Summarizing Data using Partially Ordered Set Theory: An Application to Fiscal Frameworks in 97 Countries

Julia Bachtrögler-Unger, Harald Badinger, Aurélien Fichet de Clairfontaine, Wolf Heinrich Reuter

Publication: Scientific journalJournal articlepeer-review


The widespread use of composite indices has often been motivated by their practicality to quantify qualitative data in an easy and intuitive way. At the same time, this approach has been challenged due to the subjective and partly ad hoc nature of computation, aggregation and weighting techniques as well as the handling of missing data. Partially ordered set (POSET) theory offers an alternative approach for summarizing qualitative data in terms of quantitative indices, which relies on a computation scheme that fully exploits the available information and does not require the subjective assignment of weights. The present paper makes the case for an increased use of POSET theory in the social sciences and provides a comparison of POSET indices and composite indices (from previous studies) measuring the 'stringency' of fiscal frameworks using data from the OECD Budget Practices and Procedures survey (2007/08).
Original languageEnglish
Pages (from-to)383 - 402
JournalStatistical Journal of the IAOS
Publication statusPublished - 2016

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