The aim of this study was to identify determinants of the population ageing process in 270 European cities. We analyzed the proportion of older people: men and women separately (aged 65 or above) in city populations in the years 1990–2018. To understand territorially-varied relationships and to increase the explained variability of phenomena, an explanatory spatial data analysis (ESDA) and geographically weighted regression (GWR) were applied. We used ArcGIS and GeoDa software in this study. In our research, we also took into account the spatial interactions as well as the structure of cities by size and level of economic development. Results of the analysis helped to explain why some urban areas are ageing faster than others. An initial data analysis indicated that the proportion of the elderly in the population was spatially diversified and dependent on gender, as well as the size and economic development of a unit. In general, elderly individuals were more willing to live in larger and highly developed cities; however, women tended to live in large areas and men in medium-sized to large urban areas. Then, we conducted the urban ageing modelling for men and women separately. The application of GWR models enabled not only the specification of the city population ageing determinants, but also the analysis of the variability in the strength and direction of dependencies occurring between the examined variables in individual cities. Significant differences were noted in the analysis results for specific cities, which were often grouped due to similar parameter values, forming clusters that divided Europe into the eastern and western parts. Moreover, substantial differences in results were obtained for women and men.

This study aims to extract and explain the territorially varied relation between socioeconomic factors and absence rate from work due to own illness or disability in European countries in the years 2006–2020. For this purpose, several causes were identified, depending on men and women. To explain the absenteeism and emphasize gender as well as intercountry differences, geographically weighted regression was applied. For men, there were five main variables that influenced sickness absence: body mass index, the average rating of satisfaction by job situation, employment in the manufacturing sector, social benefits by sickness/health care, and performing health-enhancing physical activity. For women, there were five main variables that increased the absence rate: the risk of poverty or social exclusion, long-standing illness or health problems, employment in the manufacturing sector, social protection benefits, and deaths due to pneumonia. Based on the conducted research, it was proven that the sickness absence observed in the analyzed countries was highly gender and spatially diverged. Understanding the multifactorial factors playing an important role in the occurrence of regional and gender-divergent sickness absence may be a good predictor of subsequent morbidity and mortality as well as be very useful to better prevent this outcome.

In this article, we analyzed the dynamics of the population aging process in Europe. The study was conducted on the basis of statistical data on the number of people aged 65 and above per 1000 of the population in 32 European countries in the years 1991–2018. The analyses also took into account the structure of the population by gender in five age groups: 65–69, 70–74, 75–79, 80–84, and 85 and above. An extensive analysis of the rate of changes in the magnitude of the phenomenon was carried out, which gave an answer to the question about how quickly Europe is aging. We applied the spatial dynamic shift–share method. The spatial variant of the method allowed, among others, indicating countries where the pace of population aging in a specific age group was faster/slower than in locations neighboring the examined country. Specific regions characterized by the fastest population aging were also indicated, and shares of structural and sectoral factors of the changes were estimated. Furthermore, based on the values of local competitiveness indicators, regions were identified where the aging of the population decelerated or accelerated the phenomenon in neighboring countries in the study period.

We analyse to what extent spatial interactions affect the labour market matching process. We apply spatial econometrics methods (including spatial panel Durbin model), which are rarely used in labour market matching analysis. We use the data on stocks and the inflows of unemployed individuals and vacancies registered at public employment offices. We conduct the analysis at the NUTS-3 and the NUTS-4 levels in Poland for the period 2003-2014. We find that (1) spatial dependency affects matching processes in the labour market; (2) both close and remote spatial interactions influence the results of the matching process; (3) spatial indirect, direct, and total spillover effects determine the scale of outflows from unemployment; and (4) spatial modelling is a more appropriate approach than classic modelling for matching function.

This article provides a quantification of the territorially varied relation between socio-economic factors and the amount of municipal waste in Polish districts. For this purpose, eight causes were identified: revenue budgets, the number and area of uncontrolled dumping sites, population density, the share of working-age population, average gross monthly wages, registrations for permanent residence, and the number of tourists accommodated. The preliminary data analysis indicated that to understand waste generation in Poland at the local level it is necessary to consider regional specificity and spatial interactions. To increase the explained variability of phenomena, and emphasise local differences in the amount of waste, geographically weighted regression was applied.

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We examine the extent to which spatial interactions affect the labour market matching process at the regional level using three frameworks: random, stock-flow, and job-queuing. We study the underexplored, regionally diversified former transition country of Poland at the LAU-1 level over the 2003-2014 time period using registered monthly data. We employ a Durbin panel model with spatial error. Results prove that spatial interactions affect matching and that the stock-flow model is in some respects superior to others in explaining the job creation process. Unemployment affects matching more than vacancies. The newly unemployed may seek jobs in adjacent regions and can cause congestion there, but vacancies tend to attract workers from adjacent markets and exert positive externalities. Policy actions should be aimed at exploiting spatial interdependencies to improve matching at the regional level by increasing the number of available job offers, improving information, and increasing labour force mobility.