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The present work looks at what we call "the multiverse of quantification", where visible and invisible numbers permeate all aspects and venues of life. We review the contributions of different authors who focus on the roles of quantification in society, with the aim of capturing different and sometimes separate voices. Several scholars, including economists, jurists, philosophers, sociologists, communication and data scientists, express concerns or identify critical areas of our relationship with new technologies of 'numericization'. While mindful of the important specificities of the different families of quantification, we use our broad and holistic canvas to explore possible spaces for a more systematic investigation of incumbent and novel quantifications, as to increase communication among disciplinary communities, and among these and society, in the pursuit a democratic agency and self-defence. [ABSTRACT FROM AUTHOR]

10.3390/en15010340 26 Zinecker M., Doubravsky K., Balcerzak A.P., Pietrzak M.B., Dohnal M. The COVID-19 disease and policy response to mitigate the economic impact in the EU: An exploratory study based on qualitative trend analysis. Of particular importance seems to be the analysis of changes in the prices of electricity and primary fuels and the relationship between these markets and the renewable energy market. Bus. Econ. 2020; 19: 114-135 7 Dmytrów K., Bieszk-Stolorz B. Comparison of changes in the labour markets of post-communist countries with other EU member states. Q. J. Econ. Econ. [Extracted from the article]

The paper aims to improve the methodology of the Digital Economic and Society Index (DESI), the European Commission's newest index to assess the development of the digital economy. In particular, we investigate whether methodological changes to the structure of DESI improve its ability to capture the digital transformation of EU economies. Using the sensitivity-based analysis, we check whether the selection of weights of individual elements included in the DESI is optimal or should be improved. We also verify the importance of DESI in explaining changes in GDP per capita in EU economies. In the literature, we find that digital transformation has enabled the creation of new business models and maximized efficiency in traditional firms. Using DESI, we empirically test whether the gap between rich and poor countries in European Union can be closed or eliminated through rapid and intensive digital transformation. Our results show that the DESI -- when modified by eliminating the pillars on internet services and digital public services -- has the same explanatory power. Connectivity is the dimension with the largest impact on digital transformation in EU countries. We also find that DESI is a significant regressor to explain changes in GDP per capita in EU countries. [ABSTRACT FROM AUTHOR]

The Global Competitiveness Index (GCI) developed by the World Economic Forum (WEF) is used as a standard for measuring a country's competitiveness. However, in literature, the GCI has been accused of numerous methodological flaws. Consequently, in 2018, the WEF introduced significant methodological changes. This study aims to examine whether the methodological modifications in the GCI's structure increase its ability to capture the real competitiveness of economies. In addition, the study considers whether the selection of weights of individual elements included in the GCI is optimal or could be improved. By employing a sensitivity-based analysis, we find that the change in methodology resulted in fewer pillars of marginal importance. In the case of the GCI 2017, there were four pillars, whereas in that of the GCI 4.0, there were only two pillars: product market and labor market. Furthermore, we reveal that the WEF weights do not reflect the measured importance of the variables. In the optimization process, numerous variables (primarily opinion-based indicators) were insignificant in explaining the GCI variance and could be eliminated from the set of diagnostic variables without affecting the index's value. For instance, in the case of the GCI 4.0, 35 out of 103 variables could be eliminated. The new rankings obtained by weight optimization and reduction of the diagnostic variables demonstrated a strong positive correlation with the original rankings. This research contributes to the literature from both a theoretical perspective (indicating the most vital indicators in the GCI) and a practical standpoint (reducing the costs and time of obtaining redundant data). [ABSTRACT FROM AUTHOR]

