Privacy is a feature inherent to the use of cash. With steadily increasing market shares of digital payment platforms, privacy in payments may no longer be attainable in the future. We explore the potential welfare impacts of reductions in privacy in payments. In our framework, firms may use data collected through payments to price discriminate future consumers. A public good aspect arises because individuals do not internalize the full cost of failing to protect their privacy and reduce social welfare by suboptimally choosing not to protect their privacy in payments. We discuss potential remedies, including the issuance of electronic cash.

This paper develops the concept of a “crypto multiplier,” which measures the equilibrium response of a cryptocurrency’s market capitalization to aggregate inflows and outflows of investors’ funds. The crypto multiplier takes high values when a large share of a cryptocurrency’s coins is held as an investment rather than being used as a means of payment. Blockchain data show that the share of coins held for the purpose of making payments is rather small for major cryptocurrencies suggesting large crypto multipliers. Our results highlight the need for market participants to be vigilant when accepting block holdings of a cryptocurrency as collateral or as compensation for seed funding. The crypto multiplier indicates that the liquidation value of block holdings of cryptocurrencies can be substantially below their prevailing market values.

Capturing financial network linkages and contagion in stress test models are important goals for banking supervisors and central banks responsible for micro- and macroprudential policy. However, granular data on financial networks is often lacking, and instead the networks must be reconstructed from partial data. In this paper, we conduct a horse race of network reconstruction methods using network data obtained from 25 different markets spanning 13 jurisdictions. Our contribution is two-fold: first, we collate and analyze data on a wide range of financial networks. And second, we rank the methods in terms of their ability to reconstruct the structures of links and exposures in networks.