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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01:15Remaining competitive in times of geo-political turbulence
IMD World Competitiveness Ranking 2022 - An overview
Released on 15 June 2022, the data explores multiple factors that affect the prosperity of 63 economies.
'Sustainability first Denmark' tops economic competitiveness ranking
 

The Nordic nation takes the lead for the first time in the IMD World Competitiveness Ranking, which also highlights how inflationary pressures are causing pressing socio-environmental concerns to take a backseat.

“Denmark has played extremely aggressively on the sustainability front and benefits from being a small country in the European market,” Professor Arturo Bris, Director of the IMD World Competitiveness Center (WCC), which creates the ranking, explained. “Operating within that framework has allowed it to announce aggressive reductions.”

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2022 Country 2021 Ranking Change  
1 Denmark 3 +2
2 Switzerland 1 -1
3 Singapore 5 +2
4 Sweden 2 -2
5 Hong Kong SAR 7 +2
6 Netherlands 4 -2
7 Taiwan, China 8 +1
8 Finland 11 +3
9 Norway 6 -3
10 USA 10 - -
11 Ireland 13 +2
12 UAE 9 -3
13 Luxembourg 12 -1
14 Canada 14 - -
15 Germany 15 - -
16 Iceland 21 +5
17 China 16 -1
18 Qatar 17 -1
19 Australia 22 +3
20 Austria 19 -1
21 Belgium 24 +3
22 Estonia 26 +4
23 United Kingdom 18 -5
24 Saudi Arabia 32 +8
25 Israel 27 +2
26 Czech Republic 34 +8
27 Korea Rep. 23 -4
28 France 29 +1
29 Lithuania 30 +1
30 Bahrain new    
31 New Zealand 20 -11
32 Malaysia 25 -7
33 Thailand 28 -5
34 Japan 31 -3
35 Latvia 38 +3
36 Spain 39 +3
37 India 43 +6
38 Slovenia 40 +2
39 Hungary 42 +3
40 Cyprus 33 -7
41 Italy 41 - -
42 Portugal 36 -6
43 Kazakhstan 35 -8
44 Indonesia 37 -7
45 Chile 44 -1
46 Croatia 59 +13
47 Greece 46 -1
48 Philippines 52 +4
49 Slovak Republic 50 +1
50 Poland 47 -3
51 Romania 48 -3
52 Turkey 51 -1
53 Bulgaria 53 - -
54 Peru 58 +4
55 Mexico 55 - -
56 Jordan 49 -7
57 Colombia 56 -1
58 Botswana 61 +3
59 Brazil 57 -2
60 South Africa 62 +2
61 Mongolia 60 -1
62 Argentina 63 +1
63 Venezuela 64 +1
Country 2018 2019 2020 2021 2022
Argentina 56 61 62 63 62
Australia 19 18 18 22 19
Austria 18 19 16 19 20
Bahrain - - - - 30
Belgium 26 27 25 24 21
Botswana - - - 61 58
Brazil 60 59 56 57 59
Bulgaria 48 48 48 53 53
Canada 10 13 8 14 14
Chile 35 42 38 44 45
China 13 14 20 16 17
Colombia 58 52 54 56 57
Croatia 61 60 60 59 46
Cyprus 41 41 30 33 40
Czech Republic 29 33 33 34 26
Denmark 6 8 2 3 1
Estonia 31 35 28 26 22
Finland 16 15 13 11 8
France 28 31 32 29 28
Germany 15 17 17 15 15
Greece 57 58 49 46 47
Hong Kong SAR 2 2 5 7 5
Hungary 47 47 47 42 39
Iceland 24 20 21 21 16
India 44 43 43 43 37
Indonesia 43 32 40 37 44
Ireland 12 7 12 13 11
Israel 21 24 26 27 25
Italy 42 44 44 41 41
Japan 25 30 34 31 34
Jordan 52 57 58 49 56
Kazakhstan 38 34 42 35 43
Korea Rep. 27 28 23 23 27
Latvia 40 40 41 38 35
Lithuania 32 29 31 30 29
Luxembourg 11 12 15 12 13
Malaysia 22 22 27 25 32
Mexico 51 50 53 55 55
Mongolia 62 62 61 60 61
Netherlands 4 6 4 4 6
New Zealand 23 21 22 20 31
Norway 8 11 7 6 9
Peru 54 55 52 58 54
Philippines 50 46 45 52 48
Poland 34 38 39 47 50
Portugal 33 39 37 36 42
Qatar 14 10 14 17 18
Romania 49 49 51 48 51
Saudi Arabia 39 26 24 32 24
Singapore 3 1 1 5 3
Slovak Republic 55 53 57 50 49
Slovenia 37 37 35 40 38
South Africa 53 56 59 62 60
Spain 36 36 36 39 36
Sweden 9 9 6 2 4
Switzerland 5 4 3 1 2
Taiwan, China 17 16 11 8 7
Thailand 30 25 29 28 33
Turkey 46 51 46 51 52
UAE 7 5 9 9 12
United Kingdom 20 23 19 18 23
USA 1 3 10 10 10
Venezuela 63 63 63 64 63