Three decades after the fall of the Berlin wall and one and a half decades after the Big Bang enlargement of the European Union (2004-2007), we revisit contrasting narratives about the benefit of both free trade and the EU enlargement for Central and Eastern European (CEE) countries. We distinguish old, pre-2004 EU countries from CEE countries that joined the EU in 2004-2007, as well as from the CEE countries that have not become part of the EU, in particular Belarus, Moldova, and Ukraine. Our analysis looks at two temporal windows: one from 1991 – the demise of the Eastern European free trade zone (COMECON) – to today, and the second zooming on the period following the enlargement process of 2004-2007. Our analysis points to an unfavourable turn of events for CEE countries, which appear to have experienced significant losses in their process of rapid integration in the world and EU economies. We are comparing these events in Central and Eastern Europe with the patterns of de-industrialisation and migration that took place in Latin America after a similar free trade shock starting in the 1970s. • With accession, 'Old' Europe managed a profit at the expense of the new one • This was caused by trade asymmetries and by deindustrialisation of new member states • Migration from accession countries became a source of cheap labour • The Euro has not helped accession countries or the EU Southern periphery • The EU enlargement has many losers – in both the old and the new EU [ABSTRACT FROM AUTHOR]

• Upward convergence occurs in 7 out of 9 variables from "planet" SDGs. • Upward divergence concerns energy consumption and greenhouse gas emissions. • Unfavourable trajectories of change in most cases concerned Poland and Lithuania. • Denmark and France compare favourably with the EU average. Sustainable development is a challenge facing humanity. EU countries not only strive to reach their specific objectives, but they also work collaboratively towards shared goals. There is a need to balance synergies and compromises to address these objectives effectively. When discussing countries' development and people's well-being, one often focuses on socio-economic development. However, it is crucial not to overlook the environmental repercussions and the need to care for the planet. Thus, our article pays attention to the sustainable development objectives of the "planets" group. We analysed upward convergence in the scope of the "Planet" goals, i.e. the analysis of improving the results of Member States and, at the same time, reducing the differences between them. Convergence trends were examined individually for each variable and then for all variables combined (Planet). Our article fills a research gap because, to our knowledge, analyses of the trajectories of achieving individual goals in such a context have not been analysed so far. The results of our study indicate a favourable situation in the case of six out of eight examined variables. Areas in which intensification of activities is necessary for some EU countries are an increase in energy productivity and a reduction in net greenhouse gas emissions of land use. The second stage of the study concerned the development paths of individual countries. The most challenging situation concerns the variable sdg_07_20 (final energy consumption in households per capita). In this case, as many as 12 countries belong to the weak group. [ABSTRACT FROM AUTHOR]

The reasons for and against composite indicators are briefly reviewed, as well as the available theories for their construction. After noting the strong normative dimension of these measures—which ultimately aim to 'tell a story', e.g. to promote the social discovery of a particular phenomenon, we inquire whether a less partisan use of a composite indicator can be proposed by allowing more latitude in the framing of its construction. We thus explore whether a composite indicator can be built to tell 'more than one story' and test this in practical contexts. These include measures used in convergence analysis in the field of cohesion policies and a recent case involving the World Bank's Doing Business Index. Our experiments are built to imagine different constituencies and stakeholders who agree on the use of evidence and of statistical information while differing on the interpretation of what is relevant and vital. [ABSTRACT FROM AUTHOR]

Research background: Composite indicators are commonly used as an approximation tool to measure economic development, the standard of living, competitiveness, fairness, effectiveness, and many others being willingly implemented into many different research disciplines. However, it seems that in most cases, the variable weighting procedure is avoided or erroneous since, in most cases, the so-called 'weights by belief' are applied. As research show, it can be frequently observed that weights do not equal importance in composite indicators. As a result, biased rankings or grouping of objects are obtained. Purpose of the article: The primary purpose of this article is to optimise and improve the Human Development Index, which is the most commonly used composite indicator to rank countries in terms of their socio-economic development. The optimisation will be done by re-scaling the current weights, so they will express the real impact of every single component taken into consideration during HDI's calculation process. Methods: In order to achieve the purpose mentioned above, the sensitivity analysis tools (mainly the first-order sensitivity index) were used to determine the appropriate weights in the Human Development Index. In the HDI's resilience evaluation process, the Monte Carlo simulations and full-Bayesian Gaussian processes were applied. Based on the adjusted weights, a new ranking of countries was established and compiled with the initial ranking using, among others, Kendall tau correlation coefficient. Findings & Value added: Based on the data published by UNDP for 2017, it has been shown that the Human Development Index is built incorrectly by putting equal weights for all of its components. The weights proposed by the sensitivity analysis better reflect the actual contribution of individual factors to HDI variability. Re-scaled Human Development Index constructed based on proposed weights allow for better differentiation of countries due to their socio-economic development. [ABSTRACT FROM AUTHOR]

