World Competitiveness Ranking

The World Competitiveness Yearbook (WCY) is a comprehensive annual report and worldwide reference point on the competitiveness of countries
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01:182021 IMD World Competitiveness Ranking Results
IMD World Competitiveness Ranking 2021 - An overview
Released today, on 17 June 2021, the data explores multiple factors that affect the prosperity of 64 economies.
Europe displays regional strength in world competitiveness ranking while Singapore slips

Innovation, digitalization, welfare benefits, and social cohesion are key to economic performance in the 2021 rankings, topped with Switzerland (1st), Sweden (2nd), Denmark (3rd), the Netherlands (4th), and Singapore (5th)

Top-performing economies are characterized by varying degrees of investment in innovation, diversified economic activities, and supportive public policy, according to the experts at the World Competitiveness Center. 

 

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2021 Country 2020 Ranking Change  
1 Switzerland 3 +2
2 Sweden 6 +4
3 Denmark 2 -1
4 Netherlands 4 - -
5 Singapore 1 -4
6 Norway 7 +1
7 Hong Kong SAR 5 -2
8 Taiwan, China 11 +3
9 UAE 9 - -
10 USA 10 - -
11 Finland 13 +2
12 Luxembourg 15 +3
13 Ireland 12 -1
14 Canada 8 -6
15 Germany 17 +2
16 China 20 +4
17 Qatar 14 -3
18 United Kingdom 19 +1
19 Austria 16 -3
20 New Zealand 22 +2
21 Iceland 21 - -
22 Australia 18 -4
23 Korea Rep. 23 - -
24 Belgium 25 +1
25 Malaysia 27 +2
26 Estonia 28 +2
27 Israel 26 -1
28 Thailand 29 +1
29 France 32 +3
30 Lithuania 31 +1
31 Japan 34 +3
32 Saudi Arabia 24 -8
33 Cyprus 30 -3
34 Czech Republic 33 -1
35 Kazakhstan 42 +7
36 Portugal 37 +1
37 Indonesia 40 +3
38 Latvia 41 +3
39 Spain 36 -3
40 Slovenia 35 -5
41 Italy 44 +3
42 Hungary 47 +5
43 India 43 - -
44 Chile 38 -6
45 Russia 50 +5
46 Greece 49 +3
47 Poland 39 -8
48 Romania 51 +3
49 Jordan 58 +9
50 Slovak Republic 57 +7
51 Turkey 46 -5
52 Philippines 45 -7
53 Bulgaria 48 -5
54 Ukraine 55 +1
55 Mexico 53 -2
56 Colombia 54 -2
57 Brazil 56 -1
58 Peru 52 -6
59 Croatia 60 +1
60 Mongolia 61 +1
61 Botswana NEW    
62 South Africa 59 -3
63 Argentina 62 -1
64 Venezuela 63 -1
Country 2017 2018 2019 2020 2021
Argentina 58 56 61 62 63
Australia 21 19 18 18 22
Austria 25 18 19 16 19
Belgium 23 26 27 25 24
Botswana - - - - 61
Brazil 61 60 59 56 57
Bulgaria 49 48 48 48 53
Canada 12 10 13 8 14
Chile 35 35 42 38 44
China 18 13 14 20 16
Colombia 54 58 52 54 56
Croatia 59 61 60 60 59
Cyprus 37 41 41 30 33
Czech Republic 28 29 33 33 33
Denmark 7 6 8 2 3
Estonia 30 31 35 28 26
Finland 15 16 15 13 11
France 31 28 31 32 29
Germany 13 15 17 17 15
Greece 57 57 58 49 46
Hong Kong SAR 1 2 2 5 7
Hungary 52 47 47 47 42
Iceland 20 24 20 21 21
India 45 44 43 43 43
Indonesia 42 43 32 40 37
Ireland 6 12 7 12 13
Israel 22 21 24 26 27
Italy 44 42 44 44 41
Japan 26 25 30 34 31
Jordan 56 52 57 58 49
Kazakhstan 32 38 34 42 35
Korea Rep. 29 27 28 23 23
Latvia 40 40 40 41 38
Lithuania 33 32 29 31 30
Luxembourg 8 11 12 15 12
Malaysia 24 22 22 27 25
Mexico 48 51 50 53 55
Mongolia 62 62 62 61 60
Netherlands 5 4 6 4 4
New Zealand 16 23 21 22 20
Norway 11 8 11 7 6
Peru 55 54 55 52 58
Philippines 41 50 46 45 52
Poland 38 34 38 39 47
Portugal 39 33 39 37 36
Qatar 17 14 10 14 17
Romania 50 49 49 51 48
Russia 46 45 45 50 45
Saudi Arabia 36 39 26 24 32
Singapore 3 3 1 1 5
Slovak Republic 51 55 53 57 50
Slovenia 43 37 37 35 40
South Africa 53 53 56 59 62
Spain 34 36 36 36 39
Sweden 9 9 9 6 2
Switzerland 2 5 4 3 1
Taiwan, China 14 17 16 11 8
Thailand 27 30 25 29 28
Turkey 47 46 51 46 51
UAE 10 7 5 9 9
Ukraine 60 59 54 55 54
United Kingdom 19 20 23 19 18
USA 4 1 3 10 10
Venezuela 63 63 63 63 64

