Quality & Quantity最新文献

In this paper we estimate monetary and non-monetary poverty measures at two sub-regional levels in the region of Tuscany (Italy) using data from the ad-hoc Survey on Vulnerability and Poverty held by Regional Institute from Economic Planning of Tuscany (IRPET). We estimate the percentage of households living in poverty conditions and three supplementary fuzzy measures of poverty regarding deprivation in basic needs and lifestyle, children deprivation, and financial insecurity. The key feature of the survey is that it was carried out after the COVID-19 pandemic, therefore, some of the items collected focus on the subjective perception of poverty eighteen months after the beginning of the pandemic. We assess the quality of these estimates either with initial direct estimates along with their sampling variance, and with a secondary small area estimation when the formers are not sufficiently accurate.

Developments in factor analysis (Spearman in Am J Psychol 15:201-292, 1904); Thurstone in Multiple factor analysis, University of Chicago Press, Chicago, 1947), multidimensional scaling (Torgerson in Theory and methods of scaling, Wiley Hoboken, New Jersey, 1958; Young and Householder in Psychometrika, 3:19-22, 1938), the Galileo model (Woelfel and Fink in The measurement of communication processes: galileo theory and method, Academic Press Cambridge, Massachusetts, 1980), and, more recently, in computer science, artificial intelligence, computational linguistics, network analysis and other disciplines (Woelfel in Qual Quant 54:263-278, 2020) have shown that human cognitive and cultural beliefs and attitudes can be modeled as movement through a high-dimensional non-Euclidean space. This article demonstrates the theoretical and methodological contribution that multidimensional scaling makes to understand attitude change associated with the COVID-19 vaccine.

The study investigates the effect of fiscal policy on the inflation rate in a panel of 44 sub-Saharan African (SSA) countries over the period 2003-2020 using a non-linear system generalized method of moments (system GMM) and the dynamic panel threshold estimation techniques. The results show that the recent increase in inflation rate has a fiscal nature and that monetary policy alone may not provide an effective response. Specifically, the results indicate that a positive shock to fiscal policy (captured by public debts) has a positive and statistically significant effect on inflation, while a negative shock to public debt has a statistically non-significant impact on the inflation rate. Also, money supply exerted a positive and insignificant impact on inflation, indicating that the current inflation rate in the region may not be induced by money supply. However, the joint effect of public debts and money supply shows that public debts aid the effect of money supply on the inflation rate, albeit, not in the proportion predicted by the quantity theory of money. Further, the results also found a public debt threshold point of 60.59% of GDP. This implies the current inflationary pressure may be rooted in fiscal policy and that further accumulation of public debts beyond the benchmark established in the study would worsen the inflationary pressure in SSA. Importantly, the study found that for fiscal policy to spur growth and reduce inflationary pressure in SSA, the inflation rate should be managed and brought within a single-digit framework of 4%. The research and policy implications are discussed.

The purpose of this study is twofold: (a) To develop digital competencies of pre-service teachers in an educational process; (b) To describing their digital competences by examining artefacts designed by pre-service teachers based on DigCompEdu framework. Holistic single case study was employed in this study and the course was examined as a single unit. The study group consisted of 40 pre-service teachers. A 14-week course has been designed to develop the digital competencies of pre-service teachers based on the DigCompEdu framework. The e-portfolios and reflection reports of 40 pre-service teachers who participated in the study were examined and evaluated according to the indicators presented for each competence within the framework of DigCompEdu. Pre-service teachers' digital competences were assessed as folows: mostly C2 level in digital resources; mostly C1 level in teaching and learning, and mostly B2 level in assessment and empowering learning. An education process that blends theoretical and practical assignments for the pre-service teachers' digital competencies to be improved was conducted in this study. It is expected that the steps that were followed in the study in the process of training pre-service teachers be directive towards researchers who wish to study this subject. It is important that contextual and cultural qualities are taken into consideration in the interpretation of the findings in the study. This study contributes to the literature in terms of evaluating the digital skills of pre-service teachers based on reflection reports and e-portfolios, instead of self-report surveys.

Spatial mobility is a distinctive feature of human history and has important repercussions in many aspects of societies. Spatial mobility has always been a subject of interest in many disciplines, even if only mobility observable from traditional sources, namely migration (internal and international) and more recently commuting, is generally studied. However, it is the other forms of mobility, that is, the temporary forms of mobility, that most interest today's societies and, thanks to new data sources, can now be observed and measured. This contribution provides an empirical and data-driven reflection on human mobility during the COVID pandemic crisis. The paper has two main aims: (a) to develop a new index for measuring the attrition in mobility due to the restrictions adopted by governments in order to contain the spread of COVID-19. The robustness of the proposed index is checked by comparing it with the Oxford Stringency Index. The second goal is (b) to test if and how digital footprints (Google data in our case) can be used to measure human mobility. The study considers Italy and all the other European countries. The results show, on the one hand, that the Mobility Restriction Index (MRI) works quite well and, on the other, the sensitivity, in the short term, of human mobility to exogenous shocks and intervention policies; however, the results also show an inner tendency, in the middle term, to return to previous behaviours.

This paper surveys the extant literature on machine learning, artificial intelligence, and deep learning mechanisms within the financial sphere using bibliometric methods. We considered the conceptual and social structure of publications in ML, AI, and DL in finance to better understand the research's status, development, and growth. The study finds an upsurge in publication trends within this research arena, with a bit of concentration around the financial domain. The institutional contributions from USA and China constitute much of the literature on applying ML and AI in finance. Our analysis identifies emerging research themes, with the most futuristic being ESG scoring using ML and AI. However, we find there is a lack of empirical academic research with a critical appraisal of these algorithmic-based advanced automated financial technologies. There are severe pitfalls in the prediction process using ML and AI due to algorithmic biases, mostly in the areas of insurance, credit scoring and mortgages. Thus, this study indicates the next evolution of ML and DL archetypes in the economic sphere and the need for a strategic turnaround in academics regarding these forces of disruption and innovation that are shaping the future of finance.