ABSTRACT
This article examines inequality in income distribution in Argentina between 2014 and 2020 in a context of stagnation and economic crisis, which coincided with the outbreak of the Covid-19 pandemic. The determining factors of income distribution were analyzed based on a household survey, and a breakdown of the Gini coefficient was implemented to determine the factors that explained the increase in inequality. From a structuralist point of view, the retraction of formal employment, the expansion of the informal sector, and greater coverage of social protection policies were the central factors that explained the increased level of inequality at that time. Social transfers helped to mitigate inequality in the face of the Covid-19 pandemic. © The Author(s) 2022.
ABSTRACT
This article examines inequality in income distribution in Argentina between 2014 and 2020 in a context of stagnation and economic crisis, which coincided with the outbreak of the Covid-19 pandemic. The determining factors of income distribution were analyzed based on a household survey, and a breakdown of the Gini coefficient was implemented to determine the factors that explained the increase in inequality. From a structuralist point of view, the retraction of formal employment, the expansion of the informal sector, and greater coverage of social protection policies were the central factors that explained the increased level of inequality at that time. Social transfers helped to mitigate inequality in the face of the Covid-19 pandemic. © 2023 Universidad Nacional Autonoma de Mexico. All rights reserved.
ABSTRACT
This article examines inequality in income distribution in Argentina between 2014 and 2020 in a context of stagnation and economic crisis, which coincided with the outbreak of the Covid-19 pandemic. The determining factors of income distribution were analyzed based on a household survey, and a breakdown of the Gini coefficient was implemented to determine the factors that explained the increase in inequality. From a structuralist point of view, the retraction of formal employment, the expansion of the informal sector, and greater coverage of social protection policies were the central factors that explained the increased level of inequality at that time. Social transfers helped to mitigate inequality in the face of the Covid-19 pandemic. (English) [ FROM AUTHOR]
ABSTRACT
Protein structures may be used to draw functional implications at the residue level, but how sensitive are these implications to the exact structure used? Calculation of the effects of SARS-CoV-2 S-protein mutations based on experimental cryo-electron microscopy structures have been abundant during the pandemic. To understand the precision of such estimates, we studied three distinct methods to estimate stability changes for all possible mutations in 23 different S-protein structures (3.69 million ΔΔG values in total) and explored how random and systematic errors can be remedied by structure-averaged mutation group comparisons. We show that computational estimates have low precision, due to method and structure heterogeneity making results for single mutations uninformative. However, structure-averaged differences in mean effects for groups of substitutions can yield significant results. Illustrating this protocol, functionally important natural mutations, despite individual variations, average to a smaller stability impact compared to other possible mutations, independent of conformational state (open, closed). In summary, we document substantial issues with precision in structure-based protein modeling and recommend sensitivity tests to quantify these effects, but also suggest partial solutions to the problem in the form of structure-averaged "ensemble" estimates for groups of residues when multiple structures are available.