Data for Inclusion: The Redistributive Power of Data Economics

This paper evaluates the redistributive and efficiency impacts of expanding access to positive credit information in a financially excluded economy. Using microdata from Uruguay's 2021 household survey, we simulate three data regimes negative only, partial positive (Score+), and synthetic full visibility and assess their effects on access to credit, interest burden, and inequality. Our findings reveal that enabling broader data sharing substantially reduces financial costs, compresses interest rate dispersion, and lowers the Gini coefficient of credit burden. While partial visibility benefits a subset of the population, full synthetic access delivers the most equitable and efficient outcomes. The analysis positions credit data as a non-rival public asset with transformative implications for financial inclusion and poverty reduction.