 

Country 2017 2018 2019 2020 2021
Argentina 56 60 61 60 59
Australia 25 19 14 23 19
Austria 40 17 20 15 20
Belgium 23 44 37 25 24
Botswana - - - - 62
Brazil 59 54 57 56 51
Bulgaria 37 28 47 34 41
Canada 16 13 12 10 14
Chile 34 41 48 50 53
China 2 2 2 7 4
Colombia 41 51 50 52 56
Croatia 57 56 55 45 50
Cyprus 28 22 19 13 13
Czech Republic 19 16 17 16 23
Denmark 20 26 26 21 17
Estonia 44 32 44 35 29
Finland 45 43 35 43 34
France 24 30 34 32 28
Germany 7 12 9 5 3
Greece 61 61 60 55 52
Hong Kong SAR 11 9 10 28 30
Hungary 36 39 46 19 8
Iceland 39 57 54 58 55
India 18 21 24 37 37
Indonesia 33 27 25 26 35
Ireland 4 11 6 12 22
Israel 31 37 40 39 36
Italy 38 47 53 42 39
Japan 14 15 16 11 12
Jordan 62 62 62 62 63
Kazakhstan 54 49 45 48 45
Korea Rep. 22 20 27 27 18
Latvia 53 53 52 53 44
Lithuania 42 36 39 33 33
Luxembourg 3 4 4 8 10
Malaysia 13 8 11 9 15
Mexico 30 35 28 38 49
Mongolia 60 48 58 59 58
Netherlands 9 6 13 1 2
New Zealand 32 33 36 40 32
Norway 48 40 32 30 25
Peru 50 55 41 51 60
Philippines 26 50 38 44 57
Poland 27 18 18 29 27
Portugal 51 42 43 41 43
Qatar 8 5 3 6 11
Romania 49 34 49 46 40
Russia 46 38 31 47 38
Saudi Arabia 21 23 30 20 48
Singapore 6 7 5 3 1
Slovak Republic 52 46 42 49 47
Slovenia 47 29 33 36 31
South Africa 58 59 59 61 61
Spain 35 31 29 31 42
Sweden 17 24 21 22 16
Switzerland 15 25 23 18 7
Taiwan, China 12 14 15 17 6
Thailand 10 10 8 14 21
Turkey 43 52 51 57 46
UAE 5 3 7 4 9
Ukraine 55 58 56 54 54
United Kingdom 29 45 22 24 26
USA 1 1 1 2 5
Venezuela 63 63 63 63 64

 

