IPRI - 2016 Levy Carciente, Sary 1 INTERNATIONAL PROPERTY RIGHTS INDEX 2016 I. Property rights in the knowledge society ‘Simplicity is the ultimate sophistication’ Leonardo da Vinci Since the end of the 20 th century it has been stated that we are in the early stages of a Third Industrial Revolution, or more accurately, of a non-Industrial one. The notion ‘knowledge based society’ is a concept which attempts to grasp the multidimensional transformations which are taking place in the current society and serves also for the analysis of those alterations. It has its origins in the 1960s when, analyzing changes, the term ‘postindustrial society’ was coined. The concept expressed transition from an economy that produces products to one based on services, led by technically qualified professionals. In a 'knowledge society,' structures and processes of material and symbolic reproduction are so immersed in knowledge operations that information processing, symbolic analysis and expert systems take precedence over other factors, like capital and labor. We are talking about the configuration of a new model of society, one in which everyone and everything is connected, all over the world and all the time, creating zillions of terabytes of data per picosecond. The topological structures of these networks are becoming the new appropriate models to look at societies, evaluated as complex systems, shaped by the collective action of individuals, and displaying emergent behaviors. Non-linearity, cascading failures, optimal interdependence and phase transitions are the focal points of current ongoing research. Innovation is critical to this economic transition and so a Schumpeterian moment is in place: when creative destruction threatens the past and promises a future; a moment that embraces disruption instead of fighting it. There is a growing consensus citing the innovation triangle (science - economy - society) and the knowledge triangle (education - research - innovation) as the key roots of the success. As always in complex systems, a linear or simple relationship among these elements is not found and much remains to deepen our understanding yet. While embracing complexity may be quixotic, ignoring it is not an option; and assessing the governance of these complex systems involves an understanding of the relevance of the underlying institutions. Appropriate and robust institutions would be those that show adaptability to changing conditions and favor appropriate synergies among individuals. In a ‘knowledge society’, structures of stiff control are more quickly eroded and this type of society is characterized by the development of new rules. Therefore, ‘knowledge societies’ gain in flexibility, but also in fragility. Heterogeneity and self-organization overlaps the pretension of homogeneity and rigid control, and simple and basic rules, respecting the nature of the agents of the system, are best applied. In other words, a complex knowledge society can prosper sufficiently if it is backed by a moldable but robust backbone of institutional arrangement. And among these basic institutions is the property rights system.
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IPRI - 2016 Levy Carciente, Sary
1
INTERNATIONAL PROPERTY RIGHTS INDEX 2016
I. Property rights in the knowledge society
‘Simplicity is the ultimate sophistication’
Leonardo da Vinci
Since the end of the 20th century it has been stated that we are in the early stages of a Third
Industrial Revolution, or more accurately, of a non-Industrial one. The notion ‘knowledge based
society’ is a concept which attempts to grasp the multidimensional transformations which are
taking place in the current society and serves also for the analysis of those alterations. It has its
origins in the 1960s when, analyzing changes, the term ‘postindustrial society’ was coined. The
concept expressed transition from an economy that produces products to one based on services,
led by technically qualified professionals. In a 'knowledge society,' structures and processes of
material and symbolic reproduction are so immersed in knowledge operations that information
processing, symbolic analysis and expert systems take precedence over other factors, like capital
and labor.
We are talking about the configuration of a new model of society, one in which everyone and
everything is connected, all over the world and all the time, creating zillions of terabytes of data
per picosecond. The topological structures of these networks are becoming the new appropriate
models to look at societies, evaluated as complex systems, shaped by the collective action of
individuals, and displaying emergent behaviors. Non-linearity, cascading failures, optimal
interdependence and phase transitions are the focal points of current ongoing research.
Innovation is critical to this economic transition and so a Schumpeterian moment is in place:
when creative destruction threatens the past and promises a future; a moment that embraces
disruption instead of fighting it. There is a growing consensus citing the innovation triangle
(science - economy - society) and the knowledge triangle (education - research - innovation) as
the key roots of the success. As always in complex systems, a linear or simple relationship
among these elements is not found and much remains to deepen our understanding yet.
