SKILL ENHANCEMENT COURSE

DATABASE AND STATISTICAL PACKAGES

ANALYSIS OF GLOBAL COMPETITIVENESS INDEX

Submitted in partial fulfilment of the requirements for the degree of

B.A. (Hons.) Business Economics
By:
Vanshika Gupta 175011
Shweta Kansal 175032
Jasmine Sidana 175046
Antra Goyal 175048
 Global Competitiveness Index Report

ACKNOWLEDGEMENT
It gives us immense pleasure to present the Project Report on REGREESION
ANALYSIS OF GLOBAL COMPETITIVENESS INDEX. This project has helped us gain
knowledge on the application of basic econometrics concepts empirically and
use of computer software like SPSS and MS-EXCEL for analysis.
We would like to express our sincere thanks and gratitude to our lecturer- Mrs.
Riyanka Jain, department of Business Economics, Sri Guru Gobind Singh College
of Commerce, who not only provided us with constant guidance, advice and
valuable inputs and suggestions but also persuasively conveyed a spirit of
adventure in regard to the research, and excitement in regard to the teaching.
This project wouldn’t have been possible without their useful guidance and
supervision.
Thanks are also due to Dr. JATINDER BIR SINGH- worthy Principal of our college
who has been a source of inspiration not only to us but also to the entire
student community and Faculty and Administrative Staff of the college.
Last but not the least we are also thankful to our friends and family members
for their kind cooperation and encouragement which helped us in the timely
completion of the project.

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DECLARATION
This is to certify that the material embodied in this project is based on our
original research work. Our indebtedness to other works, studies and
publications have been duly acknowledged at the relevant places .This project
work has not been submitted in part or in full for any other Diploma or Degree
in this or any other University.

STUDENT MEMBERS PROJECT SUPERVISOR

VANSHIKA GUPTA (175011) MRS. RIYANKA JAIN
SHWETA KANSAL (175032)
JASMINE SIDANA (175046)
ANTRA GOYAL (175048)

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INDEX

Serial No. Particulars

1 Introduction
2 Objectives
3 Research Methodology
4 Sample Data
5 Statement of Hypothesis
6 Analysis and Interpretation
7 Problem Detection
8 Conclusion
9 Recommendations
10 Bibliography

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INTRODUCTION
WHAT IS GCI?
The Global Competitiveness Index (GCI) tracks the performance of close to
140 countries on 12 pillars of competitiveness. It assesses the factors and
institutions identified by empirical and theoretical research as determining
improvements in productivity, which in turn is the main determinant of long-
term growth and an essential factor in economic growth and prosperity.
The Global Competitiveness Index is published annually by the World Economic
Forum, an independent international organization committed to improving the
state of the world by engaging leaders in partnerships to shape global, regional
and industry agendas.
The World Economic Forum defines competitiveness as the set of institutions,
policies, and factors that determine the level of productivity of an economy,
which in turn sets the level of prosperity that the economy can achieve.
Building on Klaus Schwab’s original work of 1979, the World Economic Forum
has used the Global Competitiveness Index (GCI) developed by Xavier Salai-
Martín in collaboration with the Forum since 2005. The GCI combines 114
indicators that capture concepts that matter for productivity and long-term
prosperity.
The Global Competitiveness Index provides a comparative overview of the
economic and business potential of countries. For each individual country, the
GCI enables decision makers to estimate the productivity of individual sectors
and the economy as a whole. Furthermore, the index identifies elements of the
economy that stimulate or inhibit growth.
We are including in our research, nine major factors of Global Competitiveness
Index, namely:

1. INFRASTRUCTURE
Extensive and efficient infrastructure is critical for ensuring the effective
functioning of the economy. Effective modes of transport—including high-

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quality roads, railroads, ports, and air transport—enable entrepreneurs to get

their goods and services to market in a secure and timely manner and facilitate
the movement of workers to the most suitable jobs. Economies also depend on
electricity supplies that are free from interruptions and shortages so that
businesses and factories can work unimpeded. Finally, a solid and extensive
telecommunications network allows for a rapid and free flow of information,
which increases overall economic efficiency by helping to ensure that
businesses can communicate and decisions are made by economic actors
taking into account all available relevant information.

2. INSTITUTIONS
The institutional environment of a country depends on the efficiency and the
behaviour of both public and private stakeholders. The legal and administrative
framework within which individuals, firms, and governments interact
determines the quality of the public institutions of a country and has a strong
bearing on competitiveness and growth. It influences investment decisions and
the organization of production and plays a key role in the ways in which
societies distribute the benefits and bear the costs of development strategies
and policies. Good private institutions are also important for the sound and
sustainable development of an economy. The 2007–08 global financial crisis,
along with numerous corporate scandals, has highlighted the relevance of
accounting and reporting standards and transparency for preventing fraud and
mismanagement, ensuring good governance, and maintaining investor and
consumer confidence.

3. MACROECONOMIC ENVIRONMENT
The stability of the macroeconomic environment is important for business and,
therefore, is significant for the overall competitiveness of a country. Although
it is certainly true that macroeconomic stability alone cannot increase the
productivity of a nation, it is also recognized that macroeconomic disarray
harms the economy, as we have seen in recent years, conspicuously in the
European context. The government cannot provide services efficiently if it has
to make high-interest payments on its past debts. Running fiscal deficits limits
the government’s future ability to react to business cycles. Firms cannot
operate efficiently when inflation rates are out of hand. In sum, the economy
cannot grow in a sustainable manner unless the macro environment is stable.

4. HEALTH AND PRIMARY EDUCATION

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5. A healthy workforce is vital to a country’s competitiveness and

productivity. Workers who are ill cannot function to their potential and
will be less productive. Poor health leads to significant costs to business,
as sick workers are often absent or operate at lower levels of efficiency.
Investment in the provision of health services is thus critical for clear
economic, as well as moral, considerations. In addition to health, this
pillar takes into account the quantity and quality of the basic education
received by the population, which is fundamental in today’s economy.
Basic education increases the efficiency of each individual worker.

6. HIGHER EDUCATION AND TRAINING

Quality higher education and training is crucial for economies that want to
move up the value chain beyond simple production processes and products. In
particular, today’s globalizing economy requires countries to nurture pools of
well-educated workers who are able to perform complex tasks and adapt
rapidly to their changing environment and the evolving needs of the
production system. This pillar measures secondary and tertiary enrollment
rates as well as the quality of education as evaluated by business leaders. The
extent of staff training is also taken into consideration because of the
importance of vocational and continuous on-the-job training—which is
neglected in many economies—for ensuring a constant upgrading of workers’
skills.

7. GOODS MARKET EFFICIENCY

Countries with efficient goods markets are well positioned to produce the right
mix of products and services given their particular supply-and-demand
conditions, as well as to ensure that these goods can be most effectively
traded in the economy. Healthy market competition, both domestic and
foreign, is important in driving market efficiency, and thus business
productivity, by ensuring that the most efficient firms, producing goods
demanded by the market, are those that thrive. Market efficiency also
depends on demand conditions such as customer orientation and buyer
sophistication. For cultural or historical reasons, customers may be more
demanding in some countries than in others. This can create an important
competitive advantage, as it forces companies to be more innovative and
customer-oriented and thus imposes the discipline necessary for efficiency to
be achieved in the market.

