In the last years, Social Impact Bonds (SIBs) have gained popularity in the impact investing space. A number of scholars and practitioners are debating-in theory and practice-the opportunities, challenges and obstacles of these financial models. Amongst others, social uncertainty evaluation metrics appear as a critical factor for the future development of the SIB market. The present work aims to shed some light on this issue, by realizing a practical application of a model-which is an extension of a framework previously proposed-for social uncertainty evaluation in SIBs. In our exploratory analysis, 34 SIBs were selected for the empirical tests. We combined the Analytic Hierarchical Process (AHP) with the creation of aggregate measure, deriving by suitable indicators at the end of the tree. Our findings open new avenues for future research in the field of uncertainty factors in the SIB landscape. Finally, our results represent a basis for implementing a prediction model for social uncertainty evaluation.
Social uncertainty evaluation of social impact bonds: a model and practical application
Carè, R.Membro del Collaboration Group
;
2020-01-01
Abstract
In the last years, Social Impact Bonds (SIBs) have gained popularity in the impact investing space. A number of scholars and practitioners are debating-in theory and practice-the opportunities, challenges and obstacles of these financial models. Amongst others, social uncertainty evaluation metrics appear as a critical factor for the future development of the SIB market. The present work aims to shed some light on this issue, by realizing a practical application of a model-which is an extension of a framework previously proposed-for social uncertainty evaluation in SIBs. In our exploratory analysis, 34 SIBs were selected for the empirical tests. We combined the Analytic Hierarchical Process (AHP) with the creation of aggregate measure, deriving by suitable indicators at the end of the tree. Our findings open new avenues for future research in the field of uncertainty factors in the SIB landscape. Finally, our results represent a basis for implementing a prediction model for social uncertainty evaluation.File | Dimensione | Formato | |
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