PPS231S.01 Law, Economics, and Organization

1. PPS231S.01Law, Economics, and Organization Spring 2012 III.2. Contract Enforcement

2. Contract Enforcement Introduction Recall how the previous chapter developed a theory of contracting: optimal contracts successfully trade off risk sharing and incentives. The incentive problem stems from the unobservability of the agent’s effort. In this chapter, we ask: What happens when the principal can partially observe agent effort?

3. Contract Enforcement The study of contract enforcement via monitoring is increasingly pertinent given recent technological advances: • GPS devices in trucks • Call monitoring in call centers • Lojack car security system • Supervision of employees • Etc.

4. Contract Enforcement A priori, we can define two concepts: 1. Perfect enforcement: In this case, the principal can enforce her preferred level of effort if she is willing to pay a (usually very high) fixed cost. 2. Imperfect enforcement: In this case, the principal can invest in a monitoring technology. And normally, the more she invests in it, the better the enforcement (i.e., the closer to her preferred level of effort).

5. Contract Enforcement “A good deal of literature on transactions costs takes enforcement as a given, assuming either that it is perfect or that it is [absent]. In fact, enforcement is seldom either, and the structure of enforcement mechanisms and the frequency and severity of imperfection play a major role in the costs of transacting and in the forms that contracts take.” – Douglass C. North (1990)

6. Contract Enforcement As the previous quote makes clear, we will focus on cases where supervision and/or monitoring is imperfect. Indeed, in the very rare cases where monitoring is perfect, the contracting problem reverts back to a pure insurance problem. How so?

7. Contract Enforcement Monitoring Intensity Principle In the previous framework (Milgrom Roberts, 1992), assume that the variance of the performance measure can be controlled at a cost M(V), where M is the cost required to achieve an error variance as low as V. Evidently, M’ < 0. Further, M’’ > 0. In other words, as V increases, M decreases, but at a decreasing rate.

8. Contract Enforcement Then, the total CE becomes P(e) – C(e) – 1/2rb2 – M(V), i.e., same as earlier in the semester, except that we now subtract this new cost.

9. Contract Enforcement Holding e and b fixed, we choose V so as to maximize equation 7.4b, page 218. The first-order condition – which is necessary (but not sufficient) for maximization – is such that – 1/2rb2 – M’(V) = 0, or – 1/2rb2 = M’(V) i.e., the marginal cost of reducing V must equal its marginal benefit.

10. Contract Enforcement Monitoring Intensity Principle: Comparing two situations, one with b set high and another with b set lower, we find that V is set lower and more resources are spent on measurement when b is higher: When the plan is to make the agent’s pay very sensitive to performance, it will pay to measure that performance carefully.

11. Contract Enforcement Note that we focus here on static, not dynamic contracts, i.e., contracts that are signed only once and for all. What would change with dynamic (i.e., relational) contracts? We’ll also focus heavily on empirical applications, given that the theory is fairly simple.

12. Contract Enforcement Frisvold (JDE, 1994) Looks at farm-level data in India and first tests whether hired and household labor are homogeneous, i.e., whether they have the same productivity. The homogeneity of hired and household labor hypothesis is rejected – hired labor is less productive than household labor.

13. Contract Enforcement Frisvold (JDE, 1994) Then, a test of supervision is conducted. The result indicates that family member supervision is required to increase hired labor productivity. Output loss due to moral hazard is greater than 10% on over 40% of the plots. So, even with some measure of supervision, there is still a significant moral hazard problem.

14. Contract Enforcement Hubbard (QJE, 2000) Studies the effect of on-board computers (OBCs) in the trucking industry, and tries to distinguish between their incentive and resource-allocation (i.e., coordination) effects. Two classes of OBCs: trip recorders and electronic vehicle management systems (EVMS).

15. Contract Enforcement Hubbard (2000) Uses truck-level data from 1992, Hubbard finds evidence that incentive benefits are higher: 1. When opportunistic behavior (speeding up and taking longer breaks) is attractive to drivers; 2. When on-time arrival is important; 3. When third-party verification of drivers’ actions is valuable.

