gestioncompetitiva.org · Web viewLengyel-Almos, Krisztina Eva** Universidad Autónoma de...

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National competitiveness and economic growth in emerging countries between 2007-2016*

Lengyel-Almos, Krisztina Eva**

Universidad Autónoma de Querétaro, Querétaro, México

Abstract

Emerging countries are constantly looking for better ways of convergence in order to achieve

higher living standards. National competitiveness is often associated with higher economic

performance and growth in high income countries. The purpose of this study is to evaluate if this

assumption holds true for selected emerging countries in comparison with aspiration peers. For

this reason, the Global Competitiveness Index (GCI) and the annual GDP per capita growth are

analyzed over a ten-year period. Using the World Economic Forum and World Bank data,

national competitiveness of 30 selected countries is studied between 2007 and 2016. Four clusters

of countries are compared in their performance of GCI, annual GDP growth and GDP per capita

growth during the decade studied. Correlation analysis and several hypotheses are tested with

respect to the relationships of the mentioned variables. The results show that national

competitiveness is correlated with economic growth in emerging countries. This finding is not

confirmed for high income aspirational peers. Further analysis shows that national

competitiveness is not stable over time, rather, it fluctuates significantly within a 10-year time

period. This study contributes to the literature on national competitiveness of emerging countries

with special focus on Mexico and its regional peers.

Keywords: National competitiveness, emerging countries, economic growth, Mexico

* The article is the result of a research project on national competitiveness, carried out at the postgraduate program in Administrative Economic Sciences of the Autonomous University of Querétaro. The author is grateful for the valuable support of professors Dr. Denise Gómez-Hernández and Dr. Enrique Kato-Vidal and the assistance of Idalid Alamilla-Gachuz y Marili Cervantes-Siurob. Without funding.

CIGECOM 2020CONGRESO INTERNACIONAL VIRTUAL EN GESTIÓN COMPETITIVA, TECNOLOGÍA E INNOVACIÓN CIGECOM 2020

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** Ph. D. c. Administrative Economic Sciences of the Autonomous University of Querétaro (FCA-UAQ), Querétaro, México. Email: [email protected]; https://orcid.org/0000-0002-1197-0278Introduction

This present analysis aims to analyze key drivers of national competitiveness using the World

Economic Forum´s Global Competitiveness Index (GCI) in 30 emerging countries. This annually

composed index tracks the change from year to year how a country´s performance has been

changing in terms of competitiveness. This paper tests if any improvement in competitiveness

actually translates to economic growth and improvement in living standards, therefore the annual

change in GDP and GDP per capita growth are also tracked and analyzed, in comparison with the

GCI. For this reason, based on the hypothesis that there is a positive impact of competitiveness

on the national GDP and GDP per capita, the relationships are tested among the three variables:

national competitiveness (CGI);

national economic performance (GDP);

national income per person (GDP per capita).

The justification for the present research lies in the need to understand which elements help

nations to achieve higher competitiveness so that it can develop better public policies to foment

growth and wellbeing. Fast Growth Economies (FGE), as some authors refers to a group of

emerging countries that have shown high levels of growth, about an annual 5% growth in terms

of GDP, for at least five consecutive years (Collazzo & Taieb, 2015). Nonetheless, when the

links between economic growth and competitiveness were studied by several authors (Aiginger,

2006; Collazzo & Taieb, 2015; Lall, 2001; Dagilienė et al, 2020), no clear pattern has been

identified. Alternative approaches and proposals exist (for example, by Pérez-Moreno, Rodríguez

and Luque, 2015, that offer three types of competitiveness), but no consensus is established in the

academic literature as to which the best measure is to assess competitiveness that may be

conducive for economic advancement.

Mexico, one of the major emerging Latin American countries, receives special attention in the

study, given that it has achieved the second highest level of competitiveness among its regional

peers in the past five years (only Chile achieved higher ranking); however, its annual economic

growth has not been matched to those of its emerging peers, especially outside of the region.


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Thus, one of the clusters in the sample will include six other regional peers, of which five also

fall in the same income bracket according to the World Bank classification.

