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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2021Country2020Ranking Change 
1Switzerland3+2
2Sweden6+4
3Denmark2-1
4Netherlands4--
5Singapore1-4
6Norway7+1
7Hong Kong SAR5-2
8Taiwan, China11+3
9UAE9--
10USA10--
11Finland13+2
12Luxembourg15+3
13Ireland12-1
14Canada8-6
15Germany17+2
16China20+4
17Qatar14-3
18United Kingdom19+1
19Austria16-3
20New Zealand22+2
21Iceland21--
22Australia18-4
23Korea Rep.23--
24Belgium25+1
25Malaysia27+2
26Estonia28+2
27Israel26-1
28Thailand29+1
29France32+3
30Lithuania31+1
31Japan34+3
32Saudi Arabia24-8
33Cyprus30-3
34Czech Republic33-1
35Kazakhstan42+7
36Portugal37+1
37Indonesia40+3
38Latvia41+3
39Spain36-3
40Slovenia35-5
41Italy44+3
42Hungary47+5
43India43--
44Chile38-6
45Russia50+5
46Greece49+3
47Poland39-8
48Romania51+3
49Jordan58+9
50Slovak Republic57+7
51Turkey46-5
52Philippines45-7
53Bulgaria48-5
54Ukraine55+1
55Mexico53-2
56Colombia54-2
57Brazil56-1
58Peru52-6
59Croatia60+1
60Mongolia61+1
61BotswanaNEW  
62South Africa59-3
63Argentina62-1
64Venezuela63-1
Country20172018201920202021
Argentina5856616263
Australia2119181822
Austria2518191619
Belgium2326272524
Botswana----61
Brazil6160595657
Bulgaria4948484853
Canada121013814
Chile3535423844
China1813142016
Colombia5458525456
Croatia5961606059
Cyprus3741413033
Czech Republic2829333333
Denmark76823
Estonia3031352826
Finland1516151311
France3128313229
Germany1315171715
Greece5757584946
Hong Kong SAR12257
Hungary5247474742
Iceland2024202121
India4544434343
Indonesia4243324037
Ireland61271213
Israel2221242627
Italy4442444441
Japan2625303431
Jordan5652575849
Kazakhstan3238344235
Korea Rep.2927282323
Latvia4040404138
Lithuania3332293130
Luxembourg811121512
Malaysia2422222725
Mexico4851505355
Mongolia6262626160
Netherlands54644
New Zealand1623212220
Norway1181176
Peru5554555258
Philippines4150464552
Poland3834383947
Portugal3933393736
Qatar1714101417
Romania5049495148
Russia4645455045
Saudi Arabia3639262432
Singapore33115
Slovak Republic5155535750
Slovenia4337373540
South Africa5353565962
Spain3436363639
Sweden99962
Switzerland25431
Taiwan, China141716118
Thailand2730252928
Turkey4746514651
UAE107599
Ukraine6059545554
United Kingdom1920231918
USA4131010
Venezuela6363636364

 

Country20172018201920202021
Argentina5660616059
Australia2519142319
Austria4017201520
Belgium2344372524
Botswana----62
Brazil5954575651
Bulgaria3728473441
Canada1613121014
Chile3441485053
China22274
Colombia4151505256
Croatia5756554550
Cyprus2822191313
Czech Republic1916171623
Denmark2026262117
Estonia4432443529
Finland4543354334
France2430343228
Germany712953
Greece6161605552
Hong Kong SAR119102830
Hungary363946198
Iceland3957545855
India1821243737
Indonesia3327252635
Ireland41161222
Israel3137403936
Italy3847534239
Japan1415161112
Jordan6262626263
Kazakhstan5449454845
Korea Rep.2220272718
Latvia5353525344
Lithuania4236393333
Luxembourg344810
Malaysia13811915
Mexico3035283849
Mongolia6048585958
Netherlands961312
New Zealand3233364032
Norway4840323025
Peru5055415160
Philippines2650384457
Poland2718182927
Portugal5142434143
Qatar853611
Romania4934494640
Russia4638314738
Saudi Arabia2123302048
Singapore67531
Slovak Republic5246424947
Slovenia4729333631
South Africa5859596161
Spain3531293142
Sweden1724212216
Switzerland152523187
Taiwan, China121415176
Thailand101081421
Turkey4352515746
UAE53749
Ukraine5558565454
United Kingdom2945222426
USA11125
Venezuela6363636364

 