• Sensitivity analysis is used to ensure equal importance of the variables. • In the EU, synergies among SDGs (41) are more common than trade-offs (2). • SDG5 – gender equality – has the highest associations number (all positive). • SDG7 – affordable and clean energy – is not significantly related to the others. • More developed economies lead in SDGs execution (Sweden, Denmark, the Netherlands). Sustainable development is one of the most urgent challenges facing humanity. Its basic principle is to improve people's well-being and maintain it over time. In 2015, the United Nations approved 17 sustainable development goals (SDGs) to ensure a better and sustainable future for all, balancing economic, social and environmental development. SDGs create an 'indivisible whole'; thus, examining their interactions is crucial. Our goals were twofold: (i) to assess the implementation degree of SDGs in EU countries and (ii) to examine the interactions between goals. The potential to achieve a given SDG is approximated by a composite indicator, calculated based on an innovative method of implementing tools derived from sensitivity analysis (SA). Respecting the degree of variability of individual variables and their correlation, we set the weights to equalise their importance. Moreover, the application of SA allows us to remove strongly correlated variables that are not transferring supplementary information. We use countries' linear ordering and Spearman's rank correlation coefficient to assess interactions between SDGs. Our research shows that Scandinavian countries predominately occupy the leading positions, respectively, eight times on the podium for Sweden and four by Denmark (of which three as a leader). The Netherlands also stands out, occupying the superior position in the performance of SDGs 2, 3, 5, 8, 9, 12 and 17. The top in achieving one of the most priority area – no poverty (SDG1) belongs to Czechia. Our study confirmed the results obtained by many researchers regarding the more potent synergy between SDGs compared to the trade-offs. The only trade-offs observed in our study relate to SDG15, which negatively correlated with SDG3 and 17. Furthermore, SDG7 was the only one not associated with others. [ABSTRACT FROM AUTHOR]

Our paper aims to investigate the impact of COVID-19 on private sector companies in terms of sales, production, finance and employment. We check whether the country and industry in which companies operate, government financial support and loan access matter to the behaviour and performances of companies during the pandemic. We use a microdata set from a worldwide survey of more than 15,729 companies conducted between April and September 2020 by the World Bank. Logistic regression is used to assess which factors increase the likelihood of businesses suffering due to the COVID-19 pandemic. Our results show that COVID-19 negatively impacts the performance of companies in almost all countries analysed, but a stronger effect is observed among firms from developing countries. The pandemic is more harmful to firms providing services than those representing the manufacturing sector. Due to the pandemic, firms suffer mainly in sales and liquidity decrease rather than employment reduction. The increase in the number of temporary workers is an important factor that significantly reduces the probability of sales, exports or supply decline. The analysis results indicate policy tools supporting enterprises during the pandemic, such as increasing the flexibility of the labour market or directing aid to developing countries. [ABSTRACT FROM AUTHOR]

