Methodological Quip – Sequence Analysis and Optimal Matching in Social Sciences

Leonard Seabrooke and Emelie Nillson introduce a novel methodological approach – Sequence Analysis (SA) to distinguish between trends and groups and identify narratives from large clusters of data in their recent Governance article that measures professional skills in the IMF. The approach appears to be a nifty way to examine change and outline sequences when measuring trends and patterns. After that, they deploy Optimal Matching algorithm derived from computer science and genetics to gather ‘sequences of information to assess the degree of similarity or difference among them by using pattern search algorithms.’

The algorithm enables researchers to ‘identify differences in sequences and then the ‘cost’ of manipulating the sequences to transform one into the other by way of insertions, deletions, and substitutions.’ Using these tools, they gather information on the work histories of IMF staffers that have worked in their FSAP program to examine how the nature of staffer’s professional experiences have shifted over the ten year period. They find evidence of more market-oriented managers that have had strong market experiences in the private sector before entering the IMF for this task. This trend provides evidence for the claim that international organizations are capable of instituting strong internal changes and are less isomorphist than imagined. Moreover, the shift towards more private sector staffers and their experiences and knowledge signify the resilience of market-friendly ideas at the apex of international economic governance. IOs are also becoming a locus where professionalisation is being taken more seriously as the demand for specific skills and knowledge acquire precedence over long-held institutional practices and traditions.


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