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The rapid growth of the gig economy has opened up new economic opportunities for workers but also limited access to employer-provided benefits and systematic tax reporting. Using data from the Survey of Household Economics and Decision-making (SHED) and the Nonemployer Statistics (NES), we leverage state-level variation in 1099-K income reporting thresholds to examine how formalization of gig income affects workers’ health and financial wellbeing. We combine both data sources to analyze policy effects at the structural level and at the individual level. At the structural level, we find, lowering the reporting threshold reduced the number of nonemployer establishments while increasing gross receipts, suggesting exit of lower-income workers from the gig workforce. Using a novel approach to investigate individual-level effects, the paper introduces a new measure of gig market maturity based on two indicators - (a) years of operation of large platform work providers such as Uber and Airbnb, and (b) log number of nonemployer establishments prior to policy introduction. We use Two-Way Fixed Effects models to estimate the effect of market maturity indicators on workers wellbeing at the individual-level. Our findings indicate that workers in more mature markets are more likely to benefit from gig income formalization because compared to workers in newly established gig markets. We find that gig workers in more mature markets, report a 1.9 percentage point increase in gig income, and 2 percentage point increase in health insurance purchased through exchanges. We also observe a 3.5 percentage point increase in emergency savings among workers in more mature gig markets indicating these workers have improved financial preparedness, although retirement account ownership remains unchanged. However, a key finding is that state 1099-K reporting requirements induce compositional changes in the gig workforce. Income formalization increases the likelihood of lower-income workers exiting the gig workforce. This suggests the policy has distributional impacts with broader gains in economic wellbeing accruing primarily to gig workers with higher earnings. Further analysis will explore heterogenous treatment effects by race/ethnicity, gender, and age.