This article aims to improve one of the newest energy transition measures—the World Economic Forum WEF Energy Transition Index (ETI) and find its driving forces. This paper proposes a new approach to correct the ETI structure, i.e., sensitivity analysis, which allows assessing the accuracy of variable weights. Moreover, the novelty of the paper is the use the spatial error models to estimate determinants of the energy transition on different continents. The results show that ETI is unbalanced and includes many variables of marginal importance for the shape of the final ranking. The variables with the highest weights in ETI did not turn out to be its most important determinants, which means that they differentiate the analysed countries well; nonetheless, they do not have sufficient properties of approximating the values of the ETI components. The most important components of ETI (with the highest information load) belong to the CO2 emissions per capita, the innovative business environment, household electricity prices, or renewable capacity buildout. Moreover, we identified the clustering of both ETI and its two main pillars in Europe, which is not observed in America and Asia. The identified positive spatial effects showing that European countries need much deeper cooperation to reach a successful energy transition. [ABSTRACT FROM AUTHOR]

Research background: The relationship between labour share and income inequality is a complex and multifaceted problem. Despite ongoing discussions among economists, there is still no consensus on the direction of the relationship between labour share and income inequality. Purpose of the article: The article aims to assess the impact of labour share on income inequality, which is measured in three ways: the Gini index of gross income, the Gini index of market incomes, and the Gini index of household disposable income. Methods: Dynamic panel data models were applied to estimate the relationship between Gini coefficients and socio-economic indicators. The study investigated 25 European Union countries over the 2011-2021 period. Findings & value added: Despite the long convergence process of the EU economies, there is still great diversity in the labour share, social inequalities, and the interplay between these factors. The added value of this research is the indication of labour share impact on three Gini measures covering a diverse income spectrum (from labour and capital). Based on the research findings, hypothesis 1, claiming that the more developed the national economy, the lower the share of employment income, favouring capital gains, is confirmed. Hypothesis 2 (as the share of income from work increases, the Gini coefficient of gross incomes decreases) must be rejected. There is no significant relationship between labour share and the studied Gini measures in 'old' EU countries. In 'new' EU members, there is a reverse relationship than assumed in hypothesis 2. The growth of the Gini coefficient was influenced by the rise in labour share, which can be attributed to the diversity in economic structures. [ABSTRACT FROM AUTHOR]

The energy transition is now treated in most countries as a necessary condition for their long-term development. The process of energy transformation assumes the simultaneous implementation of the Sustainable Development Goals, which are a major challenge for modern economies and introduce significant restrictions in their functioning. Our study aims to group EU member states according to their ability to achieve energy transition over time. The novelty of our approach is the assessment of energy transformation in the European Union through two aspects. The first one, "smart and efficient energy systems", assess the current, widely understood energy consumption in economy, and the second one, "macroeconomic heterogeneity", refers to the economic potential of a country. In our analysis, we included indicators from the 7th, 8th, 10th, 11th, and 12th Sustainable Development Goals. Using taxonomic methods, we created clusters of countries according to the emissivity of their economies and the socio-economic potential for the energy transition. The analysis results revealed that countries vary more due to their emissivity than economic potential. [ABSTRACT FROM AUTHOR]

The last half a century has seen spectacular progresses in computing and modelling in a variety of fields, applications, and methodologies. Over the same period, a cross-disciplinary field known as sensitivity analysis has been making its first steps, evolving from the design of experiments for laboratory or field studies, also called 'in-vivo', to the so-called experiments 'in-silico'. Some disciplines were quick to realize the importance of sensitivity analysis, whereas others are still lagging behind. Major tensions within the evolution of this discipline arise from the interplay between local vs global perspectives in the analysis as well as the juxtaposition of the mathematical complexification and the desire for practical applicability. In this work, we retrace these main steps with some attention to the methods and through a bibliometric survey to assess the accomplishments of sensitivity analysis and to identify the potential for its future advancement with a focus on relevant disciplines, such as the environmental field. • The manuscript offers a detailed timeline of sensitivity analysis, tracing its evolution from local to global methods. • The manuscript includes a bibliometric survey assessing sensitivity analysis achievements and identifying future directions and areas of growth in the field. • We believe our research significantly contributes to the academic discourse on sensitivity analysis, aligning well with the focus of your journal. [ABSTRACT FROM AUTHOR]