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Following restrictions to control the spread of COVID-19, and subsequent vaccination campaigns, sentiments against such policies were quick to arise. While individual-level determinants that led to such attitudes have drawn much attention, there are also reasons to believe that the macro context in which these movements arose may contribute to their evolution. In this study, exploiting data on business activities which supported a major Italian anti-restriction and anti-vaccine movement, IoApro, using quantitative analysis that employs both a fractional response probit and logit model and a beta regression model, we investigate the relationship between socio-economic characteristics, institutional quality, and the flourishing of this movement. Our results suggest a U-shaped relationship between income and the proliferation of the movement, meaning that support for these movements increases the greater the degree of economic decline. Our results further indicate that the share of the population between 40 and 60 years old is positively related to support for such movements, as is institutional corruption. [ABSTRACT FROM AUTHOR]

Different countries have adopted different policies in their fight against the COVID-19 outbreak. In this work we aim to model these approaches with respect to the first wave of COVID-19 (from January to October 2020) in the five Central Asian republics (Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan). By means of a multivariate approach based on principal component analysis and cluster analysis, we derived three clusters. Each of these represents a different strategy adopted by a group of governments in a given period of the first wave: pre-pandemic, initial reaction, and emergency scenarios. The contribution of these results is two-fold: on the one hand they may help us to understand differences and similarities among Central Asian republics during the first wave of the COVID-19 outbreak and guide future quantitative or qualitative studies; on the other hand, our findings suggest that, with the exception of Turkmenistan, the different countries adopted very similar strategies and that deaths, more than cases, pushed governments to impose restrictions. [ABSTRACT FROM AUTHOR]

Applications of econometrics and statistics are widely used as tools in many disciplines as a means to provide quantitative analysis to investigate hypotheses. Examples include economics, sociology, and cliometrics. While so far in a genealogy such an approach has not been attempted, in this exploratory work we suggest some possibilities for the application of such quantitative tools to the subject. In doing so, we introduce and discuss the Forecasting and Inference Tool, an application of this framework, and a tool that can help genealogists to be more productive in their fieldwork by estimating years of death, marriage, and birth. [ABSTRACT FROM AUTHOR]

Extant research on COVID-19 suggests that many socio-economic determinants, by affecting personal behavior, have influenced the evolution of the pandemic. In this paper we study the role played in this regard by average levels of self-esteem in the public. There are reasons to believe that both low and very levels of self-esteem may have an effect on the spread of COVID-19, for opposite reasons. On the one hand, people with low self-esteem may not worry enough to behave in the way recommended (and prescribed, through non-pharmaceutical interventions) by the authorities; people with very high self-esteem, on the other hand, may be over-confident and fail to follow the prescriptions, believing that they do not need them. In this study we test this hypothesis by means of a quantitative cross-country analysis, using a hybrid model and the Rosenberg self-esteem scale. Our results suggest the existence of a U-shaped relationship between the trend of COVID-19 and average levels of self-esteem in a country. • Self-esteem played a positive role in containing COVID-19 pandemic contagion when is at a low level. • There is empirical evidence of a transformation of this relationship with higher levels of self-esteem. • The relationship is therefore U-shaped. • This finding confirms previous results regarding the negative effects of high levels of self-esteem on social outcomes. [ABSTRACT FROM AUTHOR]

Corruption is considered in the literature as an activity with several externalities and spillover effects. Adding to the recent research on the corruption-COVID-19 nexus, we study the impact of corruption on coronavirus cases. High perceived levels of corruption have been proven to lead to lower institutional trust, and hence possibly to lower levels of citizen compliance with non-pharmaceutical interventions (NPIs), such as lockdowns, imposed by the authorities during the first wave of the pandemic to reduce the spread of coronavirus. Applying quantitative analysis with the use of hybrid models, we find that in countries with higher levels of perceived corruption, across alternative corruption measures, more COVID-19 cases are observed, ceteris paribus. This suggests that corruption has a detrimental effect on the spread of COVID-19, and that countries experiencing higher levels of corruption should pay extra attention when implementing NPIs. • Countries where the public is perceived as more corrupt have more COVID cases. • Corruption in politicians and public officials leads to the same increase in cases. • Results are robust to alternative operationalization of corruption. [ABSTRACT FROM AUTHOR]