“Optimal Contracts for Experimentation,” M. Halac, N. Kartik & Q. Liu (2013)

Innovative activities have features not possessed by more standard modes of production. The eventual output, and its value, are subject to a lot of uncertainty. Effort can be difficult to monitor – it is often the case that the researcher knows more than management about what good science should look like. The inherent skill of the scientist is hard to observe. Output is generally only observed in discrete bunches.

These features make contracting for researchers inherently challenging. The classic reference here is Holmstrom’s 1989 JEBO, which just applies his great 1980s incentive contract papers to innovative activities. Take a risk-neutral firm. They should just work on the highest expected value project, right? Well, if workers are risk averse and supply unobserved effort, the optimal contract balances moral hazard (I would love to just pay you based on your output) and risk insurance (I would have to pay you to bear risk about the eventual output of the project). It turns out that the more uncertainty a project has, the more inefficient the information-constrained optimal contract becomes, so that even risk-neutral firms are biased toward relatively safe, lower expected value projects. Incentives within the firm matter in many other ways, as Holmstrom also points out: giving employee multiple tasks when effort is unobserved makes it harder to provide proper incentives because the opportunity cost of a given project goes up, firms with a good reputation in capital markets will be reluctant to pursue risky projects since the option value of variance in reputation is lower (a la Doug Diamond’s 1989 JPE), and so on. Nonetheless, the first order problem of providing incentives for a single researcher on a single project is hard enough!

Holmstrom’s model doesn’t have any adverse selection, however: both employer and employee know what expected output will result from a given amount of effort. Nor is Holmstrom’s problem dynamic. Marina Halac, Navin Kartik and Qingmin Liu have taken up the unenviable task of solving the dynamic researcher contracting problem under adverse selection and moral hazard. Let a researcher be either a high type or a low type. In every period, the researcher can work on a risky project at cost c, or shirk at no cost. The project is either feasible or not, with probability b. If the employee shirks, or the project is bad, there will be no invention this period. If the employee works, the project is feasible, and the employee is a high type, the project succeeds with probability L1, and if the employee is low type, with probability L2<L1. Note that as time goes on, if the employee works on the risk project, they continually update their beliefs about b. If enough time passes without an invention, belief about b becomes low enough that everyone (efficiently) stops working on the risky project. The firm's goal is to get employees to exert optimal effort for the optimal number of period given their type.

Here’s where things really get tricky. Who, in expectation and assuming efficient behavior, stops working on the risky project earlier conditional on not having finished the invention, the high type or the low type? On the one hand, for any belief about b, the high type is more likely to invent, hence since costs are identical for both types, the high type should expect to keep working longer. On the other hand, the high type learns more quickly whether the project is bad, and hence his belief about b declines more rapidly, so he ought expect to work for less time. That either case is possible makes solving for the optimal contract a real challenge, because I need to write the contracts for each type such that the low type does not ever prefer the high type payoffs and vice versa. To know whether these contracts are incentive compatible, I have to know what agents will do if they deviate to the “wrong” contract. The usual trick here is to use a single crossing result along the lines of “for any contract with properties P, action Y is more likely for higher types”. In the dynamic researcher problem, since efficient stopping times can vary nonmonotically with researcher type, the single crossing trick doesn’t look so useful.

The “simple” (where simple means a 30 page proof) case is when the higher types efficiently work longer in expectation. The information-constrained optimum involves inducing the high type to work efficiently, while providing the low type too little incentive to work for the efficient amount of time. Essentially, the high type is willing to work for less money per period if only you knew who he was. Asymmetric information means the high type can extract information rents. By reducing the incentive for the low type to work in later periods, the high type information rent is reduced, and hence the optimal mechanism trades off lower total surplus generated by the low type against lower information rents paid to the high type.

This constrained-optimal outcome can be implemented by paying scientists up front, and then letting them choose either a contract with progressively increasing penalties for lack of success each period, or a contract with a single large penalty if no success is achieved by the socially efficient high type stopping time. Also, “Penalty contracts” are nice because they remain optimal even if scientists can keep their results secret: since secrecy just means paying more penalties, everyone has an incentive to reveal their invention as soon as they create it. The proof is worth going through if you’re into dynamic mechanism design; essentially, the authors are using a clever set of relaxed problems where a form of single crossing will hold, then showing that mechanism is feasible even under the actual problem constraints.

Finally, note that if there is only moral hazard (scientist type is observable) or only adverse selection (effort is observable), the efficient outcome is easy. With moral hazard, just make the agent pay the expected surplus up front, and then provide a bonus to him each period equal to the firm’s profit from an invention occurring then; we usually say in this case that “the firm is sold to the employee”. With adverse selection, we can contract on optimal effort, using total surplus to screen types as in the correlated information mechanism design literature. Even though the “distortion only at the bottom” result looks familiar from static adverse selection, the rationale here is different.

Sept 2013 working paper (No RePEc IDEAS version). The article appears to be under R&R at ReStud.