Methods. This study employed a scoping review methodology to rapidly map the field of the gig economy and workers' exclusion from the social safety net in the social work/welfare field. Two social service/welfare research databases and one sociology research database were searched for papers covering this domain; included were articles in English from the years 2016 - 2022. There was no regional boundary in the collected data; it covered more than ten countries, such as European countries, the U.S, Brazil, China, India, and Australia. Three researchers assessed articles, extracted data, and analyzed patterns of knowledge.
Results: Forty papers focusing on the welfare needs of platform workers were identified. Three main application domains emerged in the literature, including: (i) what is the current state of the literature addressing the gig economy; (ii) how are platform workers identified?; (iii) how are platform workers excluded from social welfare. The results suggest that workers in the gig economy have been excluded from the social safety net because they are defined as self-employed in most countries and therefore ineligible for work-based welfare benefits. Gig workers' autonomy over their labor was much weaker than the traditional self-employed, as algorithmic systems of their employers controlled their work. During the covid-19 period, platform workers have been exposed to severe vulnerability. They have worked in conditions where they cannot take care of their hygiene while the industry has become more competitive as individuals swarmed into the industry. We discovered efforts to redesign the social safety net to include them in some countries, such as Portugal. However, their discussion is still in its infancy.
Conclusions and Implications: Most previous research focused on describing platform workers' performance and difficulties with the qualitative methods. The lack of quantitative analysis can be attributed to the lack of agreement on how to legally and statistically distinguish them from traditional wage earners and how to measure the size of the population. We found a void to test theories that can be associated with platform workers' characteristics, such as theories related to stress, strain, self-esteem, loneliness, and various behaviors.