Labor Credit Scoring for Gig Workers

Gig workers often generate valuable performance data, yet their ratings remain platform-bound, opaque, and unusable for financial inclusion. This study proposes a Labor Credit Scoring (LCS) model and Labor Credit Bureau (LCB) to convert fragmented work histories into portable Skill Passports. LCS combines quantitative indicators, such as job volume, income stability, and certifications, with reputation indicators, including ratings, punctuality, complaints, and service behavior, using occupation-adjustable weights. A two-month pilot on Thailand’s krib platform with thirty workers (January–February 2025) showed favorable descriptive indicators in job completion, earnings, customer ratings, complaint rates, and retention. A subsequent analysis of krib administrative data used approximately 74,000 completed transactions from one thousand screened workers, aggregated into 32,145 worker–skill–month observations, and found a positive association between reputation scores and monthly income. The findings suggest that LCS has the potential to strengthen trust, worker mobility, and future credit access in Thailand’s gig economy.