RESUMEN
The rapid growth of Uber and analogous platform companies has led to considerable scholarly interest in the phenomenon of platform labor. Scholars have taken two main approaches to explaining outcomes for platform work-precarity, which focuses on employment classification and insecure labor, and technological control via algorithms. Both predict that workers will have relatively common experiences. On the basis of 112 in-depth interviews with workers on seven platforms (Airbnb, TaskRabbit, Turo, Uber, Lyft, Postmates, and Favor) we find heterogeneity of experiences across and within platforms. We argue that because platform labor is weakly institutionalized, worker satisfaction, autonomy, and earnings vary significantly across and within platforms, suggesting dominant interpretations are insufficient. We find that the extent to which workers are dependent on platform income to pay basic expenses rather than working for supplemental income explains the variation in outcomes, with supplemental earners being more satisfied and higher-earning. This suggests platforms are free-riding on conventional employers. We also find that platforms are hierarchically ordered, in terms of what providers can earn, conditions of work, and their ability to produce satisfied workers. Our findings suggest the need for a new analytic approach to platforms, which emphasizes labor force diversity, connections to conventional labor markets, and worker dependence.
RESUMEN
Background Crackdowns on urban sites with concentrated criminal activity are sometimes followed by geographical relocation of crime. Is this also the case in cyberspace, where illegal websites and online networks can be wiped clean, but also quickly rebuilt and replaced on new servers and URLs? Methods I address this question in three steps. First, I measure MDMA trade in a large digital market for drugs, before and after the arrest of a leading MDMA seller in the same market. Second, I count the number of available digital drug markets and vendor shops in the period February 2014-June 2018, to see if websites closed by police were replaced by new ones. Third, I track the digital movement and trading activities of individual drug sellers, before and after law enforcement shut down two large markets. Results After police arrested a leading MDMA seller, other MDMA sellers filled most - but not all - of the gap. A major law enforcement crackdown reduced the number of available markets, but new ones were created, and market counts eventually surpassed the previous peak. When law enforcement shut down two big markets, many of the sellers relocated to other e-commerce sites and continued high-earning operations there. Conclusion Arrests and market closures redirect digital drug trade to other sellers and markets. Hot spot policing in cyberspace might produce temporary results, but is arguably ineffective in the long run, as actors use information and communication technology's unique capacities to reorganize.