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8. LABOR MARKET EFFICIENCY

The efficiency and flexibility of the labour market are critical for ensuring that
workers are allocated to their most effective use in the economy and provided
with incentives to give their best effort in their jobs. Labour markets must
therefore have the flexibility to shift workers from one economic activity to
another rapidly and at low cost, and to allow for wage fluctuations without
much social disruption. Efficient labour markets must also ensure clear strong
incentives for employees and promote meritocracy at the workplace, and they
must provide equity in the business environment between women and men.
Taken together these factors have a positive effect on worker performance
and the attractiveness of the country for talent, two aspects of the labour
market that are growing more important as talent shortages loom on the
horizon.

9. FINANCIAL MARKET DEVELOPMENTS

An efficient financial sector allocates the resources saved by a nation’s
population, as well as those entering the economy from abroad, to the
entrepreneurial or investment projects with the highest expected rates of
return rather than to the politically connected. Business investment is critical
to productivity. Therefore economies require sophisticated financial markets
that can make capital available for private-sector investment from such
sources as loans from a sound banking sector, well-regulated securities
exchanges, venture capital, and other financial products. In order to fulfil all
those functions, the banking sector needs to be trustworthy and transparent,
and—as has been made so clear recently—financial markets need appropriate
regulation to protect investors and other actors in the economy at large.

10.MARKET SIZE
The size of the market affects productivity since large markets allow firms to
exploit economies of scale. Traditionally, the markets available to firms have
been constrained by national borders. In the era of globalization, international
markets have become a substitute for domestic markets, especially for small
countries. Thus exports can be thought of as a substitute for domestic demand
in determining the size of the market for the firms of a country. By including
both domestic and foreign markets in our measure of market size, we give
credit to export-driven economies and geographic areas (such as the European
Union) that are divided into many countries but have a single common market.

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OBJECTIVE
To prove that the level of global competitiveness depends on the factors
institutions, infrastructure, macroeconomic environment, health and primary
education, higher education and training, goods market efficiency, labor
market efficiency, financial market development and market size.

Y1= Global Competitiveness Index

X1= Institutions
X2= Infrastructure
X3= Macroeconomic Environment
X4= Health and Primary Education
X5= Higher Education and Training
X6= Goods Market Efficiency
X7= Labor Market Efficiency
X8= Financial Markets Development
X9= Market Size

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RESEARCH METHODOLOGY
There are three stages to our research project:
1. MODEL SPECIFICATION: It is based on available literature and theory.
Such literature helped us to identify the independent and dependent
variables and the relationship between them.

2. ESTIMATION OF THE MODEL:

a) Gathering the data: We gathered the data and determined the
type of the data collected. The data we collected was a Cross-
Sectional data for 137 countries.
b) Choosing appropriate technique: We then proceeded to
determine the appropriate econometric technique that could
justify a relationship between the dependent and independent
variables. We used SPSS to compute regression, correlation and
other tests.

3. HYPOTHESIS TESTING: This process requires a null hypothesis and an

alternative hypothesis with their respective beta coefficients. After
defining the hypothesis, we proceed towards analysing the data with the
chosen econometric technique. After the analysis, the resulting output is
then interpreted to determine if the null hypothesis should be rejected
or not. This is done by observing the results of regression, t-tests, F-tests
and descriptive statistics.

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STATEMENT OF HYPOTHESIS
Model Hypothesis
H0: The factors institutions, infrastructure, macroeconomic environment,
health & primary education, higher education and training, goods market
efficiency, labor market efficiency, financial market development and market
size don’t have a significant effect on global competitiveness index, others held
constant.
H1: The factors institutions, infrastructure, macroeconomic environment,
health & primary education, higher education and training, goods market
efficiency, labor market efficiency, financial market development and market
size have a significant effect on global competitiveness index, others held
constant.

1. X1: Institutions
H0: Institutions do not significantly affect the global competitiveness index
H1: Institutions do significantly affect the global competitiveness index
2. X2: Infrastructure
H0: Infrastructure does not significantly affect the global competitiveness
index
H1: Infrastructure does significantly affect the global competitiveness index
3. Macroeconomic Environment
H0: Macroeconomic environment does not significantly affect the global
competitiveness index
H1: Macroeconomic environment does significantly affect the global
competitiveness index

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4. Health and Primary Education

H0: Health and primary education does not significantly affect the global
competitiveness index
H1: Health and primary education does significantly affect the global
competitiveness index
5. Higher Education and Training
H0: Higher education and training does not significantly affect the global
competitiveness index
H1: Higher education and training does significantly affect the global
competitiveness index
6. Goods Market Efficiency
H0: Goods market efficiency does not significantly affect the global
competitiveness index
H1: Goods market efficiency does significantly affect the global
competitiveness index
7. Labour Market Efficiency
H0: Labour market efficiency does not significantly affect the global
competitiveness index
H1: Labour market efficiency does significantly affect the global
competitiveness index
8. Financial Market Development
H0: Financial market development does not significantly affect the global
competitiveness index
H1: Financial market development does significantly affect the global market
efficiency
9. Market Size
H0: Market size does not significantly affect the global competitiveness index
H1: Market size does significantly affect the global competitiveness index

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SAMPLE DATA
Country Global Competitive Rate Institutions Infrastructure Macroeconomic Environment Health and Primary Education
Albania 4.18 3.88 3.56 4.6 6.24
Algeria 4.07 3.63 3.56 4.63 5.77
Argentina 3.95 3.28 3.85 3.38 5.89
Armenia 4.19 4.06 3.85 4.13 5.99
Australia 5.19 5.35 5.27 5.67 6.52
Austria 5.25 5.15 5.73 5.52 6.4
Azerbaijan 4.69 4.65 4.54 4.8 5.72
Bahrain 4.54 5.04 5.07 3.98 6.22
Bangladesh 3.91 3.39 2.92 4.9 5.22
Belgium 5.23 5.02 5.42 4.87 6.63
Benin 3.47 3.53 2.31 3.94 4.69
Bhutan 4.1 4.8 3.64 4.58 5.42
Bosnia and Herzegovina 3.87 3.09 3.3 4.82 5.97
Botswana 4.3 4.36 3.64 6.09 4.83
Brazil 4.14 3.35 4.11 3.44 5.41
Brunei Darussalam 4.52 4.43 4.31 5.15 6.32
Bulgaria 4.46 3.48 4.06 5.72 5.8
Burundi 3.21 3.2 2.12 3.59 4.79
Cambodia 3.93 3.39 3.14 4.64 5.26
Cameroon 3.65 3.48 2.25 4.45 4.77
Canada 5.35 5.43 5.7 5.13 6.6
Cape Verde 3.76 3.94 3.5 4.14 5.85
Chad 2.99 2.64 1.9 4.4 3.62
Chile 4.71 4.53 4.78 5.38 5.82
China 5 4.42 4.66 6 6.21
Colombia 4.29 3.21 3.77 4.83 5.53
Congo, Democratic Rep. 3.27 3.2 2.33 3.45 4.25
Costa Rica 4.5 4.25 4.25 4.55 6.24
Croatia 4.19 3.45 4.65 4.85 6.13
Cyprus 4.3 4.18 5.11 4.19 6.21
Czech Republic 4.77 4.16 4.61 6.23 6.4
Denmark 5.39 5.46 5.51 6.22 6.41
Dominican Republic 3.87 3.05 3.3 5.1 5.07
Ecuador 3.91 3.05 4.12 4.34 5.91

