Forced volatility: earnings and incentives for gig work in quick commerce

Abstract

India's quick commerce sector has grown explosively, with platforms such as Blinkit, Zepto, Swiggy Instamart, and others delivering groceries and daily essentials through dense networks of neighbourhood dark stores. This growth has made the sector a major catalyst for India's gig economy, but its reliance on a large, flexible delivery workforce raises pressing concerns about wages, working conditions, and social security. These platforms use opaque algorithms to set pay, allocate tasks, and evaluate performance, with earnings varying by factors such as weather and worker availability. The resulting information asymmetry leaves workers unable to reconstruct how their pay is determined or how it fluctuates over time and place. We present GigSaathi, a pilot intervention designed to address this asymmetry; a chatbot that enables delivery workers to systematically collect earnings screenshots, which are processed into time series data on individual wages. We supplement this with incentive data collected from Blinkit stores in New Delhi. Our analysis reveals substantial variability in base pay, incentives, working hours, and distance travelled, with a large share of earnings contingent on undisclosed variables; Blinkit, the sector's largest firm, guarantees no minimum base pay per kilometre. We find little to no correlation between earnings and time spent per order, indicating that workers are not compensated proportionately for delivery time, and show that incentives function as a tool of labour control orders with higher per order earnings carry a lower incentive share. Incentives average roughly a quarter of weekly earnings but vary widely. These findings add to mounting evidence that quick commerce gig work in India is precarious, with wages governed by opaque algorithms that push workers into chasing unknown incentives without any guarantee of higher pay.