Optimal incentive schemes need not be complicated. In a hidden action model with lognormally distributed output, Mirrlees (1974) shows that the first-best outcome can be approached arbitrarily closely by a suitably chosen sequence of step functions. The present paper shows that this result extends to any probability distributiuon that satisfies two conditions: 1) a convexity condition which ensures that the first-order approach is valid, and 2) a likelihood ratio condition which implies that low output values are a reliable signal that the agent has shirked. Both conditions are met by the normal, lognormal, gamma, beta, chi-squared, Weibull, t-, and F-distribution.
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