While embracing complexity may be quixotic, ignoring it is not an option; and assessing the
governance of these complex systems involves an understanding of the relevance of the
underlying institutions. Appropriate and robust institutions would be those that show adaptability
to changing conditions and favor appropriate synergies among individuals.
In a ‘knowledge society’, structures of stiff control are more quickly eroded and this type of
society is characterized by the development of new rules. Therefore, ‘knowledge societies’ gain
in flexibility, but also in fragility. Heterogeneity and self-organization overlaps the pretension of
homogeneity and rigid control, and simple and basic rules, respecting the nature of the agents of
the system, are best applied. In other words, a complex knowledge society can prosper
sufficiently if it is backed by a moldable but robust backbone of institutional arrangement. And
among these basic institutions is the property rights system.
IPRI - 2016 Levy Carciente, Sary
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Since the 1990s, there is considerable empirical literature dealing with the relationship between
institutions and the improvement of social wellbeing, and particularly between property rights
and social prosperity1.
While classical economists gave a central position to the role of property rights in the process of
economic development, the core welfare results of mainstream economics assumes that property
rights are well defined and costlessly enforced. It is this new institutional approach that concerns
effective property rights as the center of thoughts about development, defining them as
endogenous to the system, evolving in time by the effects of political, economic and cultural
forces. Effective property rights means that ownership structures are well defined having
important effects on assets allocation (separating ownership from control), wealth distribution
and consumption.
Besley and Ghatak (2010) address two areas concerning the relationship between property rights
and development: the mechanisms through which property rights affect economic activity and
the determinants of property rights. In the first they emphasize some economic costs of weak
property rights by means of expropriation risk, unproductive costs to defend property, failure to
facilitate gains and supporting other transactions. Their model concludes that increasing the
security of property rights can reduce asset sub-utilization. Their results capture the mechanisms
suggested by de Soto (2000) linking property rights’ increase of the use of assets as collateral
and economic efficiency.
Other research finds similar positive links: Wang (2008) shows that the housing reform in China
(allowing employees to buy state-owned houses) increased entrepreneurial ventures using houses
as collateral; Johnson, McMillan and Woodruff (2002) found that weak property rights
discourage profit reinvestment in post-communist countries; Galiani and Schargrodsky (2005)
found that titled parcels in Argentina favored housing investment and child education; and Field
and Torero (2004) revealed that urban land titling in Peru is associated with a 9-10% increase in
loan approval rates from the public sector bank for housing construction materials, while finding
no effect on the loan approval rate from private sector lenders.
However, the analysis of the impact of the property rights system is not an easy task: Domingo
(2013) examines the evidence on the relationship between property rights and social and political
empowerment, finding ambivalent evidence, basically because it needs to take account of the
political and social relations in which property regimes are embedded; and Locke (2013) found
contradictory evidence in the relationship of land rights and growth (through investment, credit
and efficiency) due to the presence of factors other than property rights (i.e. skills) also of
primary importance for growth, recognizing a ‘cluster of institutions’ that drive economic
growth.
An important problem with economic and social dynamics, as with any other complex system, is
the so-called problem of endogeneity: institutions cause development, but development also
1See among others: F.A. Hayek, 1960; Milton Friedman, 1962; A. Rand, 1964; Alchian & Demsetz, 1973; Demsetz,
1967; Nozick, 1974; R. A. Epstein, 1985, 1995; J. M. Buchanan, 1993;J. V. Delong, 1997; North 1981, 1990,
Richard R. Pipes, 1999; Von Mises, L., 2002, De Soto, 2000; De Soto & Cheneval, 2006; Barzel, 1997, Knack&
Keefer, 1995; Hall & Jones, 1999; Acemoglu et al. 2001, 2002, 2005;Acemoglu& Johnson, 2005; Easterly &
Levine, 2003;Rodriket al. 2004;Feyrer&Sacerdote, 2009; Hansson, 2009; T. R. Machan, 2002; Sandefur, 2006;
Waldron, 2012. For dissenting views see Glaeser et al., 2004 and Angeles, 2010.
IPRI - 2016 Levy Carciente, Sary
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causes institutions. This is an issue recognized in the empirical literature but never fully solved.
