Translation | Dependence and precarity in the platform economy

date
Aug 29, 2020
slug
dependence-precarity-platform-economy
status
Published
summary
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.
tags
Academic
Sociology
Reading
Communication
Platform
Capitalism
type
Post
Schor, J. B., Attwood-Charles, W., Cansoy, M., Ladegaard, I., & Wengronowitz, R. (2020). Dependence and precarity in the platform economy. Theory and Society, 1-29.

Abstract

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.

摘要

Uber和类似平台公司的快速发展引领了学者对平台劳动的极大兴趣。学者们主要采用了两种方法解释平台工作的结果——不稳定性,即关注雇佣分类和无保障的劳动;以及通过算法进行的技术控制。两者都预测劳动者会有相对共同的经历。在对七个平台(Airbnb、TaskRabbit、Turo、Uber、Lyft、Postmates和Favor)的工人进行 112 次深度访谈的基础上,我们发现在不同平台之间和平台内部的经验的异质性。我们认为,由于平台劳动的制度化程度较弱,工人满意度、自主性和收入在不同平台之间和平台内部都有显著差异,这说明主流的解释是不充分的。我们发现,劳动者依靠平台收入支付基本开支而不是为补充收入而工作的程度解释了结果的差异,补充收入者更满意,收入更高。这说明平台对传统雇主是搭便车的(free-riding)。我们还发现,平台在提供者能赚取的收入、工作条件以及其培养满意工人的能力方面是有等级之分的。我们的研究结果表明,需要对平台采取新的分析方法,强调劳动力多样性、与传统劳动力市场的联系以及工人的依赖性。

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