Forced volatility: earnings and incentives for gig work in quick commerce
12 June 2026
The internet Research Lab is happy to release our research report, Forced Volatility: Earnings and Incentives for Gig Work in Quick Commerce.
The quick commerce sector in India is experiencing explosive growth, with companies like Swiggy, Zepto, Instamart and Blinkit marketing convenience to customers through hyperfast doorstep delivery of groceries and daily essentials. These companies utilize a dense network of neighborhood-level “dark stores”, strategically-located micro-fulfillment centers that can often stock thousands of different products. The sector has become a catalyst for India's gig economy, with an estimated 60% surge in gig worker hiring in 2025. This reliance on a vast, flexible delivery workforce poses challenges related to wages, working conditions and social security.
These platforms use algorithms to determine pay, allocate tasks and review worker performance. Task allocation and wages can change according to multiple varying factors, including weather and number of workers available. These volatile algorithmic system create an information asymmetry, leaving workers to grapple with variable task allocation and earnings. Workers try to develop strategies to maximise pay but are actively denied knowledge of how these systems work, and this information asymmetry makes it almost impossible for gig workers to collect and present evidence regarding earnings and fluctuations in payouts over time and place.
Our project, GigSaathi, was aimed at addressing this information assyemtry, which forms the key dynamic of the power relationship between quick commerce platforms and gig workers. Our project piloted a chatbot allowing delivery workers in quick commerce to systematically collect their earnings. The chatbot would then process these screenshots to produce time series data on earnings, which we could then be used by workers to gain better insight into their own wages.
We supplemented this dataset with data on daily incentives collected from Blinkit stores in New Delhi, which revealed significant variability in earnings in wages (base pay and incentives), working hours, and distance travelled. A significant proportion of wages are contingent on invisible or opaque variables that are not reported to workers. The biggest company in the sector, Blinkit, does not have a minimum base pay per kilometer.
We establish little to no correlation between earnings and time spent per order, which suggests that workers are not compensated proportionately for the total time spent in delivering a given order. We also find that incentives are clearly deployed as a tool for labour control, with. higher earnings per order corresponding to a lower share of incentives in total earnings. Incentives make up about a quarter of weekly earnings on average, with very high variability. Our findings confirm that platforms promote dependence on incentives and gamified features of control, aiming to bind workers to the platform.
Our report adds to the mounting evidence that gig work in quick commerce in India is precarious, with wages regulated through opaque algorithms that force workers onto a cycle of chasing unknown incentives without any guarantees of higher pay.
GigSaathi's research team is Abhishek Sekharan, Ameya Kasliwal, Ambika Tandon, Gurshabad Grover, Javed Siddiqui and Omir Kumar. GigSaathi's software was developed by Kiwimesh Technologies, and the report and graphics for GigSaathi were designed by Srishti Verma. The team would like to thank the Rajdhani App Workers Union for their partnership.
