by Caryn Peiffer and Rosita Armytage, April 2018, Development Leadership Program Research Paper 52. Available as pdf
Summary: Increasingly, development scholars and practitioners are reaching for exceptional examples of positive change to better understand how developmental progress occurs. These are often referred to as ‘positive outliers’, but also ‘positive deviants’ and ‘pockets of effectiveness’.
Studies in this literature promise to identify and examine positive developmental change occurring in otherwise poorly governed states. However, to identify success stories, such research largely relies on cases’ reputations, and, by doing so, overlooks cases that have not yet garnered a reputation for their developmental progress.
This paper presents a novel three-stage methodology for identifying and examining positive outlier cases that does not rely solely on reputations. It therefore promises to uncover ‘hidden’ cases of developmental progress as
well as those that have been recognised.
The utility of the methodology is demonstrated through its use in uncovering two country case studies in which surprising rates of bribery reduction occurred, though the methodology has much broader applicability. The advantage of the methodology is validated by the fact that, in both of the cases identified, the reductions in bribery that occurred were largely previously unrecognised.
Contents:
Summary
Introduction 1
Literature review: How positive outliers are selected 2
Stage 1: Statistically identifying potential positive outliers in bribery reduction 3
Stage 2: Triangulating statistical data 6
Stage 3: In-country case study fieldwork 7
Promise realised: Uncovering hidden ‘positive outliers’ 8
Conclusion 9
References 11
Appendix: Excluded samples from pooled GCB dataset 13
Rick Davies comment: This is a paper that has been waiting to be published, one that unites a qual and quant approach to identifying AND understanding positive deviance / positive outliers [I do prefer the latter term, promoted by the authors of this paper]
The authors use regression analysis to identify statistical outliers, which is appropriate where numerical data is available.. Where the data is binary/categorical is possible to use other methods to identify such outliers. See this page on the use of the EvaLC3 Excel app to find positive outliers in binary data sets.