Paldam and Gundlach (2007) address this problem using two measures of institutional quality,
democracy and corruption. In both cases they found mixed results on causality direction, but
strong support on the interactions of institutions and income and development, and so of the
creation of a virtuous circle.
In this way, enforcing a strong property rights system is a key element fostering economic
growth as a linchpin of a multidimensional prosperity goal. However, assigning and
administering property rights can be challenging. This is particularly true with respect to
knowledge-based goods and economic use of some natural resources. In this sense, the
environment and knowledge-based products will continue to be at the heart of the biggest
potential conflicts on property rights in the 21st century.
To understand this issue it has to be noted that knowledge and information are not like other kind
of physical goods widely traded in markets. They possess a specific characteristic referred as
‘non-rival in use’, that is, they can be used repeatedly and concurrently by many people, without
being ‘depleted’. In this sense the allocation of intellectual property rights does not confer
exclusive possession (as physical property rights) but of the benefits of its economic exploitation.
This creates economic incentives for people to go on research and innovation, as well as finding
new applications for old ideas. Intellectual property rights also tend to prevent ideas from
remaining in secrecy, being shared with the whole society, encouraging creativity spillovers
(David & Foray, 2003).
Most legal systems nowadays recognize three different kinds of intellectual property rights:
trademarks, copyrights and patents:
A trademark is a word, name, symbol or device which is used in trade with goods to indicate
the source of the goods and to distinguish them from others. A servicemark is the same as a
trademark except that it identifies and distinguishes the source of a service rather than a
product.
A copyright is a form of protection provided to the authors of original works of authorship
including literary, dramatic, musical, artistic and intellectual works, published or
unpublished.
A patent is the grant of a property of an invention to its creator. What is granted is not the
right to make, use, offer for sale, sell or import, but the right to exclude others from making,
using, offering for sale, selling or importing the creation.
In synthesis, trademarks distinguish products or services; copyrights apply to expressions, and
not to ideas, procedures, or methods of operation, while patents apply to specific
implementations of ideas. But in all cases we are talking about knowledge based rights.
There are other kinds of intellectual property rights: Industrial Designs and Geographical
Indicators. An industrial design is somewhat similar to a particular type of trademark known as a
‘distinguishing guise’, the aesthetic aspect of an article (its shape, patterns, lines or colors). A
geographical indication (GI) is a name or sign used on products corresponding to specific
geographical origin, acting as a quality certification.
The main goal for promoting strong intellectual property rights is to fuel the creation of
knowledge-based economies. Such legal infrastructure promotes innovation, and that new ideas
IPRI - 2016 Levy Carciente, Sary
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would become products, leading to economic growth, job creation, economic productivity,
sustained competitiveness in global markets, and improvement of social well-being.
Simultaneously there are critics addressed to instituting a system of intellectual property rights,
saying that they could threat fair competition. This critic is mainly related to health related
products and the concern that IP rights could rise their price. However, competition is not
opposed to property rights. On the contrary, strong IP rights are a complementary dimension of a
competitive economy whose main goal is the consumer’s benefit. This is because innovation is
based on a dynamical perspective of competition, which creates dynamical efficiency (creative
capacity) and not static efficiency (with fixed technology). The dynamical approach shows not
only indecisive short term impacts, but positive ones in the medium and long term, which are not
confined to a price reduction in time as a result of increased production, but also includes the
promotion of positive side effects on other social spheres: education, research and innovation,
endogenous development of technologies, and so on.
There is an important ongoing debate on this issue and, as in all social affairs; there are not easy
or general ‘one-size-fits-all’ solutions. This controversy will not vanish soon. We are talking
about complex systems, with multiple interactions and multidimensional dependence. But what it
is very important is to understand the relevance of institutional arrangements in the aim of
building productive, free and inclusive societies. A main building block of this institutional
support is, with no doubt, a strong Property Rights System.
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II. IPRI Structure and Methodology
One of the most important things to achieve a goal is to evaluate its evolution in time and space,
and for that, measuring is a key tool. Since 2007, the Property Rights Alliance (PRA) -
dedicated to the protection of property rights all around the world - instituted the Hernando de
Soto fellowship to produce a yearly edition of the International Property Rights Index, IPRI.
