Constrained Pareto efficiency

The condition of Constrained Pareto optimality is a weaker version of the standard condition of Pareto Optimality employed in Economics which accounts for the fact that a potential planner (i.e. the government) may not be able to improve upon a decentralized market outcome, even if that outcome is inefficient. This will occur if he is limited by the same informational or institutional constraints as individual agents.

The most common example is of a setting where individuals have private information (for example a labor market where own productivity is known to the worker but not to a potential employer, or a used car market where the quality of a car is known to the seller but not to the buyer) which results in moral hazard or adverse selection and a sub-optimal outcome. In such a case, a planner who wishes to improve the situation is unlikely to have access to any information that the participants in the markets do not have. Hence he cannot implement allocation rules which are based on idisoyncratic characteristics of individuals, for example "if a person is of type A, they pay price p1, but if of type B, they pay price p2" (see Lindahl prices). Essentially, only anonymous rules are allowed of the sort "Everyone pays price p" or rules based on observable behavior; "if any person chooses x at price px then they get a subsidy of ten dollars, and nothing otherwise". If there exists no allowed rule that can successfully improve upon the market outcome, then that outcome is said to be Constrained-Pareto optimal.

Note that the concept of Constrained Pareto optimality assumes benevolence on the part of the planner and hence it is distinct from the concept of government failure, which occurs when the policy making politicians fail to achieve an optimal outcome simply because they are not necessarily acting in the public's best interest.

See also

• Mechanism design
• Revelation principle
• Pivotal mechanism
• Market for lemons

References

Magill and Quinzii, Theory of Incomplete Markets, MIT Press, 202, pg. 104 [1].