platform exercises direction and control through algorithmic management (Eurofound 2021). Some countries have also mandated or encouraged voluntary registration to social insurance schemes, and have simplified the process to encourage uptake (Behrendt et al. 2018). Beyond protection through social insurance systems, some platforms have taken the initiative to offer private insurance to gig workers. For example, the platform Deliveroo has collaborated with Qover, a private insurance company, to provide private insurance for food delivery couriers in countries where Deliveroo operates, including Belgium, France, Ireland, and the Netherlands (Eurofound 2021). However, efforts to improve the protection digital platform workers have largely focused on location-based workers, given the complexity of regulating and providing protections for digital freelancers and workers performing digital microtasks, who perform tasks online and across country borders. Such efforts have also tended to be undertaken and studied more in developed country contexts, and relatively less is known about the complexity of regulation of gig work in low- and middle-income settings.

In situations where it is available, are informally employed workers willing to pay for social insurance? Using the contingent valuation method, which relies on eliciting respondents’ stated preferences, various studies have explored the willingness to pay – as well as the characteristics that affect willingness to pay – for social insurance among informally employed workers and workers employed in the informal sector, particularly in developing countries. The studies generally indicate that these workers are willing to pay for health insurance. One study in Wuhan, China, found that on average, informal sector workers were willing to pay more than the average cost of basic health insurance (Bärnighausen et al. 2007). The level of willingness to pay has been found to be moderated by income level, with workers having higher levels of income being more willing to pay, as in the case of Vietnam (Huyen and Van Minh 2014) and urban Bangladesh (Ahmed et al. 2016). The same studies found that workers with higher levels of education are willing to pay more for social insurance (Huyen and Van Minh 2014; Ahmed et al. 2016). Similarly, another study in urban Bangladesh found that the willingness to pay for health insurance among informal sector workers is higher for those who were exposed to an educational intervention on occupational solidarity and health insurance (Khan and Ahmed 2013), reflecting the important role of awareness and education. In some cases, the cost of the insurance premium has been found to affect the willingness to pay. For example, in Indonesia, 70% of informal sector workers in a survey of 400 households were willing to pay for national health insurance, but at a premium that was lower than the prevailing rate (Dartanto et al. 2016). A study in Togo also found that 92% of informal sector workers were willing to pay for mandatory health insurance, but would require subsidized premia (Djahini-Afawoubo and Atake 2018). A study on the willingness to pay for a hypothetical national health insurance scheme in Sarawak, Malaysia, found that more than 90% of respondents were willing to pay for this scheme (Ahmad, Ting and Zafar Ahmed 2018). The factors affecting the willingness to pay included education level and awareness or insurance literacy. Although these studies have sought to examine the question of affordability of (and therefore the potential need for subsidies for social insurance uptake), their reliance on stated preferences alone makes them susceptible to greater social desirability bias in response, as well as less effective to elicit the marginal valuation of particular job attributes (in this case social insurance coverage) holding other job characteristics constant. These limitations could potentially result in a gap between stated preferences and real-life choices.

This paper uses a vignette-based discrete choice experiment, which improves upon the contingent valuation method by assessing choices between scenarios based on marginal differences of attributes, to 4