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Country Global Competitive Rate Institutions Infrastructure Macroeconomic Environment Health and Primary Education
Egypt 3.9 3.94 4.13 2.59 5.54
El Salvador 3.77 2.75 3.97 4.47 5.31
Estonia 4.85 5.04 5.09 6.07 6.43
Ethiopia 3.78 3.83 2.71 4.87 4.77
Finland 5.49 6.16 5.39 5.49 6.9
France 5.18 4.84 6.1 4.82 6.39
Gambia, The 3.61 4.31 3.64 2.42 4.19
Georgia 4.28 4.2 4.19 5.1 5.79
Germany 5.65 5.3 5.96 6.1 6.52
Ghana 3.72 4.03 3.25 2.64 4.55
Greece 4.02 3.65 4.89 3.7 6.1
Guatemala 4.08 3.33 3.82 4.94 4.97
Guinea 3.47 3.42 2.43 4.12 3.54
Haiti 3.22 2.66 1.79 4.85 4.81
Honduras 3.92 3.2 3.24 5.04 5.51
Hong Kong SAR 5.53 5.69 6.7 6.28 6.38
Hungary 4.33 3.46 4.36 5.13 5.65
Iceland 4.99 5.45 5.56 5.94 6.58
India 4.59 4.44 4.22 4.54 5.5
Indonesia 4.68 4.27 4.52 5.72 5.43
Iran, Islamic Rep. 4.27 3.72 4.35 5.15 6.04
Ireland 5.16 5.35 5.11 5.77 6.48
Israel 5.31 4.94 5.4 5.24 6.34
Italy 4.54 3.5 5.37 4.24 6.39
Jamaica 4.25 3.94 4.09 3.94 6.11
Japan 5.49 5.41 6.34 4.3 6.6
Jordan 4.3 4.5 4.34 3.78 5.64
Kazakhstan 4.35 4.03 4.2 4.17 5.95
Kenya 3.98 3.82 3.46 3.57 4.76
Korea, Rep. 5.07 4.04 6.08 6.63 6.34
Kuwait 4.43 4.05 4.26 5.6 5.61
Kyrgyz Republic 3.9 3.44 3.05 4.38 5.7
Lao PDR 3.91 4.02 3.27 3.81 5.19
Latvia 4.4 3.76 4.4 5.77 6.11
Lebanon 3.84 3.18 2.79 2.46 5.76

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Country Global Competitive Rate Institutions Infrastructure Macroeconomic Environment Health and Primary Education
Lesotho 3.2 3.87 2.49 3.81 2.97
Liberia 3.08 3.52 2.41 3.34 3.17
Lithuania 4.58 4.13 4.65 5.61 6.2
Luxembourg 5.23 5.74 5.68 6.27 6.21
Madagascar 3.4 3.02 1.99 4.14 4.77
Malawi 3.11 3.5 1.79 2.19 4.75
Malaysia 5.17 4.98 5.46 5.44 6.32
Mali 3.33 3.33 2.83 4.07 3.09
Malta 4.65 4.47 4.77 5.85 6.57
Mauritania 3.09 2.93 2.1 4.64 4.16
Mauritius 4.52 4.49 4.8 4.69 6.07
Mexico 4.44 3.2 4.3 5.17 5.69
Moldova 3.99 3.2 3.74 4.53 5.4
Mongolia 3.9 3.37 3.11 4.37 5.59
Montenegro 4.15 3.9 4.16 3.71 5.91
Morocco 4.24 4.2 4.42 4.91 5.63
Mozambique 2.89 3.05 2.47 1.86 3.59
Namibia 3.99 4.39 4.21 4.02 4.77
Nepal 4.02 3.58 2.61 5.59 5.68
Netherlands 5.66 5.76 6.44 6.08 6.69
NewZealand 5.37 6.07 5.45 6.06 6.62
Nicaragua 3.95 3.24 3.58 5.09 5.55
Nigeria 3.3 3.17 2.04 3.51 3
Norway 5.4 5.82 5.04 6.64 6.59
Oman 4.31 4.96 4.9 4.7 5.9
Pakistan 3.67 3.53 3.03 4.03 4.14
Panama 4.44 3.82 4.9 6.11 5.64
Paraguay 3.71 3 2.63 5.19 5.08
Peru 4.22 3.22 3.77 5.35 5.44
Philippines 4.35 3.51 3.43 5.82 5.63
Poland 4.59 3.84 4.7 5.2 6.22
Portugal 4.57 4.4 5.59 4.04 6.44
Qatar 5.11 5.6 5.83 5.93 6.25
Romania 4.28 3.7 3.82 5.25 5.49
Russian Federation 4.64 3.75 4.93 5.03 6

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Country Global Competitive Rate Institutions Infrastructure Macroeconomic Environment Health and Primary Education
Rwanda 4.35 5.42 3.39 4.34 5.34
Saudi Arabia 4.83 5.01 5.2 4.87 6.03
Senegal 3.81 3.9 3.14 4.48 4.3
Serbia 4.14 3.42 4.09 4.61 6.02
Seychelles 3.8 3.77 4.55 4.6 5.97
Sierra Leone 3.2 3.3 2.58 3.24 4.29
Singapore 5.71 6.08 6.54 5.98 6.76
Slovak Republic 4.33 3.51 4.29 5.4 6.1
Slovenia 4.48 4.05 4.8 5.23 6.49
South Africa 4.32 3.81 4.31 4.52 4.47
Spain 4.7 4.1 5.88 4.35 6.29
Sri Lanka 4.08 3.8 3.8 4.27 6.15
Swaziland 3.35 4.02 3.22 3.28 3.63
Sweden 5.52 5.59 5.56 6.44 6.41
Switzerland 5.86 5.93 6.26 6.57 6.78
Taiwan, China 5.33 4.85 5.71 6.33 6.48
Tajikistan 4.14 4.41 3.34 4.1 5.75
Tanzania 3.71 3.85 2.77 4.6 4.28
Thailand 4.72 3.8 4.7 6.23 5.51
Trinidad and Tobago 4.09 3.49 4.32 3.84 5.93
Tunisia 3.93 3.78 3.83 3.94 5.95
Turkey 4.42 3.85 4.47 5.1 5.6
Uganda 3.7 3.48 2.49 4.59 4.64
Ukraine 4.11 3.21 3.95 3.52 6.02
United Arab Emirates 5.3 5.93 6.26 5.63 6.26
United Kingdom 5.51 5.52 5.96 4.65 6.47
United States 5.85 5.33 6.01 4.51 6.33
Uruguay 4.15 4.55 4.66 4.26 5.77
Venezuela 3.23 2.18 2.63 2.43 5.32
Viet Nam 4.36 3.79 3.9 4.59 5.81
Yemen 2.87 2.67 1.83 2.85 4.68
Zambia 3.52 3.72 2.44 3.68 4.36
Zimbabwe 3.32 3.25 2.66 3.19 4.69

