The Labor Credit Scoring Model for the Gig Economy in Developing Countries

Gig workers in developing countries are part of an informal labor system, which means unstable jobs and being far from accessible capital from the banking system. This study presents Labor Credit Scoring (LCS), a novel framework that turns worker reputation into a transferable asset across platforms. A combination of quantitative, qualitative, and alternative data that was never used in platforms before, like skilled certifications or behavior data, LCS opens the black box of rating in traditional platforms and enables credible assessments without formal records. The model contributes to lifelong learning and incentivizes upskilling, laying the groundwork for a Labor Credit Bureau (LCB) that connects labor histories with financial institutions and public services. A 12-month field experiment in Thailand involving 300 participants across three cities demonstrated LCS’s effectiveness in improving worker performance, income, and trustworthiness. The results suggest that LCS is scalable, adaptable, and fits diverse labor platforms in the Global South that are professionally licensed, weak, or volunteer. It offers a policy-relevant solution for building a skills-based labor market with a central standard across platforms that will turn informal labor back to formality at the end.