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학술논문법경제학연구2013.06 발행

Optimal Damages under Participation Constraints

Optimal Damages under Participation Constraints

김일중(성균관대학교); 김재홍(한동대학교); 김현석(Department of Economics, Iowa State University, Am)

10권 1호, 85~108쪽

초록

We design a comprehensive‘optimal damages’(OD) scheme which can elicit‘voluntary participation’from contracting parties as well as‘optimal behavior’. Particularly, the necessity to consider explicitly participation constraints has been motivated from the well-praised‘perfect expected damages’(PED). Such constraints for PED are shown to be more critical than commonly presumed. OD is then utilized to re-examine, in a fairly unified manner, the properties of popular measures in the literature. Above all, it is formally proven that PED can fail to induce promisor’participation; in that case, PED should have no efficiency implication. A few other interesting findings are confirmed such as the fragility of the efficient-performance feature under expectation damages and a likelihood of efficient breach under reliance damages. Finally, we demonstrate that

Abstract

We design a comprehensive‘optimal damages’(OD) scheme which can elicit‘voluntary participation’from contracting parties as well as‘optimal behavior’. Particularly, the necessity to consider explicitly participation constraints has been motivated from the well-praised‘perfect expected damages’(PED). Such constraints for PED are shown to be more critical than commonly presumed. OD is then utilized to re-examine, in a fairly unified manner, the properties of popular measures in the literature. Above all, it is formally proven that PED can fail to induce promisor’participation; in that case, PED should have no efficiency implication. A few other interesting findings are confirmed such as the fragility of the efficient-performance feature under expectation damages and a likelihood of efficient breach under reliance damages. Finally, we demonstrate that

발행기관:
한국법경제학회
분류:
법경제학

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Optimal Damages under Participation Constraints | 법경제학연구 2013 | AskLaw | 애스크로 AI