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Country Higher Education and Training Goods Market Efficiency Labor Market Efficiency Financial Market Development Market Size
Egypt 3.6 4.15 3.22 3.89 5.08
El Salvador 3.49 3.96 3.43 4.15 3.26
Estonia 5.52 5.09 5.02 4.85 3.1
Ethiopia 2.77 3.71 4.19 3.41 3.89
Finland 6.18 5.15 4.78 5.54 4.16
France 5.41 4.68 4.35 4.53 5.75
Gambia, The 3.44 4.51 4.64 4.04 1.53
Georgia 4.02 4.51 4.39 4.06 3.09
Germany 5.7 5.27 5.03 5.03 6
Ghana 3.67 4.3 4.3 3.78 3.77
Greece 4.87 4.12 3.72 2.49 4.28
Guatemala 3.67 4.52 3.85 4.9 3.75
Guinea 2.91 4.26 4.36 4.6 2.45
Haiti 2.65 3.03 3.89 2.45 2.6
Honduras 3.56 4.05 3.48 4.46 3.15
Hong Kong SAR 5.7 5.74 5.59 5.51 4.8
Hungary 4.33 4.38 4.21 4.31 4.33
Iceland 5.79 4.78 5.21 4.22 2.46
India 4.31 4.47 4.15 4.37 6.43
Indonesia 4.52 4.59 3.91 4.5 5.73
Iran, Islamic Rep. 4.71 4.04 3.3 3.02 5.24
Ireland 5.85 5.35 4.87 3.99 4.5
Israel 5.44 4.82 4.9 5.07 4.29
Italy 4.96 4.41 3.67 3.05 5.59
Jamaica 4.38 4.4 4.45 4.57 2.78
Japan 5.38 5.24 4.78 4.89 6.07
Jordan 4.52 4.51 3.97 3.99 3.62
Kazakhstan 4.57 4.29 4.57 3.3 4.55
Kenya 3.8 4.35 4.7 4.16 3.8
Korea, Rep. 5.34 4.97 4.18 3.9 5.53
Kuwait 3.91 4.16 3.59 4.07 4.39
Kyrgyz Republic 4.01 4.21 3.69 3.75 2.78
Lao PDR 3.47 4.28 4.56 3.89 3.06
Latvia 4.95 4.42 4.47 4.05 3.24
Lebanon 4.32 4.4 3.74 3.89 3.63

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Country Higher Education and Training Goods Market Efficiency Labor Market Efficiency Financial Market Development Market Size
Albania 4.77 4.43 3.96 3.81 2.99
Algeria 3.95 3.64 3.27 3.06 4.78
Argentina 5 3.44 3.29 3.1 4.88
Armenia 4.42 4.7 4.4 3.88 2.79
Australia 5.88 4.88 4.68 5.45 5.13
Austria 5.68 4.89 4.49 4.58 4.59
Azerbaijan 4.46 4.8 5.01 3.84 3.97
Bahrain 4.99 4.98 4.55 4.3 3.31
Bangladesh 3.1 4.11 3.6 3.6 4.72
Belgium 5.82 5.18 4.47 4.68 4.79
Benin 3.13 3.66 4.41 3.36 2.66
Bhutan 4.01 4.16 4.73 4.01 1.94
Bosnia and Herzegovina 3.98 3.7 3.49 3.5 3.15
Botswana 3.84 4.21 4.52 4.04 2.96
Brazil 4.21 3.79 3.68 3.7 5.69
Brunei Darussalam 4.47 4.34 4.44 3.75 2.89
Bulgaria 4.62 4.32 4.25 4.14 3.92
Burundi 2.62 3.74 4.26 2.8 1.78
Cambodia 2.88 4.17 4.42 4.09 3.38
Cameroon 3.52 3.94 4.14 3.62 3.4
Canada 5.77 5.15 5.43 5.44 5.44
Cape Verde 4.06 4.01 3.67 3.21 1.56
Chad 2.3 3.01 3.78 2.73 2.82
Chile 5.25 4.65 4.42 4.92 4.54
China 4.78 4.55 4.55 4.23 7
Colombia 4.5 4.03 3.98 4.64 4.76
Congo, Democratic Rep. 2.75 3.59 4.34 3.04 3.22
Costa Rica 5.13 4.38 4.22 4.45 3.45
Croatia 4.54 4.04 3.77 3.65 3.62
Cyprus 4.86 4.9 4.53 3.44 2.9
Czech Republic 5.25 4.66 4.49 4.8 4.49
Denmark 5.97 5.11 5.19 4.87 4.29
Dominican Republic 3.93 3.91 3.62 3.57 3.89
Ecuador 4.25 3.65 3.41 3.34 3.92

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 Global Competitiveness Index Report

Country Higher Education and Training Goods Market Efficiency Labor Market Efficiency Financial Market Development Market Size
Rwanda 3.24 4.68 5.37 4.52 2.64
Saudi Arabia 4.87 4.6 4.1 4.16 5.44
Senegal 3.44 4.2 3.91 3.67 3.06
Serbia 4.55 3.96 3.96 3.56 3.72
Seychelles 3.92 4.28 4.1 3.27 1.45
Sierra Leone 2.54 3.71 3.72 3.17 2.22
Singapore 6.27 5.76 5.79 5.66 4.78
Slovak Republic 4.54 4.48 4.01 4.55 4.08
Slovenia 5.37 4.64 4.1 3.45 3.41
South Africa 4.06 4.48 3.96 4.35 4.91
Spain 5.2 4.51 4.21 4.01 5.42
Sri Lanka 4.23 4.2 3.3 3.78 4.2
Swaziland 3.24 3.87 4.07 3.77 2.21
Sweden 5.59 5.23 4.87 5.13 4.66
Switzerland 6.07 5.5 5.94 5.29 4.69
Taiwan, China 5.63 5.26 4.73 4.9 5.22
Tajikistan 4.31 4.34 4.59 3.49 2.77
Tanzania 2.63 3.9 4.29 3.52 3.81
Thailand 4.56 4.72 4.26 4.44 5.24
Trinidad and Tobago 5.1 4.09 4.01 4.19 3.16
Tunisia 4.09 3.95 3.09 3.39 3.86
Turkey 4.78 4.48 3.39 3.82 5.5
Uganda 2.76 3.88 4.64 3.72 3.44
Ukraine 5.09 4.04 4.01 3.11 4.49
United Arab Emirates 5.05 5.62 5.17 4.76 4.94
United Kingdom 5.48 5.29 5.44 5.03 5.75
United States 6.12 5.47 5.64 5.73 6.86
Uruguay 4.62 4.28 3.53 4.09 3.33
Venezuela 4.56 2.76 2.72 3.1 4.38
Viet Nam 4.07 4.15 4.35 3.98 4.91
Yemen 2.25 3.44 3 2.18 3.15
Zambia 2.92 4.17 3.86 3.66 3.35
Zimbabwe 3.11 3.46 3.72 3.17 2.8

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 Global Competitiveness Index Report

