The main goal of this paper is to determine the factors responsible for economic growth at the global level. The indication of the sources of economic growth may be an important element of the sustainable economic policy for development. The novelty of this research lies in employing an analysis based on data, which consist of an average growth rate of the Gross Domestic Product (GDP) for 168 countries for the years 2002–2013. The Bayesian model averaging approach is used to identify potential factors responsible for differences in countries’ GDPs. Additionally, a jointness analysis is performed to assess the potential independence, substitutability, and complementarity of the factors of economic growth. The robustness of the results is confirmed by Bayesian averaging of classical estimates. We identify the most probable factors of economic growth, and we find that the most important determinants are variables associated with the so-called “Asian development model”.

On 11 March 2020, the WHO declared the COVID-19 epidemic to be a global pandemic. This was a consequence of the rapid increase in the number of people with positive test results, the increase in deaths due to COVID-19, and the lack of pharmaceutical drugs. Governments introduced national lockdowns, which have impacted both energy consumption and economies. The purpose of this paper is to answer the following question: do COVID-19 lockdowns affect the business cycle? We used the cycle clock approach to assess the magnitude of decrease in electricity consumption in the three waves of the epidemic, namely, April 2020, November 2021, and April 2021. Additionally, we checked the relation between energy consumption and GDP by means of spectral analysis. Results for selected 28 European countries confirm an impact of the introduced non-pharmaceutical interventions on both energy consumption and business cycle. The reduction of restrictions in subsequent pandemic waves increased electricity consumption, which suggests movement out of the economic recession.

Bayesian Model Averaging and Jointness Measures for gretl

This paper presents empirical research on the employment attitudes of Ukrainian households that fled to one of the largest cities in Poland, during a mass migration at the onset of the Russia’s full-scale invasion of Ukraine in 2022. It identifies the characteristics of households with adult members immediately employed and those planning for future employment in Poland. Based on socio-economic migration theories and empirical knowledge, the paper delves into the role of the economic motive for these households’ migration. The study is based on household-level survey data collected in Krakow in May 2022. The Bayesian Averaging of Classical Estimates technique is employed to identify the determinants of household members’ employment and their plans in this regard. The findings confirm that financial reasons partly drove Ukrainian households to migrate during the war; however, public and private assistance played distinct roles in their prompt inclusion in the labour market and their planning for employment in Poland. Additionally, the findings allow us to conclude that knowledge of the Polish language consistently played a role in their professional activation abroad.