One of the key findings of this paper is that there is evidence that the change in competitiveness

and GDP per capita growth are correlated in emerging countries. No evidence was found that the

same holds true for high income countries. This challenges the widely held view that greater

national competitiveness, which is the highest among high-income countries, is closely related to

greater economic growth. The present study contributes to the research in the field of

competitiveness at a national level in emerging countries, which, is often used as a strategy to

achieve higher level of competitiveness and economic growth that is sustainable over time.

Literature Review

Competitiveness has been a focus of many studies and lately it has received attention as a key

feature of economic development. In this paper the term competitiveness is considered at a

national level as a unit of analysis, instead of considering smaller units, such as firms, industries

or regions. Since the 1990s, different proposals have been put forth by experts, emphasizing

different aspects of national competitiveness. One of the pioneers in the past 30 years was

Michael Porter (1990) whose groundbreaking work of “The Competitiveness of Nations” focused

on national productivity as a main measure of success. His idea suggests that if a country

implements policies that enable and promote a competitiveness using his analytical framework

(referred to as Porter´s Diamond), productivity improves, hence, the country becomes more

competitive and can growth faster.

Paul Krugman (1994, 1996), the Nobel laureate economist, differed from Porter´s view, as he

considered national competitiveness very different in nature to that of corporations: Krugman

called it a “dangerous obsession” to talk about national competitiveness as countries cannot go

bankrupt like businesses can; they do not have a “bottom line” (Krugman, 1994, p. 31). Krugman

separates the terms of firm level competitiveness from national competitiveness as they have

different objectives and understanding. From his point of view, the debate on competitiveness is

misleading, and the overuse of the term national competitiveness is incorrect as they may prompt

erroneous public policies, pushing industrial policy in the name of competitiveness (Krugman,


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1994). Thus, Krugman suggests that competitiveness, if used at all, should refer to productivity

only (1996).

Retaking Porter´s concept, Cho & Moo (2009, 2013) developed the dual double diamond model

(DDD) for the assessment of competitiveness of nations, an adapted and modified Porter´s

Diamond, with new elements, such as taking into account the international environment; while in

the Nine Factor Model the authors included human factors as well. This extension of the concept

captures a broader understanding of national competitiveness. At a country level, the key

objective of competitiveness is to improve the prosperity of its people (Aiginger, 2006; Aiginger

and Vogel, 2015; Chikán, 2008).

When evaluating economic performance, one of the most commonly used key metrics has been

the gross domestic product (GDP) of a nation, which is an indicator of domestic production

within the borders of country in any given year (World Bank, n.d.). The measure has been in use

for several decades, in spite of the caution of its creator, Simon Kuznets (1934), who noted that

looking at GDP growth can be misleading and may oversimplify reality. Ever since it has been

obvious that the material conditions are only one aspect of the human life. To address this

shortcoming, in the past few decades new measures have been created that are broader and can

capture other aspects of human life, such as the UN´s Human Development Index (HDI), the

OECD´s Better Life Index, the Social Progress Index, or the Happy Planet Index, among many

others (Costanza et al, 2009; Balkyte & Tvaronavičiene, 2010). Nonetheless, the use of GDP still

prevails when emerging countries measure economic growth and convergence, which is a key

objective for countries that try to catch up with more advanced economies. Therefore, emerging

countries may follow some of the successful policies of wealthier countries, including the

policies that make them more competitive, with the ultimate objective of improving the lives of

its people. To take into account the change in population, a more accurate measure is the GDP

per capita that indicates the living standards of a nation. Hence, this measure is more useful for

international comparisons to assess economic development (World Bank, n.d.).

This paper focuses on evaluating whether national competitiveness is conducive for economic

growth as a first step, measured by the annual growth of GDP and GDP per capita,


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acknowledging the virtues as well as the limitations of this metrics. Second, the components of

global competitiveness index (GCI) are assessed to compare their relationship to the composite

index.

Methodology

The key questions that this paper aims to answer are the followings: 1) Is there a positive

relationship between the change in economic growth per capita and change in competitiveness

from 2007 and 2016 of (a) aspirational countries, (b) upper middle-income (UMC) peers, and (c)

lower middle-income countries? 2) Is there significant difference between the average GDP per

capita growth of (a) high income and upper middle-income countries; (b) upper and lower

middle-income countries; (c) regional and non-regional peers in the upper middle-income group?

