Lecture 4 in Contracts Hidden Information

1. Lecture 4 in ContractsHidden Information Our final lecture analyzes optimal contracting in situations when the principal writing the contract has less information than the agent who accepts or rejects it. In this scenarios the principal is not only limited by a participation constraint, but also by incentive compatibility and truth telling constraints as well. Read Chapters 17 and 18 of Strategic Play.

2. Contracting with specialists Often managers know less than their own workers about the value employees contribute to and take from the firm. More generally, medical doctors and specialists diagnose the illnesses for patients, strategic consultants evaluate firm performance for shareholders, and building contractors tell property owners what needs to be done. This leads us to investigate how principals (like managers) should design contracts for agents (such as workers) when the information on their employees is incomplete. Consider a game between company headquarters and its research division, which is seeking to increase its budget so that it can proceed with “product development”.

3. Research and product development There are two types of discoveries, minor and major, denoted by j = 1, 2. The probability it is minor (j = 1) is p, and the probability it is major one (j = 2) is 1 - p. It costs cjx to develop a commercial product with appeal of x, where c1 > c2, which in turn produces a present value of log(1+x) to the firm. A budget of bi is allocated to the research division to develop the product up to a consumer appeal level of xi when the research division announces a discovery of type i = 1,2.

4. Research funding policy Headquarters forms a policy on funding product development, by announcing (b1,x1) and (b2, x2). After the policy formulation stage at headquarters, the division announces whether it has made a major discovery (i=2), a minor (i=1), or none at all (i=0). If i = 0, then shareholders net 0 and the research division nets r to sustain continued operations. Otherwise shareholders net: log(1+xi) – bi and the research division nets: bi – cjxi where cj is the true discovery.

5. Full information solution In this case headquarters directly sees the discovery, and sets the budget just high enough to motivate optimal development. Thus : bj = cjxj Substituting for bj into headquarters’ objective function, it chooses xj to maximize log(1+xj) – bj= log(1+xj) – cjxj Taking the first order condition and solving we obtain xj = 1/cj – 1 Funding is undertaken only when cj < 1 and profits, as defined below, are positive – log(ci) – 1 + cj

6. Participation and incentive compatibilitywhen there is incomplete information • Suppose headquarters does not directly observe the discovery, but relies exclusively on the divisional report . • The division will truthfully report the outcome of its activities if the following two constraints are met: • The participation constraint requires for each j: bj – cjxj 0 • The incentive compatibility constraint requires: b2 – c2x2 b1 – c2x1 and vice versa. Note that both inequalities cannot be satisfied by strict equality since c1 < c2.

7. Solving for the budgets • The participation constraint binds for the minor discovery (j = 1), but not for major ones. That is: b1 – c1x1= 0 b2 – c2x2 b1 – c2x1 > b1 – c1x1 = 0 • Substituting for b1 in the incentive compatibility constraint yields : b2 b1 + c2x2 – c2x1 = c1(x1 – x2) – c2x1 • Minimizing b2 we conclude the incentive compatibility constraint binds with strict equality for major discoveries (j = 2), but not for minor ones.

8. Optimal product development • Having derived the optimal budget as a function of product development, we choose x1 and x2 to maximize: p[log(1+x1) – b1] + (1 – p) [log(1+x2) – b2] = p[log(1+x1) – c1x1] + (1 – p) [log(1+x2) – c2x2 + c2x1 – c1x1] • = p[log(1+x1) – cx1] + (1 – p) [log(1+x2) – c2x2] • In the third line, c is called the virtual cost of x1 and is defined by the equation: c = c1 + (c1 – c2) (1 – p)/p

9. Solution to the full disclosure policy Mathematically this is almost the same problem as the full information case. Taking the first order condition and solving, we obtain: x1 = 1/c – 1 x2 = 1/c2 – 1 Substituting for x1 and x2 into the profit equation derived on the previous slide, we obtain: p[log(1+x1) – cx1] + (1 – p) [log(1+x2) – c2x2] = p[c– log(c)] + (1 – p)[c2 – log(c2)] – 1

10. Two other policies An alternative to full disclosure is to treat every discovery as minor (and let the research staff consume the surplus when they make a major discovery). If all discoveries are treated as minor, the profits are: c1 – log(c1) – 1 The last option is to reward only major discoveries. If only major discoveries are reported, then profits are: (1 – p)[c2 – 1 – log(c2)]

11. Numerical Parameterization • Suppose the probability of a minor discovery is p = 0.5. • Let the marginal cost of developing a minor discovery be c1 = 0.5, and the marginal cost of developing a major discovery be c2 = 0.25 • This implies the virtual cost of a minor discovery is c = c1 + (c1 – c2) (1 – p)/p = 0.5 + (0.5 – 0.25) = 0.75

12. Comparing the policy options on research disclosure Summarizing, the profits from a full disclosure policy are: p[c– log(c)] + (1 – p)[c2 – log(c2)] – 1 = 0.5[0.75 – log(0.75)] + 0.5[0.25 – log(0.25)]– 1 = 0.336988 If all discoveries are treated as minor, the profits are: c1 – log(c1) – 1 = 0.5 – log(0.5) – 1 = 0.193147 If only major discoveries are reported, then profits are: (1 – p)[c2 – 1 – log(c2)] = 0.5[0.25 – 1 – log(0.25)]= 0.318147

13. How profits depend on the probability of a minor invention If the probability of a major invention is very high, then a full disclosure policy is optimal. If the probability is very low, a nondiscriminatory policy is more profitable. Otherwise only major inventions are rewarded.

14. Lecture Summary Private information and outside options available to agents working for principals are captured through the truth telling, incentive compatibility and participation constraints. These constraints help determine the shape of the contract but limit its value. The more attractive the outside alternative to the agent, the better informed he is about the project relative to the principal, the harder it is to monitor the agent’s activities, then the lower the value of the contract to the principal. However ignoring these constraints is even more costly to the principal, because the agent may reject the contract, misinform the principal about the business situation, or not pursue the firm’s interests.