No Arabic abstract
How are economies in a modern age impacted by epidemics? In what ways is economic life disrupted? How can pandemics be modeled? What can be done to mitigate and manage the danger? Does the threat of pandemics increase or decrease in the modern world? The Covid-19 pandemic has demonstrated the importance of these questions and the potential of complex systems science to provide answers. This article offers a broad overview of the history of pandemics, of established facts, and of models of infection diffusion, mitigation strategies, and economic impact. The example of the Covid-19 pandemic is used to illustrate the theoretical aspects, but the article also includes considerations concerning other historic epidemics and the danger of more infectious and less controllable outbreaks in the future.
The study examines the relationship between mobile financial services and individual financial behavior in India wherein a sizeable population is yet to be financially included. Addressing the endogeneity associated with the use of mobile financial services using an instrumental variable method, the study finds that the use of mobile financial services increases the likelihood of investment, having insurance and borrowing from formal financial institutions. Further, the analysis highlights that access to mobile financial services have the potential to bridge the gender divide in financial inclusion. Fastening the pace of access to mobile financial services may partially alter pandemic induced poverty.
Rapid rise in income inequality in India is a serious concern. While the emphasis is on inclusive growth, it seems difficult to tackle the problem without looking at the intricacies of the problem. Social mobility is one such important tool which helps in reaching the cause of the problem and focuses on bringing long term equality in the country. The purpose of this study is to examine the role of social background and education attainment in generating occupation mobility in the country. By applying an extended version of the RC association model to 68th round (2011-12) of the Employment and Unemployment Survey by the National Sample Survey Office of India, we found that the role of education is not important in generating occupation mobility in India, while social background plays a critical role in determining ones occupation. This study successfully highlights the strong intergenerational occupation immobility in the country and also the need to focus on education. In this regard, further studies are needed to uncover other crucial factors limiting the growth of individuals in the country.
Due to the unavailability of nationally representative data on time use, a systematic analysis of the gender gap in unpaid household and care work has not been undertaken in the context of India. The present paper, using the recent Time Use Survey (2019) data, examines the socioeconomic and demographic factors associated with variation in time spent on unpaid household and care work among men and women. It analyses how much of the gender gap in the time allocated to unpaid work can be explained by differences in these factors. The findings show that women spend much higher time compared to men in unpaid household and care work. The decomposition results reveal that differences in socioeconomic and demographic factors between men and women do not explain most of the gender gap in unpaid household work. Our results indicate that unobserved gender norms and practices most crucially govern the allocation of unpaid work within Indian households.
Social media-transmitted online information, particularly content that is emotionally charged, shapes our thoughts and actions. In this study, we incorporate social network theories and analyses to investigate how emotions shape online content diffusion, using a computational approach. We rigorously quantify and characterize the structural properties of diffusion cascades, in which more than six million unique individuals transmitted 387,486 articles in a massive-scale online social network, WeChat. We detected the degree of eight discrete emotions (i.e., surprise, joy, anticipation, love, anxiety, sadness, anger, and disgust) embedded in these articles, using a newly generated domain-specific and up-to-date emotion lexicon. We found that articles with a higher degree of anxiety and love reached a larger number of individuals and diffused more deeply, broadly, and virally, whereas sadness had the opposite effect. Age and network degree of the individuals who transmitted an article and, in particular, the social ties between senders and receivers, significantly mediated how emotions affect article diffusion. These findings offer valuable insight into how emotions facilitate or hinder information spread through social networks and how people receive and transmit online content that induces various emotions.
The article presents the results of multivariate classification of Russian regions by the indicators characterizing the population income and their concentration. The clusterization was performed upon an author approach to selecting the characteristics which determines the academic novelty in the evaluation of regional differentiation by population income and the interconnected characteristics. The performed analysis was aimed at the evaluation of the real scale of disproportions in spatial development of the country territories by the considered characteristics. The clusterization results allowed to formulate the condition of a relatively strong position of a group of high-income regions (the changes in the array of regions constituting it is highly unlikely in the foreseeable future). Additionally there has been revealed a group of Russian regions that the population is struggling to live on quite low income. These so-called poor regions, within the crisis conditions caused by Covid-19 are in need of additional public support, without which their population will impoverish.