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A Theory of Endogenous Institutional Change

Avner Greif

Stanford University - Department of Economics; Canadian Institute for Advanced Research (CIFAR)

David Laitin

Stanford University - Department of Political Science; Stanford Immigration Policy Lab

American Political Science Review, Forthcoming

This paper asks why and how institutions change. How does an institution persist in a changing environment and how do processes that it unleashes lead to its own demise? The paper shows that the game theoretic notion of self-enforcing equilibrium and the historical institutionalist focus on process are both inadequate to answer these questions. Building on a game theoretic foundation, but responding to the critique of it by historical institutionalists, the paper introduces the concepts of quasi-parameters and self-reinforcement. With these concepts, and building on repeated game theory, a dynamic approach to institutions is offered, one that can account for endogenous change (and stability) of institutions. Contextual accounts of formal governing institutions in early modern Europe and the informal institution of cleavage structure in the contemporary world provide illustrations of the approach.

Number of Pages in PDF File: 57

Keywords: Institutional Change, Institutions, Dynamics

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Date posted: July 1, 2004  

Suggested Citation

Greif, Avner and Laitin, David, A Theory of Endogenous Institutional Change. American Political Science Review, Forthcoming. Available at SSRN: https://ssrn.com/abstract=548363

Contact Information

Avner Greif (Contact Author)
Stanford University - Department of Economics ( email )
Landau Economics Building
579 Serra Mall
Stanford, CA 94305-6072
United States
650-725-8936 (Phone)
Canadian Institute for Advanced Research (CIFAR) ( email )
180 Dundas Street West, Suite 1400
Toronto, Ontario
David Laitin
Stanford University - Department of Political Science ( email )
Stanford, CA 94305
United States
Stanford Immigration Policy Lab
30 Alta Road
Stanford, CA 94305
United States
Feedback to SSRN

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