The Challenges of the Exponential Diffusion of Democratic Innovations: How to Accumulate Knowledge from Case Studies?

Abstract

In the last ten years democratic innovations have become a common feature of our polities. Hundreds of case studies on democratic innovations are written by social scientists every year. We now face the methodological challenge of combining meaningfully this knowledge. The purpose of this panel is to explore the specific challenges of comparative large-n case study analyses in the context of democratic innovations. The panel will focus on three main issues; 1) the identification of different effects (dependent variable) and causes of effects (independent variable(s)), 2) the selection of a sample of comparable cases, and 3) the selection of the appropriate method of analysis. How should we define effects of democratic innovations across a large set of case studies and how can we explain these effects is the first challenge. Is, for example, survival a good measure? How can we, for instance, define the quality of deliberation? What independent and intervening variables are helpful to explain the effects of democratic innovations? Case selection is the second challenge. How can we overcome the problem of heterogeneity of the unit of analysis? How can we analyze the impact, for example, of participatory budgeting, if participatory budgeting is a family of different democratic innovations? What is the appropriate sampling strategy? How can we create samples that can be used for statistical analysis? The third methodological challenge deals with the methods of large-n case study analysis techniques. What strategies are most appropriate for what research question? For example, when is meta-analysis appropriate? When is QCA appropriate? We are interested in empirical papers that analyze a large number of case studies in the context of democratic innovations, with statistical and non-statistical techniques. Theoretical papers are welcome if they address the methodological conceptualization of comparative large-n case study analyses.

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