The IPRI was developed to serve as a barometer for the status of property rights across the
world. A vast review of the literature on property rights was done in order to conceptualize and
operationalize a comprehensive characterization of property rights. Following convention set in
place by previously compiled indexes, several experts and practitioners in the field of property
rights were consulted to finalize the set of core categories (here referred to as “components” or
‘sub-indexes’) and the items that create the components.
The following are the three core components of the IPRI:
1. Legal and Political Environment, LP
2. Physical Property Rights, PPR
3. Intellectual Property Rights, IPR
Figure 1. IPRI Structure
International Property Rights Index IPRI
Legal and Political Environment
(LP)
Judicial Independence
Control of Corruption
Rule of Law
Political Stability
Physical Property Rights
(PPR)
Protection of Physical Property
Rights
Registering Property
Registering Property
Intellectual Property Rights (IPR)
Protection of Intellectual Property
Rights
Patent Protection
Copyright Piracy
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The Legal and Political Environment (LP) component provides an insight into the strength of the
institutions of a country, the respect of the ‘rules of the game’ among citizens; consequently, the
measures used for the LP are broad in scope. This component has a significant impact on the
development and protection of physical and intellectual property rights.
The other two components of the index - Physical and Intellectual Property Rights (PPR and
IPR) - reflect two forms of property rights, both of which are crucial to the economic
development of a country. The items included in these two categories account for both de jure
rights and de facto outcomes of the countries considered.
The IPRI is comprised of 10 items in total, grouped under one of the three components: LP, PPR,
or IPR. While considering numerous items related to property rights, the final IPRI is specific to
the core factors that are directly related to the strength and protection of physical and intellectual
property rights. Furthermore, items for which data was available both more regularly and in a
greater number of countries were given preference. This was done to ensure that scores were
comparable across countries and years.
The IPRI-2016 keeps previous years’ methodology to allow a full comparison of its results with
previous editions.
II.1. Legal and Political Environment (LP)
The Legal and Political Environment component grasps the ability of a nation to enforce a de
jure system of property rights. In this sense there are considered four dimensions or sub-
components: the independence of its judicial system, the strength of the rule of law, the control
of corruption and the stability of its political system.
Judicial Independence
This item examines the judiciary’s freedom from influence by political and business groups. The
independence of the judiciary is a central underpinning for the sound protection and sovereign
support of the court system with respect to private property. For this item the chosen data source
was the Global Competitiveness Index from the World Economic Forum’s 2015-2016.
FINLAND IRELAND SLOVAKIA BRAZIL MALINEW ZEALAND BELGIUM OMAN THAILAND MONTENEGROLUXEMBURG QATAR LITHUANIA BULGARIA DOMINICAN REPNORWAY UNITED ARAB EMIRATES BAHREIN INDONESIA BENINSWITZERLAND FRANCE POLAND MACEDONIA, FYR NEPALSINGAPORE ICELAND BOTSWANA SRI. LANKA EGYPTSWEDEN TAIWAN (China) JORDAN CROATIA MOZAMBIQUEJAPAN ESTONIA SPAIN COLOMBIA GUYANANETHERLANDS MALAYSIA COSTA RICA TUNISIA IRANCANADA MALTA LATVIA LIBERIA SIERRA LEONEDENMARK CHILE HUNGARY SENEGAL ETHIOPIAAUSTRALIA PORTUGAL ITALY SWAZILAND ARMENIAHONG KONG (SAR of China) SOUTH AFRICA JAMAICA PERU ARGENTINAUNITED KINGDOM (UK) CZECH REPUBLIC SLOVENIA ZAMBIA BOSNIA & HERZEGOVINAUNITED STATES (USA) ISRAEL GHANA MEXICO BOLIVIAGERMANY RWANDA ROMANIA EL SALVADOR ALGERIAAUSTRIA MAURITIUS CHINA KAZAKHSTAN AZERBAIJAN
KOREA, REP PANAMA ECUADOR PARAGUAYCYPRUS GREECE CôTE D'IVOIRE CAMEROONURUGUAY MOROCCO HONDURAS SERBIASAUDI ARABIA INDIA GABON ALBANIA
IPRI vs Civic Activism (CA)IPRI vs Interpersonal Safety and Trust (IST)IPRI vs Inclusion of Minorities (IM) IPRI vs Intergroup Cohesion (IC)
IPRI - 2016 Levy Carciente, Sary
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The highest correlations were found between numbers of full time researches and IPR (0.786),
followed by the IPRI (0.764) and LP (0.751). The next highest correlation was between R&D
expenditure and the IPR (0.743), followed by the IPRI (0.677) and LP (0.638). Though positive,
PPR showed moderate correlations. The number of published scientific papers showed positive
but weak to moderate correlations.