3) Is it probable that the average economic growth of emerging UMC is above 4% annually? 4) Is

it probable that the GDP/capita growth of emerging UMC is above 3% annually? 5) Is the

competitiveness of aspirational countries significantly higher than that of the upper middle-

income group? 6) Is there any significant difference between the regional peers (Latin American

& Caribbean countries) and the non-regional peers in the same income bracket? 7) Is there any

significant difference between the fluctuation of competitiveness between the aspirational and

upper middle-income countries?

The methodology section first describes the three selected variables as mentioned in the

introduction and here they can be seen in Table 1 described. Second, the data sources are

described along with the some of its key features. Third, the sample selection process and its

results are discussed. Fourth, the results of the data analysis are presented in four parts: this

includes several normality tests (part 1, answering question 1), descriptive statistics (part 2, with

respect to background to questions 1-7), hypothesis tests (part 3 that responds to questions 1-7).

First, the three variables are shown in Table 1 that will be used for analysis.


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Table 1. Selected variables

Variable Short name Description Source

Global Competitiveness Index

GCI Composite index that is calculated value based on the 12 pillars and 114 subindicators of competitiveness

World Economic Forum

GDP per capita growth rate GDPpc% Annual change in GDP per capita in percentage, (calculation using current USD data)

World Bank

GDP growth rate GDP% Annual change in GDP in percentage, (calculation using current USD data)

World Bank

Source: The author´s work.

Available information

For the analysis of competitiveness at national level the Global Competitiveness Index (GCI) will

be used, compiled annually by the World Economic Forum (WEF, 2019). The World Bank has

adopted this measure of competitiveness and makes it available on its website in a different

format. Other advantage of this dataset is that the organization provides data for 141 countries

since 2003. Well-described and consistent dataset can be downloaded freely including time series

starting from 2006 for most countries.

In 2018, there was a significant change in methodology that made adjustments in the ordering of

the 12 pillars (changing the order several pillars and the scaling of the indicators from a 1-7 value

to a 0-100 value). Since 2017 the data is presented in the new format (for 2017 the data was back-

casted with the new methodology). Due to this change, the present study will use the data

between 2007 and 2016 in which period the data is managed and summarized consistently with

the same methodology.

With respect to the two other selected variables, the World Bank Dataset will be used, given that

the World Bank that collects and provides reliable annual time series data for most countries of

the world on several metrics of human life. The two indicators that are used in this study in

addition to the competitiveness indicator, annual GDP growth and annual GDP per capita growth,

are both calculated by using constant international USD values and expressed in annual percent

change. Similar to the GCI data, a ten-year period is used between 2007-2016. Considering that


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in 2009 several countries underwent an economic recession, this time period offers insights on

the impact of the recession and recovery of the economies included in the sample.

Sample selection

In order to gain insight for the performance of emerging countries, a sample of 30 countries is

compiled for this paper. This is an availability selection (non-random and non-probabilistic) so

that the sample can provide benchmarking and adequate comparison for Mexico. Specifically, the

sampling frame included countries that meet the following criteria: i) emerging country-peers

within the Latin American & Caribbean region in the upper middle income group according to

the World Bank categorization1; ii) emerging country-peers outside the Latin American &

Caribbean region in the upper middle income group; iii) emerging peers in the lower middle

income group; iv) aspirational peers in the higher income group. An additional filter for the

selection, small countries – those with a population less than 10 million inhabitants – are not

considered, given that Mexico is a populous country with more than 120 million people. As

pointed out with the choice of the sampling method, the availability of data for the 10-year period

also limited the country selection; for example, Venezuela, another regional emerging peer of

Mexico, could not be considered for this reason as no consistent data was available for the

country since 2014. As a result, Table 2 shows the four clusters of countries chosen with the

criteria described above.

