The Timing of Complementary Innovations

Job Market Paper

Socially-valuable technologies sometimes require complementary innovations. This paper studies the development of innovations that exhibit such complementarity. At each point in time, a unit of attention is allocated across different innovation projects. The projects are completed stochastically in the form of breakthroughs. The social value of the technology depends on the set of completed projects by the time the agent decides to stop the development stage. In some cases it is optimal to develop the innovations in sequence. In others, it is optimal to develop multiple innovations simultaneously. I provide conditions that determine the efficient timing of development: sequential development is efficient when costs are high and there is more uncertainty about the innovations' rate of success. I compare the efficient allocation to the equilibrium outcome with a decentralized industry in which many firms compete for the development of the innovations.

Liability Design with Information Acquisition

with Bruno Strulovici

How to guarantee that firms perform due diligence before launching potentially dangerous products? We study the design of liability rules when (i) limited liability prevents firms from internalizing the full damage they may cause, (ii) penalties are paid only if damage occurs, regardless of the product’s inherent riskiness, (iii) firms have private information about their products' riskiness before performing due diligence. We show that (i) any liability mechanism can be implemented by a tariff that depends only on the evidence acquired by the firm if a damage occurs, not on any initial report by the firm about its private information, (ii) firms that assign a higher prior to product riskiness always perform more due diligence but less than is socially optimal, and (iii) under a simple and intuitive condition, any type-specific launch thresholds can be implemented by a monotonic tariff.

Market-Based Mechanisms

with Quitzé Valenzuela-Stookey

Decision makers frequently condition their actions on economic outcomes, e.g. asset prices, that they believe convey information about an unknown state. However the decision maker’s action, or expectations thereof, may also influence the outcome. In this paper we study the general problem of choosing decision rules mapping outcomes to actions in the presence of such feedback effects. We characterize the set of joint distributions of outcomes, actions, and states that can be implemented as the unique equilibrium by decision rules which satisfy a minimal notion of robustness to manipulation. Moreover, we show that all such equilibria are robust to model misspecification. This characterization of the feasible set greatly simplifies the problem of choosing decision rules. A simple graphical technique allows us to identify qualitative features of optimal policies. We illustrate the power of this approach with an application to corporate bailouts. The results are also useful for characterizing optimal decision rules when the requirement of unique implementation is relaxed.

A Taxation Principle with Non-Contractible Events

with Bruno Strulovici

In some legal settings it is not possible to contract with an agent ex ante. For example, a criminal process only start after the crime was committed and only if the agent is apprehended. We study a quasilinear single-agent setting with private information and private actions in which the intervention of the designer is only triggered by certain outcomes. We introduce a property of social choice functions, identifiability, and show that implementable social choice functions satisfying this property can be implemented with a tariff, that is transfers that depend only on the realized outcome.

Delayed Disclosure

with Ludvig Sinander

A principal owns a project, and recruits an agent to learn about its viability. The agent’s participation over time is observable and costly. Learning is private, allowing the agent to delay the (verifiable) disclosure of any discoveries. The principal incentivises the agent by promising a (history-dependent and possibly random) share of any revenue generated. What is the optimal contract?