Coping with precarity during COVID-19: A study of platform work in Poland

What Is on Your Gig Radar? Toward a Hierarchical Structure of Coping

Digitalized independent workers, known as gig workers, have been shown to work under high-pressure, with a lack of autonomy, a lack of feedback and perceived competence, and a high level of isolation. We conducted a literature review to investigate how gig workers cope with these sources of stress. We identified primary sources of psychological stress in gig work and the main strategies used by workers for coping with them. We show that focusing solely on identifying coping strategies depicts a fragmented literature, making it impossible to compare, link, or aggregate findings. We suggest a radar classification of coping based on the motivational action theory of coping and self-determination theory that defines coping as a process to adapt to the environment and maintain well-being. We argue that this framework is both relevant and necessary for developing research on gig-worker coping.

Hourly Wages in Crowdworking: A Meta-Analysis

In the past decade, crowdworking on online labor market platforms has become an important source of income for a growing number of people worldwide. This development has led to increasing political and scholarly interest in the wages people can earn on such platforms. This study extends the literature, which is often based on a single platform, region, or category of crowdworking, through a meta-analysis of prevalent hourly wages. After a systematic literature search, the paper considers 22 primary empirical studies, including 105 wages and 76,765 data points from 22 platforms, eight different countries, and 10 years. It is found that, on average, microtasks results in an hourly wage of less than $6. This wage is significantly lower than the mean wage of online freelancers, which is roughly three times higher when not factoring in unpaid work. Hourly wages accounting for unpaid work, such as searching for tasks and communicating with requesters, tend to be significantly lower than wages not considering unpaid work. Legislators and researchers evaluating wages in crowdworking need to be aware of this bias when assessing hourly wages, given that the majority of literature does not account for the effect of unpaid work time on crowdworking wages. To foster the comparability of different research results, the article suggests that scholars consider a wage correction factor to account for unpaid work. Finally, researchers should be aware that remuneration and work processes on crowdworking platforms can systematically affect the data collection method and inclusion of unpaid work.