Table 2. Selection criteria and the sample of countriesCluster

AbbreviationCriteria Selected countries

HIC Aspirational peers – high income countries (HIC)

Australia, Chile, Hungary, Japan, Netherlands, Poland, South Korea, Spain

UMC_LA Emerging regional peers – upper middle-income countries in Latin America & Caribbean (UMC_LA)

Argentina, Brazil, Colombia, Dominican Republic, Mexico, Peru

UMC_NLA Emerging non-regional peers – upper middle-income countries outside of Latin America (UMC_NLA)

Algeria, China, Malaysia, Romania, Russia, South Africa, Thailand, Turkey

1 The World Bank uses a classification for countries according to their income level and organizes them into four groups: low income country (LIC), lower- middle income country (LMC), higher-middle income country (UMC) and high-income countries (HIC). Since July 2019, countries in the higher-middle income group have a GNI per capita between 3,996-12,375 USD calculated using the Atlas method.


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LMC Emerging peers – lower middle-income countries (LMC)

Bolivia, Egypt, India, Indonesia, Morocco, Philippines, Tunisia, Vietnam

Aspirational countries include countries that have successfully developed in the past 30 years

starting from similar level as Mexico and have managed to achieve a higher living standard; thus,

currently are in the high-income category of the World Bank. Among the regional Latin

American peers, only Chile has transitioned and maintained its place in the high-income group

until 2020.

Based on these criteria, the 30 selected countries can be seen in Table 3 below, including the

selection criteria previously described and key data about the country (GDP per capita,

population, latest competitiveness ranking according to the World Economic Forum).

Table 3. Key data of 30 selected countries No. Country Populatio

n (millions),

(2018, WB)

GDP per capita, in constant

2010 USD (WB, 2018)

Economic Development/Income Level

Category (WB, 2018)

Region (WEF category) Global Competi-tiveness

Index (GCI Rank, 2019,

WEF)

Difference in

Ranking (2007-2019, WEF)

1 Australia 25 56,864 High Income East Asia and Pacific 16 32 Chile 19 15,130 High income Latin America and the Caribbean 33 -73 Hungary 10 16,636 High income Emerging and Developing Europe 47 04 Japan 127 48,920 High income East Asia and Pacific 6 25 Korea, Rep. 52 26,777 High Income East Asia and Pacific 13 -26 Netherlands 17 55,021 High income Europe and North America 4 67 Poland 38 16,661 High income Emerging and Developing Europe 37 148 Spain 47 32,898 High income Europe and North America 23 69 Algeria 42 4,764 Upper middle income Middle East, North Africa 89 -89

10 China 1393 7,753 Upper Middle Income Emerging and Developing Asia 28 611 Malaysia 32 12,120 Upper Middle Income Emerging and Developing Asia 27 -612 Romania 19 11,537 Upper middle income Emerging and Developing Europe 51 2313 Russia 145 11,729 Upper Middle Income Eurasia/CIS 43 1514 South Africa 58 7,434 Upper middle income Sub-Saharan Africa 60 -1615 Thailand 69 6,362 Upper Middle Income Emerging and Developing Asia 40 -1216 Turkey 82 15,069 Upper Middle Income Emerging and Developing Eurasia 61 -817 Argentina 44 10,044 Upper middle income Latin America and the Caribbean 83 218 Brazil 209 11,080 Upper middle income Latin America and the Caribbean 71 119 Colombia 50 7,692 Upper middle income Latin America and the Caribbean 57 1220 Dominican Republic 11 7,698 Upper middle income Latin America and the Caribbean 78 1821 Mexico 126 10,404 Upper middle income Latin America and the Caribbean 48 422 Peru 32 6,454 Upper middle income Latin America and the Caribbean 65 2123 Bolivia 11 2,560 Lower middle income Latin America and the Caribbean 116 -3124 Egypt 98 2,907 Lower middle income Middle East, North Africa 93 -1625 India 1353 2,101 Lower Middle Income Emerging and Developing Asia 68 -2026 Indonesia 268 4,285 Lower Middle Income Emerging and Developing Asia 50 427 Morocco 36 3,361 Lower middle income Middle East, North Africa 75 -11


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28 Philippines 107 3,022 Lower middle income Emerging and Developing Asia 64 729 Tunisia 12 4,401 Lower middle income Middle East, North Africa 87 -5530 Vietnam 96 1,964 Lower middle income Emerging and Developing Asia 67 1

Sources: Compiled with data from the World Bank (n.d.) and World Economic Forum (2019).

