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The wealth–health relationship is not unambiguous and constant. Indeed, a higher level of wealth affects individual and population health in two opposite ways. Increased risk factors raise the probability of some diseases especially non-communicable diseases (NCDs). Conversely, better healthcare and awareness reduce the chances of developing these diseases or raise the likelihood of treatment and cure. Therefore, the overall impact on health or the ‘net effect’ of wealth (positive or negative) may be challenging to determine. Moreover, this effect may not be fixed for different income groups. Thus, it states to reason that there may exist an ‘affluence point’ changing the predominant impact of wealth (positive/negative), which we will refer to as the ‘health economic threshold’. This paper aims to assess and quantify the hard-to-grasp overall impact of prosperity on the mortality of selected NCDs in European regions. In particular, we attempt to estimate both the net effect of affluence and the health economic threshold of GDP-mortality relationship, by developing a dedicated analytical tool. The original idea is based on joinpoint regression and forecasting methods. To our knowledge, no such study has been performed in health economics. Our results show that in the case of most investigated diseases in more impoverished regions, mortality rises with prosperity. After crossing the health economic threshold of around 20 thousand euros per capita, the trend changes (it stabilises or reverses).

This paper investigates factors of innovation and their role in regional economic performance for a sample of 261 EU NUTS 2 regions over 2009–2012. In our study, we identify regions with spillover as well as drain effects of innovation factors on economic performance. The spatial analysis indicates that both regional innovativeness and regional development, are strongly determined by the region’s location and neighbourhood, with severe consequences for the Eastern and Central Europe. We assessed the impact of innovation factors and their spatial counterparts on economic performance by spatial Durbin panel model. The model is designed to test the existence and strength of country-effect of innovativeness on the level of regional economic status. This allows for controlling the country-specific socio-economic factors, without reducing the number of degrees of freedom. Our model shows that regions benefit economically from their locational spillovers in terms of social capital. However, the decomposition of R&D expenditures revealed competition effect between internal R&D and external technology acquisition favouring in-house research over the outsourced ones.

The main objective of this paper is to introduce a new approach to measuring efficiency in regional studies through SDEA (Spatial Data Envelopment Analysis). The paper offers a proper mathematical elaboration and a highlight of the differences between classic DEA and newly developed method. Presented literature review and the discussion on theoretical examples, proves the need for new solution to measure regional efficiency, which takes into consideration region-specific spatial context. The introduced method, SDEA, incorporates explicitly spatial interactions (via the 𝐗� matrix) into the model through spatially weighted inputs and outputs. An example study concerning healthcare efficiency of European regions will be presented to illustrate SDEA. We confront the efficiency results of classic DEA with results of SDEA, which is expanded by the spatial equivalents of inputs and outputs. The results suggest that classic DEA undervalues the regional healthcare efficiency, underestimating the region-specific spatial context.

This paper presents a fundamentally improved statement on asymptotic behaviour of the well-known Gaussian QML estimator of parameters in high-order mixed regressive/autoregressive spatial model. We generalize the approach previously known in the econometric literature by considerably weakening assumptions on the spatial weight matrix, distribution of the residuals and the parameter space for the spatial autoregressive parameter. As an example application of our new asymptotic analysis we also give a statement on the large sample behaviour of a general fi xed effects design.

Innovation constitutes an important factor for growth in all EU countries. Regions of the EU play a principle role in shaping new innovation trajectories and in bringing out the hidden potential for national growth. However, it is not only the level of innovation that diversifies regions, but also the innovative potential and the level of its realization. Therefore, the aim of this paper is to assess the realization of innovative potential, defined as effectiveness, in EU NUTS 0 and, if possible, NUTS 2 regions. To accomplish this goal a relative effectiveness method in used. The DEA (Data Envelopment Analysis) makes it possible to analyse the relative technical effectiveness based on regional inputs and outputs, without incorporating the legal and technological specifications of innovations, thus treating it like a production process. The inputs of the process are employment in technology and knowledge-intensive sectors and R&D expenditure, while the outputs include the number of patents and GDP. All variables are standardized by the size of the economically active population. DEA results divide regions in to two groups – effective, being the leaders; and ineffective, or followers. The DEA approach was combined and extended by ESDA (Exploratory Spatial Data Analysis) in order to pinpoint spatial patterns of innovation efficiency across NUTS 2 regions. Defining the best practices and implementing the learning-from-the-best policy is important in the process of regional development and specialization.

