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Optimal Contracts under Moral Hazard

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What does it mean Moral Hazard? We will use much more often the notion of Moral Hazard as hidden action rather than ex-post hidden information Moral Hazard means that the action (effort) that the A supplies after the signature of the contract is not verifiable This means that the optimal contract cannot be contingent on the effort that the A will exert Consequently, the optimal contracts will NOT have the form that they used to have when there is SI:: If e=e opt then principal (P) pays w(x i ) to agent (A) if not, then A will pay a lot of money to P

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What does the solution to the SI case does not work when there is Moral Hazard? Say that a Dummy Risk Neutral Principal offers to a Risk Averse A the same contract under moral hazard that he would have offered him if Information is Symmetric -Threat is not credible because e is no verifiable -…plus Wage does not change with outcome: no incentives. -RESULT: Agent will exert the lowest possible effort instead of e o If e=e o then principal (P) pays to agent (A) the fixed wage of: if not, then A will pay a lot of money to P

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Anticipation to the solution to the optimal contract in case of Moral Hazard Clearly, if the P wants that the A will exert a given level of effort, she will have to give some incentives The remuneration schedule will have to change according to outcomes This implies that the A will have to bear some risk (because the outcome does not only depend on effort but also on luck) So, the A will have to bear some risk even if the A is risk averse and the P is risk neutral In case of Moral Hazard, there will not be an efficient allocation of risk

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How to compute the optimal contract under MH For each effort level e i, compute the optimal w i (x i ) Compute Ps expected utility E[B(x i - w i (x i )] for each effort level taking into account the corresponding optimal w i (x i ) Choose the effort and corresponding optimal w i (x i ) that gives the largest expected utility for the P This will be e opt and its corresponding w i (xi) So, we break the problem into two: First, compute the optimal w i (x i ) for each possible effort Second, compute the optimal effort (the one that max Ps utility)

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Moral Hazard with two possible effort levels

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Moral Hazard with two possible levels of effort For simplification, lets study the situation with only two possible levels of effort: High (e H ) and Low (e L ) There are N possible outcomes of the relationship. They follow that: x 1

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Moral Hazard with two possible levels of effort For the time being, lets work in the case in which P is risk neutral and the A is risk averse Now, we should work out the optimal remuneration schedule w(x i ) for each level of effort: -Optimal w(x i ) for L effort ( this is easy to do) -Optimal w(xi) for H effort ( more difficult)

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Optimal w(x i ) for low effort In the case of low effort, we do not need to provide any incentives to the A. We only need to ensure that the A want to participate (the participation constraint verifies) Hence, the w(x i ) that is optimal under SI is also optimal under MH, that is, a fixed wage equals to: Why is it better this fixed contract that one than a risky one that pays more when the bad outcome is realized?

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Optimal w(x i ) for High effort This is much more difficult We have to solve a new maximization problem…

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We must solve the following program: The first constraint is the Participation Constraint The second one is called the Incentive compatibility constraint (IIC) Optimal w(x i ) for High effort

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The Incentive Compatibility Constraint tell us that The remuneration scheme w(x i ) must be such that the expected utility of exerting high effort will be higher or equal to the expected utility of exerting low effort In this way, the P will be sure that the A will be exerting High Effort, because, given w(x i ), it is in the Agents own interest to exert high effort About the IIC

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The IIC can be simplified: About the IIC So:

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Rewriting the program with the simplified constraints: The first constraint is the Participation Constraint The second one is called the Incentive compatibility constraint (IIC) Optimal w(x i ) for High effort

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The Lagrangean would be: Taking the derivative with respect to w(x i ), we obtain the first order condition (foc) in page 43 of the book. After manipulating this foc, we obtain equation (3.5) that follows in the next slide… Optimal w(x i ) for High effort

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Equation (3.5) is: Optimal w(x i ) for High effort By summing equation (3.5) from i=1 to i=n, we get: This means that in the optimum, the constraint will hold with equality(=) instead of (>=)

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Notice that (eq 3.5) comes directly from the first order condition, so (eq. 3.5) characterizes the optimal remuneration scheme Eq. (3.5) can easily be re-arranged as: Optimal w(x i ) for High effort We know that λ>0. What is the sign of μ? -It cannot be negative, because Lagrange Multipliers cannot be negative in the optimum -Could μ=0?

