We study how public data infrastructure interacts with private disclosure to shape entrant financing, firm entry, and resource allocation when borrower information is scarce and uncertainty is high. Using the China Industrial and Commercial Enterprise Registration Database (2001-2022), we document that entrant voluntary disclosure is positively associated with entry and local bank loan stocks, and that this relationship weakens when macro and policy uncertainty is high. Rollouts of municipal digital government data platforms are also positively associated with entry and local bank loan stocks. We develop a quantitative DSGE model in which borrower verifiability is jointly determined by private disclosure and public data infrastructure, and affects lending terms through expected monitoring losses at default and recovery discounts in distressed sales. In the model, an uncertainty shock immediately worsens financing terms, with entrants’ external finance premium spiking on impact. It tightens credit and reduces entry and aggregate output. Data infrastructure expansion raises baseline verifiability and increases the informativeness of a given unit of private disclosure in screening. We use the model to evaluate alternative policy packages that vary disclosure requirements, data infrastructure, and enforcement against manipulation. In steady state, among the regimes we consider, higher public data verifiability with strong enforcement under voluntary disclosure delivers the strongest joint improvement in entrant credit terms and wedge measures, with higher entry and output.
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