global competitiveness report

2022.07.31
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global competitiveness report

According to the structure of said factor (which is common to both periods), a possible name could be Human development and ease of communication. This factor explains 28.4% (31.5%) of the total variability.

In 2012, Loo states the need for a third organization to measure competitiveness in order to conciliate both results WEF and IMD. In summary, and in terms of the evolution of the countries positioned in the first and fifth quintiles, it is noteworthy that for both periods, the majority of the least competitive countries are located in Africa, except for Cambodia, while the most competitive countries are located in Australia (Oceania), United States (America), Singapore (Asia), and others in Central and Northern Europe (Denmark, Finland, Holland, Norway, Sweden, and Switzerland). This index only encompasses quantitative indicators (hard data) used by the WEF and is computed by applying the multivariate exploratory technique of factor analysis, which ensures the elimination of qualitative data and, consequently, the subjectivity of the weighting. Considering the classification presented in Table 7, according to the CSI, we can see that the most competitive countries for the two analyzed periods are Singapore and Norway. This factor is identified with C3 (201011) and explains 8.8% (8.9%) of the total variability. The variables are organized into twelve pillars,[6] with each pillar representing an area considered as an important determinant of competitiveness. Moreover, in any elaboration of an index, there is subjectivity as humans are involved in the process. In regard to the WEFs methodology, we lean toward a competitiveness index based on official, quantitative data that is computed using statistical and/or mathematical procedures, which considers weights that can be implicitly determined by the inherent structure of the data. Theoretically, we cannot establish as clear of a correspondence for the remaining factors in the 200708 period and the 201011 period as those described in the previous paragraph. Companies compete on the basis of prices and sell basic products or commodities, with their low productivity reflected in low wages.

https://doi.org/10.1371/journal.pone.0265045.t007. Nevertheless, this study presents some limitations such as the existence of key indicators (hard data) not considered during the analyzed period in the elaboration of WEF-GCI and therefore in the CSI; the no representation of some pillars as these pillars do not include hard data indicators. In spite of the World Economic Forum's Global Risks Report which is increasingly identifying environmental pressures as the dominant risks to humanity, none of the indicators used to determine this report's competitiveness ranking reflect any of the countries' environmental dimensions such as energy, water, climate risks, resource or food security, etc. The Global Competitiveness Index's annual reports are somewhat similar to the Ease of Doing Business Index and the Indices of Economic Freedom, which also look at factors affecting economic growth (but not as many as the Global Competitiveness Report). Artadi. Finally, as countries move into the innovation-driven stage, they are only able to sustain higher wages and a higher standard of living if their businesses are able to compete by providing new or unique products. [8] The weights used are the values that best explain growth in recent years[9] For example, the sophistication and innovation factors contribute 10% to the final score in factor and efficiency-driven economies, but 30% in innovation-driven economies. In addition, what creates productivity in Sweden is necessarily different from what drives it in Ghana. The comparison of the dependence coefficient with these extreme cases can give us a good idea of the degree of linear dependence between the indicators used [23]. Unreliable citations may be challenged or deleted. Finally, the relevant advantages of using this index are the transparency of the information of WEF-GCI (freely available online) and continuation in yearly published since 1979. We believe that this is the only way to eliminate any political biases or individual interests. According to Loo (2012), Singapore was ranked 1st in the rank using an average between IMD-WCY and WEF-GCI.

Moreover, the 2012 GSCI report [13] indicates that countries in northern Europe are the leading countries: Denmarkrank 1, Swedenrank 2 Norwayrank 3 have the highest rakings, although this index is proposed from a sustainable perspective.