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Optimal w(x i ) for High effort If μ was 0, we would have: Intuitively, we know that it cannot be optimal that the Agent is fully insured in this case (see the example of the dummy principal at the beginning of the lecture) So, it cannot be that μ was 0 is zero in the optimum.

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Optimal w(x i ) for High effort Mathematically: If μ was 0, we would have:

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Optimal w(x i ) for High effort In summary, if μ was 0 the IC will not be verified !!! We also know that μ cannot be negative in the optimum Necessarily, it must be that μ> 0 This means that the ICC is binding !!! So, in the optimum the constraint will hold with (=), and we can get rid off (>=)

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Optimal w(x i ) for High effort Notice that these equations might be enough if we only have w(x1) and w(x2). If we have more unknowns, we will also need to use the first order conditions (3.5) or (3.7) Now that we know that both constraints (PC, and ICC) are binding, we can use them to find the optimal W(X i ):

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The condition that characterizes optimal w(x i ) when P is RN and A is RA is (3.5) and equivalently (eq 3.7): -This ratio of probabilities is called the likelihood ratio -So, it is clear that the optimal wage will depend on the outcome of the relationship because different x i will normally imply different values of likelihood ratio and consequently different values of w(x i ) ( the wage do change with x i ) !!!! Optimal w(x i ) for High effort

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The condition that characterizes optimal w(x i ) when P is RN and A is RA is (eq 3.7): We can compare this with the result that we obtained under SI (when P is RN and A is RA): So the term in brackets above show up because of Moral Hazard. It was absent when info was symmetric Optimal w(x i ) for High effort

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What does the likelihood ratio: mean? The likelihood ratio indicates the precision with which the result x i signals that the effort level was e H Small likelihood ratio: -p i H is large relative to p L i -It is very likely that the effort used was e H when the result x i is observed Example: Clearly, X 2 is more informative than X 1 about e H was exerted, so it has a smaller likelihood ratio

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What is the relation between optimal w(x i ) and the likelihood ratio when effort is high? λ >0, we saw it in the previous slides. μ>0, we saw it in the previous slides Notice: small likelihood ratio (signal of e H ) implies high w(x i )

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An issue of information Assume a RN P that has two shops. A big shop and a small shop. In each shop, the sales can be large or small. For each given of effort, the probability of large sales is the same in each shop The disutility of effort is also the same However, the big shop sells much more than the small shop For the same level of effort, will the optimal remuneration scheme be the same in the large and small shop?

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A question of trade-offs… P is RN and A is RA. This force will tend to minimize risk to the Agent Effort is no verifiable: This force will tend to make payments to the agent vary according to actual x i (introducing risk), as long as actual x i gives us information about the effort exerted The optimal remuneration schedule trades off these two forces Notice that it would not make sense to make the contract contingent on a random variable that: The agent cannot influence It is not important for the value of the relationship

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When will w(x i ) be increasing with x i ? If the likelihood ratio is decreasing in i, that is, if higher x i are more informative about e H than lower levels of effort. This is called the monotonous likelihood quotient property. Notice that this property does not necessarily have to hold: phplpl/ph x10.20.42 x20.10.44 x30.70.22/7

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Is the solution Pareto Efficient?

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Graphical analysis: P is RN and A is RA. Two outcomes: x1 and x2

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Draw f(w 2,w 1 ) in Fig 3.3, page 59 Now, we need to know how to draw the indifference curves Draw the indifference curves as in Fig 3.3, page 59

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Graphical analysis: P is RN and A is RA. Two outcomes: x1 and x2 Their slopes are:

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Graphical analysis: P is RN and A is RA. Two outcomes: x1 and x2 x 1 -w 1 x 2 -w 2 x 1 -w 1 =x 2 -w 2 For e H For e L Picture of the expected profit lines We can invert the axis, and make Fig 3.4

