Estimating Lower Tail Dependence Between Pairs of Poverty Dimensions in Europe

This study seeks to measure teachers’ readiness to use ICT in Italy by exploiting the data collected by the National Institute for the evaluation of education and training system (INVALSI) in 2018–2019. We propose a fuzzy set approach to provide a multidimensional picture of how much teachers were ready to integrate ICTs into educational practice. In addition, we use empirical bootstrap intervals to test whether significant differences exist in teacher readiness over several personal and socioeconomic characteristics. The study reveals that teachers’ readiness for ICT is composed of three dimensions and varies by teachers’ characteristics and regions. These results are a useful tool for understanding the relationship between teachers and ICTs so as to develop more appropriate educational policies.

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Measuring teachers’ readiness to use ICT before the COVID-19 pandemic in Italy

D’Agostino

Longobardi

Migali

et al. 2022

Qual Quant

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Evaluating the effects of digital finance on urban poverty

Duan,

Yuan,

Tian

2024

Socio-Economic Planning Sciences

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The risk of clustering of deprivations in Spain: a tale of two crises

García-Gómez,

Pérez,

Prieto-Alaiz

2024

AEA

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Purpose The At Risk of Poverty or Social Exclusion (AROPE) rate is a key indicator for monitoring poverty in Europe. However, it is not sensitive to the degree to which individuals face multiple deprivations simultaneously. This paper aims to fill this gap by studying the relationship between the three dimensions of the AROPE rate at the lower tail of their joint distribution in Spain in the period 2009–2022. Design/methodology/approach To capture how the different dimensions of poverty are related at the lower tail of their joint distribution, this paper proposes a multivariate left tail concentration function based on copulas. This function quantifies lower tail dependence at a finite scale, which, for practical purposes, is more suitable than estimating asymptotic measures, and can be represented in a 2D graph, facilitating interpretation and temporal comparisons. This function also provides information on overall dependence, as it is closely related to the Blomqvist’s beta. Findings There is a considerable risk of clustering of deprivations in Spain, with low positions in one poverty dimension extending to others. This risk increased after the Great Recession but did not decrease with the economic recovery that followed. The crisis linked to COVID-19 did not have a significant impact on the risk of clustering of deprivations. Lower tail dependence provides new valuable insights on the dependence structure of poverty dimensions beyond the analyses based on overall dependence. Originality/value This paper provides new theoretical results and a pioneering application of multivariate lower tail dependence measures in welfare economics using non-parametric methods.

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Estimating Lower Tail Dependence Between Pairs of Poverty Dimensions in Europe

Cited by 3 publications

References 61 publications

Measuring teachers’ readiness to use ICT before the COVID-19 pandemic in Italy

Measuring teachers’ readiness to use ICT before the COVID-19 pandemic in Italy

Evaluating the effects of digital finance on urban poverty

The risk of clustering of deprivations in Spain: a tale of two crises

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