Table 11. Pearson Correlation Indexes
Full time
researches (per
106)
R & D expenditure
(% GDP)
Scientific &
technical journal
articles
IPRI 0.764 0.677 0.315
LP 0.751 0.638 0.251
PPR 0.540 0.426 0.240
IPR 0.786 0.743 0.374
Figure 23. IPRI Correlations with R&D variables
9000
4,5
0
0
IPRI
Expe
nd
iture
in R
&D
Re
search
ers in
R&
D
2 9 2 9
R² = 0,472
R² = 0,627
IPRI vs Researchers in R&D (per million people)
IPRI vs Research and development expenditure (% of GDP)
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VII.5. Ecological performance
The ecological environment is critical for sustainable development, and a part of the recent
international climate change agreement in Paris. For this metric we ran correlations of the IPRI
and the EPI-Yale:
The Environmental Performance Index (EPI-Yale) provides a global view of environmental
performance and country by country metrics to inform decision-making. It ranks countries'
performance on high-priority environmental issues in two areas: protection of human health
and protection of ecosystems (http://epi.yale.edu/country-rankings). See Table 12 & Fig. 24.
Table 12. Pearson Correlation Indexes
EPI-Yale
IPRI 0.638
LP 0.644
PPR 0.553
IPR 0.568
We found positive correlations among the EPI and IPRI and its components. The same result can
be found at: http://marketmonetarist.com/2015/12/01/coase-was-right-the-one-graph-version/, it
follows that well defined property rights are the best way to manage economic externalities.
Usually, these results may indicate the extent to which society has stronger property rights;
eventually it will be able to apply appropriate policies protecting health and the environment
through the conservation and protection of the ecosystem.
Figure 24. IPRI Correlations with ecological measurements
100
30
IPRI
EPI
2 9
R² = 0,414
IPRI vs Environmental Performance Index (EPI)
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VIII. IPRI Cluster Analysis
Cluster analysis aims to group similar entities into clusters. It classifies individuals into groups as
homogeneous as possible based on observed variables.
The cluster analysis was performed for all the 128 countries according to their values in LP, PPR
and IPR. Additionally, we included illustrative variables that do not influence the formation of
the cluster but will bring an important contribution to describe them6. Those variables were those
we used to calculate correlations (chapter VII), mainly to expose the conditions or features in the
resulted clusters.
In order to seize the variability in the analysis -given the great differences among the countries in
the IPRI- we used Ward's Method7 with squared Euclidean distance that groups countries with
minimal loss inertia.
First, a Principal Component Analysis (PCA) was applied with the aim of handling variables by
factors, given the high correlation among them. The results of the PCA express that the three
components of the IPRI (LP, PPR, IPR) define a dimension, that was named IPRI, which collects
86.07% of the inertia. The second and third factors - with inertias of 9.55% and 4.38%
respectively - are the residue of the inertia. These entities do not contribute to first factor inertia
and are generally very close to the origin of the first factor. They could be subdivided into groups
more associated to the PPR dimension –defining the second factor – and those more associated
to LP and IPR defining the third factor.
Next, we used the mobile centers algorithm to show the inertia within groups and the criteria to
decide the optimal number of classes or clusters (see Table 13).
