In the presence of markup differences, externalities and other social considerations, the equilibrium direction of innovation can be systematically distorted. This paper builds a simple model of endogenous technology, which generalizes existing comparative static results and characterizes...

We develop a credit market competition model that distinguishes between the information span (breadth) and signal precision (quality), capturing the emerging trend in fintech/non-bank lending where traditionally subjective ("soft") information becomes more objective and concrete ("hard"). In a...

In recent years, the designs of many new blockchain applications have been inspired by the Byzantine fault tolerance (BFT) problem. While traditional BFT protocols assume that most system nodes are honest (in that they follow the protocol), we recognize that blockchains are deployed in...

This paper considers the problem of making inferences about the effects of a program on multiple outcomes when the assignment of treatment status is imperfectly randomized. By imperfect randomization we mean that treatment status is reassigned after an initial randomization on the basis of...

This chapter presents an economic approach to character and personality traits with an application to the study of virtue. Economists interpret psychological traits, including character traits and virtue, as strategies that shape responses to situations (actions) determined by underlying...

This chapter reviews recent advances in the task model and shows how this framework can be put to work to understand the major labor market trends of the last several decades. Production in each industry necessitates the completion of a range of tasks, which can be allocated to workers of...

We study specialized lending in a credit market competition model with private information. Two banks, equipped with similar data processing systems, possess "general" signals regarding the borrower's quality. However, the specialized bank gains an additional advantage through further...