Going Local: Ethnographic investigation of spatio-temporal factors in the Danish context of digital platform labor

The inequalities and displacements produced by the proliferation of platform-mediated gig work and algorithmic management have created a growing literature in recent years (e.g., van Doorn et al. 2020; Lee et al. 2015). Particularly important to our work is the fact that platforms operate largely uncontrolled in terms of employment relations and algorithmic transparency (cf. Rosenblat & Stark 2016). To delve deeper into this question in our local context of Denmark we have established the Danish Research Network for Platform Work. The research within this network has so far resulted in 28 interviews with housecleaning and food-delivery platform workers in three locations in Denmark and we have also undertaken platform work ourselves in the period from 2019 and ongoing. A commonality between our projects is our focus on utilizing ethnographic methodologies which we use to go beyond numbers and algorithms to uncover the lived experience of the platform workers. Our preliminary findings point to a range of locally contingent factors that our interlocutors navigate within. Amongst these are working-holiday visas for migrant workers, tax exemptions, vehicle availability and more general labor market particularities. Moreover, the findings point to varied temporalities of workers’ participation, shaped by the interplay between locally contingent factors and our interlocutors’ specific situations. We argue that it is essential to complement our understanding of how platform workers’ livelihoods are shaped on a local level, by investigating which part of the labor force is attracted to this type of work (and why), how it is governed and whether the effect of the platform business model is to produce, sustain or simply take advantage of pre-existing inequalities. We bring these concerns of social justice and inequality as well as methodological implications to the fore in our discussion.
van Doorn, N., Ferrari, F., & Graham, M. (2020). Migration and Migrant Labour in the Gig Economy: An Intervention. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3622589
Lee, M. K., Kusbit, D., Metsky, E., & Dabbish, L. (2015). Working with machines: The impact of algorithmic and data-driven management on human workers. In Proceedings of the 33rd annual ACM conference on human factors in computing systems (pp. 1603-1612).
Rosenblat, A., & Stark, L. (2016). Algorithmic labor and information asymmetries: A case study of Uber’s drivers. International Journal of Communication, 10, 27.