 

Country 2018 2019 2020 2021 2022
Argentina 60 61 60 59 57
Australia 19 14 23 19 16
Austria 17 20 15 20 24
Bahrain - - - - 39
Belgium 44 37 25 24 14
Botswana - - - 62 60
Brazil 54 57 56 51 48
Bulgaria 28 47 34 41 49
Canada 13 12 10 14 10
Chile 41 48 50 53 50
China 2 2 7 4 4
Colombia 51 50 52 56 45
Croatia 56 55 45 50 32
Cyprus 22 19 13 13 38
Czech Republic 16 17 16 23 18
Denmark 26 26 21 17 13
Estonia 32 44 35 29 33
Finland 43 35 43 34 44
France 30 34 32 28 17
Germany 12 9 5 3 5
Greece 61 60 55 52 51
Hong Kong SAR 9 10 28 30 15
Hungary 39 46 19 8 8
Iceland 57 54 58 55 56
India 21 24 37 37 28
Indonesia 27 25 26 35 42
Ireland 11 6 12 22 7
Israel 37 40 39 36 36
Italy 47 53 42 39 41
Japan 15 16 11 12 20
Jordan 62 62 62 63 62
Kazakhstan 49 45 48 45 58
Korea Rep. 20 27 27 18 22
Latvia 53 52 53 44 54
Lithuania 36 39 33 33 43
Luxembourg 4 4 8 10 1
Malaysia 8 11 9 15 12
Mexico 35 28 38 49 27
Mongolia 48 58 59 58 61
Netherlands 6 13 1 2 19
New Zealand 33 36 40 32 47
Norway 40 32 30 25 25
Peru 55 41 51 60 40
Philippines 50 38 44 57 53
Poland 18 18 29 27 29
Portugal 42 43 41 43 46
Qatar 5 3 6 11 9
Romania 34 49 46 40 55
Saudi Arabia 23 30 20 48 31
Singapore 7 5 3 1 2
Slovak Republic 46 42 49 47 52
Slovenia 29 33 36 31 26
South Africa 59 59 61 61 59
Spain 31 29 31 42 35
Sweden 24 21 22 16 21
Switzerland 25 23 18 7 30
Taiwan, China 14 15 17 6 11
Thailand 10 8 14 21 34
Turkey 52 51 57 46 37
UAE 3 7 4 9 6
United Kingdom 45 22 24 26 23
USA 1 1 2 5 3
Venezuela 63 63 63 64 63

 