Country Higher Education and Training Goods Market Efficiency Labor Market Efficiency Financial Market Development Market Size
Lesotho 3.03 4.29 3.83 2.39 2.11
Liberia 2.5 4.01 4.14 3.7 1.55
Lithuania 5.16 4.57 4.33 4.1 3.62
Luxembourg 4.75 5.52 5.01 4.97 3.34
Madagascar 2.91 3.94 4.34 3.1 2.99
Malawi 2.66 3.79 4.47 3.55 2.65
Malaysia 4.87 5.11 4.72 4.96 5.09
Mali 3.01 4 3.76 3.36 2.98
Malta 5.16 4.88 4.68 4.37 2.68
Mauritania 1.9 3.11 3.33 2.13 2.51
Mauritius 4.65 4.89 4.4 4.38 2.82
Mexico 4.11 4.32 3.77 4.51 5.67
Moldova 4.09 4.06 3.94 3.08 2.68
Mongolia 4.51 3.95 4.23 3 3
Montenegro 4.54 4.36 4.18 4.24 2.28
Morocco 3.58 4.43 3.58 3.93 4.34
Mozambique 2.25 3.8 3.9 2.77 3.09
Namibia 3.32 4.18 4.59 4.21 2.87
Nepal 3.44 3.97 3.9 3.91 3.37
Netherlands 6.09 5.5 5.07 4.63 5.1
NewZealand 5.97 5.3 5.47 5.81 3.94
Nicaragua 3.42 3.88 3.85 3.57 3.01
Nigeria 3.1 4.07 4.6 3.7 4.98
Norway 5.88 4.98 5.11 5.19 4.43
Oman 4.4 4.53 3.5 4.16 4.06
Pakistan 3 3.98 3.37 3.64 4.95
Panama 4.02 4.6 4.15 4.99 3.59
Paraguay 3.44 4.17 3.77 3.8 3.34
Peru 4.1 4.28 4.27 4.51 4.45
Philippines 4.59 4.03 4.02 4.19 4.97
Poland 4.98 4.55 4.14 4.17 5.17
Portugal 5.09 4.7 4.35 3.26 4.33
Qatar 5.01 5.22 4.89 4.71 4.38
Romania 4.41 4.14 3.97 3.74 4.61
Russian Federation 5.12 4.21 4.33 3.45 5.9

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 Global Competitiveness Index Report

Formulas used:
t= (beta 2 cap – beta 2) / se (beta 2 cap)
TSS= ESS + RSS
F= (ESS/k-1) / (RSS/n-k)
MSS= ESS/df and RSS/df
Adjusted R^2= 1-(1-R^2)(n-1)/(n-k)

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 Global Competitiveness Index Report

REGRESSION ANALYSIS
Variables Entered/Removeda

Model Variables Entered Variables Removed Method

1 Market_Size,
Labor_Market_Efficiency,
Health_and_primary_education,
Macroeconomic_environment,
. Enter
Financial_Market_Development,
Institutions, Infrastructure,
Higher_Education_and_training,
Goods_Market_Efficiencyb

a. Dependent Variable: Global_Competitive_Rate

b. All requested variables entered.

Model Summary

Adjusted R Std. Error of

Model R R Square Square the Estimate

1 .993a .987 .986 .08292

a. Predictors: (Constant), Market_Size, Labor_Market_Efficiency,

Health_and_primary_education, Macroeconomic_environment,
Financial_Market_Development, Institutions, Infrastructure,
Higher_Education_and_training, Goods_Market_Efficiency

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 Global Competitiveness Index Report

ANOVAa

Sum of
Model Squares df Mean Square F Sig.

1 Regression 65.919 9 7.324 1065.196 .000b

Residual .873 127 .007

Total 66.792 136

a. Dependent Variable: Global_Competitive_Rate

b. Predictors: (Constant), Market_Size, Labor_Market_Efficiency,

Health_and_primary_education, Macroeconomic_environment,
Financial_Market_Development, Institutions, Infrastructure, Higher_Education_and_training,
Goods_Market_Efficiency

Coefficientsa

Model Unstandardized Coefficients Standardized t Sig.

Coefficients

B Std. Error Beta

1 (Constant) -.013 .099 -.128 .898

Institutions .142 .019 .177 7.425 .000

Infrastructure .093 .018 .160 5.263 .000

Macroeconomic_en .106 .009 .153 11.342 .000

vironment

Health_and_primar .139 .017 .175 7.961 .000

y_education

Higher_Education_ .086 .019 .126 4.530 .000

and_training

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 Global Competitiveness Index Report

Goods_Market_Effi .083 .036 .068 2.306 .023

ciency

Labor_Market_Effici .125 .021 .108 5.913 .000

ency

Financial_Market_D .088 .017 .094 5.244 .000

evelopment

Market_Size .116 .008 .193 14.626 .000

a. Dependent Variable: Global_Competitive_Rate

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 Global Competitiveness Index Report

DATA ANALYSIS AND

INTERPRETATION
We now regress on all of the variables together. We use this to
predict the Global Competitiveness Index 2017-2018 based on all
other variables. Global Competitiveness Index indicates the ability of
countries to provide high levels of prosperity to their citizens.

The Model Summary table displays the following:

 MULTIPLE CORRELATION COEFFICIENT (R) :
 This is the Pearson’s coefficient of correlation for multiple regression
analysis.
 The value of R lies between -1 and 1
 Value of R is 0.993
 Hence, there is strong positive correlation between the dependant
variable (Global Competitive Rate) and independent variables
(Institutions, Infrastructure, Macroeconomic environment, Health and
primary education, Higher education and training, Goods market
efficiency, Labor market efficiency, Financial Market Development and
Market Size).
 COEFFICIENT OF DETERMINATION (R SQUARE)
 It is the most commonly used measure of goodness of fit of a regression
line.
 It measures the proportion of the total variation in Y explained by the
variation in independent variables.
 R square lies between 0 and 1.
 The value of R square is 0.987
 Hence we can conclude that 98% of variation in the dependant variable
(Global Competitive Rate) is explained by the independent variables
(Institutions, Infrastructure, Macroeconomic environment, Health and

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 Global Competitiveness Index Report

primary education, Higher education and training, Goods market

efficiency, Labor market efficiency, Financial Market Development and
Market Size).
 ADJUSTED R SQUARE:
 It is an indication of the adequacy of the model as it takes into
account number of degrees of freedom.
 It is always less than Coefficient of Determination.
 Its value is 0.986 which is also very high, hence proving that the
model is adequate.
 STANDARD ERROR OF THE ESTIMATE:
 It is a measure of accuracy of predictions.
 A smaller value of standard error indicates more accurate
predictions.
 Its value is 0.08292 which is a very small value, hence indicating the
predictions made by the regression model are quite accurate.
Unstandardized

Global Competitiveness Index = A + B1 (Institutions) + B2 (Infrastructure) + B3

(Macroeconomic Environment) + B4 (Health and Primary Education) +
B5(Higher Education and Training) + B6 (Goods Market Efficiency) + B7 (Labor
Market Efficiency) + B8 (Financial Market Development) + B9 (Market Size)

The regression coefficients B1, B2, B3, B4, B5, B6, B7, B8 and B9 are known as
partial regression or partial slope coefficients. The meaning of partial slope
coefficients is as follows

B1 measures the change in the mean value of Y (Global Competitiveness Index)

per unit change in X1 (Institutions)

B2 measures the change in the mean value of Y (Global Competitiveness Index)

per unit change in X2 (Infrastructure)

B3 measures the change in the mean value of Y (Global Competitiveness Index)

per unit change in X3 (Macroeconomic Environment)

