This index – the world’s most comprehensive assessment and comparison of global soft power – aims to bring new clarity and understanding to the soft power resources of the world’s major nations.
See explanation in the Notes below the table.
Countries are ranked highest to lowest in softpower
(poorest countries at the bottom).
NOTES:
The index compares the relative strength of
countries’ soft power resources; assessing the
quality of a country’s political institutions, the
extent of their cultural appeal, the strength of
their diplomatic network, the global reputation of
their higher education system, the attractiveness
of their economic model, and a country’s digital
engagement with the world. Only where absolutely
necessary metrics are controlled for population or
GDP. But this is not done often as there is ultimately
no such thing as ‘soft power per capita’.
For some composite indices, whether the measure
is government effectiveness, quality of life,
economic competitiveness or prosperity, there
is usually a single, objective outcome measure,
against which an index can be structured. This is
usually done by using multiple regression to test
the relative contribution of metrics towards the
single outcome measure. Unfortunately, there
is no single objective outcome measure for the
successful leveraging of soft power. Without an
objective outcome measure, using a regression
analysis for variable selection is impossible for
our index. As a result, the indicators across all
the objective data had to be selected based on an
analysis of existing literature on soft power.
In calculating the index, all data was normalised
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. Normalisation was
calculated according to the min-max method, which
converts raw data to a figure between the range of
0 to 1. The formula for normalising data according
to this method is given in an OECD publication on
constructing composite indicators and is as follows :
Itqc = (xtqc – minc (xqt0))/(maxc(xqt0) – (minc (xqt0))
However, some variables we also binned into
quartiles or deciles where the range of the scale
was too large to warrant a standard approach to
normalising 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 normalisation was
deemed appropriate.
Within each 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 normalised 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 prioritises the soft power
resources that exist in reality. Opinion is important,
but The Soft Power 30 aims to measure objective,
tangible assets that contribute to a countries
soft power
For the subjective data, ComRes designed and
ran new international polling to give an accurate
assessment of favourability towards specific
aspects of countries that international audiences
would find attractive. It was essentially designed
to provide a subjective account of key soft power
assets of countries. ComRes conducted the
research online between the 21st May and 8th June
The following questions were asked (each rated
on a 0-10 scale, where 0 represented a very
negative opinion, and 10 represented a very
positive opinion):
- Favourability towards foreign countries;
- Perceptions of cuisine of foreign countries;
- Perceptions of how welcoming foreign countries
are to tourists;
- Perceptions of technology products of foreign
countries;
- Perceptions of luxury goods produced by
foreign countries;
- Trust in foreign countries’ conduct in
global affairs;
- Desire to visit foreign countries for work
or study;
- Perceptions of foreign countries’ contributions
to global culture.
These eight metrics were used to develop a
regression model, where ‘favourability towards
foreign countries’ was the dependent variable,
and the remaining questions were independent
variables. This measured the extent to which
the remaining perceptions predict favourability
towards a country in the dataset. The regression
model allowed each metric to be appropriately
weighted, to minimise the impact of any bias in the
choice of questions.
Countries for the index were not selected according
to a rigid formula or set criteria, but chosen to
give a representative sample of the world’s major
powers, including countries from every geopolitical
region.
The selection process included
major OECD countries, the emerging BRIC nations
and several smaller countries that have carved
out a reputation exceeding their size. Data was
collected for 50 countries in total, and we have
published the top 30 ranking countries.
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