Country 2017 2018 2019 2020 2021
Argentina 58 60 61 63 64
Australia 18 14 13 15 16
Austria 33 32 28 25 29
Belgium 32 35 36 35 37
Botswana - - - - 42
Brazil 62 62 62 61 62
Bulgaria 39 37 42 39 47
Canada 13 9 14 10 15
Chile 26 24 26 20 22
China 45 46 35 37 27
Colombia 56 58 56 56 58
Croatia 57 56 58 59 57
Cyprus 22 28 32 21 25
Czech Republic 29 27 34 36 36
Denmark 7 6 6 4 7
Estonia 23 21 27 19 18
Finland 16 15 17 16 14
France 52 39 48 46 39
Germany 21 19 22 24 23
Greece 61 61 60 52 52
Hong Kong SAR 1 1 1 1 1
Hungary 54 48 45 47 40
Iceland 8 16 15 17 17
India 48 50 46 50 46
Indonesia 30 36 25 31 26
Ireland 9 13 11 13 13
Israel 24 20 30 27 33
Italy 53 53 53 57 55
Japan 35 41 38 41 41
Jordan 49 43 43 45 35
Kazakhstan 19 25 21 29 21
Korea Rep. 28 29 31 28 34
Latvia 36 33 33 32 32
Lithuania 34 31 29 33 31
Luxembourg 15 17 10 12 10
Malaysia 25 23 24 30 30
Mexico 51 54 52 55 59
Mongolia 60 57 59 53 54
Netherlands 12 8 9 11 12
New Zealand 5 7 8 8 11
Norway 6 5 7 6 4
Peru 43 47 49 40 48
Philippines 37 44 41 42 45
Poland 44 40 44 43 56
Portugal 40 34 37 34 38
Qatar 11 10 5 7 6
Romania 47 51 51 49 44
Russia 46 52 47 48 50
Saudi Arabia 31 30 18 22 24
Singapore 3 3 3 5 5
Slovak Republic 55 55 57 60 51
Slovenia 42 42 39 38 43
South Africa 50 49 50 54 61
Spain 38 38 40 44 49
Sweden 14 11 16 14 9
Switzerland 2 2 4 2 2
Taiwan, China 10 12 12 9 8
Thailand 20 22 20 23 20
Turkey 41 45 55 51 60
UAE 4 4 2 3 3
Ukraine 59 59 54 58 53
United Kingdom 17 18 19 18 19
USA 27 26 23 26 28
Venezuela 63 63 63 62 63

 

Country 2017 2018 2019 2020 2021
Argentina 58 49 59 62 63
Australia 27 24 24 21 34
Austria 17 14 17 16 18
Belgium 26 23 28 22 20
Botswana - - - - 61
Brazil 49 50 57 47 49
Bulgaria 56 57 54 53 59
Canada 11 7 16 10 16
Chile 31 26 41 37 40
China 18 15 15 18 17
Colombia 53 56 47 52 51
Croatia 63 62 63 63 64
Cyprus 50 53 52 35 43
Czech Republic 34 32 37 38 41
Denmark 8 3 7 1 1
Estonia 32 27 33 27 31
Finland 13 16 13 13 12
France 40 31 38 43 36
Germany 16 19 26 25 23
Greece 57 59 58 51 44
Hong Kong SAR 1 1 2 2 3
Hungary 60 58 56 59 56
Iceland 24 22 19 15 14
India 29 29 30 32 32
Indonesia 30 35 20 31 25
Ireland 3 10 3 5 11
Israel 22 18 21 26 29
Italy 45 44 42 45 35
Japan 35 36 46 55 48
Jordan 47 39 35 46 33
Kazakhstan 23 34 29 34 28
Korea Rep. 44 43 34 28 27
Latvia 39 40 43 44 42
Lithuania 33 30 23 24 30
Luxembourg 6 8 12 17 13
Malaysia 19 17 18 29 24
Mexico 36 48 49 48 47
Mongolia 62 61 61 57 60
Netherlands 4 6 4 4 4
New Zealand 20 28 22 30 22
Norway 7 5 8 8 6
Peru 55 51 55 50 53
Philippines 28 38 32 33 37
Poland 37 37 36 40 57
Portugal 46 33 45 41 38
Qatar 12 13 10 11 15
Romania 52 52 51 54 52
Russia 51 54 53 58 54
Saudi Arabia 38 45 25 19 26
Singapore 10 11 5 6 9
Slovak Republic 54 60 60 61 55
Slovenia 48 47 40 39 45
South Africa 41 46 44 56 58
Spain 42 42 39 42 39
Sweden 9 4 6 3 2
Switzerland 5 9 9 9 5
Taiwan, China 15 20 14 12 7
Thailand 25 25 27 23 21
Turkey 43 41 48 36 46
UAE 2 2 1 7 8
Ukraine 59 55 50 49 50
United Kingdom 21 21 31 20 19
USA 14 12 11 14 10
Venezuela 61 63 62 60 62

