Big techs and the credit channel of monetary policy

Summary

Focus 

Big techs are lending to small and medium-sized enterprises and vendors on their e-commerce platforms, thus encroaching on financial markets. These changes in financial intermediation could affect monetary policy transmission in at least two ways. First, the business model of big techs depends on using vast amounts of data instead of collateral to solve agency problems between borrowers and lenders. By using machine learning and big data to generate credit scores, big techs can assess a company's creditworthiness more accurately than traditional credit bureau ratings can. As a result, this may decrease the relevance of the "collateral channel" and, simultaneously, increase the responsiveness of credit to changes in firms' business conditions. Second, the threat of reputational damage, or of being excluded from the e-commerce platform, serves as an extra-legal but highly effective means of contract enforcement for big tech firms.

Contribution 

We view big tech credit through the lens of a model where big techs facilitate matching on the e-commerce platform and extend loans. While bank credit is backed by collateral, big techs reinforce credit repayment by threatening to exclude borrowers from the platform. The most significant difference between big tech credit and bank credit lies in the borrowers' cost of default. If a firm defaults on bank credit, it loses its collateral, usually real estate. In contrast, if a company defaults on big tech credit, it loses access to the e-commerce platform, thereby jeopardising future profits. 

Findings 

We find that, first, an improvement in big techs' matching efficiency on the e-commerce platform raises the value for firms of trading on the platform and accessing big tech credit. Higher future profits ease financing constraints and increase firms' production, driving aggregate output closer to the efficient level. Second, the response of credit and output to a monetary policy shock depends on how sensitive the firms' opportunity cost of default on big tech credit (ie the stream of future profits from operating on the big tech platform) is compared with the cost of defaulting on bank credit (ie the value of physical collateral). In our baseline calibration, the introduction of big tech credit mitigates the initial responses of aggregate credit and output to a monetary shock. However, it increases the persistence of the effect of monetary policy on the macroeconomy. Third, big techs' macroeconomic efficiency gains are limited by the distortionary nature of the fees collected from their users.

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.

JEL Classification: E44, E51, E52, G21, G23

Keywords: Big Techs, monetary policy, credit frictions