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 Global Competitiveness Index Report

B4 measures the change in the mean value of Y (Global Competitiveness Index)

per unit change in X4 (Health and Primary Education)

B5 measures the change in the mean value of Y (Global Competitiveness Index)

per unit change in X5 (Higher Education and Training)

B6 measures the change in the mean value of Y (Global Competitiveness Index)

per unit change in X6 (Goods Market Efficiency)

B7 measures the change in the mean value of Y (Global Competitiveness Index)

per unit change in X7 (Labor Market Efficiency)

B8 measures the change in the mean value of Y (Global Competitiveness Index)

per unit change in X8 (Financial Market Development)

B9 measures the change in the mean value of Y (Global Competitiveness Index)

per unit change in X9 (Market Size)

It gives the direct or net effect of a unit change in X on the mean value of Y so
as keeping all other factors being constant.
H0: B1 equals to 0

H1: B1 does not equal to 0

 We observe that the significance of the t-stat for B1 (0.000) is less than
0.05. Therefore we reject the null hypothesis.
 This means that B1 is significant. Therefore Institution significantly
affects the Global Competitiveness Index
H0: B2 equals to 0
H1: B2 does not equal to 0

 We observe that the significance of the t-stat for B2 (0.000) is less than
0.05. Therefore we reject the null hypothesis.

 This means that B2 is significant. Therefore Infrastructure significantly

affects the Global Competitiveness Index

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 Global Competitiveness Index Report

H0: B3 equals to 0
H1: B3 does not equal to 0

 We observe that the significance of the t-stat for B3 (0.000) is less than
0.05. Therefore we reject the null hypothesis.
 This means that B3 is significant. Therefore Macroeconomic
Environment significantly affects the Global Competitiveness Index
H0: B4 equals to 0
H1: B4 does not equal to 0

 We observe that the significance of the t-stat for B4 (0.000) is less than
0.05. Therefore we reject the null hypothesis.
 This means that B4 is significant. Therefore Health and Primary
Education significantly affects the Global Competitiveness Index
H0: B5 equals to 0
H1: B5 does not equal to 0

 We observe that the significance of the t-stat for B5 (0.000) is less than
0.05. Therefore we reject the null hypothesis.
 This means that B5 is significant. Therefore Higher Education and
Training significantly affects the Global Competitiveness Index
H0: B6 equals to 0
H1: B6 does not equal to 0

 We observe that the significance of the t-stat for B6 (0.023) is less than
0.05. Therefore we reject the null hypothesis.
 This means that B6 is significant. Therefore Goods Market Efficiency
significantly affects the Global Competitiveness Index
H0: B7 equals to 0
H1: B7 does not equal to 0

 We observe that the significance of the t-stat for B7 (0.000) is less than
0.05. Therefore we reject the null hypothesis.
 This means that B7 is significant. Therefore Labor Market Efficiency
significantly affects the Global Competitiveness Index

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 Global Competitiveness Index Report

H0: B8 equals to 0
H1: B8 does not equal to 0

 We observe that the significance of the t-stat for B8 (0.000) is less than
0.05. Therefore we reject the null hypothesis.
 This means that B8 is significant. Therefore Financial Market
Development significantly affects the Global Competitiveness Index
H0: B9 equals to 0
H1: B9 does not equal to 0

 We observe that the significance of the t-stat for B9 (0.000) is less than
0.05. Therefore we reject the null hypothesis.
 This means that B9 is significant. Therefore Market Size significantly
affects the Global Competitiveness Inde
H0: The overall model is not significant

H1: The overall model is significant

 Global Competitiveness Index = -0.013 + 0.142 (Institutions) + 0.093

(Infrastructure) + 0.106 (Macroeconomic Environment) + 0.139 (Health
and Primary Education) + 0.086 (Higher Education and Training) + 0.083
(Goods Market efficiency) + 0.125 (Labor Market Efficiency) + 0.088
(Financial Market Development) + 0.116 (Market Size)

F-TEST
F-test is a statistical test that determines the significance of the result. If
F(calculated) > F(critical), we reject the null hypothesis.
F(critical) comes out to be 4(approximately) which is less than
F(calculated) 1065.96. So, we reject the null hypothesis. Thus, we infer
that the overall model is significant.

Standardized

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 Global Competitiveness Index Report

Global Competitiveness Index = Beta 1 (Institutions) + Beta 2 (Infrastructure) +

Beta 3 (Macroeconomic Environment) + Beta 4 (Health and Primary Education)
+ Beta 5(Higher Education and Training) + Beta 6 (Goods Market Efficiency) +
Beta 7 (Labor Market Efficiency) + Beta 8 (Financial Market Development) +
Beta 9 (Market Size)
The regression coefficients Beta 1, Beta 2, Beta 3, Beta 4, Beta 5, Beta 6, Beta
7, Beta 8 and Beta 9 are known as partial regression or partial slope
coefficients. The meaning of partial slope coefficients is as follows

Beta 1 measures the change in the mean value of Y (Global Competitiveness

Index) per unit change in X1 (Institutions)

Beta 2 measures the change in the mean value of Y (Global Competitiveness

Index) per unit change in X2 (Infrastructure)

Beta 3 measures the change in the mean value of Y (Global Competitiveness

Index) per unit change in X3 (Macroeconomic Environment)

Beta 4 measures the change in the mean value of Y (Global Competitiveness

Index) per unit change in X4 (Health and Primary Education)

Beta 5 measures the change in the mean value of Y (Global Competitiveness

Index) per unit change in X5 (Higher Education and Training)

Beta 6 measures the change in the mean value of Y (Global Competitiveness

Index) per unit change in X6 (Goods Market Efficiency)

Beta 7 measures the change in the mean value of Y (Global Competitiveness

Index) per unit change in X7 (Labor Market Efficiency)

Beta 8 measures the change in the mean value of Y (Global Competitiveness

Index) per unit change in X8 (Financial Market Development)

Beta 9 measures the change in the mean value of Y (Global Competitiveness

Index) per unit change in X9 (Market Size)

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 Global Competitiveness Index Report

It gives the direct or net effect of a unit change in X on the mean value of Y so
as keeping all other factors being constant.
H0: Beta 1 equals to 0

H1: Beta 1 does not equal to 0

 We observe that the significance of the t-stat for Beta 1 (0.000) is less
than 0.05. Therefore we reject the null hypothesis.
 This means that Beta 1 is significant. Therefore Institution significantly
affects the Global Competitiveness Index

H0: Beta 2 equals to 0

H1: Beta 2 does not equal to 0

 We observe that the significance of the t-stat for Beta 2 (0.000) is less
than 0.05. Therefore we reject the null hypothesis.
 This means that Beta 2 is significant. Therefore Infrastructure
significantly affects the Global Competitiveness Index

H0: Beta 3 equals to 0

H1: Beta 3 does not equal to 0

 We observe that the significance of the t-stat for Beta 3 (0.000) is less
than 0.05. Therefore we reject the null hypothesis.
 This means that Beta 3 is significant. Therefore Macroeconomic
Environment significantly affects the Global Competitiveness Index

H0: Beta 4 equals to 0

H1: Beta does not equal to 0

 We observe that the significance of the t-stat for Beta 4 (0.000) is less
than 0.05. Therefore we reject the null hypothesis.
 This means that Beta 4 is significant. Therefore Health and Primary
Education significantly affects the Global Competitiveness Index

