Although the policy performance field acknowledges the importance of adopting a long‐term decision making perspective to attain more sustainable policy outcomes, it overlooks the way in which sustainable governance conditions and policy performance relate to each other. We address this gap by investigating why some countries succeed in terms of policy performance while others do not. Applying a fuzzy‐set QCA to 41 Organization for Economic Co‐operation and Development and European Union countries of the Sustainable Governance Indicators data, we find out that successful economic and social policy performance is mainly driven by executive accountability conditions combined with effective implementation, while the high‐quality of media plays a key role in successful environmental policy performance. Considering the multiple paths that practitioners can follow toward sustainable development benefits, they can also learn how to shift from short‐term to long‐term thinking by identifying “what works and why,” formulating good practices and guidelines for better policy performance in the respective national contexts.
What is the most appropriate QCA solution type when engaging in a multimethod design that includes QCA and in-depth process-tracing (PT)? While either the intermediate or the parsimonious solution are generally favored in QCA-only studies, we identify important challenges that can emerge when selecting those solutions in a QCA-PT multimethod study. We particularly highlight the risk of mechanistic heterogeneity, omitted conditions, and draw the attention on the issue of generalization. We discuss each of these intertwined challenges in depth, and explain why the conservative solution is useful to consider in addressing them. We substantiate our arguments by drawing on a recently completed evaluation study that was commissioned by the Flemish ESF Agency in Belgium. In the study, we combined QCA and theory-guided in-depth process-tracing to uncover under what combinations of conditions (QCA) a training programme would lead to successful training transfer and how (PT) this happened in the successful cases. The article highlights the need to carefully consider the selection of solution types in any multimethod design comprising QCA.
The aim of this article is to build up a concept-informed research design to answer "why and how" a policy can make a difference. It demonstrates the potential and challenges of an innovative multimethod approach, which combines a configurational and mechanistic view to policy effectiveness. The article hereto draws on experiences in applying Qualitative Comparative Analysis and Process Tracing in one single evaluation. The study calls for a rigorous treatment of concepts, especially to avoid the risk of mechanistic heterogeneity. It unpacks important lessons in concept formation and operationalization, so as to ensure concept validity and to make strong causal inferences.
Cet article vise à retracer le développement de l’analyse QCA ( Qualitative Comparative Analysis ), initiée par Charles Ragin il y a vingt-cinq ans. Nous examinons d’abord en quoi le contexte dans lequel ceci s’est produit a façonné QCA comme approche de recherche, et présentons les caractéristiques initiales de cette approche. Ensuite, nous présentons les principaux débats suscités par QCA et les réponses apportées, en particulier en termes d’innovations techniques. Enfin, nous examinons les différentes manières dont QCA, en tant qu’ensemble de techniques ( crisp-set QCA, multivalue QCA et fuzzy-set QCA ) a été exploité jusqu’à présent, en termes disciplinaires et en termes d’usages plus descriptifs ou explicatifs. Nous concluons par quelques réflexions sur les promesses, mais aussi les périls d’un mainstreaming de QCA alors que cette dernière est mise à profit de manière de plus en plus diversifiée.
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