Yeji Sung

This paper evaluates the impact of the second series of Targeted Longer-Term Refinancing Operations (TLTRO2) on the amount of credit granted to non-financial private corporations and on the interest rates applied to loans in Italy, using data on credit transactions, bank and firm characteristics and a difference-in-differences approach. We find that TLTRO2 had a positive impact on the Italian credit market, encouraging medium-term lending to firms and reducing credit interest rates. While firms overall benefited from TLTRO2 irrespective of their risk category and size, we document heterogeneous treatment effects. Regarding firms’ risk category, the effects on credit quantities are larger for low-risk firms while those on credit interest rate are larger for high-risk firms. Regarding firms’ size, smaller firms benefited the most both in terms of amounts borrowed and interest rates. Furthermore, our evidence suggests that monetary policy transmission of TLTRO2 is stronger for banks with a low bad debt ratio in their balance sheets.

Standard models of information frictions explain sluggish forecasts but struggle to account for cases of excessive sensitivity. I argue that this limitation arises because these models define information frictions too narrowly—emphasizing constraints on processing new information while assuming past information remains fully accessible. I propose a broader framework that integrates both attention and memory frictions, which reflects constraints on processing new and past information, respectively. This unified model better matches observed forecast biases, enables joint identification of the two frictions from survey data, and provides novel insights for optimal monetary policy responses to cost-push shocks.

We propose a model of optimal decision making subject to a memory constraint in the spirit of models of rational inattention; our theory differs from that of Sims (2003) in not assuming costless memory of past cognitive states. The model implies that both forecasts and actions will exhibit idiosyncratic random variation; that average beliefs will exhibit a bias that fluctuates forever; and that more recent news will be given disproportionate weight in forecasts. The model provides a simple explanation for the over-reaction to news observed in the laboratory by Afrouzi et al. (2023).