Country 2018 2019 2020 2021 2022
Argentina 60 61 63 64 63
Australia 14 13 15 16 16
Austria 32 28 25 29 34
Bahrain - - - - 39
Belgium 35 36 35 37 33
Botswana - - - 42 41
Brazil 62 62 61 62 61
Bulgaria 37 42 39 47 49
Canada 9 14 10 15 18
Chile 24 26 20 22 30
China 46 35 37 27 29
Colombia 58 56 56 58 59
Croatia 56 58 59 57 46
Cyprus 28 32 21 25 24
Czech Republic 27 34 36 36 22
Denmark 6 6 4 7 6
Estonia 21 27 19 18 15
Finland 15 17 16 14 10
France 39 48 46 39 40
Germany 19 22 24 23 21
Greece 61 60 52 52 55
Hong Kong SAR 1 1 1 1 2
Hungary 48 45 47 40 37
Iceland 16 15 17 17 14
India 50 46 50 46 45
Indonesia 36 25 31 26 35
Ireland 13 11 13 13 11
Israel 20 30 27 33 32
Italy 53 53 57 55 54
Japan 41 38 41 41 39
Jordan 43 43 45 35 44
Kazakhstan 25 21 29 21 25
Korea Rep. 29 31 28 34 36
Latvia 33 33 32 32 28
Lithuania 31 29 33 31 23
Luxembourg 17 10 12 10 13
Malaysia 23 24 30 30 38
Mexico 54 52 55 59 60
Mongolia 57 59 53 54 57
Netherlands 8 9 11 12 12
New Zealand 7 8 8 11 17
Norway 5 7 6 4 5
Peru 47 49 40 48 52
Philippines 44 41 42 45 48
Poland 40 44 43 56 56
Portugal 34 37 34 38 43
Qatar 10 5 7 6 7
Romania 51 51 49 44 47
Saudi Arabia 30 18 22 24 19
Singapore 3 3 5 5 4
Slovak Republic 55 57 60 51 51
Slovenia 42 39 38 43 42
South Africa 49 50 54 61 53
Spain 38 40 44 49 50
Sweden 11 16 14 9 9
Switzerland 2 4 2 2 1
Taiwan, China 12 12 9 8 8
Thailand 22 20 23 20 31
Turkey 45 55 51 60 58
UAE 4 2 3 3 3
United Kingdom 18 19 18 19 26
USA 26 23 26 28 27
Venezuela 63 63 62 63 62

 

Country 2018 2019 2020 2021 2022
Argentina 49 59 62 63 63
Australia 24 24 21 34 26
Austria 14 17 16 18 18
Bahrain - - - - 24
Belgium 23 28 22 20 19
Botswana - - - 61 57
Brazil 50 57 47 49  52
Bulgaria 57 54 53 59 59 
Canada 7 16 10 16  13
Chile 26 41 37 40  41
China 15 15 18 17  15
Colombia 56 47 52 51  60
Croatia 62 63 63 64  49
Cyprus 53 52 35 43  44
Czech Republic 32 37 38 41  29
Denmark 3 7 1 1  1
Estonia 27 33 27 31  22
Finland 16 13 13 12  5
France 31 38 43 36  35
Germany 19 26 25 23  21
Greece 59 58 51 44  46
Hong Kong SAR 1 2 2 3
Hungary 58 56 59 56  48
Iceland 22 19 15 14  8
India 29 30 32 32  23
Indonesia 35 20 31 25  31
Ireland 10 3 5 11  11
Israel 18 21 26 29  27
Italy 44 42 45 35  34
Japan 36 46 55 48  51
Jordan 39 35 46 33  45
Kazakhstan 34 29 34 28  32
Korea Rep. 43 34 28 27  33
Latvia 40 43 44 42 37 
Lithuania 30 23 24 30  25
Luxembourg 8 12 17 13  20
Malaysia 17 18 29 24  38
Mexico 48 49 48 47  47
Mongolia 61 61 57 60  61
Netherlands 6 4 4 4  3
New Zealand 28 22 30 22  36
Norway 5 8 8 6  10
Peru 51 55 50 53  53
Philippines 38 32 33 37  39
Poland 37 36 40 57  58
Portugal 33 45 41 38  42
Qatar 13 10 11 15  14
Romania 52 51 54 52  50
Saudi Arabia 45 25 19 26  16
Singapore 11 5 6 9  9
Slovak Republic 60 60 61 55  54
Slovenia 47 40 39 45 43 
South Africa 46 44 56 58 56 
Spain 42 39 42 39 40 
Sweden 4 6 3 2  2
Switzerland 9 9 9 5
Taiwan, China 20 14 12 7  6
Thailand 25 27 23 21  30
Turkey 41 48 36 46  55
UAE 2 1 7 8  17
United Kingdom 21 31 20 19  28
USA 12 11 14 10  12
Venezuela 63 62 60 62  62

 