These rankings are guides for relevant decisions such as the investment in countries as usually is going to be directly related to the more competitive countries instead of to the less competitive ones. No, PLOS is a nonprofit 501(c)(3) corporation, #C2354500, based in San Francisco, California, US, Corrections, Expressions of Concern, and Retractions, https://doi.org/10.1371/journal.pone.0265045, https://reports.weforum.org/global-competitiveness-report-2012-2013/, https://www3.weforum.org/docs/WEF_GlobalCompetitivenessReport_2010-11.pdf. The association between the rankings based on the scores of said indices, measured by Spearmans correlation coefficient, is considered to be statistically significant, positive, and high for the different pairs considered in this study, as shown in Table 10. https://doi.org/10.1371/journal.pone.0265045.t010. You can find the computation and structure of the GCI pp. The geographic representation is based on the quintiles (values that divide the corresponding distribution into five types, each with the same number of countries, approximately). According to the above, Factor C2 (200708) could be called Health, but we cannot find an appropriate name or clear interpretation for Factor C4 (201011). In order to facilitate the interpretation of said factors, in terms of the different aspects related to the competitiveness of said factors, the coefficients that have an absolute value greater than 0.4 are shaded in a darker color. The most competitive countries include the United States (America), China, Taiwan (China), and Singapore (Asia), as well as numerous countries in Central and Northern Europe (Germany, Austria, Denmark, Finland, France, Holland, Norway, United Kingdom, Sweden, and Switzerland). Therefore, the Global Competitiveness Index measures the set of institutions, policies, and factors that set the sustainable current and medium-term levels of economic prosperity."[3][4]. This factor could complement the data on ease of doing business provided by Factor C2 for the same period, but the negative correlation that said factor has with participation in primary education makes its interpretation more complicated. Since 2004, the Global Competitiveness Report ranks countries based on the Global Competitiveness Index,[1] developed by Xavier Sala-i-Martin and Elsa V. https://doi.org/10.1371/journal.pone.0265045.t005, In regard to the interpretation of the retained factors, it should be noted that the only factor that is common to both periods (C5) is the one that includes the variables related to the macroeconomic environment (Pillar 3), with the exception of inflation (Pillar3X3). To maintain competitiveness at this stage of development, competitiveness hinges mainly on well-functioning public and private institutions (pillar 1), appropriate infrastructure (pillar 2), a stable macroeconomic framework (pillar 3), and good health and primary education (pillar 4). The Global Competitiveness Index integrates the macroeconomic and the micro/business aspects of competitiveness into a single index. Said indicator was obtained as a weighted average of the scores for the seven retained factors, using the percentage of the total variance explained by each factor as the weight of the score for each factor. Similarly, all of the countries classified in the last quintile for the 200708 period, except for Israel, remain in the same quintile for the 201011 period. For more information about PLOS Subject Areas, click The Global Competitiveness Report (GCR)[1] is a yearly report published by the World Economic Forum. The last component (C7) appears to be associated with the labor market (Pillar 7) for the 200708 period, both in terms of the cost of layoffs (Pillar7X1) and womens participation in the labor market (Pillar7X2). At the same time, other countries relative competitiveness improved from one period to the next: Austria, Germany, Estonia (Europe), and Oman (Asia). The rankings provided by the proposed index (CSI) present a high degree of association with the rankings from the Global Competitiveness Index (GCI) for the two analyzed periods. Yes Similarly, although it may seem obvious, the two indices provide different rankings, both in terms of the majority of the countries that are classified as the most competitive according to the WEF-GCI (the first 20 countries) and the majority of the countries that are classified as the least competitive (the last 20 countries), which remain in the same group according to the alternative index CSI. For this analysis, the values of the KMO index are 0.824 and 0.790, respectively. This table shows that competitiveness can be synthesized in seven factors for the two analyzed periods, according to the criteria based on selecting the factors associated with higher eigenvalues than the unit [21]. It should bear in mind other relevant factors not considered in this WEF indicators, so, these missed relevant factor are given 0% of the total explained variance. Note it presents a weak, negative correlation with the normalized variable that quantifies the total tax rate (Pillar6X1) for the 200708 period, while it appears to be correlated with the Gross National Savings for the 201011 period (Pillar3X2). Data from the Global Competitiveness Index relating to the strength of auditing and reporting standards, institutions and judicial independence is used in the Basel AML Index, a money laundering risk assessment tool developed by the Basel Institute on Governance. These factors explain 77% and 76.5% of the total variability, an acceptable percentage considering that the lower limit of acceptability for studies in the social sciences is 60% [25]. The data provided by the scores of the retained factors have been synthesized in a sole index, the synthetic competitiveness index (CSI), which summarizes the situation of each of the analyzed countries in terms of competitiveness. According to the factor analysis model, the theoretical correlation coefficients calculated between each pair of unique factors are null by hypothesis. This coefficient quantifies the degree of association between the two rankings and indicates their direction, as well as the association between the WEF-GCI for the countries analyzed in this study, which is statistically significant, positive and high. Meanwhile, the C2 factor (12% of the total variability) for the 200708 period unites all of the variables that quantify health-related aspects, even those that appear to be accounted for in Factor C1. Yes Finally, the CSI shows a very similar evolution as mentioned in the previous paragraph (see Figs 3 and 5). Similarly, C4 (200708) can be called Foreign trade, since the higher correlations correspond to the percentage of imports (Pillar6X4) and exports (Pillar10X1) in the GDP. Thus, the impact of each pillar on competitiveness varies across countries, in function of their stages of economic development. Similarly, when limited to the European context, the association between the CSI index and the European Competitiveness Index (ECI) is not only maintained, but rather increases. As wages rise with advancing development, countries move into the efficiency-driven stage of development, when they must begin to develop more efficient production processes and increase product quality. We use the scores to analyze the geographic distribution of the indices within said context and the relationship between the two. It is surprising that said component presents a high negative correlation with the variable that quantifies womens participation in the labor market (Pillar7X2). One part of the report is the Executive Opinion Survey, which is a survey of a representative sample of business leaders in their respective countries. Thus, the GCI separates countries into three specific stages: factor-driven, efficiency-driven, and innovation-driven, each implying a growing degree of complexity in the operation of the economy. Table 5 shows the factor loading matrices after the varimax rotation or rotated component matrices, which are formed by the linear correlation coefficients between the factors and the indicators used to estimate them.

