The main objective of the study is to assess the similarity between the time series of energy commodity prices and the time series of daily COVID-19 cases. The COVID-19 pandemic affects all aspects of the global economy. Although this impact is multifaceted, we assess the connections between the number of COVID-19 cases and the energy commodities sector. We analyse these connections by using the Dynamic Time Warping (DTW) method. On this basis, we calculate the similarity measure—the DTW distance between the time series—and use it to group the energy commodities according to their price change. Our analysis also includes finding the time shifts between daily COVID-19 cases and commodity prices in subperiods according to the chronology of the COVID-19 pandemic. Our findings are that commodities such as ULSD, heating oil, crude oil, and gasoline are weakly associated with COVID-19. On the other hand, natural gas, palm oil, CO₂ allowances, and ethanol are strongly associated with the development of the pandemic.

At a time of rapid climate change and an uncertain geopolitical situation caused by the war in Ukraine, the problem of access to energy is a serious issue. The use of renewable energy sources and ensuring the highest possible energy independence are becoming important. They are in line with the seventh Sustainable Development Goal (SDG7). The aim of our research is to compare European countries in terms of the degree of SDG7 implementation and its dynamics from 2005 to 2020. We assess the SDG7 implementation using the COPRAS method and compare its dynamics using the Dynamic Time Warping (DTW) and hierarchical clustering. In years 2005, 2009 and 2020, we present rankings of countries in terms of the SDG7 implementation. Norway, Denmark, Sweden, Croatia, and Estonia were ranked the best, and Luxembourg, Belgium, Bulgaria, Lithuania, Iceland, and Cyprus—the worst. We obtained eight clusters with respect to dynamics of the degree of SDG7 implementation. In Poland, Romania, Belgium, Luxembourg, Latvia, and Ireland, the relative dynamics was increasing, while in the Nordic and South European countries, it was decreasing. The novelty of our research is combining the COPRAS (assessment of SDG7 implementation) and DTW methods (selection of similar countries with respect to its dynamics).

Research background: In recent times, the whole world has been severely affected by the COVID-19 pandemic. The influence of the epidemic on the society and the economy has caused a great deal of scientific interest. The development of the pandemic in many countries was analyzed using various models. However, the literature on the dissemination of COVID-19 lacks econometric analyzes of the development of this epidemic in Polish voivodeships. Purpose of the article: The aim of the study is to find similarities in time series for infected with and those who died of COVID-19 in Polish voivodeships using the method of dynamic time warping. Methods: The dynamic time warping method allows to calculate the distance between two time series of different lengths. This feature of the method is very important in our analysis because the coronavirus epidemic did not start in all voivodeships at the same time. The dynamic time warping also enables an adjustment of the timeline to find similar, but shifted, phases. Using this method, we jointly analyze the number of infected and deceased people in each province. In the next step, based on the measured similarity of the time series, the voivodeships are grouped hierarchically. Findings & value added: We use the dynamic time warping to identify groups of voivodeships affected by the epidemic to a different extent. The classification performed may be useful as it indicates patterns of the COVID-19 disease evolution in Polish voivodeships. The results obtained at the regional level will allow better prediction of future infections. Decision makers should formulate further recommendations for lockdowns at the local level, and in the long run, adjust the medical infrastructure in the regions accordingly. Policymakers in other countries can benefit from the findings by shaping their own regional policies accordingly.

The paper deals with microeconometric techniques useful for the study of differences between groups of objects, methods that go beyond simple comparison of average values. Techniques for the decomposition of differences in distributions by constructing counterfactual distributions were considered. Using the Machado-Mata quantile regression approach the empirical decomposition of the inequalities in income distributions of one-person households in urban and rural areas was performed. We employed data from the Household Budget Survey for Poland in 2012. It was found that the tendency towards increased income inequalities between urban and rural residents when moving to the right of the income distribution can be observed. The rural residents are at a disadvantage. The decomposition of the inequalities revealed a growing share of the part explained by different characteristics of people and a declining share of the unexplained part, associated with the evaluation of those characteristics.

We examined the relationships between the compositional changes in demographic and socioeconomic factors and the changes in the prevalence of material deprivation in rural and urban areas in Poland. Using the European Union Statistics on Income and Living Conditions (EU-SILC) data for 2019–2020, we applied the Fairlie decomposition approach for a logit model. We found that the important characteristics affecting a gap in material deprivation between rural and urban areas are: equivalized income, the level of education, the type of household, and the presence of disabled or unemployed people in the household. A non-significant effect of the pandemic on the material deprivation gap between rural and urban areas was observed.

Research background: Recently there has been an increase in interest in the studies of income inequalities. The findings of numerous empirical studies show that males earn higher wages than females. A variety of techniques of income inequalities decomposition are becoming popular. New procedures go far beyond the Oaxaca-Blinder decomposition. They allow to study differences of income distributions for various groups of people and to decompose them at various quantile points. Purpose of the article: The aim of the paper is to compare personal income distributions in selected countries of the European Union, taking into account gender differences. Methods: First, we examined the income inequalities between men and women in each country using the Oaxaca-Blinder decomposition procedure. The unexplained part of the gender pay gap gave us information about the wage discrimination. Second, we extended the decomposition procedure to different quantile points along the whole income distribution. To describe differences between the incomes of men and women, we constructed the so-called counterfactual distribution, which is a mixture of a conditional distribution of the dependent variable (income) and a distribution of the explanatory variables (individual people?s characteristics). Then, we utilized the residual imputation approach (JMP-approach). Findings & Value added: In the article data from EU-SILC (Statistics on Income and Living Conditions) were used. We found that there exists an important diversity in the size of the gender pay gap across members of the European Union. The results obtained for these countries allowed us to group them into clusters. In general, there are two types of countries in Europe: the countries, where the bulk of the observed income differences cannot be explained by observed characteristics, and the countries, where the explained and the unexplained effects are both positive, with even a bigger explained effect for the lower income ranges.