Country20172018201920202021
Argentina5860616364
Australia1814131516
Austria3332282529
Belgium3235363537
Botswana----42
Brazil6262626162
Bulgaria3937423947
Canada139141015
Chile2624262022
China4546353727
Colombia5658565658
Croatia5756585957
Cyprus2228322125
Czech Republic2927343636
Denmark76647
Estonia2321271918
Finland1615171614
France5239484639
Germany2119222423
Greece6161605252
Hong Kong SAR11111
Hungary5448454740
Iceland816151717
India4850465046
Indonesia3036253126
Ireland913111313
Israel2420302733
Italy5353535755
Japan3541384141
Jordan4943434535
Kazakhstan1925212921
Korea Rep.2829312834
Latvia3633333232
Lithuania3431293331
Luxembourg1517101210
Malaysia2523243030
Mexico5154525559
Mongolia6057595354
Netherlands12891112
New Zealand578811
Norway65764
Peru4347494048
Philippines3744414245
Poland4440444356
Portugal4034373438
Qatar1110576
Romania4751514944
Russia4652474850
Saudi Arabia3130182224
Singapore33355
Slovak Republic5555576051
Slovenia4242393843
South Africa5049505461
Spain3838404449
Sweden141116149
Switzerland22422
Taiwan, China10121298
Thailand2022202320
Turkey4145555160
UAE44233
Ukraine5959545853
United Kingdom1718191819
USA2726232628
Venezuela6363636263

 

Country20172018201920202021
Argentina5849596263
Australia2724242134
Austria1714171618
Belgium2623282220
Botswana----61
Brazil4950574749
Bulgaria5657545359
Canada117161016
Chile3126413740
China1815151817
Colombia5356475251
Croatia6362636364
Cyprus5053523543
Czech Republic3432373841
Denmark83711
Estonia3227332731
Finland1316131312
France4031384336
Germany1619262523
Greece5759585144
Hong Kong SAR11223
Hungary6058565956
Iceland2422191514
India2929303232
Indonesia3035203125
Ireland3103511
Israel2218212629
Italy4544424535
Japan3536465548
Jordan4739354633
Kazakhstan2334293428
Korea Rep.4443342827
Latvia3940434442
Lithuania3330232430
Luxembourg68121713
Malaysia1917182924
Mexico3648494847
Mongolia6261615760
Netherlands46444
New Zealand2028223022
Norway75886
Peru5551555053
Philippines2838323337
Poland3737364057
Portugal4633454138
Qatar1213101115
Romania5252515452
Russia5154535854
Saudi Arabia3845251926
Singapore1011569
Slovak Republic5460606155
Slovenia4847403945
South Africa4146445658
Spain4242394239
Sweden94632
Switzerland59995
Taiwan, China152014127
Thailand2525272321
Turkey4341483646
UAE22178
Ukraine5955504950
United Kingdom2121312019
USA1412111410
Venezuela6163626062

 

Country20172018201920202021
Argentina5247515256
Australia1816171823
Austria1114111012
Belgium1320211919
Botswana----63
Brazil5152545352
Bulgaria4751505054
Canada1071288
Chile4543474545
China2519162218
Colombia5858565653
Croatia4646494850
Cyprus4041423841
Czech Republic2830313231
Denmark43323
Estonia2932343330
Finland66545
France121291315
Germany911101110
Greece3940413939
Hong Kong SAR2023221416
Hungary4139394137
Iceland171713179
India6056554949
Indonesia5959535557
Ireland1921232320
Israel1613182021
Italy3331323029
Japan1415152122
Jordan5754585855
Kazakhstan4342435147
Korea Rep.2418201617
Latvia3537353735
Lithuania3029303434
Luxembourg2224252424
Malaysia3233283132
Mexico5555575758
Mongolia6262626262
Netherlands89897
New Zealand2325242525
Norway54764
Peru6161616060
Philippines5460595959
Poland3434363542
Portugal2726292727
Qatar3838404040
Romania5049484748
Russia3635374238
Saudi Arabia4444383636
Singapore786711
Slovak Republic4245444644
Slovenia3128272933
South Africa5657606161
Spain2627262626
Sweden35412
Switzerland12231
Taiwan, China2122191514
Thailand4948454443
Turkey4850464346
UAE3736332828
Ukraine5353525451
United Kingdom1510141213
USA21156
Venezuela6363636364

 

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

   

World Competitiveness

    

World Digital Competitiveness

    

World Talent

 

    

 

 

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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