Soft Power 30, 2015 - Country Rankings
SOURCE: Portland.
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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).

1 United Kingdom 75.61
2 Germany 73.89
3 United States 73.68
4 France 73.64
5 Canada 71.71
6 Australia 68.92
7 Switzerland 67.52
8 Japan 66.86
9 Sweden 66.49
10 Netherlands 65.21
11 Denmark 63.20
12 Italy 63.09
13 Austria 62.00
14 Spain 61.70
15 Finland 60.19
16 New Zealand 60.00
17 Belgium 58.85
18 Norway 57.96
19 Ireland 55.61
20 South Korea 54.32
21 Singapore 52.50
22 Portugal 48.97
23 Brazil 46.63
24 Poland 46.50
25 Greece 45.73
26 Israel 44.51
27 Czech Republic 43.26
28 Turkey 42.55
29 Mexico 42.52
30 China 40.85


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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This page was last modified 4-AUG-15
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