Calibrating and Aggregating Multi-Dimensional Concepts with Fuzzy Sets: “Human Development” and the Quality of Democracy ☆

Abstract

In the social sciences we often employ complex multi-dimensional concepts such as development, democracy or marketplace. In the past decades, these have become increasingly operationalized and documented in huge international databases such as those of the United Nations, the World Bank, and a host of other public or academic institutions. Serious problems arise when these data are taken at face value, as for example GDP per capita, without considering its specific context and meaning, or when multi-dimensional concepts are aggregated into single indices by such simple statistical procedures as arithmetic means. The validity of such concepts is then seriously put into question by misleading measurement or oversimplifying aggregation. The increasing use of set-theoretical methods, as in fuzzy-set QCA, now provides an important alternative by calibrating data more carefully and by aggregation procedures which allow to take necessary conditions of a multi-dimensional concept into account. This is the focus of this paper, which employs two examples of a broad, political-practical, relevance.