摘要
In recent years,there are many crimes related drug fraud occuring in China and many experts think that the main cause is that China Food and Drug Administration (CFDA)adopts announced inspection (AI).In order to circumvent this difficulty,CFDA has exploited unannounced inspection (UI)since 2014.In this paper,the authors study the problem of which one performs better, AI or UI.Specifically,the authors consider a supervisor,which decides the inspection approach,inspection strength and punishment to force the firm to put self-inspection effort to meet the requirements of Good Manufacturing Practice,and a firm,which produces a drug and decides its self-inspection effort. The authors use game theory to model this problem,characterize the equilibrium policies under AI, and compare the effects of the two approaches on preventing drug fraud under complete and incomplete information.The results show that under the complete information,UI performs better if the firm's technical level and the inspection cost are low and AI performs better otherwise.When the supervisor doesn't know the firm's technical level,if the low technical level is high,AI performs better.Otherwise, UI performs better if the inspection cost is low and AI performs better if the inspection cost is high.
In recent years, there are many crimes related drug fraud occuring in China and many experts think that the main cause is that China Food and Drug Administration(CFDA) adopts announced inspection(AI). In order to circumvent this difficulty, CFDA has exploited unannounced inspection(UI) since 2014. In this paper, the authors study the problem of which one performs better,AI or UI. Specifically, the authors consider a supervisor, which decides the inspection approach, inspection strength and punishment to force the firm to put self-inspection effort to meet the requirements of Good Manufacturing Practice, and a firm, which produces a drug and decides its self-inspection effort.The authors use game theory to model this problem, characterize the equilibrium policies under AI,and compare the effects of the two approaches on preventing drug fraud under complete and incomplete information. The results show that under the complete information, UI performs better if the firm’s technical level and the inspection cost are low and AI performs better otherwise. When the supervisor doesn’t know the firm’s technical level, if the low technical level is high, AI performs better. Otherwise,UI performs better if the inspection cost is low and AI performs better if the inspection cost is high.
基金
supported by Beijing Logistics Information Research Base,and the National Natural Science Foundation of China under Grant Nos.71390334 and 71661167009