However, C1 presents intense correlations with the variables related to infrastructure (Pillar 2), innovation (Pillar 9), education (Pillars 4 and 5), per capita income (Anc2) and some health-related variables (infant mortality, Pillar4X4, and life expectancy, Pillar4X5) for both periods. Regarding the countries, different sizes, geographical location, populations, political situations, or climate are other characteristics to take into account in order to elaborate an index. No, Is the Subject Area "Asia" applicable to this article? https://doi.org/10.1371/journal.pone.0265045.t009. Yearly report published by the World Economic Forum. Yes This factor, C6, which we call Overall country size, explains 6.1% (6.4%) of the total variability. At this point, competitiveness becomes increasingly driven by higher education and training (pillar 5), efficient goods markets (pillar 6), efficient labor markets (pillar 7), developed financial markets (pillar 8), the ability to harness the benefits of existing technologies (pillar 9), and its market size, both domestic and international (pillar 10). In turn, the country distribution considering their scores in the GCI and CSI for the two analyzed periods is shown in Figs 2, 3, 4, and 5. In memoriam to Mara Dolores Sarrin-Gavilan. Therefore, in the calculation of the GCI, pillars are given different weights depending on the per capita income of the nation. However, this factor is not exclusive of health for the 201011 period, but some of the variables included in this factor (such as life expectancy and cases of tuberculosis and HIV) are correlated with C4 (8.2% of total variability). We know that if one of the variables is the perfect linear combination of others, which are also included in the analysis, the correlation matrix is singular, |R| = 0, and therefore, D(R) = 1. At this stage, companies must compete by producing new and different goods using the most sophisticated production processes (pillar 11) and through innovation (pillar 12). https://doi.org/10.1371/journal.pone.0265045.t006. Since 2004, the report ranks the world's nations according to the Global Competitiveness Index,[2] based on the latest theoretical and empirical research. Learn how and when to remove this template message.

This comparison is based on the data in common among 22 countries: Germany, Austria, Belgium, Denmark, Spain, Estonia, Finland, France, Greece, Holland, Hungary, Ireland, Italy, Latvia, Lithuania, Norway, Poland, Portugal, United Kingdom, Czech Republic, Sweden, Switzerland. Meanwhile, based on the rankings shown in Table 7, the correlation coefficients have been calculated according to Spearmans rank correlation corresponding to the rankings provided by both indices in Table 8. Nevertheless, these two indices keep being the most authoritative sources in global competitiveness. The following section presents an analysis of the results of said indicator for the two analyzed periods. Table 7 shows the rankings from the CSI and WEF-GCI for the analyzed countries and periods. Intermediate values are used for economies in transition between stages. This factor also appears to be associated with other health variables, cases of malaria (Pillar4X1) and tuberculosis (Pillar4X2) for the 201011 period.

