
doi: 10.3982/ecta17386
We study a class of models of moral hazard in which a principal contracts with a counterparty, which may have its own internal organizational structure. The principal has non‐Bayesian uncertainty as to what actions might be taken in response to the contract, and wishes to maximize her worst‐case payoff. We identify conditions on the counterparty's possible responses to any given contract that imply that a linear contract solves this maxmin problem. In conjunction with a Richness property motivated by much previous literature, we identify a Responsiveness property that is sufficient—and, in an appropriate sense, also necessary—to ensure that linear contracts are optimal. We illustrate by contrasting several possible models of contracting in hierarchies. The analysis demonstrates how one can distill key features of contracting models that allow their findings to be carried beyond the bilateral setting.
linear contracts, Principal-agent models, Contract theory (moral hazard, adverse selection), principal-agent problem, robustness, hierarchical contracting
linear contracts, Principal-agent models, Contract theory (moral hazard, adverse selection), principal-agent problem, robustness, hierarchical contracting
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 9 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