Description of data

As the World Economic Forum describes it, this dataset contains proprietary and non-proprietary

data used in computation to generate the Global Competitiveness Index (WEF, 2019). The data is

made available to the public to provide insights and track progress of countries´ competitiveness

for an evidence-based and data-driven decision making. All data and its source in the WEF

dataset are defined on the Metadata tab of the spreadsheet. This includes aggregate data

computed by the WEF such as the GCI index and sub indexes, organized in pillar series. There

are 12 pillars organized in three major categories of competitiveness: 1) basic requirements, 2)

efficiency enhancers, and 3) innovation and sophistication factors. Within the twelve pillars there

are several sub indexes, with a total number of 114 sub indexes (the exact number of the sub

indexes are shown in Table 4).

Table 4. The 12 pillars of the Global Competitiveness Index and its sub indexes (2007-2016)

Pillars Number of Sub indexes

Basic Requirements 1st pillar: Institutions 21 2nd pillar: Infrastructure 9 3rd pillar: Macroeconomic environment 5 4th pillar: Health and primary education 10Efficiency enhancers 5th pillar: Higher education and training 8 6th pillar: Goods market efficiency 16 7th pillar: Labor market efficiency 10 8th pillar: Financial market development 8 9th pillar: Technological readiness 7 10th pillar: Market size 4Innovation and sophistication factors 11th pillar: Business sophistication 9 12th pillar: Innovation 7

Total of sub indexes 114 Source: World Economic Forum (2019)

The data for each pillar and subindex is a calculated value by the WEF and then based on the

values, a ranking is published among all the countries that were evaluated in that year. The basis

for calculating the indicator value can be the national statistical data such as the annual


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percentage of inflation, or it may come from executive surveys conducted by the WEF itself. For

the purpose of this paper, the calculated values (ranging from 1-7) will be used. Ranking was

used only in Table 3 to indicate the most recent comparative for each country.

Results

First, in part 1, the results of data distribution tests are presented for the three selected variables

(GCI, GGP%, GDP per capita%) for each cluster. This is important so that further analysis can be

done depending on whether the data follows a normal distribution or not.

Part 1 – Analysis of data distribution

After clustering the information, the data for each variable was tested for normality by clusters.

The results, summarized in Table 5 below, show that only the data for GDP per capita growth

shows a normal distribution in each four clusters with the use of the Anderson-Darling method

(Newbold et al., 2009). The data for competitiveness (GCI) does not show normal distribution in

two clusters cases: high income and upper middle income Latin American counties.

Table 5. Normality test per variable and per cluster

Variable tested

Cluster of Countries

Per Income

Population size

Sample size

(population * no. years)

Normality test (Anderson-Darling)

p-value

Result(data distribution)

GCI

HIC 8 80 < 0.005 not normalUMC_LA 6 60 < 0.005 not normalUMC_NLA 8 80 0.095 normalLMC 8 80 0.061 normal

GDP %

HIC 8 80 < 0.005 not normalUMC_LA 6 60 0.114 normalUMC_NLA 8 80 0.088 normalLMC 8 80 0.117 normal

GDP per capita %

HIC 8 80 0.008 normalUMC_LA 6 60 0.322 normalUMC_NLA 8 80 0.241 normalLMC 8 80 0.053 normal

Source: author´s calculation with the use of Minitab.

It is important to mention that when analyzing the GDP growth data, the non-normal distribution

indicated several outliers that are described below in part two of methodology.


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The annual GDP per capita change did not show such a major fluctuation, and in spite of outliers

(also described later), the data in this sample shows a normal distribution. As the main focus of

this paper is the improvement of living standards over time, this latter variable (GDP per capita

%) will be used for anchoring in hypothesis tests in part 3 of this section.

Part 2 – Descriptive statistics

Global Competitiveness Index (GCI). Table 6 offers descriptive statistics in panel a) while

panel b) shows the boxplot image of this variable.

