The Soft Power 30: getting to grips with the measurement challenge

Abstract

The question all countries face today is how best to achieve their foreign policy goals in an increasingly complex and inter-dependent world. Challenges and opportunities now rarely sit within national borders. Power has never been more diffuse, moving not just from west to east but also from governments to non-state actors. The digital revolution is accelerating this diffusion of power, enabling citizens to come together within, and beyond, countries in a way that has never been possible. The result is a world in which the use of soft power is increasingly important to the shaping of global outcomes – whether they be driven by state or non-state actors. This paper provides an overview of the research undertaken as part of the Soft Power 30 study in 2015 and 2016. It focuses more on the theoretical considerations that went into the development of a composite index designed to measure the soft power resources of countries in a comparable way. The aim of the paper is to give a more detailed account of the methodology used in the research, report on results so far, consider the growing implications of digital diplomacy for soft power, and give a look ahead to considerations for future research.

Keywords:

• digital diplomacy
• international relations
• soft power

Notes on Contributors

Jonathan McClory is the creator of the Soft Power 30 Index. He leads Portland’s Place Branding practice. He is a specialist in soft power, public diplomacy, cultural relations and place branding. Olivia Harvey is an Account Executive in Portland's Government Advisory team.

Notes

1. In calculating the index, all data were normalized in order to ensure that each variable was on a single scale. This allows for the comparison of data across a diverse set of metrics that would otherwise be incomparable. Normalization was calculated according to the min-max method, which converts raw data to a figure between the range of 0 to 1, expressed as a percentage. The formula for normalizing data according to the standard model for constructing composite indicators is as follows:

However, some variables were binned into quartiles or deciles where the range of the scale was too large to warrant a standard approach to normalizing the data. When a variable was deciled, countries in the bottom 10% were given a score of 10% and countries in the top 10% were given a score of 100%. There were only a few cases where a given metric was so skewed by outliers that a decile or quartile approach to normalization was deemed appropriate.

Within each objective data sub-index, metrics were given equal weighting in the calculation of the sub-index score. This was done as no justification could be found in the literature for weighting some variables more than others. The calculated score for each sub-index was then combined with the normalized scores of the seven polling categories to form a final score for each country. In calculating the final score, the objective sub-indices were weighted 70% of the final score and the average polling scores 30%. The 70%-to-30% objective-to-subjective weighting was done because the index prioritizes the soft power resources that are objective and tangible.