 

Country 2017 2018 2019 2020 2021
Argentina 52 47 51 52 56
Australia 18 16 17 18 23
Austria 11 14 11 10 12
Belgium 13 20 21 19 19
Botswana - - - - 63
Brazil 51 52 54 53 52
Bulgaria 47 51 50 50 54
Canada 10 7 12 8 8
Chile 45 43 47 45 45
China 25 19 16 22 18
Colombia 58 58 56 56 53
Croatia 46 46 49 48 50
Cyprus 40 41 42 38 41
Czech Republic 28 30 31 32 31
Denmark 4 3 3 2 3
Estonia 29 32 34 33 30
Finland 6 6 5 4 5
France 12 12 9 13 15
Germany 9 11 10 11 10
Greece 39 40 41 39 39
Hong Kong SAR 20 23 22 14 16
Hungary 41 39 39 41 37
Iceland 17 17 13 17 9
India 60 56 55 49 49
Indonesia 59 59 53 55 57
Ireland 19 21 23 23 20
Israel 16 13 18 20 21
Italy 33 31 32 30 29
Japan 14 15 15 21 22
Jordan 57 54 58 58 55
Kazakhstan 43 42 43 51 47
Korea Rep. 24 18 20 16 17
Latvia 35 37 35 37 35
Lithuania 30 29 30 34 34
Luxembourg 22 24 25 24 24
Malaysia 32 33 28 31 32
Mexico 55 55 57 57 58
Mongolia 62 62 62 62 62
Netherlands 8 9 8 9 7
New Zealand 23 25 24 25 25
Norway 5 4 7 6 4
Peru 61 61 61 60 60
Philippines 54 60 59 59 59
Poland 34 34 36 35 42
Portugal 27 26 29 27 27
Qatar 38 38 40 40 40
Romania 50 49 48 47 48
Russia 36 35 37 42 38
Saudi Arabia 44 44 38 36 36
Singapore 7 8 6 7 11
Slovak Republic 42 45 44 46 44
Slovenia 31 28 27 29 33
South Africa 56 57 60 61 61
Spain 26 27 26 26 26
Sweden 3 5 4 1 2
Switzerland 1 2 2 3 1
Taiwan, China 21 22 19 15 14
Thailand 49 48 45 44 43
Turkey 48 50 46 43 46
UAE 37 36 33 28 28
Ukraine 53 53 52 54 51
United Kingdom 15 10 14 12 13
USA 2 1 1 5 6
Venezuela 63 63 63 63 64

 

Over the past two decades, the methodology used to assess the competitiveness of countries has been fine-tuned to take into account the evolution of the global environment and new research. In this way, the WCY keeps pace with structural changes in national environments and the rapidly changing technological revolution. We make these changes gradually so that we can preserve the comparability of results from year to year and highlight the evolution of an economy’s performance relative to the competitiveness of others.

Based on analysis made by leading scholars and on our own research, all criteria is grouped into sub-factors. Each sub-factor does not necessarily include the same number of criteria (for example, it takes more criteria to assess Education than to evaluate Prices). Sub-factors, irrespective of the number of criteria they contain, have the same weight in the overall consolidation of results.