Table 13. Cluster analysis
Cluster Inertia Countries
Distance of
Centroids to
origin
Coordinates of centroids
Factor 1 Factor 2 Factor 3
Inter-classes 2,22755
Intra-classes
Class 1 / 3 0,28378 41 2,71059 -1,64302 -0,05779 -0,08804
Class 2 / 3 0,31978 56 0,02577 -0,10188 0,09733 0,07693
Class 3 / 3 0,16889 31 5,56611 2,35706 -0,09939 -0,02253
6We used the statistical software SPAD® which allows the inclusion of illustrative variables in the analysis. 7Ward’s Method joins cases looking for minimizing the variance within each group, creating homogeneous groups.
First, it calculates the media of all variables in each cluster, then the distance between each case and the cluster’
media, that will be added. Subsequently, clusters are grouped in a way to minimize increases in the sum of distances
inside each cluster.
IPRI - 2016 Levy Carciente, Sary
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The analysis showed that the three clusters were enough to explain the grouping of countries,
more specifically, where the observed inertia within each group does not exceed the inertia
among groups. In this sense the clusters are formed as shown in Table 14 and illustrated in
Figure 25.
Although the first factor contains 86.07% of inertia, which is enough to illustrate the formation
of the clusters, Fig. 35 illustrates Factors 1 and 2 as well as the three clusters centroids (yellow).
Cluster 1, with countries located in the negative coordinates of the first factor (red), groups
individuals associated with low values of the LP, IPR and PPR. Cluster 2 includes countries
(green) located very close to the origin, showing average values of the LP, IPR, and PPR. Cluster
3 contains countries (blue) located in the positive coordinates of the first factor and its members
are linked to high values of the LP, IPR and PPR. The second factor consists mostly of countries
in Cluster 2, including those whose scores are very close to the average, including both
neighboring countries between Cluster 2 and Cluster 1, and those neighboring Cluster 2 and
Cluster 3. Cluster 1 and Cluster 3 are complete opposites and their individuals are not directly
associated with each other.
Besides the clusters, Figure 25 also shows the contribution of each country explaining the inertia
gathered by the factors, hence the bigger the dot size representing the country, the higher its
contribution. Very close countries show how they are similar and how they differ as the distance
increases between them.
In the central circle are those countries that have no statistically significant contribution to the
definition of the factors. As already mentioned, they are close to the average and are mostly
members of Cluster 2. In addition, arrows represent each of the three dimensions of the IPRI,
their definite direction indicates the direct relationship with the individuals, i.e., as countries are
in the same direction of the vector, countries tend to have a higher relationship with this
dimension; and as a country direction diverts from the vector, the relationship between the
country decreases to point of being contrary to it. This can be exemplified with the case of Haiti,
which is totally opposite to the direction of vector PPR, which coincides with its low score in this
sub-index, being the bottom country of the sample.
Subsequently, clusters composed using income, population, participation in economic and
regional integration agreements and regional and development criteria are shown in Fig. 26a-
26d, where font size represent the frequency of the groupings in the cluster.
The analysis of each cluster can describe the internal characteristics of the countries within it. In
this regard Table 15 exhibits the features that are statistically significant8 in each group.
Additional statistics are shown in Table 16 and Appendix IV.
