Working Paper: CEPR ID: DP18217
Authors: Fiorella De Fiore; Leonardo Gambacorta; Cristina Manea
Abstract: We document some stylized facts on big tech credit and rationalize them through the lens of a model where big techs facilitate matching on the e-commerce platform and extend loans. The big tech reinforces credit repayment with the threat of exclusion from the platform, while bank credit is secured against collateral. Our model suggests that: (i) a rise in big techs’ matching efficiency increases the value for firms of trading on the platform and the availability of big tech credit; (ii) big tech credit mitigates the initial response of output to a monetary shock, while increasing its persistence; (iii) the efficiency gains generated by big techs are limited by the distortionary fees collected from users.
Keywords: Monetary Policy; Credit Frictions; Big Tech
JEL Codes: E44; E51; E52; G21; G23
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Improved matching efficiency (C78) | Increased firm value and credit availability (G32) |
| Monetary shock (E49) | Persistence of output response (C69) |
| Fees charged by big techs (D49) | Limited benefits from big tech credit (H81) |
| Big tech credit (L63) | Mitigation of initial response of output to monetary shock (E19) |