This paper shows that higher information technology (IT) adoption by banks was associated to a larger increase in corporate lending in the months following the COVID-19 outbreak in Italy. Examining banks with heterogeneous degrees of IT adoption, we investigate the dynamics of credit and its allocation across firms using a new database with detailed information on banks’ IT expenditures and use of innovative technologies matched with bank-firm level data on credit growth before and during the pandemic. Using a diff-in-diff approach, we find that banks with a higher share of IT spending increased their credit more than others during the pandemic. The increase was concentrated in term loans extended to smaller and financially sounder companies; the effect was stronger in the initial phase of tighter restrictions to firm activity and individual mobility, and more significant for undertakings active in the sectors most affected by the shock. We provide evidence that these results are driven by bank’s ability to offer credit entirely online and bank’s use of artificial intelligence for credit risk assessment. Physical proximity between borrowers and lenders was important for credit provision during the pandemic, but only when combined with high level of IT adoption.

We present a model of endogenous network formation to recover unobserved social networks using only observable outcomes. We propose a novel equilibrium concept that allows for a sharp characterization of equilibrium behaviour and that yields a unique prediction under testable conditions. While the equilibrium is characterized by a large number of non-linear equations, we show that it can be efficiently employed to recover the networks by an appropriately designed approximate Bayesian computation method. We apply the model to recover the network of social links between lawmakers in the U.S. Congress using data from the 109th to 113th legislatures. We show that social connections are important for legislators’ productivities, and we identify some of the key determinants of network centralities in the U.S. Congress.