8To be statistically significant the value must be less or equal -1.96 or greater or equal 1.96
IPRI - 2016 Levy Carciente, Sary
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Table 14. Clusters’ Members (Countries ordered alphabetically)
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Figure 25. Clusters’ Members and Centroids. Factor 1 and Factor 2
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Figure 26a. Clusters composition by Income classification
Figure 26b. Clusters composition by Regional and Development criteria
Figure 26c. Clusters composition and Population weight (thousands)
Cluster 1 Cluster 2 Cluster 3
Advancedeconomies
Emerging and Developing Asia
Latin America and the Caribbean
Middle East, North Africa, and Pakistan
Sub-Saharan Africa
Advancedeconomies
Latin America and the Caribbean
Emerging and Developing Europe
Middle East, North Africa, and Pakistan
Sub-SaharanAfricaEmerging and
Developing Asia
Commonwealth of Independent States
Sub-SaharanAfrica
Middle East, North Africa, and Pakistan
Latin America and the Caribbean
Commonwealth of Independent States
Emerging and Developing Asia
Emerging and Developing Europe
Cluster 1 Cluster 2 Cluster 3
1.474.552972.262
4.382.016
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Fig. 26d. Clusters composition by Economic and Regional Integration Agreements
Table 15. Cluster statistics
Statistically significant only if Value-Test ≥ ∣1.96∣
Cluster 1 Cluster 2 Cluster 3
EUNAFTA
ASEAN
GCC
EFTA
EU
PACIFIC
SADCAP
AP SADCGCC
ASEAN
PARLACEN
MCCA
ARAB M UNION
CAN
CARICOM
CIS
MERCOSUR SAARC
IGAD
NAFTA
ECOWAS
ECOWAS
SADC CEMAC
ARAB M UNION
CIS
MERCOSURPARLACEN
SAARC
ASEAN
CARICOM IGAD
MCCACAN
CEEACOPEC
TPP
TPP
OPEC
TPP
OPEC
Characteristic
VariablesValue-Test Probability
Characteristic
VariablesValue-Test Probability
Characteristic
VariablesValue-Test Probability
IM -3,11 0,001 EPI 0,59 0,278 LP 9,15 0,000
IST -3,57 0,000 EC 0,41 0,339 IPRIGE 8,98 0,000
E.R&D -3,72 0,000 PPR 0,37 0,355 IPR 8,95 0,000
Gen -4,06 0,000 Gen 0,16 0,436 Gp 8,82 0,000
R.R&D -4,12 0,000 HDI 0,14 0,444 Gp.G 8,40 0,000
FE -4,15 0,000 GEI 8,28 0,000
IC -4,26 0,000 IPRIGE -0,57 0,286 GKFpc 8,25 0,000
GKFpc -4,42 0,000 IPR -0,69 0,247 PPR 7,88 0,000
Gp.G -4,54 0,000 IC -0,79 0,214 CA 7,79 0,000
Gp -4,93 0,000 FE -1,18 0,119 CSL 7,50 0,000
CA -5,26 0,000 LP -1,39 0,082 R.R&D 6,86 0,000
CSL -5,40 0,000 CSL -1,52 0,064 HDI 6,15 0,000
HDI -5,74 0,000 IST -1,53 0,063 E.R&D 6,05 0,000
EPI -5,76 0,000 GEI -1,71 0,043 FE 5,88 0,000
EC -5,80 0,000 CA -1,83 0,034 EPI 5,65 0,000
GEI -5,87 0,000 IM -2,06 0,020 EC 5,59 0,000
LP -6,92 0,000 E.R&D -2,26 0,012 IM 5,57 0,000
IPR -7,49 0,000 Gp.G -2,51 0,006 IC 5,54 0,000
IPRIGE -7,58 0,000 R.R&D -2,60 0,005 IST 5,44 0,000
PPR -7,63 0,000 GKFpc -2,93 0,002 Gen 4,27 0,000
Gp -2,95 0,002
Cluster 1 Cluster 2 Cluster 3
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Table 16. Illustrative variables. Averages by Clusters
Table 17. Regional Integration Agreements and Cluster
Cluster 1 Cluster 2 Cluster 3
Total Countries 41 56 31
Total Population (Thousnad) 1.474.552 4.382.016 972.262