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 Global Competitiveness Index Report

H0: Beta 5 equals to 0

H1: Beta 5 does not equal to 0

 We observe that the significance of the t-stat for Beta 5 (0.000) is less
than 0.05. Therefore we reject the null hypothesis.
 This means that Beta 5 is significant. Therefore Higher Education and
Training significantly affects the Global Competitiveness Index

H0: Beta 6 equals to 0

H1: Beta 6 does not equal to 0

 We observe that the significance of the t-stat for Beta 6 (0.023) is less
than 0.05. Therefore we reject the null hypothesis.
 This means that Beta 6 is significant. Therefore Goods Market Efficiency
significantly affects the Global Competitiveness Index

H0: Beta 7 equals to 0

H1: Beta 7 does not equal to 0

 We observe that the significance of the t-stat for Beta 7 (0.000) is less
than 0.05. Therefore we reject the null hypothesis.
 This means that Beta 7 is significant. Therefore Labor Market Efficiency
significantly affects the Global Competitiveness Index

H0: Beta 8 equals to 0

H1: Beta 8 does not equal to 0

 We observe that the significance of the t-stat for Beta 8 (0.000) is less
than 0.05. Therefore we reject the null hypothesis.
 This means that Beta 8 is significant. Therefore Financial Market
Development significantly affects the Global Competitiveness Index

H0: Beta 9 equals to 0

H1: Beta 9 does not equal to 0

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 Global Competitiveness Index Report

 We observe that the significance of the t-stat for Beta 9 (0.000) is less
than 0.05. Therefore we reject the null hypothesis.
 This means that Beta 9 is significant. Therefore Market Size significantly
affects the Global Competitiveness Index

H0: The overall model is not significant

H1: The overall model is significant

 Global Competitiveness Index = 0.177 (Institutions) + 0.160

(Infrastructure) + 0.153 (Macroeconomic Environment) + 0.175 (Health
and Primary Education) + 0.126 (Higher Education and Training) + 0.068
(Goods Market efficiency) + 0.108 (Labor Market Efficiency) + 0.094
(Financial Market Development) + 0.193 (Market Size)
 The partial correlation coefficient of institutions = 0.177 implies that
with 1 unit change in score of institutions, the global competitiveness
index moves in the same direction with a magnitude of 0.177, keeping
others constant.
 The PCC of infrastructure = 0.160 implies that with 1 unit change in score
of infrastructure, the global competitiveness index moves in the same
direction with a magnitude of 0.160, keeping others constant.
 The PCC of macroeconomic environment = 0.153 implies that with 1 unit
change in score of macroeconomic environment, the global
competitiveness index moves in the same direction with a magnitude of
0.153, keeping others constant.
 The PCC of health and primary education = 0.175 implies that with 1 unit
change in score of health and primary education, the global
competitiveness index moves in the same direction with a magnitude of
0.175, keeping others constant.
 The PCC of higher education and training = 0.126 implies that with 1 unit
change in score of higher education and training, the global
competitiveness index moves in the same direction with a magnitude of
0.126, keeping others constant.

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 Global Competitiveness Index Report

 The PCC of goods market efficiency = 0.068 implies that with 1 unit
change in score of goods market efficiency, the global competitiveness
index moves in the same direction with a magnitude of 0.068, keeping
others constant.
 The PCC of labor market effieciency = 0.108 implies that with 1 unit
change in score of labor market effieciency, the global competitiveness
index moves in the same direction with a magnitude of 0.108, keeping
others constant.
 The PCC of financial market development = 0.094 implies that with 1
unit change in score of financial market development, the global
competitiveness index moves in the same direction with a magnitude of
0.094, keeping others constant.
 The PCC of market size = 0.193 implies that with 1 unit change in score
of market size, the global competitiveness index moves
in the same direction with a magnitude of 0.193, keeping others
constant.
 The P values of all 9 variables are less than 0.05 implying that these
variables are statistically significant.

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 Global Competitiveness Index Report

PROBLEM DETECTION
1. MULTI- COLLINEARITY: It means that two or more of the independent
variables in a regression model have a linear relationship. This causes a
problem in the interpretation of the regression results. If the variables
have a close linear relationship, then the estimated regression
coefficients and T-statistics may not be able to properly isolate the
unique effects of each variable and the confidence with which we can
presume these effects to be true. The close relationship of the variables
makes this isolation difficult. Variance Inflating Factor (VIF) and
Tolerance Factor (TOL) are used to detect the problem of Multi-
Collinearity. TOL is the inverse of VIF. If VIF approaches 1, it means there
is no multi-collinearity and if TOL is 0, it means there is perfect multi-
collinearity.

2. HETEROSCEDASTICITY: It is the problem of unequal variance of error

terms. The variances of the error terms should be constant or
homoscedastic. We use graphical method (scatter diagram) to detect the
problem of heteroscedasticity. If the data points exhibit any particular
pattern, it shows that the problem of heteroscedasticity exists. If the
data points do not exhibit any pattern and are random, it means that the
problem of heteroscedasticity does not exist.

3. AUTOCORRELATION: It is the problem of high correlation between the

disturbances of error terms. It means that the error term of any
observation is influenced by the disturbance term of any other
observation. It is the similarity between observations as a function of the
time lag between them. We can detect the problem of Autocorrelation
using Durbin-Watson Test. If the value of d-statistics is equal to 2, there
is no autocorrelation. If the value of d-statistics is close to 0, there is
positive autocorrelation and if the value of d-statistics is close to 4, there
is negative autocorrelation.

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 Global Competitiveness Index Report

MULTICOLLINEARITY
In addition to the assumptions of the classical linear regression model
involving 9 variables, in multiple regressions we also assume that there
is no exact linear relationship among the various independent variables.

Mathematically, a set of variables is perfectly multicollinear if there exist

one or more exact linear relationships among some of the variables.

There are a number of methods to detect multicollinearity among

regressors, 2 of them are VIF (Variance inflation factor) and tolerance
level. VIF is nothing but the reciprocal of tolerance.

To conduct a test for multicollinearity, each of the time one independent

variable was set as dependant variable and a multiple linear regression
was run. The following results were obtained :

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 Global Competitiveness Index Report

Coefficientsa

Collinearity Statistics

Model Tolerance VIF

1 Infrastructure .118 8.456

Macroeconomic_environment .565 1.770

Health_and_primary_education .214 4.667

Higher_Education_and_training .134 7.466

Goods_Market_Efficiency .134 7.443

Labor_Market_Efficiency .360 2.777

Financial_Market_Development .320 3.125

Market_Size .604 1.656

a. Dependent Variable: Institutions

b. Since the VIF of some variables is more than 6, multicollinearity exists.

