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We analyze optimal relational contracts for a group (team) of multitasking agents with hidden actions. Contracts are based on noisy signals that may be correlated across agents and between tasks. The optimal contract defines a performance measure in the form of an index (a scorecard) for each agent, and awards a bonus to the highest performing agent, provided his or her index exceeds a hurdle. An optimal index generally involves benchmarking against other agents, and this may, in combination with the hurdle requirement, introduce a cooperative element in the otherwise competitive incentive structure. For agents with separate tasks and normally distributed signals, we find that strong correlation (either positive or negative) across agents is beneficial, while larger correlation within each agent's tasks is detrimental for efficiency, and that this has implications for optimal organization of tasks. For agents with common tasks the optimal contract may have features of both tournament and team incentives. The tournament aspect incentivizes an agent to exert effort on his own task, while the hurdle necessary to receive a bonus also incentivizes an agent to help his peers. In our setting this hybrid scheme can only be optimal if signals from agents' tasks are negatively correlated. Otherwise pure team incentives are optimal.

Reward systems based on balanced scorecards typically connect pay to an index, i.e. a weighted sum of multiple performance measures. However, there is no formal incentive model that actually describe this kind of index contracts as an optimal solution. In this paper, we show that an index contract may indeed be optimal if performance measures are non-verifiable so that the contracting parties must rely on self-enforcement. Under standard assumptions, the optimal self-enforcing (relational) contract between a principal and a Multitasking agent is an index contract where the agent gets a bonus if a weighted sum of performance outcomes on the various tasks (the index) exceeds a hurdle. For a parametric (multinormal) specification, the efficiency of the contract improves with higher precision of the index measure, since this strengthens incentives. Correlations between measurements may for this reason be beneficial. For a similar reason, the principal may also want to include verifiable performance measures in the relational index contract in order to improve incentives.

This paper analyzes relational contracts under moral hazard. We first show that if the available information (signal) about effort satisfies a generalized monotone likelihood ratio property, then irrespective of whether the first-order approach (FOA) is valid or not, the optimal bonus scheme takes a simple form. The scheme rewards the agent a fixed bonus if his performance index exceeds a threshold, like the FOA contract of Levin (2003), but the threshold can be set differently. We next derive a sufficient and necessary condition for non-verifiable information to improve a relational contract. Our new informativeness criterion sheds light on the nature of an ideal performance measure in relational contracting.

We study relational contracting and renegotiation in environments with external enforcement of long-term contractual arrangements. An external, long-term contract governs the stage games the contracting parties will play in the future (depending on verifiable stage-game outcomes) until they renegotiate. In a contractual equilibrium, the parties choose their individual actions rationally, they jointly optimize when selecting a contract, and they take advantage of their relative bargaining power. Our main result is that in a wide variety of settings, in each period of a contractual equilibrium the parties agree to a semi-stationary external contract, with stationary terms for all future periods but special terms for the current period. In each period the parties renegotiate to this same external contract, effectively adjusting the terms only for the current period. For example, in a simple principal-agent model with a choice of costly monitoring technology, the optimal contract specifies mild monitoring for the current period but intense monitoring for future periods. Because the parties renegotiate in each new period, intense monitoring arises only off the equilibrium path after a failed renegotiation.

We analyze relational contracting between a principal and a team of agents where only aggregate output is observable. We deduce optimal team incentive contracts under di¤erent set of assumptions, and show that the principal can use team size and team composition as instruments in order to improve incentives. In particular, the principal can strengthen the agents' incentives by composing teams that utilize stochastic dependencies between the agents' outputs. We also show that more agents in the team may under certain conditions increase each team member's effort incentives, in particular if outputs are negatively correlated.

This paper analyses and compares optimal relational contracts be- tween a principal/firm and a set of agents when (a) only aggregate out- put can be observed, and (b) individual outputs can be observed. We show that the optimal contract under (a) is a team incentive scheme where each agent is paid a maximal bonus for aggregate output above a threshold and a minimal (no) bonus otherwise. The team's effi- ciency decreases with its size (number of agents) when outputs are non-negatively correlated, but may increase considerably with size if outputs are negatively correlated. In the case where individual output can be observed, we show that the optimal contract is a tournament scheme where the conditions for an agent to obtain the (single) bonus are stricter for negatively compared to positively correlated outputs. We finally show that if agents have bargaining power, firms may delib- erately choose to organize production as a team where only aggregate output is observable. The team alternative is more likely to be supe- rior under negatively correlated outputs.

The economics of crime and punishment postulates that higher punishment leads to lower crime levels, or less severe crime. It is how- ever hard to get empirical support for this rather intuitive relationship. This paper offers a model that can contribute to explain why this is the case. We show that if criminals can spend resources to reduce the probability of being detected, then a higher general punishment level can increase the crime level. In the context of antitrust enforcement, the model shows that competition authorities who attempt to fight cartels by means of tougher sanctions for all o¤enders may actually lead cartels to increase their overcharge when leniency programs are in place.

When a worker is offered performance related pay, the incentive effect is not only determined by the shape of the incentive contract, but also by the probability of contract enforcement. We show that weaker enforcement may reduce the worker s effort, but lead to higher- powered incentive contracts. This creates a seemingly negative rela- tionship between effort and performance pay.

We analyse tax competition when a multinational firm has invested in two countries but also has an outside option, e.g., towards a third country. An interesting finding is that more attractive outside options for firms may constitute a win-win situation; the firm as well as its present host countries may gain when this occurs. The reason that it benefits the host countries is that an enhanced outside option reduces the ineffciencies of tax competition. An implication of the result is that better outside options for multinational firms may reduce the gains from host countries policy coordination and thus reduce those countries incentives to coordinate their policies. Also, with a development where outside options become more accessible, the perceived costs of tax competition, e.g., in terms of underprovision of public goods, may be overestimated. Our findings may also have implications for international negotiations, since it provides an argument for mutual reduction of entry barriers, as this may improve outside options.