Table 6. Descriptive statistics and boxplot image of Global Competitiveness Index by cluster, 2007-2016a) b)

Variable Total N Mean StDev Min Max RangeGCI_HIC 80 4.88 0.43 4.20 5.57 1.36GCI_UMC-LA 60 4.07 0.21 3.65 4.41 0.76GCI_UMC-NLA 80 4.44 0.36 3.71 5.23 1.51GCI_LMC 80 4.12 0.30 3.42 4.65 1.23

Source: author´s calculation with Minitab.

Based on the statistics, the high-income group has the highest maximum and average values

(5.57 and 4.88, respectively) while the lower middle-income countries had the lowest

minimum value (4.12) as a group. Interestingly, the lowest average value of competitiveness

(4.07) is seen at emerging Latin American countries with upper middle income (Mexico is part

of this group), not at lower middle-income countries. Another noteworthy observation is that

greatest dispersion was found in the aspirational high-income group (HIC), as seen in panel b)

and numerically confirmed by the highest standard deviation (0.43) while the biggest spread

between the extreme GCI values (range: 1.51) was experienced by the non-Latin American

emerging countries in the upper middle income group (UMC-NLA). This suggests that their


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competitiveness has the greatest fluctuation in the decade of observation, which can indicate

important improvement or deterioration of national competitiveness.

To analyze in greater details the changes in competitiveness within each cluster, further

analysis was conducted, which is not included in this paper. Nonetheless, that study helped to

identify the best and worst performing countries within each cluster along with trend analysis

per country over the 10-year period in terms of competitiveness. As a summary, there were

big fluctuations within each group, some countries seem to be consistent in their

competitiveness over time while others show great fluctuations. No systemic improvement

was found. In case of Mexico, the best performer among its regional peers in the same income

bracket, very little change was found during the decade observed. According to the data,

Mexico became only moderately more competitive during the 10 years between 2007 and

2016.

Annual GDP growth (GDP%). The descriptive statistics can be seen in Table 7 in panel a)

while panel b) shows the corresponding boxplot image of this variable. As mentioned at the

data distribution analysis, this variable shows outliers in each cluster as it is visible in panel b)

in the boxplot image. The countries in the high-income group experienced the lowest GDP

growth average in the 10-year period, again, keeping in mind that several underwent a

recession in 2009.

Figure 7. Descriptive statistics and boxplot image of annual GDP growth by cluster, 2007-2016a) b)

Variable Total N Mean StDev Min Max RangeGDP%_HIC 80 1.97 2.50 -6.70 7.04 13.73GDP%_UMC_LA 60 3.54 3.51 -5.92 10.13 16.04GDP%_UMC_NLA 80 3.96 3.77 -7.80 14.23 22.03GDP%_LMC 80 4.98 1.93 -1.92 8.50 10.42


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At closer inspection of the outlier data in the high income group, it was confirmed that in case of

4 countries (Japan, Hungary, Netherlands and Spain) the downturn that was outside of the

standard deviation occurred indeed in 2009 when the recession resulted in a contraction of their

GDP that year, hence the GDP growth was negative, it was below -4% in each of these four

countries ´cases. There were outlier countries in each group as well, as seen panel b), similarly in

2009, due to the impact of economic recession.

With respect of the emerging countries in Latin America and the Caribbean, the two outliers were

Argentina and Mexico, both undergoing a considerable recession in 2009 (-5.9% and -5.3% GDP

growth, respectively). Within the lower middle-income group, Tunisia stood out as it experienced

an economic downturn with a contraction of 2.8% in its GDP in 2011, during the first year of the

Arab Spring that resulted in political and economic upheaval in many of the Arab countries of the

Middle East. Apart from these events, the two clusters of the upper middle-income group, both

within and outside of the Latin American region showed similar behavior, except the extremes,

which were higher and lower among the non-Latin American countries (range in UMC_LA:

16.03, whereas in UMC_NLA: 22.03), indicating more extreme changes in other regions of the

world. Considering that these are growth percentage points, the swings are quite considerable.

Annual GDP per capita growth (GDPpc%). Descriptive statistics for this variable are

presented in panel a) of Table 8.