Country 2018 2019 2020 2021 2022
Argentina 47 51 52 56 54
Australia 16 17 18 23 19
Austria 14 11 10 12 10
Bahrain - - - - 39
Belgium 20 21 19 19 20
Botswana - - - 63 61
Brazil 52 54 53 52 53
Bulgaria 51 50 50 54 51
Canada 7 12 8 8 11
Chile 43 47 45 45 47
China 19 16 22 18 21
Colombia 58 56 56 53 56
Croatia 46 49 48 50 45
Cyprus 41 42 38 41 40
Czech Republic 30 31 32 31 28
Denmark 3 3 2 3 2
Estonia 32 34 33 30 27
Finland 6 5 4 5 4
France 12 9 13 15 15
Germany 11 10 11 10 9
Greece 40 41 39 39 41
Hong Kong SAR 23 22 14 16 14
Hungary 39 39 41 37 36
Iceland 17 13 17 9 8
India 56 55 49 49 49
Indonesia 59 53 55 57 52
Ireland 21 23 23 20 23
Israel 13 18 20 21 17
Italy 31 32 30 29 31
Japan 15 15 21 22 22
Jordan 54 58 58 55 55
Kazakhstan 42 43 51 47 46
Korea Rep. 18 20 16 17 16
Latvia 37 35 37 35 35
Lithuania 29 30 34 34 32
Luxembourg 24 25 24 24 24
Malaysia 33 28 31 32 37
Mexico 55 57 57 58 58
Mongolia 62 62 62 62 62
Netherlands 9 8 9 7 5
New Zealand 25 24 25 25 29
Norway 4 7 6 4 6
Peru 61 61 60 60 59
Philippines 60 59 59 59 57
Poland 34 36 35 42 43
Portugal 26 29 27 27 30
Qatar 38 40 40 40 38
Romania 49 48 47 48 48
Saudi Arabia 44 38 36 36 34
Singapore 8 6 7 11 12
Slovak Republic 45 44 46 44 42
Slovenia 28 27 29 33 33
South Africa 57 60 61 61 60
Spain 27 26 26 26 25
Sweden 5 4 1 2 3
Switzerland 2 2 3 1 1
Taiwan, China 22 19 15 14 13
Thailand 48 45 44 43 44
Turkey 50 46 43 46 50
UAE 36 33 28 28 26
United Kingdom 10 14 12 13 18
USA 1 1 5 6 7
Venezuela 63 63 63 64 63

 

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 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 online survey takes place from end of February to beginning of May. All responses are treated as confidential. In 2022, we received 6,031 responses from the 63 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 the table below. 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.

Criteria Details

Ranking/Report

Hard Data

Survey

Background information

Total ranked criteria

World Competitiveness 2022

163

92

78

255

World Digital Competitiveness 2021

32

20

2

52

World Talent 2021

 

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 2022 we received 6,031 responses for an average of approximately 100 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

News
'Sustainability first Denmark' tops economic competitiveness ranking
Topics: Competitiveness, Economics
15 June 2022

Denmark has reached the number one spot in the IMD World Competitiveness Ranking (WCR) for the first time in the ranking’s 34-year history.

“Denmark has played extremely aggressively on the sustainability…

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Leaders: pay attention to what really motivates your workers
Topics: Competitiveness, Leadership, Talent Management
9 December 2021

Workers today are increasingly motivated by high quality of life, flexible working, and opportunities to train on the job, whereas previously, remuneration was the driving force behind their desire to stick at their jobs…

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Data shows effects of COVID-19 and climate change on citizens’ perceptions of how ‘smart’ their cities are
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The third edition of the annual IMD-SUTD Smart City Index (SCI)released today has revealed that city-dwellers’ perceptions of how technology is helping to address urban challenges has been highly affected by the…

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Talent, regulation and capital needed for Thai economy to recover
Topics: Competitiveness
27 January 2021

Thailand needs to attract digital talent and invest more in education for it to boost its competitiveness in the current economic climate. That is the key message that came out of a recent IMD webinar, organized in…

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European economies take 8 out of top 10 spots in 2020 World Talent Ranking
Topics: Competitiveness
12 November 2020

In order, from first to fifth were: Switzerland, Denmark, Luxembourg, Iceland, and Sweden.

This year’s results— which use hard data from 2019 and survey answers from January to April 2020 — underline…