Trade, competition and welfare in global online labour markets : a "gig economy" case study

This study focuses on collaborative economy platforms that specialize in purely digital tasks that require no physical delivery or proximity between workers and their clients, which we call Online Labour Markets (OLMs). They have a global reach. There is a debate on job fragmentation and deteriorating working conditions in OLMs. This study emphasizes the economic opportunities and explores (a) the drivers of global trade in digital tasks, (b) the determinants of online wages and (c) the welfare impact of OLMs on workers and employers. This is a case study based on data obtained from a single UK-based OLM. Our findings cannot necessarily be generalised to other OLMs with different characteristics. Using panel data we find that the vast majority of employers are located in high-income countries while many workers are located in low-income countries. Workers in low-income countries are motivated to participate mostly by labour productivity gains and the corresponding higher wages. Workers in high income countries combine opportunities for additional work and income with the benefits of flexible time use and other non-wage benefits. Employers are motivated by wage savings and task unbundling. Despite the global nature of digital OLMs, there is an impression of home bias in hiring. OLMs are heterogeneous markets where about half of all transactions are settled above the lowest price bid. Workers' skills and experience as well as their countries of residence have an impact on the agreed wage and the probability of being hired. Worker quality signaling induces superstar effects and a very uneven distribution of work. Taking into account only the difference between online and offline wages in the countries of residence of workers and employers we estimate that this particular OLM has positive monetized welfare effects, both for workers (17%) and even more so for employers (70%). Unobservable non-monetized benefits such as increased flexibility and savings in transport or migration costs for workers and task unbundling for employers give a further boost to welfare. The extent of unbundling is demonstrated by the very short duration of the average task in this OLM: slightly less than 8 hours. Workers in this case study platform are self-employed and responsible for compliance with regulations in their country of residence.