Bargaining Against the Machine: A Theory of Bargaining Power in the Gig Economy
Michael Maffie & Mark Gough
What the paper says
Purpose This chapter extends the concept of associational power into the context of online platform work. To do so, this chapter centers on platforms' underlying economic model – the multisided market – to better understand how workers may be able to collectively influence their terms and conditions of employment. In illuminating how labor's associational power functions in platform work arrangements, this model helps explain how collective action may function in the “gig economy” and provides a roadmap for future academic inquiry. Methodology This chapter develops a model of associational power in the ride-hail industry which can be extended to markets defined by geographically specific platforms, like ride-hail, delivery, domestic work, and home healthcare workers. Findings This chapter finds that there is substantial promise for labor unions and other worker associations in the gig economy. Additionally, we find that even well-intended regulations can harm workers' power if the regulators do not grapple with the structure of digital platforms. Originality/Value This chapter identifies the foundations of workers' associational power: network effects and multihoming. In contrast to traditional analyses of workers' power, labor's ability to withdraw its effort from a single employer is not the basis of its collective power. Instead, labor's power resides in its ability to withdraw its labor from a competitor and promise its exclusive labor to a single platform. Existing literature has explored the interaction between network effects and market power from the companies' perspective but has yet to extend this analysis to workers' perspective.
8 citations
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 1.00 × 0.4 = 0.40 |
| M · momentum | 0.80 × 0.15 = 0.12 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.