or local authorities and other regional development organizations in defining and implementing an effective system of supporting innovativeness in the region. The current scope of realizing projects connected with RIS is different in particular provinces. The author of the paper attempts to evaluate the effects of implementation of pro-innovativeness solution included in Regional Innovation Strategies with particular consideration of their influence on the growth of region innovativeness level in Poland.
As spatial diversity of economic development is one of the main problems of modern economies, researchers have attempted to define the conditions and factors influencing this phenomenon. Among others, two intangible factors are suggested: human capital and social capital (Herbst ed. 2007).
The primary objective of this work is a spatial and spatio-temporal analysis of the diversification of human and social capital within the Polish NUTS 3 subregions. The two detailed targets are constructing composite indicator of both of the mentioned types of capital as well as examining spatial interactions between human capital, social capital and the GNP level per capita.
The large diversification of human and social capital in the Polish subregions has been confirmed. Clusters of regions with low levels of human capital have been indicated, whereas in the case of social capital a grouping of its high values was observed. The research also confirmed the positive correlation between GNP per capita and human capital, with high values of both variables in the larget cities. Additionally, there are some subregions with high levels of economic development surrounded by low levels of human and social capital (Łódź, Szczecin, Wrocław). It is possible that high level of GNPpc in these regions was the incentive causing the relocation of human capital from the neighbouring regions. The correlation between GNPpc and social capital, where significant, is of the low-high type. These subregions are located in the east and south of Poland.
, Cheltenham, UK
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