A Dynamic Analysis of Schelling’s Binary Corruption Model: A Competitive Equilibrium Approach

  • Jonathan P. Caulkins
  • Gustav FeichtingerEmail author
  • Dieter Grass
  • Richard F. Hartl
  • Peter M. Kort
  • Andreas J. Novak
  • Andrea Seidl
  • Franz Wirl


Schelling (in Micromotives and Macrobehavior, Norton, New York, 1978) suggested a simple binary choice model to explain the variation of corruption levels across societies. His basic idea was that the expected profitability of engaging in corruption depends on its prevalence. The key result of the so-called Schelling diagram is the existence of multiple equilibria and a tipping point. The present paper puts Schelling’s essentially static approach into an intertemporal setting. We show how the existence of an unstable interior steady state leads to thresholds such that history alone or history in addition to expectations (or coordination) is necessary to determine the long-run outcome. In contrast to the related literature, which classifies these two cases according to whether the unstable equilibrium is a node or a focus, the actual differentiation is more subtle because even a node can lead to an overlap of solution paths such that the initial conditions alone are insufficient to uniquely determine the competitive equilibrium. Another insight is that a (transiently) cycling competitive equilibrium can dominate the direct and monotonic route to a steady state, even if the direct route is feasible.


Corruption Schelling diagram Intertemporal competitive equilibria Thresholds History versus expectations 



The authors like to thank Marjorie Carlson, two referees, and the editor for their helpful comments. This research was supported by the Austrian Science Fund (FWF) under Grant P21410-G16 and P23084-N13.


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Jonathan P. Caulkins
    • 1
  • Gustav Feichtinger
    • 2
    • 3
    Email author
  • Dieter Grass
    • 2
  • Richard F. Hartl
    • 4
  • Peter M. Kort
    • 5
    • 6
  • Andreas J. Novak
    • 4
  • Andrea Seidl
    • 2
    • 3
  • Franz Wirl
    • 4
  1. 1.H. John Heinz III CollegeCarnegie Mellon UniversityPittsburghUSA
  2. 2.Department for Operations Research and Control Systems, Institute for Mathematical Methods in EconomicsVienna University of TechnologyViennaAustria
  3. 3.Wittgenstein Centre for Demography and Global Human Capital (IIASA,VID/OEAW,WU)Vienna Institute of Demography/Austrian Academy of SciencesViennaAustria
  4. 4.Department of Business AdministrationUniversity of ViennaViennaAustria
  5. 5.Department of Econometrics and Operations Research & CentERTilburg UniversityTilburgThe Netherlands
  6. 6.Department of EconomicsUniversity of AntwerpAntwerpBelgium

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