Algorithmic Shocks: Mapping the Temporal Volatility of Gig Worker Wages via Real-Time Platform Data

Building on Renuka Anoop Kumar (2024)’s exploration of gig economy wage instability, this idea proposes constructing a unique, time-stamped dataset from digital labor platforms (e.g., Uber, TaskRabbit, Fiverr) that tracks not just worker earnings but also platform algorithm updates, pricing changes, and demand-supply fluctuations. Instead of focusing only on month-to-month averages, the project would analyze sub-daily and weekly volatility, and crucially, link these to algorithmic or operational changes announced (or detected) on the platforms. By matching these “algorithmic shocks” to worker-level outcomes—especially for women and minorities—this research can uncover how opaque technological tweaks exacerbate or, in rare cases, alleviate wage inequality in near real-time. This approach goes beyond prior literature by combining real-time platform data and quasi-experimental methods, addressing a major gap in understanding the micro-dynamics and mechanisms behind gig wage instability and inequality. The findings could inform policy on algorithmic transparency and fair digital labor standards.

References:

1. PRECARIOUS PROSPERITY: THE GIG ECONOMY’S ROLE IN EMPLOYMENT GROWTH AND WAGE INEQUALITY. Renuka Anoop Kumar (2024). INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN COMMERCE, MANAGEMENT & SOCIAL SCIENCE.