In the case of the World Competitiveness Ranking, for example, the weight of each sub-factor is 5% (20 x 5 = 100). This allows us to “lock” the weight of the sub-factors regardless of the number of criteria they include. We believe that this approach improves the reliability of the results and helps ensure a high degree of compatibility with past results. Statistics are sometimes prone to errors or omission, locking the weights of sub-factors has the same function as building “fire barriers”; it prevents problems from spreading in a disproportionate way.

The WCY uses different types of data to measure quantifiable and qualitative issues separately. Statistical indicators are acquired from international, national and regional organizations, private institutions and our Partner Institutes. These statistics are referred to in the WCY as hard data. The hard data represent a weight of two-thirds in the overall rankings.

Additional criteria are drawn from our annual Executive Opinion Survey and are referred to in the WCY as survey data. The survey questions are included in the Yearbook as individual criteria and are also used to calculate the overall rankings, representing a weight of one-third.

Our Executive Opinion Survey complements the statistics we use from international, national and regional sources. While the hard data show how competitiveness is measured over a specific period of time, the survey data measures competitiveness as it is perceived by market participants.

The survey is designed to quantify issues that are not easily measured, for example: management practices, corruption, adaptive attitudes and the agility of companies. The survey responses reflect present and future perceptions of competitiveness by business executives who are dealing with international business situations. Their responses are more recent and closer to reality since there is no time lag with the year under consideration, which is often a problem with hard data, which show a “picture of the past.”

The Executive Opinion Survey is sent to midand upper-level managers in all the economies studied. The sample of respondents is representative of the entire economy, covering a cross-section of the business community in all economic sectors. In order to be statistically representative, we select a sample size that is proportional to the GDP breakdown of economic sectors of the economy.

The survey respondents are nationals or expatriates, in domestic or international enterprises who have Methodology and Principles of Analysis 5 resided at least a year in the economy under consideration. They are asked to evaluate the present and future competitiveness conditions of the economy in which they work, drawing from their domestic and international experience.

The surveys are sent in February and are returned in April. All responses returned to IMD and are treated as confidential. In 2021, we received more than 5,800 responses from the 64 economies worldwide. The respondents assess the competitiveness issues by answering the questions on a scale of 1 to 6. The average value for each economy is then calculated and converted into a 0 to 1.

The essential building block for the rankings is the standardized value for all the criteria (i.e., STD value). The first step is to compute the STD value for each criterion using the data available for all the economies (see the next section Data Processing Methodology for more detail). We then rank the economies based on the criteria that are used in the aggregation: a combination of hard and survey data.

Additional criteria are presented for background information only; they are not included in the aggregation of data to determine the overall rankings. Details on the type and number of criteria used in the calculation of each of the rankings are presented in Table 3. In most cases, a higher value is better, for example, for Gross Domestic Product; the economy withthe highest standardized value is ranked first while the one with the lowest is last. However, for some criteria the inverse may be true, where the lowest value is the most competitive, for example, Software Piracy. In these cases, a reverse ranking is used: the economy with the highest standardized value is ranked last and the one with the lowest is first.

Table 3. Criteria Details

Ranking/Report

Hard Data

Survey

Background information

Total ranked criteria

World Competitiveness 2021

163

92

79

255

World Digital Competitiveness 2021

32

20

2

52

World Talent 2020

 

14

17

-

31

 

 

Standard Deviation Method

As distinct criteria exhibit different scales and units, a comparable standard measure – the Standard Deviation Method (SDM) – is used to compute the overall, factor and sub-factor results. It measures the relative difference between the economies’ performances, resulting in a more accurate assessment of each country’s relative position in the final rankings.

First, for each criterion, we compute the average value for the entire population of economies. Then, the standard deviation is calculated using the following formula:

formula.png

x = original value
x ̅= average value of all the economies
N = number of economies
S = standard deviation

Subsequently, we compute each of the economies’ STD values for the all the ranked criteria. The STD is calculated by subtracting the average value of the 64 economies from the economy’s original value and then dividing the result by the standard deviation.

