This paper shows that local productivity spillovers propagate throughout the economy through the plant-level networks of multi-region firms. Using confidential Census plant-level data, we show that large manufacturing plant openings not only raise the productivity of local plants but also of...

Our paper evaluates recent regulatory proposals mandating the deferral of bonus payments and claw-back clauses in the financial sector. We study a broadly applicable principal agent setting, in which the agent exerts effort for an immediately observable task (acquisition) and a task for which...

This paper provides a complete characterization of optimal contracts in principal-agent settings where the agent's action has persistent effects. We model general information environments via the stochastic process of the likelihood-ratio. The martingale property of this performance metric...

This paper shows that local productivity spillovers propagate throughout the economy through the plant-level networks of multi-region firms. Using confidential Census plant-level data, we show that large manufacturing plant openings not only raise the productivity of local plants but also of...

This paper provides a complete characterization of optimal contracts in principal-agent settings where the agent's action has persistent effects. We model general information environments via the stochastic process of the likelihood-ratio. The martingale property of this performance metric...

This paper proposes an indirect tax approach to derive positive and normative implications of regulatory interference in compensation contracts, focusing on recent mandatory deferral and clawback requirements in the financial sector. Moderate deferral requirements for bonus-pay typically lead to...

This paper characterizes optimal compensation contracts in principal-agent settings where the agent's action has persistent effects. As additional informative signals arrive over time, deferred compensation has the benefit of exploiting better performance measurement which, for any information...