Article
European Management Review (2006) 3, 44–59. doi:10.1057/palgrave.emr.1500048
Exploring complexity when diversity is limited: institutional complementarity in theories of rule of law and national systems revisited
Bruce Kogut1 and Charles Ragin2
- 1INSEAD, Department of Strategy, Boulevard de Constance, Fontainebleau, France
- 2Department of Sociology, University of Arizona, Social Sciences Building, Tucson, AZ, USA
Correspondence: B Kogut, INSEAD, Department of Strategy, Boulevard de Constance, 77300 Fontainebleau, France. Tel: +33 1 6071 4205; Fax: +33 1 6074 5582; E-mail: Bruce.kogut@insead.edu
Abstract
Categories reflect particular theories about the world in the form of causal and performative claims. Unlike attributes that are the mainstay of statistical analysis, these discrete entities consist of the contradiction of being easy to understand and yet hard to analyze. An important obstacle to the exploration of causal claims about categories (e.g. nations) is the limited diversity of observed cases. We propose the use of methodologies that take greater exploratory account of causal complexity and that respects the importance of case narratives for the explicit decisions made to arrive at theoretical claims. One such methodology is qualitative comparative analysis developed by Charles Ragin. This method is applied to data provided by two independent lines of study (i.e. rule of law and governance and varieties of capitalism) to show how the identification, and adoption, of prototypes is complex. Through the use of logic (e.g. De Morgan's law) and reductive inferences, we explore the space of observed and unobserved configurations, showing how the identification of institutional configurations relies upon logical assumptions that are rarely made explicit. The analysis rejects the hypothesis of rule of law and financial development and qualifies the institutional prototypes of corporatism and market as useful descriptions of the varieties of capitalism.
Keywords:
categories, institutional complementarity, Boolean inference, complexity