Additionally, the rankings provided by the WEF for the two analyzed periods include the countries for which there is no data for certain quantitative indicators (hard data) in the database the WEF provides as a basic instrument to analyze global competitiveness with the GCI. However, it is not questionable that inside the subjectivity, our proposed index is less manipulable for humans as no survey opinions are included. Some results are supported by literature as in the case of Singapore. Based on these determinants, we have calculated the values of the effective dependence coefficients associated with them, D07-08(R) = 0.6366 and D10-11(R) = 0.6345, which indicate the existence of a considerable degree of linear dependence between the variables involved in each of the analyzed periods. It presents a weak correlation with the womens participation in the labor market (Pillar7X2) for the 200708 period and the number of procedures required to start a business (Pillar6X2). Depending on what indicators are used to measure competitiveness, the outcome will be different. This paper proposes an objective global competitiveness index that exclusively uses the data provided by the WEF and quantifies to what degree the resulting rankings are associated with those corresponding to the GCI. One year later, in 2013, the first report of SolAbility-GSCI [13] only using quantitative indicators was published by this South Korean company and maintains the publication currently but from the perspective of sustainability. https://doi.org/10.1371/journal.pone.0265045.t004. This in turn depends on how productively a country uses available resources. As a complement to the linear dependence analysis, we calculated the KMO index (Kaiser-Meyer-Olkin). Yes Source: Prepared by authors using ArcGIS software. For the 201011 period, this component presents moderate, positive levels of correlation with the variables that quantify the cost of layoffs, the total tax rate (Pillars6X1) and the number of procedures required to start a business (Pillars6X2), as well as a moderate, negative correlation with the participation in primary education (Pillar5X1). https://doi.org/10.1371/journal.pone.0265045.t008. This section presents a comparison of the rankings provided by the proposed index (CSI) and the WEF-GCI, on a global scale. Meanwhile, other factors related to the 200708 period, such as C3, C4, and C6, could be identified in 201011 with C2, C3, and C6, respectively, since the variables that have higher correlations with each of these factors are the same for both periods. The report notes that as a nation develops, wages tend to increase, and that in order to sustain this higher income, labor productivity must improve for the nation to be competitive. Depend on the economic and political interest of countries: from the USAs point of view, from Switzerlands point of view or Singapores perspective, among other countries.

Is the Subject Area "Statistical data" applicable to this article? The eternal question: What is the best index? These are: In the factor-driven stage countries compete based on their factor endowments, primarily unskilled labor and natural resources. Moreover, Loo states there is still a controversial opinion concerning the different rankings provided by WEF and IMD, both Switzerland-based institutions. No, Is the Subject Area "Factor analysis" applicable to this article? According to Loo [26], Singapore was ranked 3rd using an average between IMD-WCY and WEF-GCI during the period 20072011, and in the period 20092011 was ranking the first one. Yes Meanwhile, if the linear correlation between different pairs of variables is null, the correlation matrix coincides with the identity, its determinant is 1 and, therefore, D(R) = 0. 4950 of the Global Competitiveness Report 2013-2014, Full Data Edition. They are both higher than the minimum recommended value for this type of study (0.5) and, therefore, the application of this methodology is considered to be acceptable. Accordingly, C3 (200708) has high correlations with the variables that quantify inflation (Pillar3X3) and the number of days and procedures required to start a business (Pillar6X3). This coefficient is defined as D(R) = 1-|R|1/(P-1) where |R| is the determinant of the correlations. Global Competitiveness Report 2013-2014, Full Data Edition. No, Is the Subject Area "Labor markets" applicable to this article? Respondent numbers have increased every year and is currently just over 13,500 in 142 countries (2010).[7]. Meanwhile, the most competitive countries for both periods belong to Oceania (Australia), America (United States), Central and Northern Europe (Denmark, Finland, Holland, Norway, Sweden, and Switzerland), and Asia (Singapore). The Global Competitiveness Report 2018[10] and 2019[11] used the ecological footprint as a context indicator, but the footprint was not included in the scoring algorithm that determines the ranking. No, Is the Subject Area "Geographic distribution" applicable to this article? No, Is the Subject Area "Body weight" applicable to this article?

