G16 is an essential reading. It provides a starting point for our model. The main complication is that, the agent has nonlinear utility over the payment. So how the payments are distributed over time matters a great deal. As a consequence, we cannot reduce the firm’s payoff to the function of virtual surplus—we must account of the non-transferability of the the firm’s cost to the agent.
Technically, we need to derive two conditions to guarantee IC and IR constraints: envelope and monotonicity. The envelope condition shows that each type should derive the premium beyond the low type for truthtelling. The premium consists of two terms: the weighted rents for his information advantage, and the gap to ensure within-period truthtelling. The monotonicity condition ensures that the net present value of the cash flow increases in the current type. The envelope condition puts constraints the payment scheme, while the monotonicity condition restricts the cash flow policy.
Once these two conditions are in place, we can apply the variational approach to derive sharp insights. In particular, we focus on the inefficiency loss, i.e., the distortion from the first best. The dynamics of the distortion depends on the interplay of risk aversion and market volatility. Depending on how they play out, the distortion can go either way. This property allows us to explain a wide range of salesforce practices.
Firms often rely on salespeople to promote demand and gather information. Because of the proximity to customers, salespeople have better information on customer preference, sales potential, and market trend. Such information is critical for new product development, production planning, and salesforce compensation. A critical question is, how can a firm motivate its salesforce so that they work hard to stimulate demand, and at the same time, truthfully disclosure market information they gather?
It is now well-known that a menu of linear contracts can elicit the market information and encourage hard work [Chen05]. This insight relies on a stylized static framework with several restrictions: Linear-contract, Exponential-utility, and Normal distribution (LEN model), and binary effort. Yet salespeople may work in a changing market, adjusting their efforts continuously over time. So the conventional insight may be a poor guide of what is really going on in practice. In this project, we develop a new model, accounting for continuous efforts and dynamic incentive. We highlight the blind spot of the existing framework, as well developing new insights.
Our main story goes as follows. There are two forces. First, the diminishing forecasting accuracy of the agent means that he will face more uncertainty in the future, because both the firm and the agent are equally uncertainty of the future. Second, the agent is risk averse, so that he hates the future uncertainty. The firm should take the advantage of this situation by offering him a long-term contract. Doing so can take out the agent’s information/forecasting advantage in the future.