ABSTRACT
Workers from a variety of industries rapidly shifted to remote work at the onset of the COVID-19 pandemic. While existing work has examined the impact of this shift on office workers, little work has examined how shifting from in-person to online work affected workers in the informal labor sector. We examine the impact of shifting from in-person to online-only work on a particularly marginalized group of workers: sex workers. Through 34 qualitative interviews with sex workers from seven countries in the Global North, we examine how a shift to online-only sex work impacted: (1) working conditions, (2) risks and protective behaviors, and (3) labor rewards. We find that online work offers benefits to sex workers' financial and physical well-being. However, online-only work introduces new and greater digital and mental health risks as a result of the need to be publicly visible on more platforms and to share more explicit content. From our findings we propose design and platform governance suggestions for digital sex workers and for informal workers more broadly, particularly those who create and sell digital content. © 2022 Owner/Author.
ABSTRACT
The COVID-19 outbreak is considered as one of the most severe infectious viruses experienced by the world during the 21st century. This pandemic has economic, social, and psychological consequences on all countries, so the main purpose of this paper is to determine the impact of COVID-19 on the Egyptian stock return specifically as Egypt has been one of the countries that were strongly affected. The impact of COVID-19 on the Egyptian Stock Exchange (EGX100) was investigated using a multiple regression model and historical data from 20 listed firms in the EGX100 index between February 2020 and March 2022. Additionally, we included inflation as a control variable in our model. The results indicated that COVID-19 significantly impacted the stock's cumulative returns when used as an independent variable and measured using the cumulative coronavirus cases (CCC) and cumulative coronavirus deaths (CCD) collected for the time period of February 2020 through March 2022 from the World Health Organization (WHO) database. The findings also showed a negative correlation between these elements and the cumulative returns of the stock. Furthermore, The outcome of our model also showed that there was no significant relationship between inflation as measured by headline CPI and the stock's cumulative returns. © 2022 The Authors.
ABSTRACT
We suggest a new method for constructing an efficient point predictor for the future order statistics when the sample size is a random variable. The suggested point predictor is based on some characterization properties of the distributions of order statistics. For several distributions, including the mixture distribution, the performance of the suggested predictor is evaluated by means of a comprehensive simulation study. Three examples of real lifetime data-sets are analyzed by using this method and compared with an efficient recent method given by Barakat et al. [1], that deals with non-random sample sizes. One of these examples predicts the accumulative new cases per million for infection of the new Coronavirus (COVID-19).