Table 8. Descriptive statistics and boxplot image of annual GDP per capita growth by cluster, 2007-2016a) b)

Variable Total N Mean StDev Min Max RangeGDP pc% HIC 80 1.50 2.44 -6.56 7.09 13.65GDP pc%_UMC-LA 60 2.57 3.33 -6.85 9.30 16.15GDP pc%_UMC-NLA 80 3.15 3.82 -7.83 13.64 21.46GDP pc% LMC 80 3.51 1.97 -2.89 7.04 9.93


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It is noticeable that the poorer countries (those in the lower middle-income group) showed the

highest average growth in their GDP per capita, while the richer countries (HIC) showed the

lowest average increase (mean in LMC: 3.51% while mean in HIC: 1.5%). Among the upper

middle-income countries, the non-Latin American countries performed better, attaining an annual

average of 3.15% growth in their per capita GDP. The greatest fluctuations were also experienced

in this cluster (UMC-NLA), showing the highest dispersion (standard deviation 3.82, range:

21.46 among the extremes) during the 10-years of evaluation (this is also visible on Table 8,

panel b, on the boxplot image). This group includes China, that experienced steady growth and

achieved above 14% GDP per capita growth in one single year (in 2007, in panel b, the upper

outlier data at UMC_NLA). On the other extreme, the biggest drop in GDP per capita was

experienced by Russia, within the same cluster, in 2009 when it contracted by almost 8%, due a

major recession (in panel b, lowest outlier data at UMC_NLA).

Within the Latin American region, the biggest decrease its GDP per capita occurred in 2009,

when it dropped by almost 7% in Argentina, as a consequence of a severe recession (Table 8,

panel b, UMC_LA outlier data).

Part 3 – Hypotheses tests regarding the GCI, GDP growth and GDP per capita growth

In this part several hypotheses are tested that aim to answer the research questions from 1-7 and

analyze the relationships among the four clusters´ performance regarding competitiveness,

economic growth and economic growth per person. For tests 1, 3, 4, 5 and 7 the two clusters of

the emerging peers with upper middle income (from and outside Latin America & the Caribbean)

were grouped into one single group (UMC) as the regional focus was less relevant in these tests.


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In tests 2 and 6 the focus was to compare the Latin America & the Caribbean region with the rest

of the world; therefore, they were handled separately. The complete test results generated with

Minitab are not included in the table due to the limitation of the length of this paper.

Table 9. Research assumptions, hypothesis tests and resultsResearch assumption Hypothesis test Result

1) There is a significant correlation between growth rate of GDP per capita vs change in GCI, from 2007 to 2016 within

(a) high income countries (HIC);(b) upper middle-income counties

(UMC, including LA and NLA clusters);

(c) lower middle-income countries (LMC).

H 0 : ρ=0H1: ρ≠ 0

p-value <0.05for acceptance of H0

There is a no significant correlation between the change GDP growth and the change in competitiveness in all clusters: (a) among the high-income countries (HIC) (p-value: 0.72)There is significant correlation within(b) among the upper middle-income countries (UMC) (p-value: 0.040)(c) among the lower middle income (LMC) countries. (p-value: 0.021)

2) There is significant difference between the average GDP per capita growth of the

(a) HIC and UMC groups;(b) UMC and LCM groups;(c) UMC-LA and UMC-NLA

groups.

H 0 : μ1=μ2

H 1: μ1≠ μ2

p-value >0.05for acceptance of H0

There is a 95% probability that there is no significant difference between the annual GDP/capita growth rates during 2007-2016 between(a) HIC and UMC (p-value = 0.002)There is significant difference between the averages of GDP/capita(b) UMC and LCM (p-value = 0.061)(c) UMC-LA and UMC-NLA groups (p-value = 0.296)

3) There is a 95% probability that the mean GDP per capita growth of the upper middle countries (UMC) will be minimum an annual 4%.

Null hypothesis H₀: μ = 4

Alternative hypothesis H₁: μ < 4

T-Value: P-Value:

-0.71 0.239

There is a 95% probability that the annual GDP growth of upper middle countries was minimum 4% between 2007 and 2016.