Diseases of affluence (diseases of the XXI c., Western diseases) by definition should have higher prevalence and/or mortality rates in richer and more developed countries than in poorer, underdeveloped states. Therefore, it has been indicated that it is the civilizational progress (directly or indirectly via changes in lifestyle, diet, physical activity, stress, etc.) that stimulates epidemic outbreaks of some illnesses (cancer, diseases of respiratory and cardiovascular systems, diabetes, mental disorders). On the other hand substantial financial resources, highly qualified medical personnel, and cutting-edge technology of richer states, should allow for effective preventions, diagnostics, and treatment of these diseases. The European Union as a whole, as well as all its member states and their regions, may be considered “highly developed” in economic sense. Does it, however, mean that EU can be perceived as homogeneous in the sense of diseases of affluence epidemiology? Are the relatively small differences in economic regional development (compared to worldwide inequalities) significant factor in the spatial distribution of diseases of affluence? To evaluate the possible dispersion in the epidemiology of some of potential Western diseases and their relation with regional development tools of spatial statistics have been incorporated. The research covers 261 EU NUTS 2 regions for the years 2003-2010. These research may provide some answers to the existence and epidemiology of hypothetical diseases of affluence as well as in recognizing spatial patterns of prevalence and mortality rates for these illnesses.

For years now, developed countries face an epidemic of high blood pressure, diabetes and high cholesterol, risk factors related to heart and circulatory disease, and a suite of psychological disorders ranging from depression, anxiety, to compulsive behaviours. These health risks have traditionally been associated with affluence however by 2008 there is no clear link between national income and these diseases. E.g. according to Danaei, there was no relationship between national income and blood pressure in men, and in women blood pressure was even higher in poorer countries. Therefore, this paper provides deepen analysis of this correlation applying spatial econometrics tools. The spatial aspect of the prevalence of western diseases does not seem to be obvious and, to our knowledge, is not widely explored in the literature. In particular, the paper investigates the spatial processes of selected diseases of affluence in regions of the European Union. The research covers 261 NUTS 2 regions for the period 2003-2010. This study provides the spatial analysis of Circulatory and Mental diseases. In our opinion, the presented spatial econometric approach may constitute an important contribution to the field of epidemiology.

This paper is an attempt to explain variations across EU regions in productivity growth and takes into consideration the important structure of the age-productivity relation of Human Capital. The study is fundamentally based on the theory of Fingleton?s model which analyses the spatial process of productivity growth on the on the foundations of the theory of New Economic Geography. The applied specification links manufacturing productivity growth to the growth of manufacturing output by the means of Verdoorn?s law. The model incorporates productivity-adjusted human capital understood as Total Human Capital Productivity corrected with age structure with the use of productivity as a function of age. Moreover, a new approach to defining the age-productivity curve has been introduced. Based on the previous studies found in the literature the age-productivity function has been interpolated by the means of Radial Basis Function method with thin-plate spline. The age-productivity function allows to describe how the work performance differs over the life period and thus allows for differences in age structure of employees in regions under research. This study covers 261 NUTS 2 regions of EU excluding some French, Portuguese and Spanish regions due to their isolated position and Croatia because of the lack of comparable data. All data used in the empirical part of this study are published by Eurostat and refer to the years 2000-2013. The regional productivity is explained by the quotient of regional GDP and the number of Economically Active Population. The productivity growth is approximated by the exponential change of regional productivity in these years to regional productivity in the year 2000. The regional GDP is expressed in millions of Euro in constant prices (year 2000), where Economically Active Population is in thousands of people 15 years or over. The Human capital is defined by the Employment in Technology and Knowledge-intensive Sectors as a percentage of Economically Active Population. The model has been tested through implemented methodology, namely a spatial panel model with fixed effects. The model presented provides evidence of the importance of increasing returns to scale for regional economic growth, which lead to divergence effects for EU regions. Similar implications can be observed in the case of regionally differentiated human capital. Furthermore, the country fixed effects turned out to be significant. The findings also suggest that productivity in jobs requiring problem solving and learning skills reaches a plateau for the 35-45 age bracket and has its peak around the age of 40. We suggest that the applied approach constitutes an innovation providing additional information hence a deeper analysis of the investigated problem.

This study presents some remarks on procedure for space-time process investigation by the use of multidimensional panel spatial autoregressive model. It is shown that information on the strength and significance of the spatial interactions is given by the model. Motivation for the use of multidimensional dependence structure as well as some empirical examples are provided. It is argued that such approach could allow for more accurate description of the spatial dependence, whose true form often has a spatio-temporal character. It is emphasised that failure to recognise these multidimensional effects may lead to incorrect inference and therefore to biased conclusions. Przedmiotem referatu jest ocena procesu przestrzenno-czasowego z zastosowaniem wielowymiarowej macierzy wag przestrzennych. W szczególności zakłada się, że podejście wielowymiarowe pozwala na lepszy opis struktury zależności przestrzennych. Praca ma na celu pokazać nowo opracowaną metodologię dotyczącą wielowymiarowego autoregresyjnego modelu przestrzennego WAMP z uwzględnieniem wymiaru czasowego. Zatem całość rozważań stanowi nowy element ekonometrii przestrzennej, a poprzez włączenie dodatkowej informacji na temat badanego zjawiska umożliwia wnikliwszą jego analizę.