Lemons in the Labour Market: Information Asymmetry, Adverse Selection, and Credit Exclusion in India's Gig Economy

India’s gig economy is projected to grow from 7.7 million workers in FY2020–21 to 23.5 million by 2029–30, yet the overwhelming majority of platform workers remain excluded from formal credit markets. The mechanism is informational, not financial: traditional credit-scoring systems cannot interpret the data that gig workers produce. Drawing on Akerlof’s (1970) model of adverse selection and Spence’s (1973) signalling theory, this paper argues that India’s gig credit market exhibits the structural properties of a lemons market, where lenders, unable to distinguish creditworthy from non-creditworthy informal borrowers, either price credit beyond reach or exit altogether.

Two existing data sources, Unified Payments Interface (UPI) transaction histories and e-Shram registration records, are identified as credible, low-cost signals capable of resolving this failure. Using secondary data from NITI Aayog, the Reserve Bank of India, the Ministry of Labour and Employment, and recent NBER empirical work, the paper proposes a dual-pillar policy framework: institutionalising platform behavioural data as an alternative underwriting input through the Unified Lending Interface (ULI), and mandating e-Shram registration for gig aggregators to establish formal worker identity. The central finding is that the infrastructure required to correct this market failure is already operational; what is missing is the institutional commitment to integrate it.