Average IPRI-2015 4,01 5,34 7,55
Average LP 3,51 4,87 7,74
Average PPR 4,78 5,92 7,24
Average IPR 3,73 5,22 7,66
Average GE 6,26 7,50 9,01
Average IPRI-GE 5,24 6,84 9,40
Average EPI 61,12 72,20 83,87
Average FE 50,93 55,92 68,40
Average HDI 0,61 0,73 0,88
Average CSL -1,46 -0,42 2,00
Average CA 0,48 0,51 0,59
Average IC 0,63 0,68 0,76
Average IST 0,41 0,45 0,57
Average IM 0,44 0,46 0,55
Average EC -0,68 0,19 1,06
Average GEI 23,58 35,21 62,28
Average Gp 2192,54 8260,15 37686,87
Average Gp.G 798,49 2544,83 11783,54
Average GKFpc 840.771.717,22 2.373.408.820,48 11.016.206.649,34
Average E.R&D 0,34 0,74 1,88
Average R.R&D 339,17 1228,81 4106,15
Total Cluster 1 % Cluster 2 % Cluster 3 %
EU European Union 28 13 46,43 15 53,57
SADC Southern African Development Community 10 5 50,00 4 40,00 1 10,00
ECOWAS Economic Community Of West African States 8 3 37,50 5 62,50
ASEAN Association of Southeast Asian Nations 7 2 28,57 3 42,86 2 28,57
PARLACEN Central American Parliament 6 2 33,33 4 66,67
GCC Gulf Cooperation Council 6 4 66,67 2 33,33
AP Pacific Alliance 6 5 83,33 1 16,67
MERCOSUR Southern Common Market 5 3 60,00 2 40,00
SAARC South Asian Association for Regional Cooperation 5 3 60,00 2 40,00
CEMAC Central African Economic and Monetary Community 3 3 100,00
MCCA Central American Common Market 5 1 20,00 4 80,00
CIS Commonwealth of Independent States 6 4 66,67 2 33,33
ARAB M UNION Arab Mahgreb Union 4 2 50,00 2 50,00
CARICOM Caribbean Community 4 2 50,00 2 50,00
CAN Andean Community 4 1 25,00 3 75,00
EFTA European Free Trade Association 3 3 100,00
IGAD Intergovernmental Authority on Development 3 2 66,67 1 33,33
NAFTA North American Free Trade Agreement 3 1 33,33 2 66,67
PACIFIC PACIFIC 2 2 100,00
OPEC Organization of the Petroleum Exporting Countries 10 4 40,00 4 40,00 2 20,00
CEEAC La Communauté Economique des Etats de l'Afrique Centrale 4 4 100,00
REST OF EUROPE 14 ALBANIA,ARMENIA,BOSNIA AND HERZEGOVINA,GEORGIA,ICELAND,MACEDONIA FYR,MOLDOVA,MONTENEGRO,NORWAY,RUSSIA,SERBIA,SWITZERLAND,TURKEY,UKRAINE
CARICOM 4 GUYANA,HAITI,JAMAICA,TRINIDAD AND TOBAGO
CAN 4 BOLIVIA,COLOMBIA,ECUADOR,PERU
EFTA 3 ICELAND,NORWAY,SWITZERLAND
IGAD 3 ETHIOPIA,KENYA,UGANDA
NAFTA 3 CANADA,MEXICO,UNITED STATES (USA)
PACIFIC 2 AUSTRALIA,NEW ZEALAND
CEEAC 4 BURUNDI,CAMEROON,CHAD,GABON
TPP 11 AUSTRALIA,CANADA,CHILE,JAPAN,MALAYSIA,MEXICO,NEW ZEALAND,PERU,SINGAPORE,UNITED STATES (USA),VIETNAM
OPEC 10 ALGERIA,ECUADOR,INDONESIA,IRAN,KUWAIT,NIGERIA,QATAR,SAUDI ARABIA,UNITED ARAB EMIRATES,VENEZUELA BOLIVARIAN REPUBLIC OF
High income: nonOECD 19ARGENTINA,BAHREIN,CROATIA,CYPRUS,HONG KONG (SAR of China),KUWAIT,LATVIA,LITHUANIA,MALTA,OMAN,QATAR,RUSSIA,SAUDI ARABIA,SINGAPORE,TAIWAN
(China),TRINIDAD AND TOBAGO,UNITED ARAB EMIRATES,URUGUAY,VENEZUELA BOLIVARIAN REPUBLIC OF
SALVADOR,GUATEMALA,GUYANA,HAITI,HONDURAS,JAMAICA,MEXICO,NICARAGUA,PANAMA,PARAGUAY,PERU,TRINIDAD AND TOBAGO,URUGUAY,VENEZUELA, BOLIVARIAN
REPUBLIC OF
Middle East, North
Africa, and Pakistan15 ALGERIA,BAHREIN,EGYPT,IRAN,JORDAN,KUWAIT,LEBANON,MAURITANIA,MOROCCO,OMAN,PAKISTAN,QATAR,SAUDI ARABIA,TUNISIA,UNITED ARAB EMIRATES