4) There is a 95% probability that the mean GDP per capita growth of the upper middle countries (UMC) will be minimum an

Null hypothesis H₀: μ = 3

Alternative hypothesis H₁: μ < 3

There is a 95% probability that the annual GDP per capita growth of upper middle countries was minimum 3% between 2007 and


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annual 3%. T-Value: P-Value:

-0.32 0.375

2016.

5) There is a significant difference between the average GCI of the high-income countries (HIC) and upper-middle income group (UMC); the mean of the HIC group is significantly higher than that of UMC.

H 0 : μ1=μ2

H 1: μ1>μ2

p-value = 0.000

There is a significant difference with 95% probability that the average competitiveness of HIC countries is higher than that of those of UMC.

6) There is significant difference between the average GCI of the regional emerging upper income countries (UMC_LA) and non-regional upper middle-income group (UMC-NLA); the mean of the UMC-LA group is the same as that of UMC-NLA group.

H 0 : μ1=μ2

H 1: μ1≠ μ2

p-value = 0.000

There is a significant difference with a 95% of confidence interval and 95% probability that the average competitiveness of upper-middle income countries outside of Latin America is different than that of those of UMC in the region (Latin America & Caribbean).

Research assumption Hypothesis test Result7) There is no difference in

fluctuations of the GCI value of the high-income countries’ vis-á vis that of the UMC countries between 2007-2016.

Null hypothesis H₀: σ₁ / σ₂ = 1

Alternative hypothesis H₁: σ₁ / σ₂ ≠ 1

Significance level α = 0.05

Method P-ValueBonett 0.027Levene 0.000

There is a significant difference in the variance of GCI of HIC and UMC, with 95% probability that the variance of competitiveness of HI countries was different than that of those of UMC. (Note: The null hypothesis rejected based on the Bonett method´s p-value which was considered as the GCI data does not show normal distribution).

Source: author´s calculations with Minitab.

Conclusions

There are several findings that offer some insights and add to the debate on national

competitiveness.

First, based on the data of the Global Competitiveness Index between 2007 and 2016 of the 30

countries used in the sample, there is significant correlation between competitiveness (GCI) and

annual GDP growth per capita within the emerging countries (both upper-middle income and

lower middle income countries, but not in the high income (aspirational) countries. This finding

suggests that there are closely related factors between economic growth and competitiveness as

measured by the Global Competitiveness Index of World Economic Forum. With respect to

Krugman´s observation, it can be noted the national competitiveness need not be an obsession,

but rather an additional measure that may offer insights to economic development, especially

among countries who seek convergence.


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When analyzing the four clusters, statistical evidence was found that the emerging peers outside

of the Latin American & Caribbean region achieved higher competitiveness than the regional

peers. Mexico, among its regional peers was the most competitive between 2007 and 2016, with

the smallest fluctuation. This latter observation may require further research as to why the

country did not manage to improve its competitiveness significantly in the decade observed. It is

important to mention that several countries, mostly developed and emerging countries in the

upper middle-income bracket, including Mexico, suffered a considerable recession in 2009. This

setback was later recovered in terms of GDP per capita and GDP growth and seemed to have

small impact on the competitiveness of the country, based on the lack of fluctuation in GCI

before and after the recession.

Recommendations

For further studies, several adjustments can be suggested. For one, the present research can be

extended to longer time series from 2017 on, to include the latest available data. To do this, first

the two methodologies of the World Economic Forum -the time series before and after 2017-

would have to be homologized to match the pillars and the scaling between the two

methodologies. Considering the sample selection, the number of countries could be increased so

that the tests are based on a more ample sample for each cluster. More extended data series with

higher sample size may impact the correlation between the change of competitiveness and GDP

per capita among the high-income countries as well.

Finally, in addition to the different tests presented in this paper, it would be useful to conduct

regression analysis to identify which pillars of the twelve have the highest impact on the overall

outcome of the aggregate Global Competitiveness Index. Time series analysis could also offer

other insights on the development of this metrics. Lastly, other indicators could be considered

and tested, such as the Human Development Index, the Social Progress Index, or the Genuine

Progress Index for the timeframe and countries that data is available, as other authors pointed

out as well (e.g. Costanza et al, 2009). These tests could add further insights into the questions

related to national competitiveness and its impact on human living conditions.


18

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