Coefficientsa

Collinearity Statistics

Model Tolerance VIF

1 Institutions .191 5.238

Macroeconomic_environment .565 1.770

Health_and_primary_education .226 4.415

Higher_Education_and_training .153 6.552

Goods_Market_Efficiency .146 6.842

Labor_Market_Efficiency .324 3.082

Financial_Market_Development .321 3.115

Market_Size .650 1.539

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 Global Competitiveness Index Report

a. Dependent Variable: Infrastructure

Coefficientsa

Collinearity Statistics

Model Tolerance VIF

1 Institutions .181 5.533

Infrastructure .112 8.933

Health_and_primary_education .223 4.478

Higher_Education_and_training .134 7.445

Goods_Market_Efficiency .120 8.347

Labor_Market_Efficiency .311 3.219

Financial_Market_Development .333 3.006

Market_Size .595 1.681

a. Dependent Variable: Macroeconomic_environment

Since the VIF of some variables is more than 6, multicollinearity exists.

Coefficientsa

Collinearity Statistics

Model Tolerance VIF

1 Institutions .180 5.551

Infrastructure .118 8.479

Macroeconomic_environment .587 1.704

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 Global Competitiveness Index Report

Higher_Education_and_training .204 4.892

Goods_Market_Efficiency .121 8.275

Labor_Market_Efficiency .313 3.196

Financial_Market_Development .319 3.138

Market_Size .609 1.641

a. Dependent Variable: Health_and_primary_education

Since the VIF of some variables is more than 6, multicollinearity exists.

Coefficientsa

Collinearity Statistics

Model Tolerance VIF

1 Institutions .180 5.551

Infrastructure .127 7.865

Macroeconomic_environment .565 1.771

Health_and_primary_education .327 3.058

Goods_Market_Efficiency .120 8.361

Labor_Market_Efficiency .313 3.198

Financial_Market_Development .320 3.121

Market_Size .610 1.640

a. Dependent Variable: Higher_Education_and_training

Since the VIF of some variables is more than 6, multicollinearity exists.

b.

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 Global Competitiveness Index Report

Coefficientsa

Collinearity Statistics

Model Tolerance VIF

1 Institutions .203 4.938

Infrastructure .136 7.329

Macroeconomic_environment .565 1.771

Health_and_primary_education .217 4.616

Higher_Education_and_training .134 7.460

Labor_Market_Efficiency .339 2.950

Financial_Market_Development .355 2.818

Market_Size .596 1.679

a. Dependent Variable: Goods_Market_Efficiency

Since the VIF of some variables is more than 6, multicollinearity exists.

Coefficientsa

Collinearity Statistics

Model Tolerance VIF

1 Institutions .210 4.755

Infrastructure .117 8.522

Macroeconomic_environment .567 1.763

Health_and_primary_education .217 4.601

Higher_Education_and_training .136 7.366

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 Global Competitiveness Index Report

Goods_Market_Efficiency .131 7.613

Financial_Market_Development .324 3.082

Market_Size .605 1.653

a. Dependent Variable: Labor_Market_Efficiency

Since the VIF of some variables is more than 6, multicollinearity exists.

Coefficientsa

Collinearity Statistics

Model Tolerance VIF

1 Institutions .181 5.519

Infrastructure .113 8.884

Macroeconomic_environment .589 1.698

Health_and_primary_education .215 4.660

Higher_Education_and_training .135 7.414

Goods_Market_Efficiency .133 7.502

Labor_Market_Efficiency .315 3.179

Market_Size .613 1.630

a. Dependent Variable: Financial_Market_Development

Since the VIF of some variables is more than 6, multicollinearity exists.

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 Global Competitiveness Index Report

Coefficientsa

Collinearity Statistics

Model Tolerance VIF

1 Institutions .184 5.447

Infrastructure .122 8.174

Macroeconomic_environment .565 1.769

Health_and_primary_education .220 4.539

Higher_Education_and_training .138 7.255

Goods_Market_Efficiency .120 8.326

Labor_Market_Efficiency .315 3.177

Financial_Market_Development .329 3.037

a. Dependent Variable: Market_Size

Since the VIF of some variables is more than 6, multicollinearity exists.

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HETEROSCADASTICITY
An assumption of the classical linear regression model is that the variance of
error term is the same, regardless of the values of X (or independent variables).
This is known as homoscedasticity. When this assumption is violated, we say
that there is a situation of heteroscedasticity. In other words, it is a situation in
which the conditional variance of Y varies with X. We have incorporated the
analysis of 3 charts (obtained from running regression) to prove that the
variance of error term is fixed, regardless of the value of X i.e., there is
homoscedasticity.

Since the data points are not forming any pattern , the problem of heteroscedasticity does not
exist.

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AUTO CORRELATION

Model Summaryb

Adjusted R Std. Error of

Model R R Square Square the Estimate Durbin-Watson

1 .993a .987 .986 .08292 1.917

Since the d-statistic equals to 1.917, which is very close to 2, so there is no autocorrelation in
the data and the model is a significant fit.

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CONCLUSION
In our research of determinants of Global Competitiveness Index 2018, we
have studied the impact of nine factors on the competitiveness of a country
namely – Institutions, Infrastructure, Macroeconomic Environment, Health and
Primary Education, Higher Education and Training, Goods Market Efficiency,
Labour Market Efficiency, Financial Markets Development and Market Size.
These factors have been studied over a pool of 137 countries. We used
multiple regression analysis to measure this impact. Based on the analysis, the
following results can be noted: All nine variables -Institutions, Infrastructure,
Macroeconomic Environment, Health and Primary Education, Higher Education
and Training, Goods Market Efficiency, Labour Market Efficiency, Financial
Markets Development and Market Size came out to be significant.
Ho: The factors Institutions, Infrastructure, Macroeconomic Environment,
Health and Primary Education, Higher Education and Training, Goods Market
Efficiency, Labour Market Efficiency, Financial Markets Development and
Market Size don’t have a significant effect on level of global competitiveness,
other factors held constant.
H1: The factors Institutions, Infrastructure, Macroeconomic Environment,
Health and Primary Education, Higher Education and Training, Goods Market
Efficiency, Labour Market Efficiency, Financial Markets Development and
Market Size have a significant effect on level of global competitiveness, other
factors held constant.
In this model, we reject null hypothesis, thus overall model comes out to be
significant.
Based on standard error and t stat, we can say that Market Size has the
maximum effect on Global Competitiveness Index of different countries as in
regression analysis the independent factor Market Size has maximum value of
t-stat and minimum standard error. Based on several tests, there exists the
problem of
multicollinearity in independent factors. Heteroscedasticity and
autocorrelation does not exist in the regression model.

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RECOMMENDATIONS

Multicollinearity: We can correct for the multicollinearity problem by dropping

some variables or by transforming variables (like taking their log or first
difference etc) or by getting new or additional data or by just increasing the
sample size because large samples reduce the possibility of multicollinearity.
This will increase the efficiency and precision of our estimators.

Autocorrelation: The problem of autocorrelation can be corrected using

generalized least squares (GLS). The procedure for transforming the original
variables in such a way that the transformed variables satisfy the assumptions
of the classical model and then apply ordinary least squares to them (OLS) is
known as the method of the generalized least squares method (GLS).

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BIBLIOGRAPHY
Web Sources
 http://www3.weforum.org/docs/GCR2017-
2018/05FullReport/TheGlobalCompetitivenessReport2017–
2018.pdf
 http://www.oceanhealthindex.org/methodology/components/global-
competitiveness-index

Text Sources
 Basic Econometrics: Damodar N. Gujarati

 Christopher Dougherty, Introductory Econometrics 3rd Edition

Oxford University Press

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