This is the top 30 of the 2022 report:[12], This is the full ranking of the 2019 report:[11], This is the top 30 of the 2018 report:[10], This is the top 30 of the 20172018 report:[13], This is the top 30 of the 20162017 report:[14], This is the top 30 of the 20152016 report:[15], This is the top 30 of the 20142015 report:[1], This is the top 30 of the 20132014 report:[16], This is the top 30 of the 20122013 report:[17], This is the top 30 of the 20112012 report:[18][19], This is the top 30 of the 20102011 report:[20], This is the top 30 of the 20092010 report:[21], This is the top 30 of the 20082009 report:[22]. Said factor explains approximately 7% and can be called Government budget balance, savings and debt.. Be aware, there is not included hard data into the following pillars: the Pillar 1 Institutions, the Pillar 2 Financial market development, the Pillar 11 Business sophisticationand the Pillar 12Innovation. "Global Competitiveness Report 2014-2015 - Reports - World Economic Forum", "Global Competitiveness Network: Frequently Asked Questions", http://www3.weforum.org/docs/WEF_GCR_Report_2011-12.pdf, http://www.columbia.edu/~xs23/papers/WEC_00220_00701_Snowdon.pdf, https://imd.cld.bz/IMD-World-Competitiveness-Booklet-2022/34/, "Global Competitiveness Report 2017-2018", "Global Competitiveness Report 2016-2017", "Global Competitiveness Report 2015-2016", http://www3.weforum.org/docs/WEF_GlobalCompetitivenessReport_2013-14.pdf, http://www3.weforum.org/docs/WEF_GlobalCompetitivenessReport_2012-13.pdf, "US Competitiveness Ranking Continues to Fall; Emerging Markets Are Closing the Gap | World Economic Forum - US Competitiveness Ranking Continues to Fall; Emerging Markets Are Closing the Gap", "Table 4: The Global Competitiveness Index 20102011 rankings and 20092010 comparisons", "Table 4: The Global Competitiveness Index 20092010 rankings and 20082009 comparisons", "The Global Competitiveness Index rankings and 20072008 comparisons", "Interactive Global Competitiveness Report", Top 20 countries of 2010 by competitiveness, International Institute for Management Development publications, Timeline of geopolitical changes (before 1900), Timeline of geopolitical changes (1900present), https://en.wikipedia.org/w/index.php?title=Global_Competitiveness_Report&oldid=1100274132, All articles with bare URLs for citations, Articles with bare URLs for citations from March 2022, Articles with PDF format bare URLs for citations, Short description is different from Wikidata, Articles lacking reliable references from June 2019, Creative Commons Attribution-ShareAlike License 3.0, This page was last edited on 25 July 2022, at 02:23. In terms of the competitiveness of European countries for the 200708 period, as previously mentioned, this section compares the results obtained for the two aforementioned indicators with those corresponding to the European Competitiveness Index (ECI) for the 200607 period. Furthermore, in terms of the evolution of the WEF-GCI over time (see Figs 2 and 4), it should be noted that the composition of the first quintile for the two time periods is very similar and over 50% of the countries classified in this quintile are on the African continent. Yes https://doi.org/10.1371/journal.pone.0265045.g002, https://doi.org/10.1371/journal.pone.0265045.g003, https://doi.org/10.1371/journal.pone.0265045.g004, https://doi.org/10.1371/journal.pone.0265045.g005. The name (Labor market) is clear and explains 5.4% of the total variability. Based on a review of the methodology used by the WEF to compute the global competitiveness index, we can conclude that there is a very high percentage of qualitative data in the total data used (approximately 75%), which results in the subjectivity of the index. It could be questioned the importance in using the WEF-GCI but the transparency of the information (freely available online) and continuation in yearly published since 1979 are relevant advantages of using this index. [5] It is made up of over 110 variables, of which two thirds come from the Executive Opinion Survey, and one third comes from publicly available sources such as the United Nations. No, Is the Subject Area "United States" applicable to this article? It is a guide for governments, enterprises, investors, citizens among others, to manage to progress in prosperity or to achieve high living of standards. Table 9 presents a summary of the countries that are among the least competitive (first quintile) and the most competitive (last quintile), respectively, for the two analyzed periods, according to both indices. Additionally, limiting our study to countries in the European Union, we have compared the results obtained for the 200708 period for the two aforementioned indices with those from the European Competitiveness Index (ECI) for the 200607 period. Yes Values of the KMO measurement below 0.5 are not acceptable [24]. The report "assesses the ability of countries to provide high levels of prosperity to their citizens". Table 6 presents a summary of the names of the factors identified in the two periods under consideration, their identification with the corresponding component, and the pillars that encompass the variables that allow for their interpretation. here. No, Is the Subject Area "Macroeconomics" applicable to this article? However, as we mentioned previously it is not questionable that inside the subjectivity, our proposed index is less manipulable for humans as no survey opinions are included.