Which Inspection Approach Is Better to Prevent Drug Fraud: Announced or Unannounced?

Manman Zhang, Juliang Zhang, T. C.E. Cheng, Jose Maria Sallan, Guowei Hua

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)1571-1590
Number of pages20
JournalJournal of Systems Science and Complexity
Volume31
Issue number6
DOIs
Publication statusPublished - 1 Dec 2018

Keywords

  • Drug inspection
  • game theory
  • healthcare
  • incomplete information
  • public policy

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Information Systems

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