The STD value for criteria i is calculated as follows:

STD value.png
 
Aggregation of Data and Rankings

In the WCY some criteria are provided as background information only and are not included in the determination of the rankings. Some background data, however, are presented in ranking order while others are shown alphabetically.

STD values are calculated for each individual criterion, based on the STD method described above. All hard data indicators are reviewed to determine the shape of the distribution. Non- normally distributed data are normalized by taking the log. The STD is then calculated using the logged values.

The sub-factor rankings are determined by calculating the average of the STD values of all criteria comprising the sub-factor. All the hard data have a weight of 1. The survey data are weighted so that the survey accounts for one- third in the determination of the overallranking. When data are unavailable for a particular economy, the missing values are replaced by STD values that are imputed from the average of existing data within the sub-factor. Taking the average for each sub-factor enables us to “lock”

the weight of all the sub-factors irrespective of the number of criteria they contain so thateach sub-factor has an equal impact on the overall rankings.

Next, we aggregate the sub-factor STD values to determine the factor rankings. Only ranked criteria are aggregated to obtain these rankings. The STD values of the factors are then aggregated to determine the overall rankings. All the ranked criteria comprised in the factors are thus included in the consolidation of data.

Since all the statistics are standardized, they can be aggregated to compute indices. We use these index values, which we call “scores,” to compute the Factors and the Overall Rankings. It should be noted that across the factors, only one economy has a value equal to 100 and one economy a value equal to 0. To calculate the overall rankings, we take the average of the factors’ scores of the respective ranking (Competitiveness, Digital or Talent) and then convert them into an index with the leading economy given a value of 100.

Survey Criteria

Each year we conduct a survey to quantify issues related to competitiveness for which there are no hard statistics. The survey is an in- depth 92-point questionnaire sent to middle and upper level managers in the economies included in the rankings. The distribution reflects a breakdown of industry by sectors: primary, industry/manufacturing and services/finance.

In 2021 we received more than 5,800 responses for an average of approximately 90 replies per economy. The target list is determined by IMD and has been developed over many years with the collaboration of our Partner Institutes worldwide. Confidentiality is ensured and the list is updated every year. Respondents answer only for the economy in which they have worked and resided in the past year. Results, therefore, reflect widespread knowledge about each economy and draw on the wealth of their international experience.

The respondents assess the competitiveness issues by answering the questions on a scale of 1-6, with 1 indicating a negative perception and 6 indicating the most positive perception. The WCY calculates the average value for each economy, then the data is converted from a1-6 scale to a 0-10 scale, using the formula below.

Finally, the survey responses are transformed into their standard deviation values, fromwhich the rankings are calculated.

deviation values.png

where X = average value.

 Trends

A trend or growth rate offers a more dynamic assessment than absolute values. The formulas used to calculate trends and growth rates are explained below:

1. Annual real growth rate (i = inflation rate):

Annual real growth rate.png

2. Average annual percentage growth rate (n = number of periods):

annual percentage growth.png
 

Growth formulas, however, may have shortcomings. The average annual growth rate fails to reveal the real extent of changes, as it flattens or inflates year-to-year growth rates. For example, an average growth rate over two years might be calculated at 15%, while in reality there was 5% growth between the first and second years, and 25% between the second and third years. The average annual growth is used only when data vary widely in the middle years of a period, and less widely between the first and last years of the period. It is also used in cases where it is impossible to combine negative and positive initial and final values. This approach gives a more accurate picture than the compound rate under these circumstances.

 

Deflated Values

The following formula is used when calculating real growth rates from nominal values, because it takes into account cumulative inflation (e.g., real growth in Household Consumption Expenditure). The final deflated value is then used to obtain the annual real growth rate.

Taking a five-year time span as an example: Deflated final value (i = inflation rate):

Deflated final value.png

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