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Draw Figures 3.5 and 3.6 -First draw contracts L and H (but call them A & B) -Draw the Indifference curve for low effort through them (in order to measure the utility), and high effort through B -Then say that A will be the optimal contract under SI for e L. - Then say that B is the optimal contract under SI for e H - Explain why A and B are not incentive compatible under MH -Draw the optimal contract under MH: (H) -Show it is not Pareto Efficient Graphical analysis: P is RN and A is RA. Two outcomes: x1 and x2

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Optimal Contract with two levels of effort After we have computed the optimal remuneration scheme for High and Low effort, The principal will assess if she prefers High or Low effort levels The optimal contract will be the one that implements her preferred level of effort

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Optimal Contract with Moral Hazard So far, we have studied the case where P is RN and A is RA. If the P wants to implement High Effort, the SI solution (fixed wage) is not incentive compatible, hence a new optimal contract that takes into account the ICC must be computed Notice that if P is RA and A is RN, then the optimal solution in case of SI (the P will get a fixed rent, and the A will get the outcome minus the rent) is incentive compatible (the A will exert high effort). Consequently: Moral Hazard does not create problems when the P is RA and the A is RN. The SI solution can be implemented

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Moral Hazard with continuous effort

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Given the differentiable function F(x). If the point x 0 is its maximum, then it must be the case that the first derivative of F(x) evaluated at x 0 is equal to zero. That is F(x 0 )=0 However, other points that are not a maximum, can also satisfy the condition that the first derivative evaluated at them is zero (do a graph…) Just to remind you…

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The (IIC) is the last one. It tell us that e 0 should maximize the agents expected utility given w(x i ), so that it is in the Agents own interest to carry out e 0 Problem with continuous effort. Optimal contract to implement e 0

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The problem is very difficult to solve as it is because it is a maximization problem within another maximization problem. To simplify it: -If e 0 maximizes the agents expected utility, it must be the case that the derivative of the agents expected utility with respect to effort, evaluated at e 0 is zero, that is: Problem with continuous effort. Optimal contract to implement e 0 Is this restriction equivalent to the ICC of the previous slide? No always… draw a concave and a non-concave function… In a non-concave function, the effort levels that satisfy this second restriction are more than the ones that satisfy the ICC

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Substituting the real ICC by the simplified constraint is called the First Order Approach. When this approach is correct, economists says that the conditions for the first order approach verifies If the expected utility function is concave, then the First Order Approach is valid Problem with continuous effort. Optimal contract to implement e 0

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Problem with continuous effort. Optimal contract to implement e 0 Using First Order Approach:

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Notice that w(x i ) will depend on the result (sales) because the ratio of the right hand side depends on the results. So, the agent is not fully insured

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Problem with continuous effort. Optimal contract to implement e 0 Using First Order Approach: The previous analysis has given us the optimal remuneration scheme for a given level of effort (e 0 ) Now, we would have to study the optimal level of effort but we will not do that because it is too complicated from a mathematical point of view.

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Other issues in optimal contracts under Moral Hazard Limited liability Value of information Contracts based on severe punishments What happens when it is the agent who offers the contract?

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Limited liability Contracts where a P is RN and A is RA under SI followed the following scheme: –If the agent exerts effort e 0, he will get the fixed wage w 0 if he exerts another effort, he will have to pay to the principal a large sum of money This contract incorporates a threat to penalize the agent. This threat ensures that the agent does not find attractive to exert a level of effort that is not desired by the principal. Sometimes, the penalization is not legal or is not credible: –An employee cannot pay to the firm. The firm has always to obey the minimum wage –A bank cannot make the shareholders of a company to pay the company debts if the company goes bankrupt

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Limited liability If the penalization is not legal or it is not credible, the agent can exert a low level of effort even if: –Information is symmetric (no MH) –P is requesting a high level of effort So, the P will have to use the Incentive Compatibility constraint even if information is symmetric So, when there is limited liability, the optimal contract might give incentives to the agent even if the P is RN and information is symmetric

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The value of information under MH So far, we have studied that the contract will be contingent only on the result of the relationship (sales). This has been done for simplicity. Clearly, the principal is interested in using in the contracts signals that reveal new information on the agents effort These signals could be: –Others agents results –Control activities –State of Nature (lets see an example with this)