16. Contract Enforcement Ayres and Levitt (QJE, 1998) Study the effect of the Lojack versus, say, the Club. The Club: A car thief sees the club in a desirable vehicle, is likely to be deterred by its presence, and thus is likely to move on to another car. Lojack: A car thief does not know whether a desirable vehicle has such a system. Thus, it should have no direct effect on the likelihood of theft.

17. Contract Enforcement Ayres and Levitt (QJE, 1998) The Lojack, however, can be used to trace stolen vehicles. As such, if there is a significant number of cars that have Lojack in a city, there is in principle a significant externality effect. How so?

18. Contract Enforcement Ayres and Levitt (QJE, 1998) As expected, the availability of Lojack in a city entails a sharp decline in auto theft, and other crime rates do not decrease. The estimated marginal social benefit of Lojack is fifteen times its marginal cost. Those who install Lojack receive less than 10% of the social benefit, so Lojack adoption is underprovided by the market.

19. Contract Enforcement Jacob and Levitt (QJE, 2003) Develop an algorithm to detect teacher cheating on standardized tests, and test it using data from Chicago public schools. The algorithm essentially tries to detect teachers who systematically “help” their students do better on standardized tests.

20. Contract Enforcement Jacob and Levitt (QJE, 2003) Algorithm combines information on unexpected test score fluctuations and suspicious answer patterns. They estimate that severe cheating takes place in 4 to 5% of classrooms, and cheating frequency responds sharply to small changes in incentives.

21. Contract Enforcement Jacob and Levitt (QJE, 2003) The results highlight that high-powered incentives can induce behavioral distortions, and that “bright line” incentive systems make this even more true. Algorithm was used in public policy: teachers who were strongly suspected of cheating have already been fired.

22. Contract Enforcement Nagin et al. (AER, 2002) Look at the effect of monitoring in call centers. Innovation: Randomization over the extent of monitoring (i.e., percentage of calls monitored). Randomization allows truly identifying the causal effect of monitoring.

23. Contract Enforcement Nagin et al. (AER, 2002) Results indicate that if less than 3% of called are monitored, then moral hazard kicks in and is maximal when no call is monitored. Above 3%, however, the effect is flat, i.e., no moral hazard anymore. Thus, the threat of monitoring is as effective as monitoring itself (perhaps individuals overweigh small probabilities.)

24. Contract Enforcement Jacoby and Mansuri (2007) Study supervision in land tenancy contracts (i.e., sharecropping) in Pakistan. Their work is born out of the observation that almost no one has ever attempted to test for the effects of supervision in sharecropping.

25. Contract Enforcement Jacoby and Mansuri (2007) To study supervision, they develop a theoretical framework in which the enforcement cost varies between tenants. But then, heterogeneity of enforcement cost affects (i) contract choice; and (ii) intensity of tenant effort.

26. Contract Enforcement Jacoby and Mansuri (2007) First off, productivity differentials are fairly small between sharecropped and owned or (fixed) rented plots. Second, yields on plots of unsupervised tenants are significantly lower.

27. Contract Enforcement Jacoby and Mansuri (2007) The presence of unsupervised tenants, however, is not enough to make a big productivity difference between sharecropped and rented plots. They argue that taking supervision into account can help settle the sharecropper productivity debate.

28. Contract Enforcement Bellemare (2010) Data on contract farming arrangements in Madagascar. One principal, 200 agents (small producers). Most agents grow more than one contracted crop on their one contracted plot, which allows controlling for agent and plot unobserved heterogeneity. How so?

29. Contract Enforcement Bellemare (2010) Focuses on the impact of supervision. Here, supervision is a mixture of contract enforcement and agricultural extension. Problem: Need to control for adverse selection and moral hazard (here, “type” is technical ability.)

30. Contract Enforcement Bellemare (2010) The use of district-agent-plot fixed effects allows controlling for agent technical ability and unobserved plot characteristics. This allows focusing on extension/supervision. Production function estimates indicate that supervision increases yields; there is a concave relationship between yield and productivity; and supervision and agent education are substitutes.