Celem pracy jest przedstawienie zmian poziomu rozwoju gospodarczego województw w okresie 2010–2019. Utrzymujące się od lat dysproporcje przestrzenne w zakresie rozwoju jednostek terytorialnych uzasadniają potrzebę takich badań, zwłaszcza w kontekście polityki wyrównywania regionalnych różnic rozwojowych. Badania prze- prowadzono na podstawie danych GUS. Zastosowano metodę porządkowania liniowego obiektów wielowymiarowych w ujęciu dynamicznym. Na podstawie dynamicznego miernika syntetycznego, który obejmował 13 wybranych cech diagnostycznych, sporządzono rankingi województw oraz obliczono skalę zmian w poziomie rozwoju województw. Badania potwierdzają dysproporcje w rozwoju gospodarczym województw. W 2019 r. najlepsza sytuacja pod względem przyjętego zestawu cech diagnostycznych panowała w województwach: mazowieckim, śląskim, dolnośląskim, pomorskim i wielkopolskim, a najniższe wskaźniki odnotowano w województwach: lubelskim, podlaskim, warmińsko--mazurskim i świętokrzyskim. We wszystkich województwach poziom rozwoju gospodarczego wzrósł w badanym okresie, ale skala zmian była nierównomierna. Największy wzrost wskaźnika poziomu rozwoju nastąpił w województwach o najwyższym poziomie rozwoju, w wyniku czego wzrosły dysproporcje między województwami pod względem badanego zjawiska.

The aim of the work is to compare the level of progress of social development in voivodeships during the period 2010–2019 with the application of a dynamic synthetic measure. The work is based on data published by the Central Statistical Office characterizing the social situation in individual voivodeships for 2010 and 2019. The linear ordering of multi-feature objects method was used, where the normalization of the variables was carried out using the zero unitarization method. The analysis was carried out in static and dynamic terms. In addition to the indicative values, which included 11 features describing the demographic situation, labor market, education and living conditions, the rankings of voivodeships were prepared in terms of the level of social development in the analyzed years using a static and a dynamic approach. Based on the dynamic synthetic measure, the direction and size of changes that took place in individual voivodeships in the period 2010–2019 were ranked. Research shows that only in 7 voivodeships (Mazowieckie, Małopolskie, Pomorskie Dolnośląskie, Podkarpackie, Kujawsko-Pomorskie and Podlaskie) did the situation improve in terms of the adopted set of diagnostic features, while in the other cases it worsened. In 2019, the best situation occurred in the following voivodeships: Mazowieckie, Małopolskie, Pomorskie and Wielkopolskie, and the lowest indexes were recorded in the following voivodeships: Świętokrzyskie, Warmińsko-Mazurskie, Opolskie and Zachodniopomorskie. In the examined period, the disproportions between voivodeships increased in terms of the studied phenomenon. The application of a dynamic index of development growth allowed not only linear ordering of objects, but also evaluation of the direction and magnitude of the changes in individual objects in the analyzed period.

Changes in the agrarian structure are among the indicators of Polish agriculture’s adaptation to competition with the EU agriculture and therefore require a thorough examination. The purpose of this paper is to determine the scale, direction and level of diversification of Polish agricultural farm size structures in 2010–2020 by typological groups of voivodeships. The research was carried out on the basis of Statistics Poland (GUS) data on the number of farms by size groups for the years 2010 and 2020. The following farm size groups were analysed: up to 2 ha of agricultural land (AL), 2–5 ha, 5–10 ha, 10–20 ha, 20–50 ha and over 50 ha. Based on fuzzy classification, four typological groups were distinguished, comprising voivodeships with similar farm size structure. Then, changes in the structure of the typological groups and changes in the number of farms in absolute terms were presented. The structure of the typological groups changed only marginally, while changes in the number of farms in absolute terms were very significant. In all groups, a decrease in the total number of farms was observed, notably farms of up to 10 ha in size, as well as an increase in the number of farms larger than 20 ha, with the rate and scale of those changes varying regionally. The largest decrease in the number of farms was recorded for voivodeships with the most fragmented agrarian structure.

The research on the economic development of voivodships is important in the context of the policy of levelling regional disparities and has been necessitated by the fact the disproportions in the level of the economic development of Polish voivodships have persisted for years. The main aim of this study was to determine the direction and scope of changes affecting the level of economic development of particular voivodships. To this end, the study used linear ordering methods in the dynamic approach. The additional aim, of the methodological nature, was to compare two methods of the normalisation of variables – the Strahl quotient transformation and the zero unitarisation method. The research was conducted on the basis of relevant data from Statistics Poland’s Local Data Bank database for the years 2010–2020. The results of the study include the calculation of the synthetic measure of the level of development of Polish voivodships in the studied period, the compilation of the relevant ranking of the voivodships, and the comparison of changes thereof. The performed analysis demonstrated that all the voivodships developed economically in the examined period, but the changes took place at a different pace and the disparities between them remained. The indicator of economic development grew to the largest extent in the voivodships which were already the most developed in economic terms, i.e. in the Mazowieckie, Dolnośląskie, Wielkopolskie and Małopolskie voivodships. Both of the applied methods of the variable normalisation led to similar conclusions. The differences between them concerned the value of the development level indicator, and affected to some degree the assessment of the scope and direction of changes, along with the assessment of the distance between the objects.

Celem badań jest ocena skali zmian poziomu rozwoju gospodarczego województw w czasie pandemii COVID-19, ze szczególnym uwzględnieniem województwa wielkopolskiego, które zajmuje w całym badanym okresie stosunkowo wysokie miejsce w rankingu. Badania przeprowadzono na podstawie danych GUS. Zastosowano metodę porządkowania liniowego obiektów wielowymiarowych przy normowaniu zmiennych metodą Strahl w ujęciu dynamicznym. Wyznaczono syntetyczny wskaźnik poziomu rozwoju obejmujący 13 zmiennych diagnostycznych i porównano poziom rozwoju gospodarczego województw w latach 2019–2021 oraz w 2010 i 2015 r. Analiza wykazała niewielkie obniżenie wskaźnika w 2020 r., ale w 2021 r. we wszystkich województwach osiągnął on wartość wyższą niż przed pandemią. Dynamika wzrostu była nierównomierna – zmiany w województwach bardziej rozwiniętych były szybsze niż w pozostałych, co spowodowało pogłębienie regionalnych różnic rozwojowych.