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Sales HSales L Effort H 0.60.4 Effort L 0.150.85 Example with state of nature.. In this case, it might be very costly to provide incentives so that the agent exerts high effort. This is because even if the agent exerts high effort, the probability of low sales is quite high. This might be because the probability of raining is too high…

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If it rainsIf it does not rain Sales HSales LSales HSales L Effort H0.30.70.90.1 Effort L0.20.80.10.9 Example cont… In this case, if it does not rain, the sales are quite good predictors of the effort, so it will not be very risky for the agent to exert high effort when it is not raining The optimal contract will depend on the sales level and whether it is raining or not Conditioning on the state of nature is useful because it allows better estimations of the agents effort thus reducing the risk inherent in the relationship

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The value of information under MH –On one side, a contract should exploit all available information in order to reduce the risk inherent in the relationship –On the other side, one must also consider the cost of obtaining the information Knowing whether it rained or not is free However, monitoring activities are not free Conditioning the contract in others agent results is not free (they could collude)

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Mechanisms based on severe punishments Assume that the P wants that the A exerts high effort Sometimes, very bad results are only possible if effort exerted is low In this case, a optimal contract could include very bad punishment in case the result obtained is very bad In this case, the P will ensure that the A does not exert low effort

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What happens when it is the agent who offers the contract? In some situations, it is the person that is going to carry out the job the one that offers the contract (ie. State agents when they are hired to sell a house) The Problem would be: –MAX Agent Expected Utility st (1) Principal expected utility >= reservation utility (2) Incentive compatibility constraint for the Agent

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What happens when it is the agent who offers the contract? (2) Needs to be taken into account because the P will only accept those contracts that are credible, that is, those contracts in which it is credible that the agent is going to exert the level of effort that he claims is going to exert The solution to this problem will have the same features than the one that we have studied (P will offer the contract to the agent) in terms of incentives and risk sharing, but what changes is who obtains the reservation utility…

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Multitask So far, we have analysed the case where the A works in one task However, it could be that the A will need to carry out two tasks (or more, but lets consider just two…) How will the optimal contract be in those circumstances?

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Multitask We can consider that the task are substitute or complements Complements: having exerted an effort for task 1, the effort for task 2 is reduced Substitutes: when exerting more effort on one increases the cost of the other

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Multitask If tasks are Complements, the principal is interested in motivating task 1, since in this way she simultaneously motivates the agent to work on task 2 If the tasks are Substitutes, then giving incentives for one task can be achieved in two ways: –Through the payments associated with each task –By reducing the opportunity cost through reductions in the incentives of the other tasks that the agent must do

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Multitask Multitasking can explain why incentive schemes might not be used even if there is MH…lets see why… Consider two substitute tasks, task 1 provide results that can be measured, but task 2 does not Hence, the principal could only give explicit incentives for Task 1 but not for Task 2

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Multitask For instance: –Task 1: carry out hip surgeries –Task 2: treat patients well, study about new illnesses, carry out medical research The principal must think what is best: –Provide strong incentives for Task 1 knowing that the A will abandon Task 2 at all –Do not provide incentives for Task 1, knowing that the Agent A will exert low effort in Task 1 but he will not abandon Task 2 so much The optimal solution might be not to give incentives at all, even if there is MH

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Multitask Other examples: –Bureaucratic systems: filling forms correctly, filling forms correctly… cannot be measured, so it might be better not to provide incentives for cases attended –Finishing dates for home construction: if we give incentives for the builder to finish the work by some date… it might happen at the expense of quality which is difficult to measure –These are examples where incentives might no be optimal even if there is MH because there is multitasking and the result of one Task cannot be measured

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Multitask Multitask is also relevant for the following: –The A can work in the task that gives profits to the principal –And in a private task that gives profits to himself –The A has to exert an effort for each task –Example: doctor that works for the NHS and works in his private practice Will the P allow the A to carry out his private task?

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Multitask Will the P allow the A to carry out his private task? If she does, The P will have to pay less to the A if she allows him to carry out his private task The final decision depends on a trade off… The P will not allow the A to carry out his private task if it is difficult to motivate the A to exert effort in the activity that he must carry out for the P, probably due to measurement problems

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